Wiring the Brain

From miswired brain to psychopathology – modelling neurodevelopmental disorders in mice

2012-01-25T12:45:00.000-08:00

It takes a lot of genes to wire the human brain. Billions of cells, of a myriad different types have to be specified, directed to migrate to the right position, organised in clusters or layers, and finally connected to their appropriate targets. When the genes that specify these neurodevelopmental processes are mutated, the result can be severe impairment in function, which can manifest as neurological or psychiatric disease.

How those kinds of neurodevelopmental defects actually lead to the emergence of particular pathological states – like psychosis or seizures or social withdrawal – is a mystery, however. Many researchers are trying to tackle this problem using mouse models – animals carrying mutations known to cause autism or schizophrenia in humans, for example. A recent study from my own lab (open access in PLoS One) adds to this effort by examining the consequences of mutation of an important neurodevelopmental gene and providing evidence that the mice end up in a state resembling psychosis. In this case, we start with a discovery in mice as an entry point to the underlying neurodevelopmental processes.

In just the past few years, over a hundred different mutations have been discovered that are believed to cause disorders like autism or schizophrenia. In many cases, particular mutations can actually predispose to many different disorders, having been linked in different patients to ADHD, epilepsy, mental retardation or intellectual disability, Tourette’s syndrome, depression, bipolar disorder and others. These clinical categories may thus represent more or less distinct endpoints that can arise from common neurodevelopmental origins.

For a condition like schizophrenia, the genetic overlap with other conditions does not invalidate the clinical category. There is still something distinctive about the symptoms of this disorder that needs to be explained. I have argued that schizophrenia can clearly be caused by single mutations in any of a very large number of different genes, many with roles in neurodevelopment. If that model is correct, then the big question is: how do these presumably diverse neurodevelopmental insults ultimately converge on that specific phenotype? It is, after all, a highly unusual condition. The positive symptoms of psychosis – hallucinations and delusions, for example – especially require an explanation. If we view the brain from an engineering perspective, then we can say that the system is not just not working well – it is failing in a particular and peculiar manner.

To try to address how this kind of state can arise we have been investigating a particular mouse – one with a mutation in a gene called Semaphorin-6A. This gene encodes a protein that spans the membranes of nerve cells, acting in some contexts as a signal to other cells and in other contexts as a receptor of information. It has been implicated in controlling cell migration, the guidance of growing axons, the specification of synaptic connectivity and other processes. It is deployed in many parts of the developing brain and required for proper development in the cerebral cortex, hippocampus, thalamus, cerebellum, retina, spinal cord, and probably other areas we don’t yet know about.

Despite widespread cellular disorganisation and miswiring in their brains, Sema6A mutant mice seem overtly pretty normal. They are quite healthy and fertile and a casual inspection would not pick them out as different from their littermates. However, more detailed investigation revealed electrophysiological and behavioural differences that piqued our interest.

Because these animals have a subtly malformed hippocampus, which looks superficially like the kind of neuropathology observed in many cases of temporal lobe epilepsy, we wanted to test if they had seizures. To do this we attached electrodes to their scalp and recorded their electroencephalogram (or EEG). This technique measures patterned electrical activity in the underlying parts of the brain and showed quite clearly that these animals do not have seizures. But it did show something else – a generally elevated amount of activity in these animals all the time.

What was particularly interesting about this is that the pattern of change (a specific increase in alpha frequency oscillations) was very similar to that reported in animals that are sensitised to amphetamine – a well-used model of psychosis in rodents. High doses of amphetamine can acutely induce psychosis in humans and a suite of behavioural responses in rodents. In addition, a regimen of repeated low doses of amphetamine over an extended time period can induce sensitisation to the effects of this drug in rodents, characterised by behavioural differences, like hyperlocomotion, as well as the EEG differences mentioned above. Amphetamine is believed to cause these effects by inducing increases in dopaminergic signaling, either chronically, or to acute stimuli.

This was of particular interest to us, as that kind of hyperdopaminergic state is thought to be a final common pathway underlying psychosis in humans. Alterations in dopamine signaling are observed in schizophrenia patients (using PET imaging) and also in all relevant animal models so far studied.

To explore possible further parallels to these effects in Sema6A mutants we examined their behaviour and found a very similar profile to many known animal models of psychosis, namely hyperlocomotion and a hyper-exploratory phenotype (in addition to various other phenotypes, like a defect in working memory). The positive symptoms of psychosis can be ameliorated in humans with a number of different antipsychotic drugs, which have in common a blocking action on dopamine receptors. Administering such drugs to the Sema6A mutants normalised both their activity levels and the EEG (at a dose that had no effect on wild-type animals).

These data are at least consistent with (though they by no means prove) the hypothesis that Sema6A mutants end up in a hyperdopaminergic state. But how do they end up in that state? There does not seem to be a direct effect on the development of the dopaminergic system – Sema6A is at least not required to direct these axons to their normal targets.

Our working hypothesis is that the changes to the dopaminergic system emerge over time, as a secondary response to the primary neurodevelopmental defects seen in these animals.

It is well documented that early alterations, for example to the hippocampus, can have cascading effects over subsequent activity-dependent development and maturation of brain circuits. In particular, it can alter the excitatory drive to the part of the midbrain where dopamine neurons are located, in turn altering dopaminergic tone in the forebrain. This can induce compensatory changes that ultimately, in this context, may prove maladaptive, pushing the system into a pathological state, which may be self-reinforcing.

For now, this is just a hypothesis and one that we (and many other researchers working on other models) are working to test. The important thing is that it provides a possible explanation for why so many different mutations can result in this strange phenotype, which manifests in humans as psychosis. If this emerges as a secondary response to a range of primary insults then that reactive process provides a common pathway of convergence on a final phenotype. Importantly, it also provides a possible point of early intervention – it may not be possible to “correct” early differences in brain wiring but it may be possible to prevent them causing transition to a state of florid psychopathology.

Rünker AE, O'Tuathaigh C, Dunleavy M, Morris DW, Little GE, Corvin AP, Gill M, Henshall DC, Waddington JL, & Mitchell KJ (2011). Mutation of Semaphorin-6A disrupts limbic and cortical connectivity and models neurodevelopmental psychopathology. PloS one, 6 (11) PMID: 22132072

Mitchell, K., Huang, Z., Moghaddam, B., & Sawa, A. (2011). Following the genes: a framework for animal modeling of psychiatric disorders BMC Biology, 9 (1) DOI: 10.1186/1741-7007-9-76

Mitchell, K. (2011). The genetics of neurodevelopmental disease Current Opinion in Neurobiology, 21 (1), 197-203 DOI: 10.1016/j.conb.2010.08.009

Howes, O., & Kapur, S. (2009). The Dopamine Hypothesis of Schizophrenia: Version III--The Final Common Pathway Schizophrenia Bulletin, 35 (3), 549-562 DOI: 10.1093/schbul/sbp006

Jump-starting regeneration of injured nerves

2012-01-08T07:51:00.001-08:00

Unlike in many other animals, injured nerve fibres in the mammalian central nervous system do not regenerate – at least not spontaneously. A lot of research has gone in to finding ways to coax them to do so, unfortunately with only modest success. The main problem is that there are many reasons why central nerve fibres don’t regenerate after an injury – tackling them singly is not sufficient. A new study takes a combined approach to hit two distinct molecular pathways in injured nerves and achieves substantial regrowth in an animal model.

Many lower vertebrates, like frogs and salamanders, for example, can regrow damaged nerves quite readily. And even in mammals, nerves in the periphery will regenerate and reconnect, given enough time. But nerve fibres in the brain and spinal cord do not regenerate after an injury. Researchers trying to solve this problem focused initially on figuring out what is different about the environment in the central versus the peripheral nervous system in mammals.

It was discovered early on that the myelin – the fatty sheath of insulation surrounding nerve fibres – in the central nervous system is different from that in the periphery. In particular, it inhibits nerve growth. A number of groups have tried to figure out what components of central myelin are responsible for this activity. Myelin is composed of a large number of proteins, as well as lipid membranes. One of these, subsequently named Nogo, was discovered to block nerve growth. This discovery prompted understandable excitement, especially because an antibody that binds that protein was found to promote regrowth of injured spinal nerves in the rat. (It even prompted a film, Extreme Measures, with Gene Hackman and Hugh Grant – an under-rated thriller with some surprisingly accurate science and some very serious medical malfeasance).

Unfortunately, the regrowth in rats that is promoted by blocking the Nogo protein is very limited. Similarly, mice that are mutant for this protein or its receptor show very minor regeneration. What is observed in some cases is extra sprouting of uninjured axons downstream of the spinal injury site. This can lead to some minor recovery of function but it’s really remodelling, rather than regeneration.

But it does suggest an answer to the question: why would we have evolved a system that seems actively harmful, that prevents regeneration after an injury? Well, first, the selective pressure in mammals to be able to regenerate damaged nerves is probably not very great, simply because injured animals would not typically get the chance to regenerate in the wild. And second, it suggests that the function of proteins like Nogo may not be to prevent regeneration but to prevent sprouting of nerve fibres after they have already made their appropriate connections. A lot of effort goes in to wiring the nervous system, with exquisite specificity – once that wiring pattern is established, it probably pays to actively keep it that way.

There are a number of reasons why blocking the Nogo protein does not allow nerves to fully regenerate. First, it is not the only protein in myelin that blocks growth – there are many others. Second, the injury itself can give rise to scarring and inflammation that generates a secondary barrier. And third, neurons in the mature nervous system may simply not be inclined to grow. (Not only that – the distances they may have to travel in the fully grown adult may be orders of magnitude longer than those required to wire the nervous system up during development. There are nerves in an adult human that are almost a metre long but these connections were first formed in the embryo when the distance was measured in millimetres.)

This last problem has been addressed more recently, by researchers asking if there is something in the neurons themselves that changes over time – after all, neurons in the developing nervous system grow like crazy. That propensity for growth seems to be dampened down in the adult nervous system – again, once the nervous system is wired up, it is important to restrict further growth.

Researchers have therefore looked for biochemical differences between young (developing) neurons and mature neurons that have already formed connections. The hope is that if we understand the molecular pathways that differ we might be able to target them to “rejuvenate” damaged neurons, restoring their internal urge to grow. The lab of Zhigang He at Harvard Medical School has been one of the leaders in this area and has previously found that targeting either of two biochemical pathways allowed some modest regeneration of injured neurons. (They study the optic nerve as a more accessible model of central nerve regrowth than the spinal cord).

In a new study recently published in Nature, they show that simultaneously blocking both these proteins leads to remarkably impressive regrowth – far greater than simply an additive effect of blocking the two proteins alone. The two proteins are called PTEN and SOCS3 – they are both intracellular regulators of cell growth, including the ability to respond to extracellular growth factors. The authors used a genetic approach to delete these genes two weeks prior to an injury and found that regrowth was hugely promoted. That is obviously not a very medically useful approach however – more important is to show that deleting them after the injury can permit regeneration and indeed, this is what they found. Presumably, neurons in this “grow, grow, grow!” state are either insensitive to the inhibitory factors in myelin or the instructions for growth can override these factors.

They went on to characterise the changes that occur in the neurons when these genes are deleted and observed that many other proteins associated with active growth states are upregulated, including ones that get repressed in response to the injury itself. The hope now is that drugs may be developed to target the PTEN and SOCS3 pathways in human patients, especially those with devastating spinal cord injuries, to encourage damaged nerves to regrow. As with all such discoveries, translation to the clinic will be a difficult and lengthy process, likely to take years and there is no guarantee of success. But compared to previous benchmarks of regeneration in animal models, this study shows what looks like real progress.

Sun F, Park KK, Belin S, Wang D, Lu T, Chen G, Zhang K, Yeung C, Feng G, Yankner BA, & He Z (2011). Sustained axon regeneration induced by co-deletion of PTEN and SOCS3. Nature, 480 (7377), 372-5 PMID: 22056987

What is a gene "for"?

2011-11-07T02:35:00.000-08:00

“Scientists discover gene for autism” (or ovarian cancer, or depression, cocaine addiction, obesity, happiness, height, schizophrenia… and whatever you’re having yourself). These are typical newspaper headlines (all from the last year) and all use the popular shorthand of “a gene for” something. In my view, this phrase is both lazy and deeply misleading and has caused widespread confusion about what genes are and do and about their influences on human traits and disease.

The problem with this phrase stems from the ambiguity in what we mean by a “gene” and what we mean by “for”. These can mean different things at different levels and unfortunately these meanings are easily conflated. First, a gene can be defined in several different ways. From a molecular perspective, it is a segment of DNA that codes for a protein, along with the instructions for when and where and in what amounts this protein should be made. (Some genes encode RNA molecules, rather than proteins, but the general point is the same). The function of the gene on a cellular level is thus to store the information that allows this protein to be made and its production to be regulated. So, you have a gene for haemoglobin and a gene for insulin and a gene for rhodopsin, etc., etc. (around 25,000 such genes in the human genome). The question of what the gene is for then becomes a biochemical question – what does the encoded protein do?

But that is not the only way or probably even the main way that people think about what genes do – it is certainly not how geneticists think about it. The function of a gene is commonly defined (indeed often discovered) by looking at what happens when it is mutated – when the sequence of DNA bases that make up the gene is altered in some way which affects the production or activity of the encoded protein. The visible manifestation of the effect of such a mutation (the phenotype) is usually defined at the organismal level – altered anatomy or physiology or behaviour, or often the presence of disease. From this perspective, the gene is defined as a separable unit of heredity – something that can be passed on from generation to generation that affects a particular trait. This is much closer to the popular concept of a gene, such as a gene for blue eyes or a gene for breast cancer. What this really means is a mutation for blue eyes or a mutation for breast cancer.

The challenge is in relating the function of a gene at a cellular level to the effects of variation in that gene, which are most commonly observed at the organismal level. The function at a cellular level can be defined pretty directly (make protein X) but the effect at the organismal level is much more indirect and context-dependent, involving interaction with many other genes that also contribute to the phenotype in question, often in highly complex and dynamic systems.

If you are talking about a simple trait like blue eyes, then the function of the gene at a molecular level can actually be related to the mutant phenotype fairly easily – the gene encodes an enzyme that makes a brown pigment. When that enzyme is not made or does not work properly, the pigment is not made and the eyes are blue. Easy-peasy.

But what if the phenotype is in some complex physiological trait, or even worse, a psychological or behavioural trait? These traits are often defined at a very superficial level, far removed from the possible molecular origins of individual differences. The neural systems underlying such traits may be incredibly complex – they may break down due to very indirect consequences of mutations in any of a large number of genes.

For example, mutations in the genes encoding two related proteins, neuroligin-3 and neuroligin-4 have been found in patients with autism and there is good evidence that these mutations are responsible for the condition in those patients. Does this make them “genes for autism”? That phrase really makes no sense – the function of these genes is certainly not to cause autism, nor is it to prevent autism. The real link between these genes and autism is extremely indirect. The neuroligin proteins are involved in the formation of synaptic connections between neurons in the developing brain. If they are mutated, then the connections that form between specific types of neurons are altered. This changes the function of local circuits in the brain, affecting their information-processing parameters and changing how different regions of the brain communicate. Ultimately, this impacts on neural systems controlling things like social behaviour, communication and behavioural flexibility, leading to the symptoms that define autism at the behavioural level.

So, mutations in these genes can cause autism, but these are not genes for autism. They are not even usefully or accurately thought of as genes for social behaviour or for cognitive flexibility – they are required, along with the products of thousands of other genes, for those faculties to develop.

But perhaps there are other genetic variants in the population that affect the various traits underlying these faculties – not in such a severe way as to result in a clinical disorder, but enough to cause the observed variation across the general population. It is certainly true that traits like extraversion are moderately heritable – i.e., a fair proportion of the differences between people in this trait are attributable to genetic differences. When someone asks “are there genes for extraversion?”, the answer is yes if they mean “are differences in extraversion partly due to genetic differences?”. If they mean the function of some genetic variant is to make people more or less extroverted, then they have suddenly (often unknowingly) gone from talking about the activity of a gene or the effect of mutation of that gene to considering the utility of a specific variant.

This suggests a deeper meaning – not just that the gene has a function, but that it has a purpose – in biological terms, this means that a particular version of the gene was selected for on the basis of its effect on some trait. This can be applied to the specific sequence of a gene in humans (as distinct from other animals) or to variants within humans (which may be specific to sub-populations or polymorphic within populations).

While geneticists may know what they mean by the shorthand of “genes for” various traits, it is too easily taken in different, unintended ways. In particular, if there are genes “for” something, then many people infer that the something in question is also “for” something. For example, if there are “genes for homosexuality”, the inference is that homosexuality must somehow have been selected for, either currently or under some ancestral conditions. Even sophisticated thinkers like Richard Dawkins fall foul of this confusion – the apparent need to explain why a condition like homosexual orientation persists. Similar arguments are often advanced for depression or schizophrenia or autism – that maybe in ancestral environments, these conditions conferred some kind of selective advantage. That is one supposed explanation for why “genes for schizophrenia or autism” persist in the population.

Natural selection is a powerful force but that does not mean every genetic variation we see in humans was selected for, nor does it mean every condition affecting human psychology confers some selective advantage. In fact, mutations like those in the neuroligin genes are rapidly selected against in the population, due to the much lower average number of offspring of people carrying them. The problem is that new ones keep arising – in those genes and in thousands of other required to build the brain. By analogy, it is not beneficial for my car to break down – this fact does not require some teleological explanation. Breaking down occasionally in various ways is not a design feature – it is just that highly complex systems bring an associated higher risk due to possible failure of so many components.

So, just because the conditions persist at some level does not mean that the individual variants causing them do. Most of the mutations causing disease are probably very recent and will be rapidly selected against – they are not “for” anything.

Jamain S, Quach H, Betancur C, Råstam M, Colineaux C, Gillberg IC, Soderstrom H, Giros B, Leboyer M, Gillberg C, Bourgeron T, & Paris Autism Research International Sibpair Study (2003). Mutations of the X-linked genes encoding neuroligins NLGN3 and NLGN4 are associated with autism. Nature genetics, 34 (1), 27-9 PMID: 12669065

Does brain plasticity trump innateness?

2011-10-01T08:59:00.000-07:00

The fact that the adult brain is very plastic is often held up as evidence against the idea that many psychological, cognitive or behavioural traits are innately determined. At first glance, there does indeed appear to be a paradox. On the one hand, behavioural genetic studies show that many human psychological traits are strongly heritable and thus likely determined, at least in part, by innate biological differences. On the other, it is very clear that even the adult brain is highly plastic and changes itself in response to experience.

The evidence on both sides is very strong. In general, for traits like intelligence and personality characteristics such as extraversion, neuroticism or conscientiousness, among many others, the findings from genetic studies are remarkably consistent. Just as for physical traits, people who are more closely related resemble each other for psychological traits more than people with a more distant relationship. Twin study designs get around the obvious objection that such similarities might be due to having been raised together. Identical twins tend to be far more like each other for these traits than fraternal twins, though the family environment is shared in both cases. Even more telling, identical twins who are raised apart tend to be pretty much as similar to each other as pairs who are raised together. Clearly, we come fairly strongly pre-wired and the family environment has little effect on these kinds of traits.

Yet we know the brain can “change itself”. You could say that is one of its main jobs in fact – altering itself in response to experience to better adapt to the conditions in which it finds itself. For example, as children learn a language, their auditory system specialises to recognise the typical sounds of that language. Their brains become highly expert at distinguishing those sounds and, in the process, lose the ability to distinguish sounds they hear less often. (This is why many Japanese people cannot distinguish between the sounds of the letters “l” and “r”, for example, and why many Westerners have difficulty hearing the crucial tonal variations in languages like Cantonese). Learning motor skills similarly improves performance and induces structural changes in the relevant brain circuits. In fact, most circuits in the brain develop in an experience-dependent fashion, summed up by two adages: “cells that fire together, wire together” and “use it or lose it”.

Given the clear evidence for brain plasticity, the implication would seem to be that even if our brains come pre-wired with some particular tendencies, that experience, especially early experience, should be able to override them.

I would argue that the effect of experience-dependent development is typically exactly the opposite – that while the right kind of experience can, in principle, act to overcome innate tendencies, in practice, the effect is reversed. The reason is that our innate tendencies shape the experiences we have, leading us to select ones that tend instead to reinforce or even amplify these tendencies. Our environment does not just shape us – we shape it.

A child who is naturally shy – due to innate differences in the brain circuits mediating social behaviour, general anxiety, risk-aversion and other parameters – will tend to have less varied and less intense social experience. As a result, they will not develop the social skills that might make social interaction more enjoyable for them. A vicious circle emerges – perhaps intense practice in social situations would alter the preconfigured settings of a shy child’s social brain circuits but they tend not to get that experience, precisely because of those settings. In contrast, their extroverted classmates may, by constantly seeking out social interactions, continue to develop this innate faculty.

This circle may be most vicious in children with autism, most of whom have a reduced level of innate interest in other people. They tend, for example, not to find faces as intrinsically fascinating as other infants. This may contribute to a delay in language acquisition, as they miss out on interpersonal cues that strongly facilitate learning to speak.

A similar situation may hold for children who have difficulties in reading or with mathematics. Dyslexia seems to be caused by an innate difficulty in associating the sounds and shapes of letters. This can be traced to genetic effects during early development of the brain, which may cause interruptions in long-range connections between brain areas. This innate disadvantage is cruelly amplified by the typical experience of many dyslexics. Learning to read is hard enough and requires years of practice and active instruction. For children who have basic difficulties in recognising letters and words, reading remains effortful for far longer and they will therefore tend to read less, missing out on the intensive practice that would help their brain circuitry specialise for reading.

Though less widely known, dyscalculia (a selective difficulty in mathematics) is equally common and shares many characteristics with dyslexia. The initial problem is in innate number sense – the ability to estimate and compare small numbers of objects. This faculty is present in very young infants and even shared with many other animal species, notably crows. Formal mathematical instruction is required to build on this innate number sense but also crucially relies on it. As with reading, mathematics requires hard work to learn and if numbers are inherently mysterious then this will change the nature of the child’s experience, lessen interest and reduce practice. At the other end of the spectrum, those with strong mathematical talent may gravitate towards the subject, further amplifying the differences between these two groups.

Thus, while a certain type of experience can alter the innate tendency, the innate tendency makes getting that experience far less likely. Brain plasticity tends instead to amplify initial differences.

That sounds rather fatalistic, but the good news is that this vicious circle can be broken if innate difficulties are recognised early enough – by actively changing the nature of early experience. There is good evidence that intense early intervention in children with autism (such as Applied Behaviour Analysis) allows them to compensate for innate deficits and lead to improvements in cognitive, communication and adaptive skills. Similarly intense intervention in children with dyslexia has also proven effective. Thus, even if it is not possible to reverse whatever neurodevelopmental differences lead to these kinds of deficits, it should at least be possible to prevent their being amplified by subsequent experience.

Duff FJ, & Clarke PJ (2011). Practitioner Review: Reading disorders: what are the effective interventions and how should they be implemented and evaluated? Journal of child psychology and psychiatry, and allied disciplines, 52 (1), 3-12 PMID: 21039483

Vismara, L., & Rogers, S. (2010). Behavioral Treatments in Autism Spectrum Disorder: What Do We Know? Annual Review of Clinical Psychology, 6 (1), 447-468 DOI: 10.1146/annurev.clinpsy.121208.131151

Engineering viruses to trace neural connections

2011-09-28T05:46:00.000-07:00

I have a new post on BigThink on engineering viruses to trace neural connections.

A brief hiatus

2011-09-20T11:14:00.000-07:00

Apologies for not posting anything recently. I have something in the works at BigThink and a few more in the pipeline but it has been hard finding the time to blog recently. I hope to be back to it in a couple of weeks.

Split brains, autism and schizophrenia

2011-08-11T01:06:00.000-07:00

A new study suggests that a gene known to be causally linked to schizophrenia and other psychiatric disorders is involved in the formation of connections between the two hemispheres of the brain. DISC1 is probably the most famous gene in psychiatric genetics, and rightly so. It was discovered in a large Scottish pedigree, where 18 members were affected by psychiatric disease.
The diagnoses ranged from schizophrenia and bipolar disorder to depression and a range of “minor” psychiatric conditions. It was found that the affected individuals had all inherited a genetic anomaly – a translocation of genetic material between two chromosomes. This basically involves sections of two chromosomes swapping with each other. In the process, each chromosome is broken, before being spliced back to part of the other chromosome. In this case, the breakpoint on chromosome 1 interrupted a gene, subsequently named Disrupted-in-Schizophrenia-1, or DISC1.

That this discovery was made using classical “cytogenetic” techniques (physically looking at the chromosomes down a microscope) and in a single family is somehow pleasing in an age where massive molecular population-based studies are in vogue. (A win for “small” science).

The discovery of the DISC1 translocation clearly showed that disruption of a single gene could lead to psychiatric disorders like schizophrenia. This was a challenge to the idea that these disorders were “polygenic” – caused by the inheritance in each individual of a large number of genetic variants. As more and more mutations in other genes are being found to cause these disorders, the DISC1 situation can no longer be dismissed as an exception – it is the norm.

It also was the first example of a principle that has since been observed for many other genes – namely that the effects of the mutation can manifest quite variably - not as one specific disorder, but as different ones in different people. Indeed, DISC1 has since been implicated in autism as well as adult-onset disorders. It is now clear from this and other evidence that these apparently distinct conditions are best thought of as variable outcomes that arise, in many cases at least, from disturbances of neurodevelopment.

Since the initial discovery, major research efforts of a growing number of labs have been focused on the next obvious questions: what does DISC1 do? And what happens when it is mutated? What happens in the brain that can explain why psychiatric symptoms result?

We now know that DISC1 has many different functions. It is a cytoplasmic protein - localised inside the cell - that interacts with a very large number of other proteins and takes part in diverse cellular functions, including cell migration, outgrowth of nerve fibres, the formation of dendritic spines (sites of synaptic contact between neurons), neuronal proliferation and regulation of biochemical pathways involved in synaptic plasticity. Many of the proteins that DISC1 interacts with have also been implicated in psychiatric disease.

This new study adds another possible function, and a dramatic and unexpected one at that. This function was discovered from an independent angle, by researchers studying how the two hemispheres of the brain get connected – or more specifically, why they sometimes fail to be connected. The cerebral hemispheres are normally connected by millions of axons which cross the midline of the brain in a structure called the corpus callosum (or “tough body” – (don’t ask)). Very infrequently, people are born without this structure – the callosal axons fail to cross the midline and the two hemispheres are left without this major route of communication (though there are other routes, such as the anterior commissure).

The frequency of agenesis of the corpus callosum has been estimated at between 1 in 1,000 and 1 in 6,000 live births – thankfully very rare. It is associated with a highly variable spectrum of other symptoms, including developmental delay, autistic symptoms, cognitive disabilities extending into the range of mental retardation, seizures and other neurological signs.

Elliott Sherr and colleagues were studying patients with this condition, which is very obvious on magnetic resonance imaging scans (see Figure). They initially found a mother and two children with callosal agenesis who all carried a deletion on chromosome 1, at position 1q42 – exactly where DISC1 is located. They subsequently identified another patient with a similar deletion, which allowed them to narrow down the region and identify DISC1 as a plausible candidate (among some other genes in the deleted region). Because the functions of proteins can be affected not just by large deletions or translocations but also by less obvious mutations that change a single base of DNA, they also sequenced the DISC1 gene in a cohort of callosal agenesis patients and found a number carrying novel mutations that are very likely to disrupt the function of the gene.

While not rock-solid evidence that it is DISC1 that is responsible, these data certainly point to it as the strongest candidate to explain the callosal defect. This hypothesis is strongly supported by findings from DISC1 mutant mice (carrying a mutation that mimics the effect of the human translocation), which also show defects in formation of the corpus callosum. In addition, the protein is strongly expressed in the axons that make up this structure at the time of its development.

The most obvious test of whether disruption of DISC1 really causes callosal agenesis is to look in the people carrying the initial translocation. Remarkably, it is not known whether the original patients in the Scottish pedigree who carry the DISC1 translocation show this same obvious brain structural phenotype. They have, very surprisingly, never been scanned.

This new paper raises the obvious hypothesis that the failure to connect the two hemispheres results in the psychiatric or cognitive symptoms, which variously include reduced intellectual ability, autism and schizophrenia. This seems like too simplistic an interpretation, however. All we have now is a correlation. First, the implication of DISC1 in the acallosal phenotype is not yet definitive – this must be nailed down and replicated. But even if it is shown that disruption of DISC1 causes both callosal agenesis and schizophrenia (or other psychiatric disorders or symptoms), this does not prove a causal link. DISC1 has many other functions and is expressed in many different brain areas (ubiquitously in fact). Any, or indeed, all of these functions may in fact be the cause of psychopathology.

One prediction, if it were true that the lack of connections between the two hemispheres is causal, is that we would expect the majority of patients with callosal agenesis to have these kinds of psychiatric symptoms. In fact, the rates are indeed very high – in different studies it has been estimated that up to 40% of callosal agenesis patients have an autism diagnosis, while about 8% have the symptoms of schizophrenia or bipolar disorder. (Of course, these patients may have other, less obvious brain defects as well, so even this is not definitive).

Conversely, we might naively expect a high rate of callosal agenesis in patients with autism or schizophrenia. However, we know these disorders are extremely heterogeneous and so it is much more likely that this phenotype might be apparent in only a specific (possibly very small) subset of patients. This may indeed be the case – callosal agenesis has been observed in about 3 out of 200 schizophrenia patients (a vastly higher rate than in the general population). Another study, just published, has found that mutations in a different gene – ARID1B – are also associated with callosal agenesis, mental retardation and autism. More generally, there may be subtle reductions in callosal connectivity in many schizophrenia or autism patients (including some autistic savants).

Whether this defect can explain particular symptoms is not yet clear. For the moment, the new study provides yet another possible function of DISC1, and highlights an anatomical phenotype that is apparently present in a subset of autism and schizophrenia cases and that can arise due to mutation in many different genes (of which DISC1 and ARID1B are only two of many known examples).

One final note: formation of the corpus callosum is a dramatic example of a process that is susceptible to developmental variation. What I mean is this: when patients inherit a mutation that results in callosal agenesis, this phenotype occurs in some patients but not all. This is true even in genetically identical people, like monozygotic twins or triplets (or in lines of genetically identical mice). Though the corpus callosum contains millions of nerve fibres, the initial events that establish it involve very small numbers of cells. These cells, which are located at the medial edge of each cerebral hemisphere, must contact each other to enable the fusion of the two hemispheres, forming a tiny bridge through which the first callosal fibres can cross. Once these are across, the rest seem able to follow easily. Because this event involves very few cells at a specific time in development, it is susceptible to random “noise” – fluctuations in the precise amounts of various proteins in the cells, for example. These are not caused by external forces – the noise is inherent in the system. The result is that, in some people carrying such a mutation the corpus callosum will not form at all, while in others it forms apparently completely normally (see figure of triplets, one on left with normal corpus callosum, the other two with it absent). So, an all-or-none effect can arise, without any external factors involved.

This same kind of intrinsic developmental variation may also explain or at least contribute to the variability in phenotypic outcome at the level of psychiatric symptoms when these kinds of neurodevelopmental mutations are inherited. Even monozygotic twins are often discordant for psychiatric diagnoses (concordance for schizophrenia is about 50%, for example). This is often assumed to be due to non-genetic and therefore “environmental” or experiential factors. If these disorders really arise from differences in brain wiring, which we know are susceptible to developmental variation, then differences in the eventual phenotype could actually be completely intrinsic and innate.

Osbun N, Li J, O'Driscoll MC, Strominger Z, Wakahiro M, Rider E, Bukshpun P, Boland E, Spurrell CH, Schackwitz W, Pennacchio LA, Dobyns WB, Black GC, & Sherr EH (2011). Genetic and functional analyses identify DISC1 as a novel callosal agenesis candidate gene. American journal of medical genetics. Part A, 155 (8), 1865-76 PMID: 21739582

Halgren C, Kjaergaard S, Bak M, Hansen C, El-Schich Z, Anderson CM, Henriksen KF, Hjalgrim H, Kirchhoff M, Bijlsma EK, Nielsen M, den Hollander NS, Ruivenkamp CA, Isidor B, Le Caignec C, Zannolli R, Mucciolo M, Renieri A, Mari F, Anderlid BM, Andrieux J, Dieux A, Tommerup N, & Bache I (2011). Corpus Callosum Abnormalities, Mental Retardation, Speech Impairment, and Autism in Patients with Haploinsufficiency of ARID1B. Clinical genetics PMID: 21801163

Welcome to your genome

2011-08-03T02:14:00.000-07:00

There is a common view that the human genome has two different parts – a “constant” part and a “variable” part. According to this view, the bases of DNA in the constant part are the same across all individuals. They are said to be “fixed” in the population. They are what make us all human – they differentiate us from other species. The variable part, in contrast, is made of positions in the DNA sequence that are “polymorphic” – they come in two or more different versions. Some people carry one base at that position and others carry another. The idea is that it is the particular set of such variations that we inherit that makes us each unique (unless we have an identical twin). According to this idea, we each have a hand dealt from the same deck.

The genome sequence (a simple linear code made up of 3 billion bases of DNA in precise order, chopped up onto different chromosomes) is peppered with these polymorphic positions – about 1 in every 1,250 bases. That makes about 2,400,000 polymorphisms in each genome (and we each carry two copies of the genome). That certainly seems like plenty of raw material, with limitless combinations that could explain the richness of human diversity. This interpretation has fuelled massive scientific projects to try and find which common polymorphisms affect which traits. (Not to mention personal genomics companies who will try to tell you your risk of various diseases based on your profile of such polymorphisms).

The problem with this view is that it is wrong. Or at least woefully incomplete.

The reason is it ignores another source of variation: very rare mutations in those bases that are constant across the vast majority of individuals. There is now very good evidence that it is those kinds of mutations that contribute most to our individuality. Certainly, they are much more likely to affect a protein’s function and much more likely to contribute to genetic disease. We each carry hundreds of such rare mutations that can affect protein function or expression and are much more likely to have a phenotypic impact than common polymorphisms.

Indeed, far from most of the genome being effectively constant, it can be estimated that every position in the genome has been mutated many, many times over in the human population. And each of us carries hundreds of new mutations that arose during generation of the sperm and egg cells that fused to form us. New mutations may spread in the pedigree or population in which they arise for some time, depending in part on whether they have a deleterious effect or not. Ones that do will likely be quickly selected against.

A new paper from the 1000 genomes project consortium shows that:

“the vast majority of human variable sites are rare and that the majority of rare variants exhibit, at most, very little sharing among continental populations”.

This is a much more fluid picture of genetic variation than we are used to. We are not all dealt a genetic hand from the same deck – each population, sub-population, kindred, nuclear family has a distinct set of rare genetic variants. And each of these decks contains a lot of jokers – the new mutations that arise each time a hand is dealt.

Why have such rare mutations generally been ignored while the polymorphic sites have been the focus of intense research? There are several reasons, some practical and some theoretical. Practically, it has until recently been almost impossible to systematically find very rare mutations. To do so requires that we sequence the whole genome, which has only recently become feasible. In contrast, methods to survey which bases you carry at all the polymorphic sites across the genome were developed quite some time ago now and are relatively cheap to use. (They rely on sampling about 500,000 such sites around the genome – because of unevenness in the way different bits of chromosomes get swapped when sperm and eggs are made, this sample actually tells you about most of the variable sites across the whole genome). So, there has been a tendency to argue that polymorphic sites will be major contributors to human phenotypes (especially diseases) because those have been the only ones we have been able to look at.

Unfortunately, the results of genome-wide association studies, which aim to identify common variants associated with traits or diseases, have been disappointing. This is especially true for disorders with large effects on fitness, such as schizophrenia or autism. Some variants have been found but their effects, even in combination are very small. Most of the heritability of most of the traits or diseases examined to date remains unexplained. (There are some important exceptions, especially for diseases that strike only late in life and for things like drug responses, where selective pressures to weed out deleterious alleles are not at play).

In contrast, many more rare mutations causing disease are being discovered all the time, and the pace of such discoveries is likely to increase with technological advances. The main message that emerges from these studies has been called by Mary-Claire King the “Anna Karenina principle”, based on Tolstoy’s famous opening line:

“Happy families are all alike; every unhappy family is unhappy in its own way”

But can such rare variants really explain the “missing heritability” of these disorders? Some people have argued that they cannot, but this seems to me to be based on a pervasive misconception of how the heritability of a trait is measured and what it means. According to this misconception, if a trait is heritable across the population, that heritability cannot be accounted for by rare variants. After all, if a mutation only occurs in one or a few individuals, it could only minimally (nearly negligibly) contribute to heritability across the whole population. That is true. However, heritability is not measured across the population – it is measured in families and then averaged across the population.

In humans, it is usually derived by comparing phenotypes between people of different genetic relatedness (identical versus fraternal twins, siblings, parents, cousins, etc.). The values of these comparisons are then averaged across large numbers of pairs to allow estimates of how much genetic variance affects phenotypic variance – the population heritability. While a specific rare mutation may only affect the phenotype within a single family, such mutations could, collectively, explain all of the heritability. Completely different sets of mutations could be affecting the trait or causing the disease in different families.

The next few years will reveal the true impact of rare mutations. We should certainly expect complex genetic interactions and some real effects of common polymorphisms. But the idea that our traits are determined simply by the combination of variants we inherit from a static pool in the population is no longer tenable. We are each far more unique than that.

(And if your personal genomics company isn’t offering to sequence your whole genome, it’s not personal enough).

Gravel S, Henn BM, Gutenkunst RN, Indap AR, Marth GT, Clark AG, Yu F, Gibbs RA, The 1000 Genomes Project, & Bustamante CD (2011). Demographic history and rare allele sharing among human populations. Proceedings of the National Academy of Sciences of the United States of America, 108 (29), 11983-11988 PMID: 21730125

Walsh CA, & Engle EC (2010). Allelic diversity in human developmental neurogenetics: insights into biology and disease. Neuron, 68 (2), 245-53 PMID: 20955932

McClellan, J., & King, M. (2010). Genetic Heterogeneity in Human Disease Cell, 141 (2), 210-217 DOI: 10.1016/j.cell.2010.03.032

Hallucinating neural networks

2011-07-25T12:06:00.000-07:00

Hearing voices is a hallmark of schizophrenia and other psychotic disorders, occurring in 60-80% of cases. These voices are typically identified as belonging to other people and may be voicing the person’s thoughts, commenting on their actions or ideas, arguing with each other or telling the person to do something. Importantly, these auditory hallucinations are as subjectively real as any external voices. They may in many cases be critical or abusive and are often highly distressing to the sufferer.

However, many perfectly healthy people also regularly hear voices – as many as 1 in 25 according to some studies, and in most cases these experiences are perfectly benign. In fact, we all hear voices “belonging to other people” when we dream – we can converse with these voices, waiting for their responses as if they were derived from external agents. Of course, these percepts are actually generated by the activity of our own brain, but how?

There is good evidence from neuroimaging studies that the same areas that respond to external speech are active when people are having these kinds of auditory hallucinations. In fact, inhibiting such areas using transcranial magnetic stimulation may reduce the occurrence or intensity of heard voices. But why would the networks that normally process speech suddenly start generating outputs by themselves? Why would these outputs be organised in a way that fits speech patterns, as opposed to random noise? And, most importantly, why does this tend to occur in people with schizophrenia? What is it about the pathology of this disorder that makes these circuits malfunction in this specific way?

An interesting approach to try and get answers to these questions has been to model these circuits in artificial neural networks. If you can generate a network that can process speech inputs and find certain conditions under which it begins to spontaneously generate outputs, then you may have an informative model of auditory hallucinations. Using this approach, a couple of studies from several years ago from the group of Ralph Hoffman have found some interesting clues as to what may be going on, at least on an abstract level.

Their approach was to generate an artificial neural network that could process speech inputs. Artificial neural networks are basically sets of mathematical functions modelled in a computer programme. They are designed to simulate the information-processing functions carried out by individual neurons and, more importantly, the computational functions carried out by an interconnected network of such neurons. They are necessarily highly abstract, but they can recapitulate many of the computational functions of biological neural networks. Their strength lies in revealing unexpected emergent properties of such networks.

The particular network in this case consisted of three layers of neurons – an input layer, an output layer, and a “hidden” layer in between – along with connections between these elements (from input to hidden and from hidden to output, but crucially also between neurons within the hidden layer). “Phonetic” inputs were fed into the input layer – these consisted of models of speech sounds constituting grammatical sentences. The job of the output layer was to report what was heard – representing different sounds by patterns of activation of its forty-three neurons. Seems simple, but it’s not. Deciphering speech sounds is actually very difficult as individual phonetic elements can be both ambiguous and variable. Generally, we use our learned knowledge of the regularities of speech and our working memory of what we have just heard to anticipate and interpret the next phonemes we hear – forcing them into recognisable categories. Mimicking this function of our working memory is the job of the hidden layer in the artificial neural network, which is able to represent the prior inputs by the pattern of activity within this layer, providing a context in which to interpret the next inputs.

The important thing about neural networks is they can learn. Like biological networks, this learning is achieved by altering the strengths of connections between pairs of neurons. In response to a set of inputs representing grammatical sentences, the network weights change in such a way that when something similar to a particular phoneme in an appropriate context is heard again, the pattern of activation of neurons representing that phoneme is preferentially activated over other possible combinations.

The network created by these researchers was an able student and readily learned to recognise a variety of words in grammatical contexts. The next thing was to manipulate the parameters of the network in ways that are thought to model what may be happening to biological neuronal networks in schizophrenia.

There are two major hypotheses that were modelled: the first is that networks in schizophrenia are “over-pruned”. This fits with a lot of observations, including neuroimaging data showing reduced connectivity in the brains of people suffering with schizophrenia. It also fits with the age of onset of the florid expression of this disorder, which is usually in the late teens to early twenties. This corresponds to a period of brain maturation characterised by an intense burst of pruning of synapses – the connections between neurons.

In schizophrenia, the network may have fewer synapses to begin with, but not so few that it doesn’t work well. This may however make it vulnerable to this process of maturation, which may reduce its functionality below a critical threshold. Alternatively, the process of synaptic pruning may be overactive in schizophrenia, damaging a previously normal network. (The evidence favours earlier disruptions).

The second model involves differences in the level of dopamine signalling in these circuits. Dopamine is a neuromodulator – it alters how neurons respond to other signals – and is a key component of active perception. It plays a particular role in signalling whether inputs match top-down expectations derived from our learned experience of the world. There is a wealth of evidence implicating dopamine signalling abnormalities in schizophrenia, particularly in active psychosis. Whether these abnormalities are (i) the primary cause of the disease, (ii) a secondary mechanism causing specific symptoms (like psychosis), or (iii) the brain attempting to compensate for other changes is not clear.

Both over-pruning and alterations to dopamine signalling could be modelled in the artificial neural network, with intriguing results. First, a modest amount of pruning, starting with the weakest connections in the network, was found to actually improve the performance of the network in recognising speech sounds. This can be understood as an improvement in the recognition and specificity of the network for sounds which it had previously learned and probably reflects the improvements seen in human language learners, along with the concomitant loss in ability to process or distinguish unfamiliar sounds (like “l” and “r” for Japanese speakers).

However, when the network was pruned beyond a certain level, two interesting things happened. First, its performance got noticeably worse, especially when the phonetic inputs were degraded (i.e., the information was incomplete or ambiguous). This corresponds quite well with another symptom of schizophrenia, especially those who experience auditory hallucinations - sufferers show phonetic processing deficits under challenging conditions, such as a crowded room.

The second effect was even more striking – the network started to hallucinate! It began to produce outputs even in the absence of any inputs (i.e., during “silence”). When not being driven by reliable external sources of information, the network nevertheless settled into a state of activity that represented a word. The reason the output is a word and not just a meaningless pattern of neurons is that the previous learning that the network undergoes means that patterns representing words represent “attractors” – if some random neurons start to fire, the weighted connections representing real words will rapidly come to dominate the overall pattern of activity in the network, resulting in the pattern corresponding to a word.

Modeling alterations in dopamine signalling also produced both a defect in parsing degraded speech inputs and hallucinations. Too much dopamine signalling produced these effects but so did a combination of moderate over-pruning and compensatory reductions in dopamine signalling, highlighting the complex interactions possible.

The conclusion from these simulations is not necessarily that this is exactly how hallucinations emerge. After all, the artificial neural networks are pretty extreme abstractions of real biological networks, which have hundreds of different types of neurons and synaptic connections and which are many orders of magnitude more complex numerically. But these papers do provide aat least a conceptual demonstration of how a circuit designed to process speech sounds can fail in such a specific and apparently bizarre way. They show that auditory hallucinations can be viewed as the outputs of malfunctioning speech-processing circuits.

They also suggest that different types of insult to the system can lead to the same type of malfunction. This is important when considering new genetic data indicating that schizophrenia can be caused by mutations in any of a large number of genes affecting how neural circuits develop. One way that so many different genetic changes could lead to the same effect is if the effect is a natural emergent property of the neural networks involved.

Hoffman, R., & Mcglashan, T. (2001). Book Review: Neural Network Models of Schizophrenia The Neuroscientist, 7 (5), 441-454 DOI: 10.1177/107385840100700513

Hoffman, R., & McGlashan, T. (2006). Using a Speech Perception Neural Network Computer Simulation to Contrast Neuroanatomic versus Neuromodulatory Models of Auditory Hallucinations Pharmacopsychiatry, 39, 54-64 DOI: 10.1055/s-2006-931496

Environmental influences on autism - splashy headlines from dodgy data

2011-07-08T05:56:00.000-07:00

A couple of recent papers have been making headlines in relation to autism, one claiming that it is caused less by genetics than previously believed and more by the environment and the other specifically claiming that antidepressant use by expectant mothers increases the risk of autism in the child. But are these conclusions really supported by the data? Are they strongly enough supported to warrant being splashed across newspapers worldwide, where most readers will remember only the headline as the take-away message? The legacy of the MMR vaccination hoax shows how difficult it can be to counter overblown claims and the negative consequences that can arise as a result.

So, do these papers really make a strong case for their major conclusions? The first gives results from a study of twins in California. Twin studies are a classic method to determine whether something is caused by genetic or environmental factors. The method asks, if one twin in a pair is affected by some disorder (autism in this case), with what frequency is the other twin also affected? The logic is very simple: if something is caused by environmental factors, particularly those within a family, then it should not matter whether the twins in question are identical or fraternal – their risk should be the same because their exposure is the same. On the other hand, if something is caused by genetic mutations, and if one twin has the disorder, then the rate of occurrence of the disorder in the other twin should be much higher if they are genetically identical than if they only share half their genes, as fraternal twins do.

Working backwards, if the rate of twin concordance for affected status are about the same for identical and fraternal twins, this is strong evidence for environmental factors. If the rate is much higher in monozygotic twins, this is strong evidence for genetic factors. Now to the new study. What they found was that the rate of concordance for monozygotic (identical) twins was indeed much higher than for dizyogotic (fraternal) twins – about twice as high on average.

For males: MZ: 0.58, DZ: 0.21
For females: MZ: 0.60, DZ: 0.27

Those numbers are for the diagnosis of strict autism. The rate of “autism spectrum disorder”, which encompasses a broader range of disability, showed similar results:

Males: MZ: 0.77, DZ: 0.31
Females: MZ: 0.50, DZ: 0.36.

These numbers fit pretty well with a number of other recent twin studies, all of which have concluded that they provide evidence for strong heritability of the disorder – i.e., that whether or not someone develops autism is largely (though not exclusively) down to genetics.

So, why did these authors reach a different conclusion and should their study carry any more weight than others? On the latter point, the study is significantly larger than many that have preceded it. This study looked at 192 twin pairs, each with at least one affected twin. However, some recent studies have been comparable or even larger: Lichtenstein and colleagues looked at 117 twin pairs and Rosenberg and colleagues looked at 277 twin pairs. These studies found eveidence for very high heritability and negligible shared environmental effects.

Another potentially important difference is in how the sample was ascertained. Hallmayer and colleagues claim that their assessment of affected status was more rigorous than for other studies and this may be true. However, it has previously been found that less rigorous assessments correlate extremely well with the more standardised assessments, so this is unlikely to be a major factor. In addition, there is very strong evidence that disorders like autism, ADHD, epilepsy, intellectual disability, tic disorders and others all share common etiology – having a broader diagnosis is therefore probably more appropriate.

In any case, the numbers they came up with for concordance rates were pretty similar across these studies. So, why did they end up with a different conclusion? That’s not a rhetorical question – I actually don’t know the answer and if anyone else does I would love to hear it. Given the data, I don’t know how they conclude that they provide evidence for shared environmental effects.

The methodology involves some statistical modeling that tries to tease out the sources of variance. However, this modeling is based completely on a multifactorial threshold model for the disorder - the idea that autism arises when the collective burden of individually minor genetic or environmental insults passes some putative threshold. Sounds plausible, but there is in fact no evidence - at all - that this model applies to autism. In fact, it seems most likely that autism really is an umbrella term for a collection of distinct genetic disorders caused by mutations in separate genes, but which happen to cause common phenotypes (or symptoms).

If that is the case, then what the twin concordance rates actually measure is the penetrance of such mutations – if one inherits mutation X, how often does that actually lead to autism? For monozygotic twins, let us assume that the affected proband (the first twin diagnosed) has such a mutation. Because they are genetically identical, the other one must too. The chance that the other twin will develop autism thus depends on the penetrance of the mutation – some mutations are more highly penetrant than others, giving a much higher probability of developing a specific phenotype. If we average across all MZ twin pairs we therefore get an average penetrance across all such putative mutations. Now, if such mutations are dominant, as many of the known ones are, then the chance that a dizygotic twin will inherit it is 50%, while the penetrance should remain the same. So, this model would predict that the rate of co-occurrence in DZ twins should be about half that of MZ twins, exactly as observed. (No stats required).

The conclusions from this study that the heritability is only modest and that a larger fraction of variance (55%!) is caused by shared environment thus seem extremely shaky. This is reinforced by the fact that the confidence intervals for these estimates are extremely wide (for the effect of shared environment the 95% confidence interval ranges from 9% to 81%). Certainly not enough to overturn all the other data from other studies.

What about epidemiological studies that have shown statistical evidence of increased risk of autism associated with a variety of other factors, including maternal diabetes, antidepressant use, season and place of brith? All of these factors have been linked with modest increases in the risk of autism. Don’t these prove there are important environmental factors? Well, first, they don’t prove causation, they provide a statistical evidence for an association between the two factors, which is not at all the same thing. Second, the increase in risk is usually on the order of about two-fold. Twice the risk may sound like a lot, but it's only a 1% increase (from 1 to 2%), compared with some known mutations, which increase risk by 50-fold or more.

The main problem with these kinds of studies (and especially with how they are portrayed in the media) is that they are correlational and so you cannot establish a causal link directly from them. In some cases, two different correlated parameters (like red hair and freckles, for example) may actually be caused by an unmeasured third parameter. For example, in the recently published study, the use of antidepressants of the SSRI (selective serotonin reuptake inhibitor) class in mothers was associated with modestly increased risk of autism in the progeny. This association could be because SSRIs disrupt neural development in the fetus (perfectly plausible) but could alternatively be due to the known genetic link between risk of depression and risk of autism. Rates of depression are known to be higher in relatives of autistic people, so SSRI use could just be a proxy for that condition. The authors claim to have corrected for that by comparing rates of autism in the progeny of depressed mothers who were not prescribed SSRIs versus those who were but one might imagine that the severity of depression would be higher among those prescribed an antidpressant. In addition, the authors are careful to note that their findings were based on a small number of children exposed and that "Further studies are needed to replicate and extend these findings". As with many such findings, this association may or may not hold up with additional study.

As for season and place of birth, those findings are better replicated and, interestingly, also found for schizophrenia. There is a theory that these effects may relate to maternal vitamin D levels, which can also affect neural development. This also seems plausible enough. However, the problem in really having confidence in these findings and in knowing how to interpret them is that they are population averages with small effect sizes. Overall, it seems quite possible that the environment - especially the prenatal environment - can play a part in the etiology of autism. At the moment, splashy headlines notwithstanding, genetic factors look much more important and genetic studies much more likely to give us the crucial entry points to the underlying biology.

Hallmayer J, Cleveland S, Torres A, Phillips J, Cohen B, Torigoe T, Miller J, Fedele A, Collins J, Smith K, Lotspeich L, Croen LA, Ozonoff S, Lajonchere C, Grether JK, & Risch N (2011). Genetic Heritability and Shared Environmental Factors Among Twin Pairs With Autism. Archives of general psychiatry PMID: 21727249

Lichtenstein P, Carlström E, Råstam M, Gillberg C, & Anckarsäter H (2010). The genetics of autism spectrum disorders and related neuropsychiatric disorders in childhood. The American journal of psychiatry, 167 (11), 1357-63 PMID: 20686188

Rosenberg, R., Law, J., Yenokyan, G., McGready, J., Kaufmann, W., & Law, P. (2009). Characteristics and Concordance of Autism Spectrum Disorders Among 277 Twin Pairs Archives of Pediatrics and Adolescent Medicine, 163 (10), 907-914 DOI: 10.1001/archpediatrics.2009.98

Croen LA, Grether JK, Yoshida CK, Odouli R, & Hendrick V (2011). Antidepressant Use During Pregnancy and Childhood Autism Spectrum Disorders. Archives of general psychiatry PMID: 21727247

On discovering you’re an android

2011-07-04T02:59:00.000-07:00

Deckard: She's a replicant, isn't she?
Tyrell: I'm impressed. How many questions does it usually take to spot them?
Deckard: I don't get it, Tyrell.
Tyrell: How many questions?
Deckard: Twenty, thirty, cross-referenced.
Tyrell: It took more than a hundred for Rachael, didn't it?
Deckard: [realizing Rachael believes she's human] She doesn't know.
Tyrell: She's beginning to suspect, I think.
Deckard: Suspect? How can it not know what it is?

A very discomfiting realisation, discovering you are an android. That all those thoughts and ideas and feelings you seem to be having are just electrical impulses zapping through your circuits. That you are merely a collection of physical parts, whirring away. What if some of them break and you begin to malfunction? What if they wear down with use and someday simply fail? The replicants in BladeRunner rail against their planned obsolescence, believing in the existence of their own selves, even with the knowledge that those selves are merely the products of machinery.

The idea that the self, or the conscious mind, emerges from the workings of the physical structures of the brain – with no need to invoke any supernatural spirit, essence or soul – is so fundamental to modern neuroscience that it almost goes unmentioned. It is the tacitly assumed starting point for discussions between neuroscientists, justified by the fact that all the data in neuroscience are consistent with it being true. Yet it is not an idea that the vast majority of the population is at all comfortable with or remotely convinced by. Its implications are profound and deeply unsettling, prompting us to question every aspect of our most deeply held beliefs and intuitions.

This idea has crept along with little fanfare – it did not emerge all at once like the theory of evolution by natural selection. There was no sudden revolution, no body of evidence proffered in a single moment that overturned the prevailing dogma. While the Creator was toppled with a single, momentous push, the Soul has been slowly chipped away at over a hundred years or more, with most people blissfully unaware of the ongoing assault. But its demolition has been no less complete.

If you are among those who is skeptical of this claim or who feels, as many do, that there must be something more than just the workings of the brain to explain the complexities of the human mind and the qualities of subjective experience (especially your own), then first ask yourself: what kind of evidence would it take to convince you that the function of the brain is sufficient to explain the emergence of the mind?

Imagine you came across a robot that performed all the functions a human can perform – that reported a subjective experience apparently as rich as yours. If you were able to observe that the activity of certain circuits was associated with the robot’s report of subjective experience, if you could drive that experience by activating particular circuits, if you could alter it by modifying the structure or function of different circuits, would there be any doubt that the experience arose from the activity of the circuits? Would there be anything left to explain?

The counter-argument to this thought experiment is that it would never be possible to create a robot that has human-like subjective experience (because robots don’t have souls). Well, all those kinds of experiments have, of course, been done on human beings, tens of thousands of times. Functional magnetic resonance imaging methods let us correlate the activity of particular brain circuits with particular behaviours, perceptions or reports of inward states. Direct activation of different brain areas with electrodes is sufficient to drive diverse subjective states. Lesion studies and pharmacological manipulations have allowed us to map which brain areas and circuits, neurotransmitters and neuromodulators are required for which functions, dissociating different aspects of the mind. Finally, differences in the structure or function of brain circuits account for differences in the spectrum of traits that make each of us who we are as individuals: personality, intelligence, cognitive style, perception, sexual orientation, handedness, empathy, sanity – effectively everything people view as defining characteristics of a person. (Even firm believers in a soul would be reluctant recipients of a brain transplant, knowing full well that their “self” would not survive the procedure).

The findings from all these kinds of approaches lead to the same broad conclusion: the mind arises from the activity of the brain – and nothing else. What neuroscience has done is correlated the activity of certain circuits with certain mental states, shown that this activity is required for these states to arise, shown that differences in these circuits affect the quality of these states and finally demonstrated that driving these circuits from the outside is sufficient to induce these states. That seems like a fairly complete scientific explanation of the phenomenon of mental states. If we had those data for our thought-experiment robot, we would be pretty satisfied that we understood how it worked (and could make useful predictions about how it would behave and what mental states it would report, given enough information of the activity of its circuits).

However, many philosophers (and probably a majority of people) would argue that there is something left to explain. After all, I don’t feel like an android – one made of biological rather than electronic materials, but a machine made solely of physical parts nonetheless. I feel like a person, with a rich mental life. How can the qualities of my subjective experience be produced by the activity of various brain circuits?

Many would claim, in fact, that subjective experience is essentially “ineffable” – it cannot be described in physical terms and cannot thus be said to be physical. It must therefore be non-physical, immaterial or even supernatural. However, the fact that we cannot conceive of how a mental state could arise from a brain state is a statement about our current knowledge and our powers of imagination and comprehension, not about the nature of the brain-mind relationship. As an argument, what we currently can or cannot conceive of has no bearing on the question. The strong intuition that the mind is more than just the activity of the brain is reinforced by an unfortunate linguistic accident – that the word “mind” is grammatically a noun, when really it should be a verb. At least, it does not describe an object or a substance, but a process or a state. It is not made of stuff but of the dynamic relations between bits of stuff.

When people argue that activity of some brain circuit is not identical to a subjective experience or sufficient to explain it, they are missing a crucial point – it is that activity in the context of the activity of the entire rest of the nervous system that generates the quality of the subjective experience at any moment. And those who dismiss this whole approach as scientific reductionism ad absurdum, claiming that the richness of human experience could not be explained merely by the activity of the brain should consider that there is nothing “mere” about it – with hundreds of billions of neurons making trillions of connections, the complexity of the human brain is almost incomprehensible to the human mind. (“If the brain were so simple that we could understand it, then we would be so simple that we couldn’t”).

To be more properly scientific, we should ask: “what evidence would refute the hypothesis that the mind arises solely from the activity of the brain”? Perhaps there is positive evidence available that is inconsistent with this view (as opposed to arguments based merely on our current inability to explain everything about the mind-brain relationship). It is not that easy to imagine what form such positive evidence would take, however – it would require showing that some form of subjective experience either does not require the brain or requires more than just the brain.

With respect to whether subjective experience requires the brain, the idea that the mind is associated with an immaterial essence, spirit or soul has an extension, namely that this soul may somehow outlive the body and be said to be immortal. If there were strong evidence of some form of life after death then this would certainly argue strongly against the sufficiency of neuroscientific materialism. Rather depressingly, no such evidence exists. It would be lovely to think we could live on after our body dies and be reunited with loved ones who have died before us. Unfortunately, wishful thinking does not constitute evidence.

Of course, there is no scientific evidence that there is not life after death, but should we expect neuroscience to have to refute this alternative hypothesis? Actually, the idea that there is something non-physical at our essence is non-refutable – no matter how much evidence we get from neuroscience, it does not prove this hypothesis is wrong. What neuroscience does say is that it is not necessary and has no explanatory power – there is no need of that hypothesis.

Complex interactions among epilepsy genes

2011-06-28T01:15:00.001-07:00

A debate has been raging over the last few years over the nature of the genetic architecture of so-called “complex” disorders. These are disorders - such as schizophrenia, epilepsy, type II diabetes and many others - which are clearly heritable across the population, but which do not show simple patterns of inheritance. A new study looking at the profile of mutations in hundreds of genes in patients with epilepsy dramatically illustrates this complexity. The possible implications are far-reaching, especially for our ability to predict risk based on an individual’s genetic profile, but do these findings apply to all complex disorders?

Complex disorders are so named because, while it is clear that they are highly heritable (risk to an individual increases the more closely related they are to someone who has the disorder), their mode of inheritance is far more difficult to discern. Unlike classical Mendelian disorders (such as cystic fibrosis or Huntington’s disease), these disorders do not show simple patterns of segregation within families that would peg them as recessive or dominant, nor can they be linked to mutations in a single gene. This has led people to propose two very different explanations for how they are inherited.

One theory is that such disorders arise due to unfortunate combinations of large numbers of genetic variants that are common in the population. Individually, such variants would have little effect on the phenotype, but collectively, if they surpass some threshold of burden, they could tip the balance into a pathological state. This has been called the common disease/common variant (CD/CV) model.

The alternative model is that these “disorders” are not really single disorders at all – rather they are umbrella terms for collections of a large number of distinct genetic disorders, which happen to result in a similar set of symptoms. Within any individual or family, the disorder may indeed be caused by a particular mutation. Because many of the disorders in question are very severe, with high mortality and reduced numbers of offspring, these mutations will be rapidly selected against in the population. They will therefore remain very rare and many cases of the disorder may arise from new, or de novo, mutations. This has therefore been called the multiple rare variants (MRV) model.

Lately, a number of mixed models have been proposed by various researchers, including myself. Even classical Mendelian disorders rarely show strictly Mendelian inheritance – instead the effects of the major mutations are invariably affected by modifiers in the genetic background. (These are variants with little effect by themselves but which may have a strong effect in combination with some other mutation). If this sounds like a return to the CD/CV model, there are a couple important distinctions to keep in mind. One is the nature of the mutations involved – the mixed model would still invoke some rare mutation that has a large effect on protein function. It may not always cause the disorder by itself (i.e., not every one who carries it will be affected), but could still be called causative in the sense that if the affected individual did not carry it one would expect they would not suffer from the disorder. The other is the number of mutations or variants involved – under the CD/CV model this could number in the thousands (a polygenic architecture), while under the mixed model one could expect a handful to be meaningfully involved (an oligogenic architecture – see diagram from review in Current Opinion in Neurobiology).

The new study, from the lab of Jeff Noebels, aimed to test these models in the context of epilepsy. Epilepsy is caused by an imbalance in excitation and inhibition within brain circuits. This can arise due to a large number of different factors, including alterations in the structural organisation of the brain, which may be visible on magnetic resonance imaging. Many neurodevelopmental disorders are therefore associated with epilepsy as a symptom (usually one of many). But it can also arise due to more subtle changes, not in the gross structure of the brain or the physical wiring of different circuits, but in the way the electrical activity of individual neurons is controlled.

The electrical properties of any neuron – how excitable it is, how long it remains active, whether it fires a burst of action potentials or single ones, what frequency it fires at and many other important parameters – are determined in large part by the particular ion channel proteins it expresses. These proteins form a pore crossing the membrane of the cell, through which electrically charged ions can pass. Different channels are selective for sodium, potassium or calcium ions and can be activated by different types of stimuli – binding a particular neurotransmitter or a change in the cell’s voltage for example. Many channels are formed from multiple subunits, each of which may be encoded by a different gene. There are hundreds of these genes in several large families, so the resultant complexity is enormous.

Many familial cases of epilepsy have been found to be caused by mutations in ion channel genes. However, most epilepsy patients outside these families do not carry these particular mutations. Therefore, despite these findings and despite the demonstrated high heritability, the particular genetic cause of the vast majority of cases of epilepsy has remained unknown. Large genome-wide association studies have looked for common variants that are associated with risk of epilepsy but have turned up nothing of note. The interpretation has been that common variants do not play a major role in the etiology of idiopathic epilepsy (epilepsy without a known cause).

The rare variants model suggests that many of these cases are caused by single mutations in any of the very large number of ion channel genes. A straightforward experiment to test that would be to sequence all these candidate genes in a large number of epilepsy patients. The hope is that it would be possible to shake out the “low hanging fruit” – obviously pathogenic mutations in some proportion of cases. The difficulty lies in recognising such a mutation as pathogenic when one finds it. This generally relies on some statistical evidence – any individual mutation, or such mutations in general, should be more frequent in epilepsy patients than in unaffected controls. The experiment must therefore involve as large a sample as possible and a control comparison group as well as patients.

Klassen and colleagues sequenced 237 ion channel genes in 152 patients with idiopathic epilepsy and 139 healthy controls. What they found was surprising in several ways. They did find lots of mutations in these genes, but they found them at almost equal frequency in controls as in patients. Even the mutations predicted to have the most severe effects on protein function were not significantly enriched in patients. Indeed, mutations in genes already known to be linked to epilepsy were found in patients and controls alike (though 96% of patients had such a mutation, so did 67% of controls). Either these specific mutations are not pathogenic or their effects can be strongly modified by the genetic background.

More interesting results emerged from looking at the occurrence of multiple mutations in these genes in individuals. 78% of patients vs 30% of controls had two or more mutations in known familial epilepsy genes. A similar trend was observed when looking at specific ion channel gene families, such as GABA receptors or sodium channels.

These data would seem to fit with the idea that an increasing mutational load pushes the system over a threshold into a pathological state. The reality seems more complicated, however, and far more nuanced. Though the average load was lower, many controls had a very high load and yet were quite healthy. It seems that the specific pattern of mutations is far more important than the overall number. This fits very well with the known biology of ion channels and previous work on genetic interactions between mutations in these genes.

Though one might expect a simple relationship between number of mutations and severity of phenotype, that is unlikely to be the case for these genes. It is well known that the effects of a mutation in one ion channel gene can be suppressed by mutation in another gene – restoring the electrical balance in the cell, at least to a degree sufficient for performance under normal conditions. The system is so complex, with so many individual components, that these interactions are extremely difficult to predict. This is complicated further by the fact that there are active processes within the system that act to normalise its function. It has been very well documented, especially by Eve Marder and colleagues, that changes to one ion channel in a neuron can be compensated for by homeostatic mechanisms within the cell that aim to readjust the electrical set-points for optimal physiological function. In fact, these mechanisms do not just happen within one cell, but across the circuit.

The upshot of the study is that, though some of the mutations they discovered are indeed likely to be the pathogenic culprits, it is very difficult to discern which ones they are. It is very clear that there is at least an oligogenic architecture for so-called “channelopathies” – the phenotype is determined by several mutations in each individual. (Note that this is not evidence for a highly polygenic architecture involving hundreds or thousands of genetic variants with tiny individual effects). The important insight is that it is not the overall number or mutational load that matters but the pattern of specific mutations in any individual that is crucial. Unfortunately, given how complicated the system is, this means it is currently not possible to predict an individual’s risk, even with this wealth of data. This will likely require a lot more biological information on the interactions between these mutations from experimental approaches and computational modelling.

What are the implications for other complex disorders? Should we expect a similarly complicated picture for diseases like schizophrenia or autism? Perhaps, though I would argue against over-extrapolating these findings. For the reasons described above, mutations in ion channel genes will show especially complex genetic interactions – it is, for example, even possible for two mutations that are individually pathogenic to suppress each other’s effects in combination. This is far less likely to occur for classes of mutations affecting processes such as neurodevelopment, many of which have been implicated in psychiatric disorders. Though by no means unheard of, it is far less common for the effects of one neurodevelopmental mutation to be suppressed by another – it generally just makes things worse. So, while modifying effects of genetic background will no doubt be important for such mutations, there is some hope that the interactions will be more straightforward to elucidate (mostly enhancing, far fewer suppressing). Others may see it differently of course (and I would be pleased to hear from you if you do); similar sequencing efforts currently underway for these disorders may soon tell whether that prediction is correct.

Klassen T, Davis C, Goldman A, Burgess D, Chen T, Wheeler D, McPherson J, Bourquin T, Lewis L, Villasana D, Morgan M, Muzny D, Gibbs R, & Noebels J (2011). Exome sequencing of ion channel genes reveals complex profiles confounding personal risk assessment in epilepsy. Cell, 145 (7), 1036-48 PMID: 21703448

Kasperaviciute, D., Catarino, C., Heinzen, E., Depondt, C., Cavalleri, G., Caboclo, L., Tate, S., Jamnadas-Khoda, J., Chinthapalli, K., Clayton, L., Shianna, K., Radtke, R., Mikati, M., Gallentine, W., Husain, A., Alhusaini, S., Leppert, D., Middleton, L., Gibson, R., Johnson, M., Matthews, P., Hosford, D., Heuser, K., Amos, L., Ortega, M., Zumsteg, D., Wieser, H., Steinhoff, B., Kramer, G., Hansen, J., Dorn, T., Kantanen, A., Gjerstad, L., Peuralinna, T., Hernandez, D., Eriksson, K., Kalviainen, R., Doherty, C., Wood, N., Pandolfo, M., Duncan, J., Sander, J., Delanty, N., Goldstein, D., & Sisodiya, S. (2010). Common genetic variation and susceptibility to partial epilepsies: a genome-wide association study Brain, 133 (7), 2136-2147 DOI: 10.1093/brain/awq130

Mitchell KJ (2011). The genetics of neurodevelopmental disease. Current opinion in neurobiology, 21 (1), 197-203 PMID: 20832285

Synaesthesia and savantism

2011-06-21T05:10:00.000-07:00

“We only use 10% of our brain”. I don’t know where that idea originated but it certainly took off as a popular meme – taxi drivers seem particularly taken with it. It’s rubbish of course – you use more than that just to see. But it captures an idea that we humans have untapped intellectual potential – that in each of us individually, or at least in humans in general lies the potential for genius.

Part of what has fed into that idea is the existence of so-called “savants” – people who have some isolated area of special intellectual ability far beyond most other individuals. Common examples of savant abilities include prodigious mental calculations, calendar calculations and remarkable feats of memory. These can arise due to brain injuries, or be apparently congenital. In congenital cases, savant abilities are often encountered against a background of the general intellectual, social or communicative symptoms of autism. (The portrayal by Dustin Hoffman in Rain Man is a good example, based on the late, well known savant Kim Peek).

A new hypothesis proposes that savantism arises due to a combination of autism and another condition, synaesthesia. Synaesthesia is commonly thought of as a cross-sensory phenomenon, where, for example, different sounds will induce the experience of particular colours, or tastes will induce the tactile experience of a shape. But in most cases the stimuli that induce synaesthesia are not sensory, but conceptual categories of learned objects, such as letters, numbers, days of the week, months of the year. The most common types involve coloured letters or numbers and what are called mental “number forms”.

These go beyond the typical mental number line that most of us can visualise from early textbooks. They are detailed, stable and idiosyncratic forms in space around the person, where each number occupies a specific position. They may follow complicated trajectories through space, even wrapping around the individual’s body in some cases. These forms can be related to different reference points (body, head or gaze-oriented) and can sometimes be mentally manipulated by synaesthetes to examine them more closely at specific positions.

The suggestion in relation to savantism is that such forms enable arithmetical calculations to be carried out in some kind of spatial, intuitive way that is distinct from the normal operations of formal arithmetic – but only when the brain is wired in such a way to take advantage of these special reprepsentations of numbers, as apparently can arise due to autism.

It has been proposed that the intense and narrowly focused interests typical of autism can lead to prolonged practice of these skills, which thus emerge and improve over time. While certainly likely to be involved in the development of these skills, on its own this explanation seems insufficient. It seems more likely that these special abilities arise from more fundamental differences in the way the brains of autistic people process information, with a greater degree of processing of local detail, paralleled by greater local connectivity in neural circuits and reductions in long-range integration.

Local processing may normally be actively inhibited. This idea has been referred to as the tyranny of the frontal lobes (especially of the left hemisphere), which impart top-down expectations with such authority that they override lower areas, conscripting them into service for the greater good. The potential of the local elements to process detailed information is thus superseded in order to achieve optimal global performance. The idea that local processing is actively suppressed is supported by the fact that savant abilities can sometimes emerge after frontal lobe injuries or in cases of frontotemporal dementia. Increased skills in numerical estimation can also, apparently, be induced in healthy people by using transcranial magnetic stimulation to temporarily inactivate part of the left hemisphere.

This kind of focus on local details, combined with an exceptional memory, may explain many types of savant skills, including musical and artistic ones. As many as 10% of autistics show some savant ability. These “islands of genius” (including things like perfect pitch, for example) are typically remarkable only on the background of general impairment – they would be less remarkable in the general population. Really prodigious savants are much more rare – these are people who can do things outside the range of normal abilities, such as phenomenal mathematical calculations. In these cases, the increased local processing typical of autism may not be, by itself, sufficient to explain the supranormal ability.

The idea is that such prodigious calculations may also rely on the concrete visual representations of numbers found in some types of synaesthesia. This theory was originally proposed by Simon Baron-Cohen and colleagues and arose from case studies of individual savants, including Daniel Tammett, an extraordinary man who has both Asperger’s syndrome and synaesthesia.

I had the pleasure of speaking with Daniel recently about his particular talents on the FutureProof radio programme for Dublin’s Newstalk Radio. (The podcast, from Nov 27th, 2010, can be accessed, with some perseverance, here). Daniel is unique in many ways. He has the prodigious mental talents of many savants, for arithmetic calculations and memory, but also has the insight and communicative skills to describe what is going on in his head. It is these descriptions that have fueled the idea that the mental calculations he performs rely on his synaesthetic number forms.

Daniel experiences numbers very differently from most people. He sees numbers in his mind’s eye as occupying specific positions in space. They also have characteristic colours, textures, movement, sounds and, importantly, shapes. Sequences of numbers form “landscapes in his mind”. This is vividly portrayed in the excellent BBC documentary “The Boy With the Incredible Brain” and described by Daniel in his two books, “Born on a Blue Day” and “Embracing the Wide Sky”.

His synaesthetic experiences of numbers are an intrinsic part of his arithmetical abilities. (I say arithmetical, as opposed to mathematical, because his abilities seem to be limited to prodigious mental calculations, as opposed to a talent for advanced calculus or other areas of mathematics). Daniel describes doing these calculations by some kind of mental spatial manipulation of the shapes of numbers and their positions in space. When he is performing these calculations he often seems to be tracing shapes with his fingers. He is, however, hard pressed to define this process exactly – it seems more like his brain does the calculation and he reads off the answer, apparently deducing the value based at least partly on the shape of the resultant number.

Daniel is also the European record holder for rembering the digits of the number pi - to over 20,000 decimal places. This feat also takes advantage of the way that he visualises numbers – he describes moving along a landscape of the digits of pi, which he sees in his mind’s eye and which enables him to recall each digit in sequence. The possible generality of this single case study is bolstered by reports of other savants, who similarly utilise visuospatial forms in their calculations and who report that they simply “see” the correct answer (see review by Murray).

Additional evidence to support the idea comes from studies testing whether the concrete and multimodal representations of numbers or units of time are associated with enhanced cognitive abilities in synaesthetes who are not autistic. Several recent studies suggest this is indeed the case.

Many synaesthetes say that having particular colours or spatial positions for letters and numbers helps them remember names, phone numbers, dates, etc. Ward and colleagues have tested whether these anecdotal reports would translate into better performance on memory tasks and found that they do. Synaesthetes did show better than average memory, but importantly, only for those items which were part of their synaesthetic experience. Their general memory was no better than non-synaesthete controls. Similarly, Simner and colleagues have found that synaesthetes with spatial forms for time units perform better on visuospatial tasks such as mental rotation of 3D objects.

Synaesthesia and autism are believed to occur independently and, as each only occurs in a small percentage of people, the joint occurrence is very rare. Of course, it remains possible that, even though most people with synaesthesia do not have autism and vice versa, their co-occurrence in some cases may reflect a single cause. Further research will be required to determine definitively the possible relationship between these conditions. For now, the research described above, especially the first-person accounts of Daniel Tammett and others, gives a unique insight into the rich variety of human experience, including fundamental differences in perception and cognitive style.

Murray, A. (2010). Can the existence of highly accessible concrete representations explain savant skills? Some insights from synaesthesia Medical Hypotheses, 74 (6), 1006-1012 DOI: 10.1016/j.mehy.2010.01.014

Bor, D., Billington, J., & Baron-Cohen, S. (2008). Savant Memory for Digits in a Case of Synaesthesia and Asperger Syndrome is Related to Hyperactivity in the Lateral Prefrontal Cortex Neurocase, 13 (5), 311-319 DOI: 10.1080/13554790701844945

Simner, J., Mayo, N., & Spiller, M. (2009). A foundation for savantism? Visuo-spatial synaesthetes present with cognitive benefits Cortex, 45 (10), 1246-1260 DOI: 10.1016/j.cortex.2009.07.007

Yaro, C., & Ward, J. (2007). Searching for Shereshevskii: What is superior about the memory of synaesthetes? The Quarterly Journal of Experimental Psychology, 60 (5), 681-695 DOI: 10.1080/17470210600785208

Where do morals come from?

2011-06-13T07:27:00.001-07:00

Review of “Braintrust. What Neuroscience Tells Us about Morality”, by Patricia S. Churchland

The question of “where morals come from” has exercised philosophers, theologians and many others for millennia. It has lately, like many other questions previously addressed only through armchair rumination, become addressable empirically, through the combined approaches of modern neuroscience, genetics, psychology, anthropology and many other disciplines. From these approaches a naturalistic framework is emerging to explain the biological origins of moral behaviour. From this perspective, morality is neither objective nor transcendent – it is the pragmatic and culture-dependent expression of a set of neural systems that have evolved to allow our navigation of complex human social systems.

“Braintrust”, by Patricia S. Churchland, surveys the findings from a range of disciplines to illustrate this framework. The main thesis of the book is grounded in the approach of evolutionary psychology but goes far beyond the just-so stories of which that field is often accused by offering not just a plausible biological mechanism to explain the foundations of moral behaviour, but one with strong empirical support.

The thrust of her thesis is as follows:

Moral behaviour arose in humans as an extension of the biological systems involved in recognition and care of mates and offspring. These systems are evolutionarily ancient, encoded in our genome and hard-wired into our brains. In humans, the circuits and processes that encode the urge to care for close relatives can be co-opted and extended to induce an urge to care for others in an extended social group. These systems are coupled with the ability of humans to predict future consequences of our actions and make choices to maximise not just short-term but also long-term gain. Moral decision-making is thus informed by the biology of social attachments but is governed by the principles of decision-making more generally. These entail not so much looking for the right choice but for the optimal choice, based on satisfying a wide range of relevant constraints, and assigning different priorities to them.

This does not imply that morals are innate. It implies that the capacity for moral reasoning and the predisposition to moral behaviour are innate. Just as language has to be learned, so do the codes of moral behaviour, and, also like language, moral codes are culture-specific, but constrained by some general underlying principles. We may, as a species, come pre-wired with certain biological imperatives and systems for incorporating them into decisions in social situations, but we are also pre-wired to learn and incorporate the particular contingencies that pertain to each of us in our individual environments, including social and cultural norms.

This framework raises an important question, however – if morals are not objective or transcendent, then why does it feel like they are? This is after all, the basis for all this debate – we seem to implicitly feel things as being right or wrong, rather than just intellectually being aware that they conform to or violate social norms. The answer is that the systems of moral reasoning and conscience tap into, or more accurately emerge from ancient neural systems grounded in emotion, in particular in attaching emotional value or valence to different stimuli, including the imagined consequences of possible actions.

This is, in a way, the same as asking why does pain feel bad? Couldn’t it work simply by alerting the brain that something harmful is happening to the body, which should therefore be avoided? A rational person could then take an action to avoid the painful stimulus or situation. Well, first, that does not sound like a very robust system – what if the person ignored that information? It would be far more adaptive to encourage or enforce the avoidance of the painful stimulus by encoding it as a strong urge, forcing immediate and automatic attention to a stimulus that should not be ignored and that should be given high priority when considering the next action. Even better would be to use the emotional response to also tag the memory of that situation as something that should be avoided in the future. Natural selection would favour genetic variants that increased this type of response and select against those that decoupled painful stimuli from the emotional valence we normally associate with them (they feel bad!).

In any case, this question is approached from the wrong end, as if humans were designed out of thin air and the system could ever have been purely rational. We evolved from other animals without reason (or with varying degrees of problem-solving faculties). For these animals to survive, neural systems are adapted to encode urges and beliefs in such a way as to optimally control behaviour. Attaching varying levels of emotional valence to different types of stimuli offers a means to prioritise certain factors in making complex decisions (i.e., those factors most likely to affect the survival of the organism or the dissemination of its genes).

For humans, these important factors include our current and future place in the social network and the success of our social group. In the circumstances under which modern humans evolved, and still to a large extent today, our very survival and certainly our prosperity depend crucially on how we interact and on the social structures that have evolved from these interactions. We can’t rely on tooth and claw for survival – we rely on each other. Thus, the reason moral choices are tagged with strong emotional valence is because they evolved from systems designed for optimal control of behaviour. Or, despite this being a somewhat circular argument, the reason they feel right or wrong is because it is adaptive to have them feel right or wrong.

Churchland fleshes out this framework with a detailed look at the biological systems involved in social attachments, decision-making, executive control, mind-reading (discerning the beliefs and intentions of others), empathy, trust and other faculties. There are certain notable omissions here: the rich literature on psychopaths, who may be thought of as innately deficient in moral reasoning, receives surprisingly little attention, especially given the high heritability of this trait. As an illustration that the faculty of moral reasoning relies on in-built brain circuitry, this would seem to merit more discussion. The chapter on Genes, Brains and Behavior rightly emphasises the complexity of the genetic networks involved in establishing brain systems, especially those responsible for such a high-level faculty as moral reasoning. The conclusion that this system cannot be perturbed by single mutations is erroneous, however. Asking what does it take, genetically speaking, to build the system is a different question from what does it take to break it. Some consideration of how moral reasoning emerges over time in children would also have been interesting.

Nevertheless, the book does an excellent job of synthesising diverse findings into a readily understandable and thoroughly convincing naturalistic framework under which moral behaviour can be approached from an empirical standpoint. While the details of many of these areas remain sketchy, and our ignorance still vastly outweighs our knowledge, the overall framework seems quite robust. Indeed, it articulates what is likely a fairly standard view among neuroscientists who work in or who have considered the evidence from this field. However, one can presume that jobbing neuroscientists are not the main intended target audience and that both the details of the work in this field and its broad conclusions are neither widely known nor held.

The idea that right and wrong - or good and evil - exist in some abstract sense, independent from humans who only somehow come to perceive them, is a powerful and stubborn illusion. Indeed, for many inclined to spiritual or religious beliefs, it is one area where science has not until recently encroached on theological ground. While the Creator has been made redundant by the evidence for evolution by natural selection and the immaterial soul similarly superfluous by the evidence that human consciousness emerges from the activity of the physical brain, morality has remained apparently impervious to the scientific approach. Churchland focuses her last chapter on the idea that morals are absolute and delivered by Divinity, demonstrating firstly the contradictions in such an idea and, with the evidence for a biological basis of morality provided in the rest of the book, arguing convincingly that there is no need of that hypothesis.

Somatic mutations make twins’ brains less identical

2011-05-25T06:43:00.000-07:00

There is a paradox at the heart of behavioural and psychiatric genetics. On the one hand, it is very clear that practically any psychological trait one cares to study is partly heritable - i.e., the differences in the trait between people are partly caused by differences in their genes. Similarly, psychiatric disorders are also highly heritable and, by now, mutations in hundreds of different genes have been identified that cause them.

However, these studies also highlight the limits of genetic determinism, which is especially evident in comparisons of monozygotic (identical) twins, who share all their genetic inheritance in common. Though they are obviously much more like each other in psychological traits than people who are not related to each other, they are clearly NOT identical to each other for these traits. For example, if one twin has a diagnosis of schizophrenia, the chance that the other one will also suffer from the disorder is about 50% - massively higher than the population prevalence of the disorder (around 1%), but also clearly much less than 100%.

What is the source of this extra variance? What forces make monozygotic twins less identical? I have argued previously that random variation in the course of development is a major contributor. The developmental programme that specifies brain connectivity is less like a blueprint than a recipe (a recipe without a cook) – an incredibly complicated set of processes carried out by mindless biochemical algorithms mediated by local interactions between billions of individual components. As each of these processes is subject to some level of “noise” at the molecular level, it is not surprising that the outcome of this process varies considerably, even between monozygotic twins.

While such developmental variation can be referred to as “non-genetic”, a new study suggests that one important component of this variation may be genetic after all, just not inherited. Mutations can be passed on from parents to offspring or arise during generation of sperm or eggs and thus be inherited, but they can also arise any time DNA is replicated. So, each time a cell divides as an embryo grows and develops, there is a very small chance of new mutations being introduced. These “somatic” mutations (meaning ones that happen in the body and not in the germline) will be inherited by all the cells that are descendants of that new cell and so will be present in some fraction of the final cells of the individual. Mutations arising earlier in development will be inherited by more cells than those arising later.

Each person will therefore be a mosaic of cells with slightly different genetic make-up. The vast majority of such mutations will not have any effect of course (with the obvious exception of those that cause dysregulation of cellular differentiation and result in cancer). But sometimes a new mutation will affect a trait and cause a detectable difference. The most obvious examples are in genes affecting hair or eye colour – where a patch of hair may be a different colour, or the two eyes may be different colours.

But what if the mutations in question are linked to a psychiatric disorder? If such a mutation arises early in the development of the brain and is therefore inherited by many of the cells in the brain then this could lead to the psychiatric disorder, just as if the mutation had been inherited in a germ cell.

A new study adds to the evidence that such mutations do indeed occur at an appreciable frequency and may help explain the discordance in phenotype between pairs of twins where one has schizophrenia and the other does not. The authors analysed the DNA from blood cells of pairs of twins discordant for schizophrenia and their parents. They were looking for two different kinds of mutation: ones that changes the identity of a single base of DNA (one letter of the genetic code to another), called point mutations, and ones that delete or duplicate whole chunks of chromosomes, called copy number variants, or CNVs.

As expected, they were able to detect both inherited mutations (present in one of the parents) and de novo mutations (present in both twins but not in the blood cells of either parent). What is more remarkable though, is that they also detected de novo mutations present in the blood cells of one twin but not the other – lots of them. About 1,000 point mutations and 2-3 new CNVs not shared by the other twin. The implication is that these mutations arose during the somatic development of one twin. They identify a couple CNVs in the twins affected by schizophrenia, raising the (very speculative) possibility that those mutations may contribute to the development of the disorder. It will obviously require a lot more work to test that specific hypothesis.

An earlier study also found a high rate of somatic mosaicism for CNVs – this time by analysing the DNA of multiple tissues taken from single (deceased) individuals. Across 34 tissue samples from 3 subjects they identified six CNVs present in one tissue but not others. What this implies is that not only do we carry additional mutations making us even more different from one another, our cells and tissues can also be genetically different from each other.

Time will tell whether such mutations really do contribute to psychiatric disorders, but it certainly seems plausible that they might. This adds to a couple other potential mechanisms of increasing individual variance: the transposition of mobile DNA elements in somatic tissues, especially neurons, and the “epigenetic” silencing of regions of the genome, which may be clonally inherited in groups of cells and contribute to differences between twins.

This has one immediate and important consequence for clinical genetics. When a mutation in an offspring is not carried by either parent it is usually interpreted as having arisen de novo. The implication is that the risk of another offspring carrying the same mutation is negligible. Clinical geneticists are finding this is not necessarily always the case, however – apparently de novo mutations may have actually arisen at an early stage in the germline and not just at the final division generating the sperm or egg. The parent in question may not actually “carry” the mutation, but their germline does. Great care must therefore be taken when advising parents with one affected child of the risk to future offspring.

Maiti S, Kumar KH, Castellani CA, O'Reilly R, & Singh SM (2011). Ontogenetic de novo copy number variations (CNVs) as a source of genetic individuality: studies on two families with MZD twins for schizophrenia. PloS one, 6 (3) PMID: 21399695

Piotrowski, A., Bruder, C., Andersson, R., de Ståhl, T., Menzel, U., Sandgren, J., Poplawski, A., von Tell, D., Crasto, C., Bogdan, A., Bartoszewski, R., Bebok, Z., Krzyzanowski, M., Jankowski, Z., Partridge, E., Komorowski, J., & Dumanski, J. (2008). Somatic mosaicism for copy number variation in differentiated human tissues Human Mutation, 29 (9), 1118-1124 DOI: 10.1002/humu.20815

Fraga, M. (2005). From The Cover: Epigenetic differences arise during the lifetime of monozygotic twins Proceedings of the National Academy of Sciences, 102 (30), 10604-10609 DOI: 10.1073/pnas.0500398102

The miswired brain; making connections from neurodevelopment to psychopathology

2011-05-14T04:42:00.000-07:00

Recent evidence indicates that psychiatric disorders can arise from differences, literally, in how the brain is wired during development. Psychiatric genetic approaches are finding new mutations associated with mental illness at an amazing rate, thanks to new genomic array and sequencing technologies. These mutations include so-called copy number variants (deletions or duplications of sections of a chromosome) or point mutations (a change in the code at one position of the DNA sequence). At the recent Wiring the Brain conference, we heard from Christopher Walsh, Guy Rouleau, Michael Gill and others of the identification of a number of new genes associated with neurological disorders, epilepsy, autism and schizophrenia.

The emerging picture is that each of these disorders can be caused by mutations in any one of a large number of genes. Strikingly, many of these genes play important roles in neural development, with mutations affecting patterns of cell migration, the guidance of growing nerve fibres and their connectivity to other cells. Even more remarkable has been the observation that most such mutations predispose to not just one specific illness (such as schizophrenia) but to mental illness in general, with a strong overlap in the genetics of schizophrenia, autism, bipolar disorder, epilepsy, mental retardation, attention-deficit hyperactivity disorder and other diagnostic categories. These different categories may thus represent arguably distinct endpoints arising from common origins in neurodevelopmental insults.

What we do not yet know is why. How does a mutation in a gene controlling say, the formation of connections between specific types of nerve cells, ultimately result in someone having paranoid delusions? (While another person carrying the same mutation may develop the quite different symptoms of autism at a much earlier age). Answering such questions will require much greater integration of efforts across a wide range of disciplines.

These efforts must include neurodevelopmental biologists. Over the past couple of decades, tremendous progress has been made in elucidating the molecular mechanisms underlying nervous system development. In many cases, these advances have been made using fairly simply model systems – fruit flies and nematode worms have been favourites in this field, as well as simple parts of the vertebrate nervous system such as the spinal cord and retina. While more and more researchers are trying to figure out how these mechanisms apply in the vastly more complicated mammalian brain, we are still a long way from understanding how this structure develops. This is especially the case as much of the circuitry of the brain is not prespecified by genetic instructions down to the last synapse, but is strongly affected by patterns of electrical activity within developing circuits. Nevertheless, it has been possible to use animals with mutations in particular genes to figure out what the functions of these genes are in the development of specific brain circuits.

The logic of these approaches is fairly straightforward: in order to discover the normal function of Gene X, mutate it, look at what happens to some part of the brain and work backwards to deduce the cellular processes that have been affected. What is needed now, if neurodevelopmental biologists are to make a contribution to the study of mental illness, is a different approach. We must develop an interest in the phenotypes themselves, not just as tools to elucidate the gene’s normal functions. If mutations in Gene X can cause autism, for example, then a mouse with the same mutation becomes a valuable and informative model of disease. It becomes of interest to analyse not just the direct processes affected by the mutation but all of the knock-on consequences. While these questions may start with neurodevelopmental biologists they rapidly require additional expertise to address.

This will entail a framework to link investigations across levels of analysis typically carried out by researchers in quite different disciplines. For example, if the mutation affects formation of synaptic connections between certain types of cells in certain brain regions, then how does this change the function of the circuits involved? If this changes the activity of the circuit, then how does this affect further activity-depdendent development of interconnected regions? How does that affect the information processing capabilities of these networks? What cognitive functions are carried out by these networks and how are they impacted? At what level can we most directly translate findings in animals to humans? Each of these questions requires researchers in different disciplines to work together.

The imperative to do this could not be more stark. Roughly 10% of the world’s population is affected by mental illness at any one time, and over 25% will have some mental health problem over their lifetime. As well as the costs to individuals and their families, the public health and economic burdens from these disorders are massive, as large as that of cancer and cardiovascular disease. In fact, the proportional burden is growing as we are making good progress in treating the latter disorders, while mental illnesses have lagged far behind. This is mainly because we have not been able to apply the tools of molecular genetics to the problem. This is now changing, thanks to the revolutionary advances in psychiatric genetics. The challenge now will be to translate these discoveries into real understanding of disease mechanisms and ideas for novel therapies.

This post is based on a brief article that introduces a thematic series of reviews and primary research papers on the theme of Wiring the Brain. This series will appear across various journal titles of the open access publisher BioMed Central and can be accessed here.

Mitchell KJ (2011). The miswired brain: making connections from neurodevelopment to psychopathology. BMC biology, 9 (1) PMID: 21489316

The sources of human uniqueness

2011-02-08T07:11:00.000-08:00

I am currently taking a short break from blogging to concentrate on teaching and on organising the Wiring the Brain conference, which will be held in April. Should be back in the saddle in a few weeks time. In the meantime, here's one of my original posts from a couple years ago.

It argues that intrinsic variation in how the program of brain development runs is a major source of individual differences in all manner of behavioural traits. In short, if you cloned yourself a hundred times you would end up with a hundred quite individual brains, each wired in its own way. Of course they would be more similar to each other than to some random person, because genetic effects are very important, but they would also be more innately unique than might be expected from the results of behavioural genetic experiments. Read the full post here: Nature, nurture and noise

Knowing without knowing

2011-01-18T06:50:00.000-08:00

I have a new post over on the Scientific American Mind Matters website. It describes new research which suggests that tune deafness and face blindness - two examples of conditions known as agnosias, both of which can be genetic - are caused not by a failure of the brain to recognise previously seen faces or detect incongruous musical notes, but a failure to communicate these events to frontal brain regions where conscious awareness is triggered. In essence, your brain knows something but can't tell you. Read more...

Hotheads by nature

2011-01-12T01:14:00.001-08:00

If some guy spilt your beer by accident, would you punch him in the face? If he was unapologetic, you might at least consider it – you might in fact feel a pretty strong urge to do it. What stops you? Or, if you’re the type who acts on those urges, what doesn’t stop you? New research has found a mutation in one gene that may contribute to these differences in temperament.

Self-control is the ability to inhibit an immediate course of action in the pursuit of a longer-term goal or to consciously override a base urge. Some people show far more inhibitory control than others. This trait is very stable – indeed, inhibitory control in children, which can be assessed using the famous “marshmallow test”, is predictive of their score on scales of impulsivity as adults. (The marshmallow test must go down as one of the cruellest experiments in psychology – it involves asking four-year olds not to eat a lovely yummy marshmallow for five minutes, after which they will be given another one to go with it if they have resisted. The videos of these poor kids as they struggle to resist this urge are priceless). Impulsivity is also partly heritable – that is, more closely related people are more similar in this trait.

This is generally true of all personality traits, suggesting they are influenced by genetic variation. However, the specific genes involved are almost entirely unknown. Indeed, a recent study that failed to find any such genes was interpreted by many (e.g., 1, 2) as evidence that either personality was not really genetic or that measures of personality traits were effectively meaningless. In fact, this was a gross misinterpretation of the results of this study. What these researchers did was look for common genetic variants that were associated with differences in personality traits, across a sample of over 5,000 people. Common variants are ancient differences at specific positions in the DNA code, where some proportion of the population carries one base, say a “C”, and the rest carry another base, say an “A”. There are millions of such variable positions across the human genome. Most of them do not do anything - they do not affect the sequence of a protein or how much of it is made. And, it seems, none of them affects personality significantly.

This does NOT mean that these traits are not affected by genetic variation. The genome-wide association analysis could not detect rare variants – ones that only a few people in the population carry. These are mutations that have arisen in the much more recent past and which have been passed on to only a small proportion of the population. In general, such mutations are far more likely to affect a protein and have some influence on the observable traits of an organism (its phenotype). Why? Because usually such effects are not very positive and natural selection pretty rapidly weeds them out – if a variant becomes common it is usually because it does not have any effect. (Not always, but usually).

So, how can these rare variants be found? Well, advances in sequencing technologies now make it possible to sequence the entire genetic code of a person or determine the entire sequence of a specific gene or genes across large numbers of people. This approach will pick up all the genetic differences, whether they are rare or common. This is what researchers from the National Institutes of Health and from Helsinki have done in a new study that led to the identification of a mutation in the Finnish population that apparently affects impulsivity.

They started with the hypothesis that this trait might be affected by variation in genes involved in the synthesis or signalling pathways of the neuromodulators dopamine and serotonin. These molecules act in the brain to alter the responsiveness of neurons to other signals – they set the tone, the internal context that helps determine how the organism will respond to various stimuli at any given moment. Differences in these pathways may also explain why different people will respond differently to the same stimulus (like that guy spilling your pint). There is a good deal of pharmacological evidence implicating these pathways in mood and temperament, as well as some prior genetic evidence for a couple specific genes.

To look for variation specifically affecting impulsivity, the researchers sequenced fourteen genes involved in the dopamine and serotonin pathways in a sample of the most impulsive people they could find – prisoners who had been convicted of violent, spontaneous crimes. All of these subjects had one of several psychiatric diagnoses that specifically include impulsive behaviour as a core symptom: borderline personality disorder, antisocial personality disorder or intermittent explosive disorder.

The scientists found one mutation that had never been seen in any other population – in the gene HTR2B, which encodes a receptor for serotonin. The mutation completely abolishes the production of the protein, so that people who carry one copy of this mutant version of the gene have only half the normal amount of the receptor protein. The mutant version was found to be greatly over-represented (7.5% frequency) among a set of 228 violently impulsive subjects, compared to 295 controls from the general population (1.2%). Among family members of the violent offenders who carried the mutation there was also an increased rate of the psychiatric disorders listed above, specifically in those relatives who also inherited the mutation.

These findings therefore suggest that this mutation increases the risk of this kind of violent, impulsive behaviour. It must only be one factor, however, as most of the 1% in the Finnish population who carry it are not violent criminals. Being male and alcohol abuse are two other likely risk factors. Almost all of the violent impulsive cases had committed crimes under the influence of alcohol, mostly unpremeditated “disproportionate reactions to minor irritations”. (Note the difference with psychopaths, who show much more cold-blooded and goal-directed violence). Two-thirds had also attempted suicide at least once, with an average of over 3 attempts.

So, does this mutation really affect the personality trait of impulsivity specifically, or is that just one component of a wider and more severe phenotype? The authors did look for effects on cognitive measures across a large Finnish twin sample, identifying significant effects on working memory in males, but do not report a test of association with impulsivity as a trait in this sample. We shall therefore have to wait to see if that more general association holds.

Their case is supported by observations in mice which carry mutations in the same gene – mice with both copies of this gene mutated score higher on a range of test used to measure impulsivity (yes, mice can be more or less impulsive). Also, the protein encoded by the HTR2B gene, the serotonin receptor 5-HT2B, is the target for the mood-altering drug ecstasy (3,4-methylene-dioxymethamphetamine, MDMA). When this drug binds the 5-HT2B receptor it induces serotonin release in the brain and a subsequent chain of events including dopamine release in the reward area of the brain.

These data naturally lead to the idea that the mutation found in this study has its effect by altering the amount of this receptor protein in the adult brain, thereby altering the tone of serotonin signalling. There is an alternative hypothesis, however, which is that the brain develops differently due to this mutation. There is good reason to think this may be the case as it is known that serotonin plays important roles in brain wiring at early stages of neural development. More on that possibility in a later post.

Whether the mechanism is acute or developmental, these findings emphasise the importance of rare variants – which may occur only in one population, in one kindred or family, or even in a single individual – in determining an individual’s phenotype.

Verweij KJ, Zietsch BP, Medland SE, Gordon SD, Benyamin B, Nyholt DR, McEvoy BP, Sullivan PF, Heath AC, Madden PA, Henders AK, Montgomery GW, Martin NG, & Wray NR (2010). A genome-wide association study of Cloninger's temperament scales: implications for the evolutionary genetics of personality. Biological psychology, 85 (2), 306-17 PMID: 20691247

Bevilacqua L, Doly S, Kaprio J, Yuan Q, Tikkanen R, Paunio T, Zhou Z, Wedenoja J, Maroteaux L, Diaz S, Belmer A, Hodgkinson CA, Dell'osso L, Suvisaari J, Coccaro E, Rose RJ, Peltonen L, Virkkunen M, & Goldman D (2010). A population-specific HTR2B stop codon predisposes to severe impulsivity. Nature, 468 (7327), 1061-6 PMID: 21179162

Self-organising principles in the nervous system

2010-12-21T01:13:00.000-08:00

The circuitry of the brain is too complex to be completely specified by genetic information – at least not down to the level of each connection. There are hundreds of billions of neurons in your brain, each making an average of 1,000 connections to other cells. There are simply not enough genes in the genome to specify all of these connections.

What the genetic program can achieve is a very good wiring diagram of initial projections between neurons in different brain areas (or layers or between particular cell types). This circuitry is then refined and elaborated at the cellular level by processes of activity-dependent development, under the principle that “cells that fire together, wire together”. The circuitry of the brain is thus a self-organising system, which assembles under the influence of local interactions, mediated first by molecular interactions and second by patterns of electrical activity.

A new study highlights an important additional factor that allows global patterns of nerve projections, or “neural maps”, to emerge from these local interactions. Neural maps are systematic representations of sensory information across the surface of the brain. A study of the structures of visual maps across a range of quite distantly related species reveals a universal pattern and argues strongly that it cannot be explained by either genetic or environmental instructions but instead arises due to self-organising principles. Remarkably, mathematical descriptions of these principles fit the observed structures extremely well and reveal that one important structural parameter is constant across all species and equal to the mathematical constant π.

Obtaining such a robust mathematical result in any biological system is a rare event and reinforces the view that it reflects a fundamental principle of self-organising systems. To understand the significance of this result, we need to examine the organisation of the visual system in more detail. Starting in the retina, the visual system is built up in a hierarchical series of relays. At each level, the system is wired to combine and compare inputs from neighbouring cells in the preceding level. In this way, more and more complex and global patterns of visual objects can be extracted (starting with dots, then lines, then parts of shapes, simple geometrical shapes and eventually complex objects).

Photons of light are initially detected by photoreceptors in the retina. Each single photoreceptor at any given moment registers light coming into the retina from a particular point of visual space. These cells relay information through a series of layers to the retinal ganglion cells, which are the output cells of the retina. Importantly, each ganglion cell integrates information from multiple, neighbouring photoreceptors. These connections can be either excitatory or inhibitory. A single ganglion cell is usually most strongly activated when a central photoreceptor is active but its neighbours are not. This means that ganglion cells are particularly sensitive to areas of visual space with high contrast – where there is an edge of an object, for example. (If the light across the visual field is uniform then the ganglion cells are less active).

Retinal ganglion cells project in turn to the visual thalamus, which relays this information to the primary visual cortex (area V1). Cells in V1 integrate information from multiple retinal ganglion cells, extracting more high-level features of the visual information. In particular, many cells in V1 respond best to short lines – you can imagine how such a response can be achieved by integrating inputs from neighbouring retinal ganglion cells, each responding to high contrast in a central domain (a line in visual space would then maximally excite these cells, compared to a solid block for example).

Depending on the layout of the ganglion cells whose inputs are integrated, each cell in V1 will be most sensitive to lines of a particular orientation (vertical, horizontal, diagonal). This sensitivity can be directly observed by using electrodes to record the responses of cells in V1 when an animal is shown various visual stimuli. The ground-breaking work of Hubel and Wiesel first revealed the remarkable preferences of individual cells for lines of different orientation. It also revealed another important principle, which is that the organisation of these cells with respect to each other is highly structured.

This structure is apparent at two levels: first, cells with similar orientation selectivity form small clusters, called columns (because the selectivity actually extends in a column across the six layers of the cortex). Second, clusters are laid out across the surface of V1 in a non-random pattern characterised by a “pinwheel” structure, where the direction of orientation selectivity varies smoothly across neighbouring columns, which are arranged in a spiral fashion around the pinwheel centre. (The diagram represents the layout of columns with different orientation selectivities, denoted by the colour code).

Not all species show these properties. Cells in visual cortex of rodents, for example, are selective for particular orientations of stimuli but they are not clustered – individual cells are effectively scattered across V1. But wherever clustering is observed, the pinwheel organisation is also observed. This is true across multiple species where it must have evolved independently. This result is not trivial – there are many other ways that these maps could theoretically be structured (stripes, lattices, etc.). So why do they emerge in this particular pattern?

To investigate this, Matthias Kaschube, Fred Wolf and colleagues analysed the orientation maps in three distantly related species: ferrets, tree shrews and galagos. Tree shrews, despite their name, are not rodents but a sister group of primates. Ferrets are on the carnivore branch and galagos, also known as bush-babies, are primates. Importantly, these three species have quite different habits and ecological habitats, arguing against any commonalities in environmental experience as driving similarities in the organisation of visual maps.

All three species show orientation columns and all show the pinwheel organisation. However, the sizes of individual columns vary considerably across these species and even across individuals within each species. To determine whether there was really any universality in the organisation of these maps, the authors painstakingly measured a range of parameters across many individuals. These parameters include the average column size, the average distance between columns of the same orientation preference and the density of pinwheel centres. They found that the pinwheel density, in relation to the other parameters, was constant across all species.

Not fairly constant or kind of constant – really constant (or as close as one could ever expect in a biological system). And not only was it constant in the sense that it was consistent – the value was equal to a mathematical constant: π (pi, the ratio of a circle’s circumference to its diameter). This had been predicted from mathematical models of the underlying processes, which I wish I understood better. Even though they are all Greek to me, the fact that the value is not just some arbitrary number indicates that it reflects a fundamental mathematical constraint on the self-organisation of this system.

The authors show that this constraint is most likely imposed by the pattern of long-range connections, which link columns of similar orientation selectivity. These horizontal connections, which are formed in an activity-dependent manner, impose a more global structure on the layout of columns and constrain the possible organisation of the map as a whole.

The results of this study argue strongly that neither genetic nor environmental instruction is sufficient to generate the observed pattern. Instead, given a set of initial conditions and biochemical algorithms instructing changes in connectivity based on local interactions, global patterns will emerge based on very general mathematical principles of self-organising systems.

Kaschube M, Schnabel M, Löwel S, Coppola DM, White LE, & Wolf F (2010). Universality in the evolution of orientation columns in the visual cortex. Science (New York, N.Y.), 330 (6007), 1113-6 PMID: 21051599

New insights into Rett syndrome

2010-11-29T02:51:00.001-08:00

A pair of papers from the lab of Fred Gage has provided new insights into the molecular and cellular processes affected in Rett syndrome. This syndrome is associated with arrested development and autistic features. It affects mainly girls, who typically show normal development until around age two, followed by a sudden and dramatic deterioration of function, regression of language skills and the emergence of autistic symptoms. It is caused mainly by mutations in the gene encoding MeCP2, which resides on the X chromosome. Complete removal of the function of this gene is effectively lethal, explaining why Rett syndrome is not observed in boys – males who inherit that mutation are not viable. Females, who have a back-up copy of the X chromosome survive but subsequently show the symptoms of the disease.

The function of the MeCP2 protein seems very far removed from the kinds of symptoms observed when it is deleted. The job of MeCP2 is to bind to DNA that carries a specific chemical tag – a methyl group – which marks DNA for repression. When MeCP2 binds, it recruits a host of other proteins which shut down that section of DNA and prevent any genes within it from being expressed. How a defect in a process that is so fundamental could result in such specific symptoms has been a mystery.

A major barrier in understanding these processes has been the inability to assay the effects of the mutation in this gene in neurons of people who carry it. After all, unlike some other cell types, one cannot easily simply extract neurons from patients. (They tend to be using them). New stem cell technologies developed over the last few years offer a way around this problem. It is possible to extract fibroblasts from patients with a simple skin biopsy. By transfecting these cells with genes that are normally expressed in embryonic stem cells it is possible to “de-differentiate” them – to turn them back into a stem cell. (The difference between a skin cell and a stem cell lies in the genes that are being expressed – transfecting the cells with the master regulatory genes that determine embryonic stem cell identity forces the expression profile back to that state). These “induced pluripotent stem cells” (iPS cells) can then be encouraged to differentiate into any of the cell-types of the body, including neurons. In this way, a virtual biopsy of a patient’s neurons can be obtained.

Gage and colleagues did exactly that, generating neurons in a dish from patients with Rett syndrome. I make that technique sound simple, but of course it isn’t, and these experiments represent a technical tour de force. They were then able to characterise various parameters of these neurons to assay more directly the molecular and cellular effects of MeCP2 mutation. These experiments revealed a not unexpected defect in the formation of synapses between Rett mutation neurons. Neurons from Rett mutation-carriers developed normally and showed normal electrophysiological properties but made fewer synapses with each other and showed a concomitant decrease in network activity. I say not unexpected because it had previously been shown that mouse neurons carrying a MeCP2 mutation show similar effects. This fits with highly convergent findings from autism genetics showing that many other implicated genes function in synapse formation.

What is important about the iPS cells, compared to the information that can be learned from studying mouse cells with MeCP2 knocked out, is that they give a picture of the effects, first, of the specific mutation in this gene in each patient, and second, of the genetic background of each patient, which may modify the effects of the MeCP2 mutation. This gives a far more direct view of the specific effects of each patient’s complete genotype on the development and function of their neurons.

While defects in synapse formation suggest a fundamental role for MeCP2 in neural development, which might imply an irreversible defect, in fact several lines of evidence suggest that the requirement for the function of MeCP2 may be ongoing, in processes of activity-dependent wiring, where neurons within networks strengthen connections based on their patterns of activity. This fits with the apparently normal early development, prior to age two, of girls with Rett syndrome, and also with evidence from mouse models that restoring MeCP2 function in adults can largely reverse the symptoms. These discoveries therefore hold out the promise that intervention in Rett syndrome patients, even in older children, may be effective.

Gage and colleagues tested a couple potential therapies on the neuronal networks derived from Rett syndrome patients and were able to show some degree of rescue of the defects. One of these, the protein insulin-like growth factor-1 (IGF-1), was previously shown to be effective in partially rescuing the defects in MeCP2 mutant mice, most likely by stimulating greater synapse production and compensating for the loss of MeCP2 activity. Clinical trials are now planned to test the efficacy of this approach in patients. Having the cells derived from patients should also greatly facilitate screening for new drugs that can correct the neuronal network defects.

Another paper from the same group, also analysing these cells, revealed a far less expected effect – one that suggests (far more speculatively) the possible involvement of a totally different pathogenic mechanism. One of the functions of the system that methylates DNA is to defend the genome against invaders. Our genome is riddled with parasitic elements – pieces of DNA that can replicate themselves and “jump” around the genome. Fully 45% of our “human” genome is made up of these so-called transposable elements. Most of the copies of these elements are inactive but a subset can generate new copies that will integrate at random into the genome. What has this got to do with Rett syndrome?

Well, MeCP2 is apparently one of the proteins whose job it is to shut down these transposable elements. Gage and colleagues could show that one particular class of these elements, called L1 elements, was far more active in cells derived from Rett syndrome patients. The L1 elements expressed higher levels of the proteins they encode and they generated additional copies of themselves, which were scattered around the genome. Interestingly, this effect seems to be restricted to neurons, presumably because the function of MeCP2 is especially required in that cell-type.

Though highly speculative, this raises the idea that high rates of somatic mutation (somatic meaning it happens in the body, not in the germline and thus will not be inherited), caused by L1 elements jumping around and landing in the middle of genes, may contribute to the severity and also the variability of the phenotype caused by MeCP2 mutations. The alternative is that the L1 transposition has no pathogenic effect but is simply a consequence of the Rett syndrome mutations. Future experiments will be required to tell which of these possibilities is correct.

Marchetto MC, Carromeu C, Acab A, Yu D, Yeo GW, Mu Y, Chen G, Gage FH, & Muotri AR (2010). A model for neural development and treatment of rett syndrome using human induced pluripotent stem cells. Cell, 143 (4), 527-39 PMID: 21074045

Muotri AR, Marchetto MC, Coufal NG, Oefner R, Yeo G, Nakashima K, & Gage FH (2010). L1 retrotransposition in neurons is modulated by MeCP2. Nature, 468 (7322), 443-6 PMID: 21085180

A synaesthetic mouse?

2010-11-22T12:17:00.000-08:00

An amazing study just published in Cell starts out with fruit flies insensitive to pain and ends up with what looks very like a synaesthetic mouse. Penninger and colleagues were interested in the mechanisms of pain sensation and have been using the fruit fly as a model to investigate the underlying biological processes. Like any good geneticist faced with profound ignorance of how a process works, they began by screening for mutant flies that are insensitive to pain. Making use of a very powerful genetic resource developed in Vienna (a bank of fly lines expressing RNA interference constructs for every gene in the genome) they screened through all these genes to see which ones were required in neurons for flies to respond to pain. (In particular, pain caused by excessive heat).

Why should anyone care how a fly feels pain? Well, like practically everything else you can think of, the basic physiology and molecular biology of pain sensation is very highly conserved from flies to mammals. It starts with specialized proteins called TRP channels, which are ion channels that span the cell membrane and allow ions to pass across it in response to various stimuli. Some of these TRP channels respond specifically to painful stimuli, some even more specifically to painful heat, and these molecules are highly conserved. The hope was that by screening for other genes they would identify additional conserved elements of the pathway.

This was exactly what they found. Among hundreds of new mutants that were insensitive to pain, they focused in this report on one, a gene called straightjacket. This gene codes for a protein called alpha2delta3, or CACNA2D3, which is a member of a conserved family of proteins that make up part of a calcium channel. These proteins are involved in modulating neurotransmission and also in some aspects of development, including the formation of synapses. Interestingly, mutations in other members of this gene family are associated with bipolar disorder, schizophrenia, Timothy syndrome (the symptoms of which include autism), epilepsy and migraine.

This particular gene is conserved in mammals and the authors show that mutation of the gene in mice also leads to insensitivity to pain induced by heat, but not to painful mechanical stimuli – a remarkably specific functional conservation. In addition, they show suggestive evidence that variants in the gene in humans are also associated with a higher pain tolerance. These latter data will have to be replicated but tantalizingly suggest that variation in this gene in humans may contribute to differences in pain sensitivity.

Mutation of this gene seems to cause pain insensitivity not by blocking pain responses in the sensory neurons or by blocking transmission of this signal to the brain, but by blocking transmission from the first relay station of the brain, the thalamus, to the cortex, where it must pass to be consciously perceived. The authors could show that the sensory neurons still respond to painful stimuli and that a spinal pain reflex was intact. They also used functional magnetic resonance imaging in mice to show that the thalamus was active as normal in response to painful stimuli. However, a network of areas in the cortex (the “pain matrix”) was completely unresponsive. Somehow, deletion of CACNA2D3 alters connectivity within the thalamus or from thalamus to cortex in a way that precludes transmission of the signal to the pain matrix areas.

This is where the story really gets interesting. While they did not observe responses of the pain matrix areas in response to painful stimuli, they did observe something very unexpected – responses of the visual and auditory areas of the cortex! What’s more, they observed similar responses to tactile stimuli administered to the whiskers. Whatever is going on clearly affects more than just the pain circuitry.

The authors suggest that this kind of sensory cross-activation may represent a model for synaesthesia, which is characterised by very similar effects. While this condition is highly familial, no genes have yet been isolated for it. Could CACNA2D3 be a viable candidate? It certainly seems possible, though one point suggests that whatever is happening, while similar to developmental synasthesia, may be somewhat distinct.

Synaesthesia usually involves an extra percept in response to some stimulus, without any decrement in the response to the stimulus itself. So, people who see colours when they hear music hear the music normally – the colour is just part of that experience. This is rather different from a situation where one sense is deficient and is taken over by another. That situation can arise due to injury, for example, and can even be surgically induced in animal models (used to study brain plasticity). One recent report (see below) described a patient who had a lesion in the thalamus in the somatosensory nucleus. This region was subsequently invaded by fibres carrying auditory information so that the patient was able to feel sounds. (The auditory fibres were activated by sound, which cross-activated the somatosensory area, which communicated this activity to the somatosensory cortex, where it was perceived as a touch on the surface of the body).

Could such an effect explain what was happening in these mice? Perhaps for the pain circuits, though one would typically expect that they would be invaded by other senses, rather than the other way around. But for the tactile stimuli, the message was apparently still getting through to the somatosensory cortex, it was just also activating visual and auditory areas. That starts to look like a pretty good model for synaesthesia. Whether it really is would most convincingly be demonstrated by finding a mutation in this gene in someone with synaesthesia. A good place to start might be testing the carriers of the variants in this gene in humans which affected pain sensitivity for any signs of synaesthesia.

Even if it does not correspond exactly to what we call developmental synaesthesia, one can predict that something pretty strange would result from mutation of this gene in humans. Given that every base of the genome is probably mutant in someone on the planet it seems certain that such mutations will eventually crop up.

It is not yet clear what cellular mechanism can explain the cross-activation observed in the mutant mice. One can imagine any number of scenarios, including structural rewiring between thalamic nuclei (which are specialized to transmit different types of sensory information) or from thalamus to cortex. Alternatively, changes in neurotransmission might explain the effects, for example by damping down cross-inhibitory processes that normally sharpen responses to one sense at a time. One way to dissociate these would be to see whether blocking the function of the protein just in adults is sufficient to induce the effect or if it has to be blocked during development. This might be achieved using drugs – a close relative of CACNA2D3 is blocked by gabapentin, a drug used in humans as an antiepileptic and also to block neuropathic pain (like that which can arise due to shingles, for example). Whether this or a similar drug could affect the A2D3 subunit is not, I think, known, but no doubt someone is now looking for a drug that can.

Neely GG, Hess A, Costigan M, Keene AC, Goulas S, Langeslag M, Griffin RS, Belfer I, Dai F, Smith SB, Diatchenko L, Gupta V, Xia CP, Amann S, Kreitz S, Heindl-Erdmann C, Wolz S, Ly CV, Arora S, Sarangi R, Dan D, Novatchkova M, Rosenzweig M, Gibson DG, Truong D, Schramek D, Zoranovic T, Cronin SJ, Angjeli B, Brune K, Dietzl G, Maixner W, Meixner A, Thomas W, Pospisilik JA, Alenius M, Kress M, Subramaniam S, Garrity PA, Bellen HJ, Woolf CJ, & Penninger JM (2010). A Genome-wide Drosophila Screen for Heat Nociception Identifies α2δ3 as an Evolutionarily Conserved Pain Gene. Cell, 143 (4), 628-38 PMID: 21074052

Beauchamp MS, & Ro T (2008). Neural substrates of sound-touch synesthesia after a thalamic lesion. The Journal of neuroscience : the official journal of the Society for Neuroscience, 28 (50), 13696-702 PMID: 19074042

Announcing the Wiring the Brain conference 2011

2010-11-03T13:38:00.000-07:00

I am pleased to announce the Wiring the Brain conference, which will be held over the 12th-15th April 2011, in Ireland. This is an international scientific conference which aims to explore how the brain is wired and what happens when that wiring is faulty.

It will bring together world-leaders in developmental neurobiology, psychiatric genetics, molecular and cellular neuroscience, systems and computational neuroscience, cognitive science and psychology. A major goal is to break down traditional boundaries between these disciplines to enable links to be made between differing levels of observation and explanation.

We will explore, for example, how mutations in genes controlling the formation of synaptic connections between neurons can alter local circuitry, changing the interactions between brain regions, thus altering the functions of large-scale neuronal networks, leading to specific cognitive dysfunction, which may ultimately manifest as the symptoms of schizophrenia or autism. Though the subjects dealt with will be much broader than that, this example illustrates the kind of explanatory framework we hope to develop, level by level, from molecules to mind.

A list of confirmed speakers is provided below. We are excited to have an outstanding programme of leading researchers across many different fields. The full programme is available at http://www.wiringthebrain.com. Registration and abstract submission are now open. You can follow updates on the meeting and pre-meeting discussion topics on the Wiring the Brain Facebook group.

The conference is being held in association with Neuroscience Ireland and with BioMed Central and we are delighted to have them both involved. We have also received generous support from Science Foundation Ireland and from other sponsors (listed on the conference website).

The venue is the beautiful Ritz Carlton hotel in Powerscourt, Co. Wicklow, a convenient drive from Dublin airport and one of the most scenic areas of the country.

We hope to see some of you there!

The Organising Committee

Kevin Mitchell, Trinity College Dublin
Aiden Corvin, Trinity College Dublin  
Isabella Graef, Stanford University 
Edward Hubbard, Vanderbilt University 
Franck Polleux, The Scripps Research Institute

Keynote lectures

Gyorgy Buzsaki, Rutgers University
- brain oscillations and cognitive functions

Carla Shatz, Stanford University,
- activity-dependent mechanisms of neural development

Chris Walsh, Harvard Medical School
- genetics of cortical development and cortical malformations

Plenary speakers

Rosa Cossart, INSERM U901, Université de la Méditerranée, Marseilles
- neuronal network development and function

Ricardo Dolmetsch, Stanford University
- neuronal signaling pathways; molecular mechanisms in autism

Dan Geschwind, University of California, Los Angeles
- genetics and pathogenic mechanisms of autism; brain systems biology

Michael Gill, Trinity College Dublin
- genetics and pathogenic mechanisms of psychiatric disorders

Anirvan Ghosh, University of California, San Diego
- molecular mechanisms of neuronal connectivity

Melissa Hines, University of Cambridge
- sexual differentiation of the nervous system

Josh Huang, Cold Spring Harbor Laboratories
- molecular mechanisms of synaptogenesis

Heidi Johansen-Berg, University of Oxford
- diffusion-weighted tractography in the human brain

Mark Johnson, Birkbeck College, University of London
- cognitive development, neuroconstructivism

Maria Karayiorgou, Columbia University
- genetics and pathogenic mechanisms of schizophrenia

Isabelle Mansuy, University of Zurich
- epigenetic mechanisms of synaptic plasticity and dysfunction

Andreas Meyer-Lindenberg, University of Heidelberg
- functional and structural neuroimaging in psychiatric disorders

Bita Moghaddam, University of Pittsburgh
- network development and mechanisms of psychiatric dysfunction

Tomas Paus, University of Nottingham
- maturation of cortical connectivity in adolescence

Linda Richards, Queensland Brain Institute
- axon guidance, cortical connectivity

Akira Sawa, Johns Hopkins University
- molecular and cellular functions of psychiatric risk genes

Bradley Schlaggar, Washington University, St. Louis
- functional connectivity networks

Klaas Stephan, University of Zurich
- computational modeling of brain connectivity

Pierre Vanderhaeghen, University of Brussels
- molecular mechanisms of cortical development

Searching for a needle in a needle-stack

2010-10-24T09:51:00.000-07:00

Whole-genome sequencing is a game-changer for human genetics. It is now possible to deduce every base of an individual’s genome (all 6 billion of them – two copies of 3 billion each) for a couple of thousand euros, and dropping. (Yes, euros). Even Ozzy Osbourne just got his genome sequenced! For researchers searching for the causes of genetic disease (or resistance to vast quantities of drugs and alcohol), this means they no longer have to infer where a mutation is by tracking a sampling of “markers” spaced across the genome – they can directly see all of the genetic information.

The problem is, they directly see all of the genetic information. If each of us carries thousands of mutations – changes that are very rare or may even have never been seen before in any other person – then telling which one of those changes is actually causing the condition is a tough task. Researchers in psychiatric genetics are currently grappling with how to handle this glut of information.

The problem is particularly acute in this field, where there is a (very slowly) growing realisation that many so-called common disorders, such as schizophrenia and autism – are really umbrella terms for collections of very rare disorders. Each of these conditions can be caused by mutations in single genes. The reason they are so common is that there are so many genes required to wire the brain properly – mutations in any of probably hundreds of genes can lead to the kinds of neurodevelopmental defects that ultimately result in psychopathology. (At least, that is the working hypothesis - see review below for a discussion of the evidence supporting it).

Very large studies are now underway to sequence the genomes of thousands of people with schizophrenia, autism or other psychiatric disorders, along with “control” individuals from different populations. The hope is that by comparing the spectrum of mutations in patients with those in controls, it will be possible to deduce which mutations are pathogenic. The most obvious ones will be those which recur in multiple individuals with a psychiatric disorder, are not present in the control population and are predicted to affect the biochemical function of the encoded protein. Those parameters can be used to prioritise candidate mutations for further study.

So far, however, it has been far more difficult to generate the type of statistical evidence that psychiatric geneticists have been used to from genome-wide association or linkage studies. One major problem is that, while it is true that mental illness can be caused by single mutations, it is also true that the situation is likely more complicated than that in many cases. Most such mutations that have been identified to date are only partially “penetrant” – that means that not all of the people who carry the mutation have the disorder in question. Another way of describing that is to say that the mutations have “variable expressivity” – that means the phenotypes they result in vary widely across mutation-carriers. This makes it crucially important for genetic studies to very carefully define the phenotype being mapped – in many cases a particular clinical diagnosis will not be the best phenotype to choose.

One reason for such variable phenotypes due to a mutation in any single gene is that its effects may be modified by other mutations that each person carries. That situation is not unique to psychiatric disease – it’s actually true of all so-called Mendelian disorders. Even in classical examples like cystic fibrosis, which is caused by mutations in a single gene, the effects of such mutations are quite variable and are strongly affected by genetic background.

But it does pose a major problem – if you find a mutation in two or three people with disease and one person without disease, how can you assign a p-value to the likelihood of that mutation being causative? And how do you distinguish mutations in that gene from those that happen to occur in all the other genes in the genome? Hopefully, this problem will partly solve itself as larger samples of patients and control individuals are sequenced. A move back to family-based studies will also be hugely helpful as it will provide evidence based on which mutations segregate with illness (or, even better, with some more fundamental neurobiological “endophenotype”).

However, we will still likely be left with a situation where the statistical evidence we can get from considering the spectrum of mutations in single genes will run into mathematical limits. At some point it will be necessary to look for other types of evidence from outside the system. One type of evidence will come from analysing the biochemical pathways of the implicated genes – it is already becoming apparent that many such genes encode proteins that interact with each other (see review below for examples).

For example, mutations in the gene Contactin-associated protein 2 (CNTNAP2) have been found in patients with autism, schizophrenia, epilepsy, Tourette’s syndrome, ADHD and other disorders. The evidence for this gene by itself is extremely strong. Recently, mutations in genes encoding the related proteins CNTNAP4 and CNTNAP5 have also been found in patients with epilepsy and autism, respectively. By themselves, the evidence for each of these genes is not at all convincing – in fact it is not possible to even generate a p-value for how likely it is that they are causative. But taken together, the findings of mutations in each of these genes greatly strengthens the implication of the pathway in general. Findings of mutations in the genes encoding the interacting proteins Contactin-3, -4 and -5, similarly add to the weight of evidence.

These proteins are all involved in forming synaptic contacts between neurons, as are many other genes identified in patients, further implicating defects in this process as one route to mental illness.

The effects of mutations in particular genes can also be investigated in genetically modified mice. If a mutation in Gene A causes neurodevelopmental defects and physiological or behavioural phenotypes that are similar to those seen in mice with mutations in a gene known to cause psychiatric illness, then that is strong evidence that Gene A may be the culprit in individuals carrying a mutation that disrupts it.

The next few years will be tremendously exciting as the data from sequencing projects become available. To fully interpret these it will be necessary to look beyond statistical measures from the human data themselves and include evidence of biological plausibility, converging biochemical pathways and neurobiological phenotypes in both humans and animal models.

Mitchell KJ (2010). The genetics of neurodevelopmental disease. Current opinion in neurobiology PMID: 20832285

Colour my world

2010-10-18T06:21:00.000-07:00

Colour does not exist. Not out in the world at any rate. All that exists in the world is a smooth continuum of light of different wavelengths. Colour is a construction of our brains. A lot is known about how the brain does this, beginning with complicated circuits in the retina itself. Thanks to a new paper from Greg Field and colleagues we now have an even more detailed picture of how retinal circuits are wired to enable light to be categorized into different colours. This study illustrates a dramatic and fundamental principle of brain wiring – namely that cells that fire together, wire together.

Colour discrimination begins with the absorption of light of different wavelengths. This is accomplished by photopigment proteins, called opsins, which are expressed in cone photoreceptor cells in the retina. Humans have three opsin genes, which encode proteins that preferentially absorb light of different wavelengths: short (S, in what we perceive as the blue part of the spectrum), medium (M, green) and long (L, red). Each cone expresses only one of these opsin genes and is thus particularly sensitive to light of the corresponding wavelength. However, by itself the response of a single cone cell cannot be used to determine the colour (wavelength) of incoming light. The reason is that each cone is responsive to both the wavelength and the intensity of the light – so an M-cone would respond equally to a dim green light or a strong red light.

Colour information only arises by comparing the responses of multiple cone cells. This is accomplished in two distinct channels – one which compares the inputs of L and M cones (the red-green channel) and one which compares the inputs of S cones to the combined inputs of L and M cones (the blue-yellow channel). The latter of these is the original, evolutionarily older system, dating back at least 500 million years. It is found in most mammals, in which there are only two opsin genes – an S opsin and one whose absorbance is midway between L and M.

The L/M system evolved much more recently, due to a gene duplication that occurred in the lineage of Old World primates, probably around 40 million years ago. The duplication of the primordial L/M opsin gene allowed the two resultant genes to diverge from each other in sequence, generating proteins with different absorption spectra, which could then be compared. Something similar can actually be achieved even in species with only one copy of the L/M gene. This gene is on the X chromosome, so females will carry two copies of it. Due to the random inactivation of one X chromosome in each cell in females, each cone will express only one of the two copies of this opsin gene. If the two copies differ from each other, encoding proteins with alterations in the amino acid sequence that affect their light absorbance, then what will arise is a set of L cones and a set of M cones.

All of this raises an important question – how are the inputs to these different cone cells compared? If the cells which express L and M cones are essentially the same, with the sole difference being that they express different opsin genes, then how is the wiring in the retina set up so that their inputs are distinguished, allowing their subsequent comparison? Cells in the retina are arranged in a series of layers. Cone cells connect, through bipolar and other cells, to retinal ganglion cells, which in turn convey visual information to the brain. Retinal ganglion cells integrate inputs from multiple cones, but in a very specialized way – some cones connect through ON bipolar cells (which are activated by light) and others through OFF bipolar cells (which are inactivated). Typically, one cone in the centre of an array of cells is connected to an ON bipolar cell, while surrounding cones connect to the same retinal ganglion cell target via OFF bipolar cells. The result is that the light signal hitting an array of cones is integrated – if the central cone is an L cell and the surrounding cones are M cells then the retinal ganglion cell will be most strongly activated by red light.

This has been known for quite a long time now. What has not been clear is how this system gets wired up during development. S, M and L cones are distributed randomly across the retina. S cones, which are the least frequent, are molecularly distinct from L/M cones in many ways and connect to a dedicated set of S channel bipolar and retinal ganglion cells. The development of the wiring that carries out the comparison between S and L/M cones is thus molecularly specified. This cannot be the case for the comparison between L and M cones, which differ only in the opsin gene they express.

The new study by Field and colleagues worked out in breathtaking detail the circuitry of the retina at a cellular level. Their results reveal the beauty and elegance of this circuitry but also resolve an important question relating to how L and M cone cells are wired. Each retinal ganglion cell in the centre of the retina receives ON inputs from a single cone and OFF inputs from the surrounding cones. In the periphery, however, the ON “centre” is composed of up to twelve cones. For the ganglion cell to discriminate colours there must be a bias in how many L or M cone cells wire up to it through the ON and OFF channels.

Their results reveal exactly such a bias and further show that it cannot be explained simply by random clumping of L or M cones in the photoreceptor array. What this indicates is that there is some additional mechanism whereby inputs from just one type of cone are strengthened in each of the ON and OFF channels. In effect, the L and M cones are competing for inputs in each channel, presumably through so-called “Hebbian mechanisms” whereby inputs to a cell are strengthened if they fire at the same time and asynchronous inputs are actively weakened. Despite their being no molecular differences between these cone cells, the brain is thus primed to wire them into distinct channels based on their patterns of activity.

A remarkable experiment performed a few years ago dramatically illustrates this principle. Mice are naturally dichromatic – they only have two opsin genes (S and L/M). Researchers in Jeremy Nathans’s group replaced one copy of the L/M gene with a version of the human L gene. This meant that female mice could be generated which carried one mouse opsin (L/M) and one human version (L). Cone cells could express one or the other of these genes. The result was astonishing – in visual tests, these mice could clearly distinguish between light of wavelengths which they were previously unable to discriminate. (They could now tell red from green). Despite normally having only two channels, their nervous system was clearly primed to perform this comparison.

Amazingly, this may extend to humans as well. The opsin genes in humans can also be polymorphic – each one comes in several different versions. Females who carry one version of, say, the L gene on one X chromosome, and another on the other X chromosome, can effectively have four different channels of absorption: S, M, L and L’. If the retina is primed to compare inputs based on their patterns of activity then one would predict that such females would be tetrachromatic – they should be able to distinguish between more colours than trichromatic individuals (just as trichromats can distinguish more colours than dichromats – people with a mutation in one of the L or M opsin genes, who are red-green colourblind).

This increased ability to discriminate colours is, apparently, indeed present in about 50% of females and can be revealed by a very simple test. Consider the picture of the colour spectrum shown below. If you print this out and mark on it with a pencil everywhere there seems to be a clear border between two distinct colours, then what you will find is that most trichromats mark out about 7 colour domains, while tetrachromats mark out between 9-10 (and dichromats about 5).

So, where a man may just see “green”, a woman may see chartreuse or olive. Realising that people literally see things differently (and not just colours) could avoid needless argument. (That said, the woman is clearly more right, and it is usually best to concede graciously).

Field GD, Gauthier JL, Sher A, Greschner M, Machado TA, Jepson LH, Shlens J, Gunning DE, Mathieson K, Dabrowski W, Paninski L, Litke AM, & Chichilnisky EJ (2010). Functional connectivity in the retina at the resolution of photoreceptors. Nature, 467 (7316), 673-7 PMID: 20930838

Jacobs, G., Williams, G., Cahill, H., & Nathans, J. (2007). Emergence of Novel Color Vision in Mice Engineered to Express a Human Cone Photopigment Science, 315 (5819), 1723-1725 DOI: 10.1126/science.1138838

Jameson KA, Highnote SM, & Wasserman LM (2001). Richer color experience in observers with multiple photopigment opsin genes. Psychonomic bulletin & review, 8 (2), 244-61 PMID: 11495112

Mice with fully functioning human brains

2010-10-04T12:49:00.000-07:00

I wouldn’t usually discuss politics in a blog like this, but a recent story caught my eye, as it provides an example of the depressing and sometimes bizarre level of scientific illiteracy among elected officials or some people who hope to be elected. The example is from the United States, which is an easy target in this regard, but we have had a similar episode in Ireland recently so I don’t think we (or indeed any other non-Americans) can feel particularly smug about it. This one is especially funny, though.

Christine O’Donnell has recently won the Republican nomination in Delaware for the upcoming election to the Senate. I just love her – for comic entertainment this woman is very good value. She makes Sarah Palin look like the most reasonable, well-informed, level-headed person around. Among many clangers that she has dropped in the past, the one that really got my attention was the following assertion, made during a debate on stem cells on The O’Reilly Factor show on Fox News a few years ago:

"American scientific companies are cross-breeding humans and animals and coming up with mice with fully functioning human brains. So they're already into this experiment."

That’s right, cross-breeding humans and animals. I’m not sure how she imagines that to have taken place and would rather not know. And yes, she did say: mice with fully functioning human brains. Now, the average mouse weighs around 20 grams. The average human brain (clearly there are exceptions) weighs around 1.4 kilograms. I’m not sure Ms. O’Donnell really thought that through, even from a purely mechanical standpoint. However, she apparently had the opportunity to qualify or alter her assertion but did not, so one can assume she meant something like what she actually said.

(She also thinks evolution is a myth, because if we evolved from monkeys, then how come the monkeys are not still evolving into humans? That some people buy that kind of “argument” exemplifies the poor grasp that many people have of geological time. And of the fact that we did not evolve from monkeys – monkeys and humans evolved from a common ancestor. It reminds me of an even funnier comment I read from another creationist: if we evolved from monkeys, then how come we don’t speak monkey? There’s just no answer to that.)

What the imaginative Ms. O’Donnell may have been trying to refer to was a story that got some press coverage at the time of scientists who had transplanted a small number of human cells into a mouse brain to see if they would migrate and integrate normally. Apparently, about 100 such cells survived, in a brain that contains over 20 million cells. So, transplantation, not cross-breeding, and not fully functioning human brains, but to be fair to her she did, in an incredibly inept and confused manner, raise an interesting issue.

That is the question of whether it is ever a good idea (or morally or ethically right) to create an organism whose cells derive from two different species – a so-called chimera (named after the mythically mixed-up creature). This is especially touchy when some of the cells are of human origin. Why, you might legitimately ask, would anyone want to do such a thing?

Well, there are lots of reasons, none of which involves playing God just for fun, or actually wanting to create a hybrid organism. Most of the studies that have carried out such experiments are designed to test the potential of stem cells for regeneration of damaged parts of the brain. Stem cells can be obtained from many different sources, including early human embryos, umbilical cord blood and bone marrow. New technologies now allow fully differentiated adult cells from various tissues to be retro-differentiated into stem cells (so-called induced pluripotent stem cells). All of these cell types hold great promise for regenerative medicine, especially ones that are of the same genotype as the prospective patient.

But how to test them? Just injecting them into patient’s brains doesn’t seem like the best approach, though actually it has been done in some cases of seriously ill patients in the late stages of Parkinson’s and Huntington’s disease. Initial results seemed to suggest some clinical improvement but larger, more carefully controlled trials have been largely disappointing. These studies involved injection of primary human fetal cells into the brains of adult patients and were not particularly sophisticated in terms of how these cells were treated prior to injection.

With better defined populations of stem cells it is now possible, for example, to differentiate them into particular types of neurons (or their direct progenitors) prior to transplantation. To determine the efficacy of such treatments, animal models have of these disorders have been used. Human cells will integrate fairly happily into a rodent or even a chick brain. (No chick jokes, please). The brain is immune privileged and grafts of foreign cells are generally well tolerated by the host. Using this approach it is possible to determine how such transplanted cells survive, migrate and integrate into the brain (under the assumption that such processes would be much the same in a human brain). More importantly, it is possible to determine whether transplantation of such cells results in any improvement in the animal’s condition.

Such studies are generating promising results in models of stroke, spinal cord injury and neurodegenerative diseases such as Alzheimer’s, Huntington’s and Parkinson’s diseases (see a few recent examples below). It is still early days, however, and a lot more pre-clinical research like this will have to be carried out to characterise how these cells behave after transplantation, before they will be approved for clinical use. So, nothing sinister, no witchcraft (sorry, Christine, I know you like that), no hybrid mouse-humans scuttling into the dark corner of the lab when the lights are turned on. Just scientists and clinicans trying hard to find cures for serious diseases. Nothing sensationalist at all really. Sorry.

Snyder BR, Chiu AM, Prockop DJ, & Chan AW (2010). Human multipotent stromal cells (MSCs) increase neurogenesis and decrease atrophy of the striatum in a transgenic mouse model for Huntington's disease. PloS one, 5 (2) PMID: 20179764

Salazar DL, Uchida N, Hamers FP, Cummings BJ, & Anderson AJ (2010). Human neural stem cells differentiate and promote locomotor recovery in an early chronic spinal cord injury NOD-scid mouse model. PloS one, 5 (8) PMID: 20806064

Lee HJ, Lim IJ, Lee MC, & Kim SU (2010). Human neural stem cells genetically modified to overexpress brain-derived neurotrophic factor promote functional recovery and neuroprotection in a mouse stroke model. Journal of neuroscience research PMID: 20818776

Ancient origins of the cerebral cortex

2010-09-17T05:59:00.001-07:00

Just how special is the human brain? Compared to other mammals, the thing that stands out most is the size of the cerebral cortex – the thick sheet of cells on the outside of the brain, which is so expanded in humans that it has to be folded in on itself in order to fit inside the skull. The cortex is the seat of higher brain functions, the bit of the brain we see with, hear with, think with. In particular, one of its main functions is association – bringing sensory information together with information on internal states and motivation to enable flexible and context-dependent decisions to be taken, rather than simple reflexive actions in response to isolated stimuli. While undoubtedly vastly more developed in humans, a new study suggests the cerebral cortex may have much more ancient origins than previously suspected.

All mammals have a cortex and it generally increases in size over evolution. Mice and rats have a smooth cortex, while that of cats is somewhat expanded and folded. Monkeys and apes show progressively bigger cortices as they get evolutionarily closer to humans. Dolphins and elephants also show highly expanded and folded cortices, so we are not the only species to arrive at this arrangement.

Expansion is coupled with an increase in the complexity of the cortex, as defined by the number of distinct cortical areas. This is mostly due to the emergence of additional association areas, where information from different inputs is integrated, and, in humans, an increase in distinct areas in the frontal and prefrontal cortex – the seat of the most sophisticated executive functions, including decision-making and long-term planning.

But when in evolution did the cortex actually evolve? Does it have some ancient precursor or did it arise as a new invention at some point? There has been considerable debate for decades over whether birds and reptiles have a counterpart of the cortex. They do have some regions that occupy the dorsal parts of the brain and perform somewhat similar functions, but their organization is so different from that of the cortex in mammals (which is arranged into discrete layers, while these regions in birds and reptiles are arranged into clusters of cells) that it has been difficult to establish their relationship.

Whether particular brain structures in different species are related to each other (i.e., whether they diverged from a single structure in a common ancestor) is often a subject of debate and controversy. It can be difficult if not impossible to determine based solely on location, anatomical organization or functional similarity. This is because it is relatively easy for these parameters to change over the course of evolution – they can be affected by changes to one or two genes, which means there is plenty of variation in these phenotypes within the population – the raw material for evolution by natural selection.

If the final phenotypic end-point of any particular region is quite variable, the opposite is true of the genetic pathways that specify the identity of the region at earlier stages of development. These involve master regulatory genes, whose expression is turned on or off in various parts of the embryo in response to earlier pathways that specify positional information (head from tail, back from belly, etc.). So, there are genes that differentiate nervous system tissue from the rest of the embryo, that differentiate forebrain, midbrain and hindbrain and that differentiate later subdivisions, including the cerebral cortex.

These genes act as “transcription factors”, controlling the expression of sets of proteins which define the mature characteristics of any particular region. While it is relatively easy for one of these effector proteins to change over evolution – affecting some specific characteristic of the region – it is much harder for the master regulatory genes to change. This is because they do not work alone – each area is defined by the expression of a combination of such genes, which are often turned on or off in a specific sequence. These genes interact in a complicated network of feedforward and feedback loops to orchestrate this complicated sequence. The networks in which they operate are so interlocked and involved in so many different parts of the embryo that mutations to any one gene tend to have very drastic consequences and will be rapidly selected against. These early regulatory systems are thus far less variable and tend to be highly conserved across evolution. So much so, in fact, that in many cases the function of one of these genes in one species can be carried out perfectly well by a copy of the gene from even a very distantly related species.

It is thus possible to tell whether a brain region in one species is homologous to one in another species (which may look quite different in its mature characteristics) by looking at how those regions were specified. If they derive from regions of the embryo which are specified by the same sets of regulatory genes then one can infer they both evolved from the same region in a common ancestor, no matter how different they may look now.

Similar patterns of gene expression argue that the cortex of mammals and the “pallium” of birds and reptiles are indeed related to each other, but a new study from the lab of Detlev Arendt goes much further back in evolutionary time. They compared the pattern and sequence of genes involved in specifying the cerebral cortex in mammals and the mushroom bodies, a sensory-associative brain centre in a much simpler organism, an annelid worm, called Platynereis. While it had previously been suspected that there might be a relationship between these structures (particularly between the cortex and mushroom bodies in insects) it had been impossible to determine definitively due to technical difficulties in examining the expression patterns of more than one gene at a time. The researchers in Arendt’s lab solved this problem by developing a new image-registration technique so that many different gene expression patterns could be mapped onto a common template and compared.

They found the same set of genes is expressed in these regions, in the same temporal sequence, under the influence of the same patterning mechanisms (those that specify where different structures develop in the embryo). Even further, very similar profiles of gene expression were observed in specific types of neurons in the mushroom bodies and in the cerebral cortex. This similarity extend to mushroom bodies in the brain of the fruitfly Drosophila, which are well known to be involved in sensory-associative integration, as well as learning and memory.

The conclusion from all these data is that the common ancestor of annelids, insects and vertebrates (the common ancestor of protostomes and deuterostomes, for those keeping score) already possessed some brain structure, specified by this defined set of genes, which was involved in integrating sensory information and performing associative functions. The morphology and connectivity of this structure has diverged significantly in each lineage since then, but the underlying similarities in function remain.

The extension of this principle of conservation in the genetic mechanisms specifying various organs or cell-types – which has been observed in eyes, limbs, hearts, and practically everywhere else one looks – to the part of the brain that most defines our humanity reinforces the notion espoused by Darwin, that “the difference in mind between man and the higher animals, great as it is, certainly is one of degree and not of kind."

Tomer R, Denes AS, Tessmar-Raible K, & Arendt D (2010). Profiling by image registration reveals common origin of annelid mushroom bodies and vertebrate pallium. Cell, 142 (5), 800-9 PMID: 20813265

Wild-type humans

2010-09-08T03:48:00.000-07:00

Wild-type is the term geneticists use to refer to non-mutants. It literally means organisms that are the same, genetically, as those in the wild, compared to ones that have been grown under coddled conditions in the lab for generations, going soft in the absence of natural selection, or that are specifically mutant at some gene or other. There are no wild-type humans.

Well, maybe there are a few, somewhere, but even they are not really non-mutants. We all carry millions of mutations in our genome – positions where the sequence in our genome differs from the typical sequence. Where everyone else has a “T”, you might have an “A”, for example. Most of these mutations have no consequence – they are simply neutral variation in DNA that has no discernible function. It turns out that most of the genome is not made of genes – the bits of DNA that code for proteins actually comprise only about 2-3% of the total sequence. Mutations that change the code for proteins are by far the most likely to cause disease or to result in an obvious phenotypic difference.

New DNA sequencing technologies have revealed how many mutations of that type each of us carries, on average. Lots: around 10,000 mutations that change the amino acid code of a protein. Those can be broken down based on frequency in the population. Some mutations are seen in many individuals in the population – this implies that they occurred long ago in some individual and have subsequently spread in the descendants of that individual. The inference is that such a mutation does not have a deleterious effect as it would have been selected against if it did. About 90% of protein-changing mutations fall into this common, ancient class. In fact, in many such cases it can be difficult to say which allele (which version of the sequence at a specific position) is “wild-type”.

Some of these common mutations are actually adaptive and may be much more common in some populations than others. These include mutations that affect skin colour, for example, reflecting adaptation to either high sunlight (requiring protective melanin) or lower sunlight (requiring less melanin to allow vitamin D production), as well as variants affecting diet, such as lactose tolerance, adaptation to environmental conditions, such as high altitude, or resistance to specific pathogens or parasites. So, what is wild-type in one population may be mutant in another.

The remaining 10% of mutations are either very rare or “private”, having only ever been observed in one individual. When searching for mutations responsible for genetic diseases, these are the variants that researchers go after. Of course, not all of these will have phenotypic effects. Many changes to the code of amino acids in a protein can be tolerated without compromising function. It is possible to estimate how many rare mutations each of us carries that are likely to affect protein function – this is between 100 and 200, quite a small number, really. As well as mutations that change one DNA base to another, these also include a different class – mutations which result in the deletion or duplication of a whole chunk of a chromosome (copy number variants).

This got me to idly musing about what would happen if you took someone’s DNA sequence and “corrected” all those mutations to the wild-type version. What would the result be? Those 200 or so rare mutations may generally be tolerated (they are clearly not lethal at least) but could still result in suboptimal performance of any number of biochemical, cellular or physiological processes in each one of us. They may also contribute to differences in morphology by subtly affecting processes of growth and development. As these mutations tend to reduce the function of the encoded protein, presumably it should be “better” to have the wild-type version. (For good measure, let’s imagine we can “correct” all the mutations predicted to affect protein function, even if they are slightly more common – say up to 5-10% frequency in the population, but not so common that we can’t say what the wild-type version is).

This was the premise of the excellent movie GATTACA. Apparently the book that inspired it was also good, but I haven’t read it because it didn’t have Uma Thurman in it. The movie did, Uma being somebody’s vision of what a wild-type human female would look like (and who would argue?). Her male counterpart, Jude Law, reinforces the impression that they would be, most importantly, ridiculously good-looking. Poor Ethan Hawke was cast as the guy born by traditional procreative methods, mutations and all.

Beauty is only skin deep, of course, and what really interests me is what would their brains look like? It takes a lot of genes to assemble a human brain and all of us carry mutations in many of those genes. Those differences affect how our brains are wired and influence many aspects of our personality, perception, cognition and behaviour (as pretty much all the posts on this blog describe). What would the brain of someone with each of those deleterious mutations corrected be like? Would they be a genius? Especially empathetic? A naturally coordinated athlete? Would they be left or right-handed? What would their personality be like? Is there a wild-type level of extroversion or neuroticism or open-mindedness?

For some of those traits the optimal level may be different from the maximal level. For brain size, for example, which is correlated with intelligence, there is a trade-off in, first, being able to make it out the birth canal and also in metabolic demand – big brains use a lot of energy. And for may personality traits it is difficult to define a single optimal point along the spectrum – there are many different strategies that may succeed better in different contexts. Being fearless and aggressive may attract the ladies, but could also get you killed young. So, our wild-type humans may have perfect vision and perfect teeth, but it’s much harder to define a perfect personality.

Another consideration is that natural selection has only ever acted on individuals with a genetic burden of mutations – we may thus in some way be adapted to that situation. Some mutations that decrease the function of one protein may be beneficial in the context of another mutation in a different protein. Perhaps putting all the perfect proteins together in one person would not actually generate an optimal system.

In the movie, the generation of these “genetically perfect” beings was accomplished by gradually selecting out all such mutations by screening embryos created by in vitro fertilization. The fatal flaw in this idea is that it considers the spectrum of mutations as static in the population, suggesting that once all the bad ones are weeded out, that will be that. This ignores the fact that the rate of new mutations is actually quite high. Each of us carries about 70 new mutations that are not inherited from our parents. Most of these arise during generation of sperm. The reason that mutations in sperm are more common than in eggs is that women are born with all their eggs already generated. The cells that generate sperm, in contrast, are constantly dividing throughout life. Each division increases the chance of incorporating an error. That is the reason why the rate of dominant Mendelian diseases – which are those caused by single mutations and which include many cases of common diseases such as schizophrenia and autism – increases with paternal age.

Of course, all of the discussion above is based on the premise that genetic effects on physical and psychological traits are predominant. This extreme form of genetic determinism was also espoused in GATTACA, to the point of predicting the cause and date of a person’s death! In reality, genetic factors have a large influence on many of these traits but by no means an exclusive one – intrinsic developmental variation, environmental effects and experience will all also contribute to varying extents. On the other hand, introducing mutations tends not only to change a phenotype but to increase the variance in the phenotype – as the system becomes more compromised, its output becomes more variable.

It would be interesting to ask, therefore, exactly how much variation in these traits would be left across our wild-type humans.

Ng, S., Turner, E., Robertson, P., Flygare, S., Bigham, A., Lee, C., Shaffer, T., Wong, M., Bhattacharjee, A., Eichler, E., Bamshad, M., Nickerson, D., & Shendure, J. (2009). Targeted capture and massively parallel sequencing of 12 human exomes Nature, 461 (7261), 272-276 DOI: 10.1038/nature08250

Roach, J., Glusman, G., Smit, A., Huff, C., Hubley, R., Shannon, P., Rowen, L., Pant, K., Goodman, N., Bamshad, M., Shendure, J., Drmanac, R., Jorde, L., Hood, L., & Galas, D. (2010). Analysis of Genetic Inheritance in a Family Quartet by Whole-Genome Sequencing Science, 328 (5978), 636-639 DOI: 10.1126/science.1186802

Coloured hearing in Williams syndrome

2010-08-27T03:48:00.000-07:00

The idea that our genes can affect many of the traits that define us as individuals, including our personality, intelligence, talents and interests is one that some people find hard to accept. That this is the case is very clearly and dramatically demonstrated, however, by a number of genetic conditions, which have characteristic profiles of psychological traits. Genetic effects include influences on perception, sometimes quite profound, and other times remarkably selective. A recent study suggests that differences in perception in two conditions, synaesthesia and Williams syndrome, may share some unexpected similarities.

Williams syndrome is a genomic disorder caused by deletion of a specific segment of chromosome 7. Due to the presence of a number of repeated sequences, this region is prone to errors during replication that can result in deletion of the intervening stretch of the chromosome, which contains approximately 28 genes. The disorder is characterised by typical facial morphology, heart defects and a remarkably consistent profile of cognitive and personality traits. These include mild intellectual disability, with relative strength in language and extreme deficits in visuospatial abilities (including being able to perceive the relationships of objects in 3D space and to construct and mentally manipulate 3D representations). Williams patients are also highly social – often to the point of being over-friendly – empathetic and very talkative. This behaviour may belie a high level of anxiety, however.

One of the most remarkable features of Williams syndrome is the strong attraction of patients for music. Many show a strong interest in music from an early age and greater emotional responses to music. They are also more likely to play a musical instrument, some using music to reduce anxiety. A recent study from Elisabeth Dykens and colleagues adds a new twist to this story. They found in a neuroimaging experiment that in addition to activating the auditory cortex, music also stimulates visual activity and perceptions in Williams patients. In fact, this is not specific to music – non-musical sounds had the same or even stronger effects.

This is very reminiscent of what happens in a form of synaesthesia, called “coloured hearing”. In this condition, which is also heritable, sounds, sometimes specifically music or words, sometimes general sounds, are accompanied by a visual percept. These percepts are typically fairly simple – patches of colour, for example – and can be experienced out in the world or “in the mind’s eye”. (They are alternatively sometimes felt more as an “association” with a visual property, so that the sound of a trombone might be blue, while a piano might be green). Importantly, these visual percepts are both idiosyncratic and extremely consistent – middle C may evoke the image of a small purple cloud, a dog’s bark may set off yellow starbursts, etc.

Neuroimaging experiments in synaesthesia have also found activation of visual areas in response to sounds. Various models have been proposed to account for this, which I have discussed previously. They all involve cross-activation from auditory circuits to those that represent visual information. This may be mediated by extra physical connections in the brains of synaesthetes, presumably due to genetic effects on how the brain is wired during development. Alternatively, the wires could be there in everyone but just working differently in synaesthetes. It has so far been very difficult to distinguish between these possibilities.

The situation in Williams syndrome may be much more amenable to investigation. Unlike synaesthesia, we know what the genetic cause is in Williams syndrome. We know which genes are deleted and researchers are beginning to dissect which ones are associated with which symptoms. Some of these genes are known to function in nerve growth and guidance. It has also been demonstrated very clearly using diffusion tensor imaging that there are large differences in various circuits in the brains of Williams patients, including the presence of additional fibre bundles to or from the intraparietal sulcus, a region involved in visuospatial construction. It will be very interesting to determine whether similar extra connections can be detected between auditory and visual areas.

It is important to recognise, however, some crucial differences between the auditory-visual effects observed in Williams syndrome and in synaesthesia. The visual percepts reported in Williams syndrome are far more complex than those reported in synaesthesia. The former reportedly involve objects and scenes, more like a dreamscape than a simple blob of colour. They also lack the consistency which is one of the defining characteristics of synaesthesia. There may thus be more than one way to end up with coloured hearing.

Whatever the cause in these conditions, they both highlight the fact that genetic differences can have profound effects on how people perceive the world.

Thornton-Wells, T., Cannistraci, C., Anderson, A., Kim, C., Eapen, M., Gore, J., Blake, R., & Dykens, E. (2010). Auditory Attraction: Activation of Visual Cortex by Music and Sound in Williams Syndrome American Journal on Intellectual and Developmental Disabilities, 115 (2) DOI: 10.1352/1944-7588-115.172

Marenco, S., Siuta, M., Kippenhan, J., Grodofsky, S., Chang, W., Kohn, P., Mervis, C., Morris, C., Weinberger, D., Meyer-Lindenberg, A., Pierpaoli, C., & Berman, K. (2007). Genetic contributions to white matter architecture revealed by diffusion tensor imaging in Williams syndrome Proceedings of the National Academy of Sciences, 104 (38), 15117-15122 DOI: 10.1073/pnas.0704311104

When to blame your parents, and for what

2010-08-20T04:45:00.000-07:00

Studies linking some aspect of parental behaviour with some trait in their offspring are depressingly common in the sociological literature. Though these studies typically only report a correlation between parental behaviour and whatever the trait is in the offspring, the implication, and often the explicit conclusion, is that one causes the other. These kinds of stories get huge play in the popular press (and in the blogosphere), where the conclusion of a causative relationship is rarely challenged. For example, the finding that children who grow up with more books in the house are more successful academically is taken as evidence that simply having books around makes kids smarter.

This kind of thinking illustrates a common and fundamental flaw in interpreting sociological or epidemiological findings – correlation does not imply causation. Red hair and freckles are highly correlated but one does not cause the other. Both are caused by something else (a mutation in a gene controlling pigmentation). It seems a simple enough distinction but it is astonishing how pervasive this mistake is, even among academics supposedly trained in statistical methodology.

In the case of books, the conclusion that having them around is the causative factor on academic success is simply not warranted by the findings. The data from this kind of study design do not pertain to that question. The books could simply be an indicator of the real cause (like freckles). It seems quite possible that the underlying link is between the IQ of the parents (or some other cognitive trait predicting both academic success and bookishness – curiosity, open-mindedness, interest in more abstract topics) and that of their children. (It is well established that such traits are quite heritable).

I am not claiming that that actually is the explanation – just that it is a highly plausible one that must be considered. In fact, the study design does not permit this conclusion to be drawn either, and that illustrates one of the major problems in dissecting the possible effects of nature and nurture. It is hugely difficult to separate confounding genetic effects on behaviour of both parents and offspring from the effects of the behaviours themselves. Adoption studies – especially of identical twins reared apart – do provide one way to dissociate genetic effects from those of the family environment. These have consistently found large effects of shared genes and very little effect of family environment on a wide range of behavioural traits.

A far more tricky task is to dissociate the effects of parental behaviour prior to birth on the future behaviour of their offspring – adoption studies obviously cannot accomplish that. However, researchers in Cardiff, led by Anita Thapar, have come up with a clever and powerful new study design which does the trick. They have made use of the growing frequency of in vitro fertilisation to examine the effects of smoking during pregnancy. It is well known that smoking during pregnancy is associated with low birth weight and a number of other health issues. It is also associated with higher rates of antisocial behaviour in the offspring. Do these correlations really reflect the effects of smoking itself or could smoking be an indicator of a distinct underlying cause?

The IVF study design, which looked at records of 779 children, allowed these factors to be dissociated by splitting the mothers into two groups – those who were biologically related to their offspring and those who had used donor eggs and thus were unrelated to their offspring. These two groups were then examined for a correlation between the smoking behaviour of the mother during pregnancy and the birth weight and a measure of antisocial behaviour of their offspring. The findings were remarkably clear – smoking was associated with lower birth weight regardless of genetic relatedness. This effect is congruent with results from experimental animal studies on the effects of nicotine, cigarette smoke or carbon monoxide on birth weight and there are a variety of biological mechanisms postulated to explain the effect. So, all the evidence is consistent with this being a genuine effect of prenatal smoking per se.

But a very different picture was observed with respect to antisocial behaviour. High rates of antisocial behaviour were observed only in those mothers who smoked during pregnancy and who were related to their offspring. So, prenatal smoking itself does not seem to influence antisocial behaviour – it is more likely an indicator of some underlying genetic effect on behaviour of both the mother and the offspring. (See here for more on this).

So, smoking while pregnant is bad, mkay, for lots of reasons, but it will not make your child antisocial. And I would never argue against having books around, but articles proclaiming “Want smart kids? Here’s what to do” are uncritically promulgating an unfounded conclusion (also known as “talking shite”).

Evans, M., Kelley, J., Sikora, J., & Treiman, D. (2010). Family scholarly culture and educational success: Books and schooling in 27 nations Research in Social Stratification and Mobility, 28 (2), 171-197 DOI: 10.1016/j.rssm.2010.01.002

Rice, F., Harold, G., Boivin, J., Hay, D., van den Bree, M., & Thapar, A. (2009). Disentangling prenatal and inherited influences in humans with an experimental design Proceedings of the National Academy of Sciences, 106 (7), 2464-2467 DOI: 10.1073/pnas.0808798106

Defining developmental disorders through genetics

2010-08-13T02:03:00.000-07:00

To many people, the term “autism” suggests a specific disorder – one with characteristic and recognizable symptoms, presumably reflecting the same underlying cause. In fact, no such disorder exists. Autism refers to a variable spectrum of symptoms – including deficits in social interaction, impaired communication (especially a delay in developing language), narrow, restricted interests and stereotyped behaviours. Any one child who is diagnosed with autism may show only some of these symptoms. There is a wide range of IQ in autism, including very high levels seen in what has been known as Asperger’s syndrome, but the average is about 70. There is also a high incidence of epilepsy (around 10%).

Psychiatrists have long recognized this variability and use the term “autism spectrum disorder” to encompass the entire range. Until recently, with a couple of exceptions, they have not had the means to distinguish different subtypes of autism based on their underlying cause. One of these exceptions, which has been known for some time, is the gene responsible for Fragile X syndrome. Routine screening for Fragile X mutations allows pediatric psychiatrists to define up to 5% of autism referrals as arising from this cause. We now know that the symptoms of autism can be caused by a mutation in any of a large number of different genes (or by mutations which affect several adjacent genes on a chromosome).

Screening for the latter type of mutation – deletions or duplications of several genes, collectively called copy number variants – is already being proposed as a routine step of clinical genetic testing in patients presenting with symptoms of autism. These mutations are easy to detect but will probably be responsible for no more than 10% of cases. Point mutations – changes to a single base of DNA – are likely to account for the rest. Fortunately, it is now possible to sequence the entire genome, or at least the entire “exome” – the part of the genome that codes for proteins – in an individual for about 3,000 dollars and in under a week. A far cry from the 3 billion dollars and ten years it took to sequence the first human genome!

This approach has already been used to identify mutations in genes on the X chromosome in autism or schizophrenia cases but can now be extended to the entire genome. It will not always be obvious which mutation is causative in any individual, and we should expect a good deal of complexity due to combinations of mutations, but it should at least be possible to identify a primarily responsible mutation in a large proportion of cases.

The obvious question then is whether mutations in different genes are associated with distinct profiles of symptoms. By grouping together patients with mutations in the same locus, it may be possible to recognise specific profiles of symptoms that are otherwise obscured by variability among carriers and phenotypic overlap with other patients. This approach has been used very successfully in diagnosing cases of mental retardation, intellectual disability or other forms of developmental delay based on genetic lesion and has led to the identification and clinical characterisation of many new, distinct neurodevelopmental syndromes.

The first study to attempt this in autism spectrum disorder has been published recently by Bruining and colleagues. They looked at cases of autism due to Klinefelter syndrome (caused by an extra X chromosome in males: XXY), deletion in a region of chromosome 22 (22q11) or a group with unknown causes. By analyzing the profile of a list of clinical symptoms across these groups they were able to distinguish the Klinefelter and 22q11 cases from each other and from the cases with unknown etiology. There is still a lot of variability in each type and substantial overlap between them in “clinical symptom space”, but the carriers of specific mutations are significantly more similar in profile to each other than to the general cases.

This knowledge is tremendously useful in several ways. First, it becomes possible to make clinical predictions about an individual’s prognosis, by comparison with other carriers of the same mutation. Second, it allows prediction of genetic risk to relatives – this can be hugely important to parents of an autistic child who are considering having additional children. Third, identification of the mutated gene is the first step to elucidating the underlying defect at a neurobiological level. Ultimately, this may suggest routes to therapies which are rationally designed and personalised.

The promise of new therapeutics is illustrated by progress in understanding the biology underlying Fragile X syndrome. The fragile X gene encodes a protein, FMR1, which functions in nerve terminals to hold a set of RNA molecules in a state where they are ready to be translated into protein when the synapse is active. The new proteins are needed when a synapse has to be strengthened after use (a core mechanism of learning). Another protein, a glutamate channel called mGluR1, performs the opposing function – when activated it signals for these mRNAs to be translated. If the FMR1 protein is mutated then the RNA molecules get translated too early. This effect can be counter-balanced by turning down the activity of the mGluR1 protein. Remarkably, this results in very significant amelioration of the “symptoms” of FMR1 deletion in a mouse model of Fragile X syndrome. These results are so impressive that drugs to block mGluR proteins are now in small-scale clinical trials of human Fragile X patients.

This example illustrates how discovery of the responsible gene and elucidation of its functions at a molecular level can suggest highly specific ways to correct or compensate for the effect of the mutation, specifically in those patients with that lesion. We can expect this kind of approach to be similarly successful in discriminating patients with other disorders such as schizophrenia or epilepsy into genetically distinct subgroups. This promises to radically transform how patients with these diverse symptoms are diagnosed and treated – no longer lumped together into categories of questionable validity and usefulness, but based on their individual genetic profile.

Shen, Y., Dies, K., Holm, I., Bridgemohan, C., Sobeih, M., Caronna, E., Miller, K., Frazier, J., Silverstein, I., Picker, J., Weissman, L., Raffalli, P., Jeste, S., Demmer, L., Peters, H., Brewster, S., Kowalczyk, S., Rosen-Sheidley, B., McGowan, C., Duda, A., Lincoln, S., Lowe, K., Schonwald, A., Robbins, M., Hisama, F., Wolff, R., Becker, R., Nasir, R., Urion, D., Milunsky, J., Rappaport, L., Gusella, J., Walsh, C., Wu, B., Miller, D., & , . (2010). Clinical Genetic Testing for Patients With Autism Spectrum Disorders PEDIATRICS, 125 (4) DOI: 10.1542/peds.2009-1684

Piton, A., Gauthier, J., Hamdan, F., Lafrenière, R., Yang, Y., Henrion, E., Laurent, S., Noreau, A., Thibodeau, P., Karemera, L., Spiegelman, D., Kuku, F., Duguay, J., Destroismaisons, L., Jolivet, P., Côté, M., Lachapelle, K., Diallo, O., Raymond, A., Marineau, C., Champagne, N., Xiong, L., Gaspar, C., Rivière, J., Tarabeux, J., Cossette, P., Krebs, M., Rapoport, J., Addington, A., DeLisi, L., Mottron, L., Joober, R., Fombonne, E., Drapeau, P., & Rouleau, G. (2010). Systematic resequencing of X-chromosome synaptic genes in autism spectrum disorder and schizophrenia Molecular Psychiatry DOI: 10.1038/mp.2010.54

Bruining H, de Sonneville L, Swaab H, de Jonge M, Kas M, van Engeland H, & Vorstman J (2010). Dissecting the clinical heterogeneity of autism spectrum disorders through defined genotypes. PloS one, 5 (5) PMID: 20526357

Migrating neurons clear their path

2010-08-09T05:15:00.001-07:00

Most neurons in the brain are not born in their final position – they are generated by cell division in one part of the brain and have to migrate, sometimes over long distances, along complicated routes, to finally arrive at their pre-specified destination. This process entails an incredibly complex and dynamic set of genetic instructions and interactions between different cell types.

A prime example is the migration of interneurons to the cerebral cortex – these inhibitory neurons make up one half of a balancing act that controls all cognitive functions in the cortex, but, unlike the excitatory neurons of the cortex, they are born in a completely different part of the brain (what will become the striatum). Many researchers have been trying to understand how these neurons find their way specifically to the cortex. A number of genes have been found which encode guidance cues which can attract or repel the migrating neurons and which mark out their correct pathway. These cues are sensed by the receptor proteins expressed on the surface of the migrating neurons. This basic mechanism has been at the core of most models of neuronal cell migration.

A new paper adds a surprising new twist to this story, one that may be especially important for the migration of neurons generated in the adult brain. One of the major sites of generation of new neurons in adults is called the sub-ventricular zone (SVZ), where an actively dividing population of neural stem cells persists in adults. New neurons born here are destined to repopulate the olfactory bulbs – structures at the very front of the brain which receive information from odorant receptor neurons – where there is a high rate of turnover of neurons (in contrast to most of the brain).

The migration of these new neurons from the SVZ occurs along a very discrete route called the rostral migratory stream (RMS). One challenge for the migration of these neurons is that the mature cellular structures in the adult brain present a physical and molecular barrier to their movement. This is a similar problem faced by nerve fibres trying to regenerate in the adult spinal cord or brain – as the nervous system matures, glial cells (a type of non-neuronal cell that outnumber neurons by about 10:1) begin to form an environment which actively restricts the movement of cells and the growth of new axons. This makes some sense as a mechanism to keep everything in place once the complicated manoeuvres of neural development have been completed.

The migrating neurons in the RMS thus have some hostile terrain to cross. It now turns out that they accomplish this by turning the tables on the cells in their environment. Rather than simply responding to attractive or repulsive cues that they encounter, they actively secrete a repulsive molecule themselves, which helps to clear out glial cells from their path. These star-shaped glial cells, called astrocytes, then form a tunnel through which the migrating cells are free to pass. If the migrating neurons do not make the repulsive protein, called Slit-1, or the astrocytes do not express the receptors for this protein (Robo-1 and -2), then the neurons cannot clear this pathway and many fail to reach their destination.

This mechanism is a nice example of a reversal of a prominent paradigm – of course, these neurons are still themselves guided by other cues in their environment, but this adds a new and unexpected twist to the story. More importantly, perhaps, it could have general implications as a mechanism to encourage the migration of new neurons or of damaged nerve fibres in the adult nervous system. If such neurons can be encouraged to express a path-clearing molecule like Slit-1, their chances of successful navigation or regrowth may be greatly enhanced.

Kaneko N, Marín O, Koike M, Hirota Y, Uchiyama Y, Wu JY, Lu Q, Tessier-Lavigne M, Alvarez-Buylla A, Okano H, Rubenstein JL, & Sawamoto K (2010). New Neurons Clear the Path of Astrocytic Processes for Their Rapid Migration in the Adult Brain. Neuron, 67 (2), 213-223 PMID: 20670830

Remote control neurons

2010-07-30T04:21:00.000-07:00

Clever, elegant and extremely powerful – techniques to activate specific sets of neurons with light have the potential to revolutionise cellular and systems neuroscience. Optogenetics has already been used to address a number of questions which have been resistant to answer by other techniques, and also holds great promise for neurotherapeutics and prosthetics. A new paper adds another approach to the toolkit – the ability to activate neurons with a radio frequency magnetic field. While very much a proof of principle, with a ways to go before it proves its worth, this approach offers some obvious advantages over optogenetics, most obviously that magnetic fields pass into brains much more readily than light.

When trying to figure out what different brain circuits do, one of the most obvious experimental approaches is to ask: what happens if I make these neurons fire? Neuroscientists have traditionally used electrodes to activate neurons in the brain or in slices of brain tissue. This approach is powerful but crude – even with microelectrodes it is difficult to stimulate just the neurons you want. In fact, in many cases it is impossible as different types of neurons tend to be intermingled with each other. If you want to figure out the job of just one of those types then it’s no good sticking an electrode in that part of the brain and zapping all of them at the same time.

The technique of optogenetics takes advantage of the fact that different types of neurons express distinct profiles of genes (each encoding a separate protein) – that’s what makes them different. A gene consists of two parts – one stretch of DNA that encodes the protein and another, adjacent stretch of DNA that encodes the instructions of when and where that protein should be made. The trick is this – that second bit of DNA can be cloned and attached to a different piece of DNA that codes for a different protein – your favourite protein. Now if that new combined DNA construct is put back into the organism then your favourite protein will be made in only the cells that normally make the original protein. As we learn more and more of the molecular details of different types of neurons in the brain our ability to express proteins in more selective subsets of cells increases all the time.

The second trick in optogenetics was to find a protein that would make neurons responsive to light. The answer came from an unlikely source – green algae. These organisms detect light with an opsin protein, called Channelrhodopsin-2 (ChR2), which is related to those that we use in our own photoreceptor cells in our eyes. But the algal protein is different in that it performs this function without the help of any other proteins – when light (specifically blue light) hits the protein, which sits in the membrane of the cell, the protein changes its conformation, opening a channel and letting sodium ions flow into the cell. Algae use this as a chemical signal but neurons use the flow of sodium ions as an electrical signal to trigger firing of an action potential.

So, it is possible to make a DNA construct fusing the control elements of some gene with the coding elements of ChR2. The resultant construct can then be inserted into the cells of the organism you wish to study in two main ways: one is to make a transgenic animal by inserting the construct DNA into the organism’s own chromosomes in the germline – then all the cells of the offspring will carry the transgene. The other, which has for more therapeutic potential, is to use a virus to carry the transgene into cells of the organism. Both techniques have been used to create animals where some specific neurons express the ChR2 protein (or other variants which respond to different wavelengths of light, some of which turn off neurons by letting chloride ions into the cell). If you shine light on these neurons then you can almost instantaneously activate them and when you turn the light off they rapidly stop firing.

This has allowed a series of incredibly powerful experiments to dissect the functions of very specific sets of cells with unprecedented precision. These have, for example, dissected the cellular circuitry underlying the motor symptoms of Parkinson’s disease, elucidated the roles of a specific class of inhibitory neuron in coupling the firing of ensembles of neurons, establishing high-frequency brain rhythms and shown that a class of non-neuronal cells called astrocytes are involved in controlling breathing rate. Optogenetics has also been used to directly show what kinds of neuronal activity drives the signals seen in functional magnetic resonance imaging scans, which had only been inferred previously, and even to restore some degree of vision in a mouse model of retinitis pigmentosa, where the normal rhodopsin protein is absent.

The only problem with this approach from an experimental point of view is that getting light deep into the brain is not so easy. It has been accomplished to date by inserting micro-optical fibres or light-emitting diodes. While the results have been amazing, it is still far from ideal to have to employ these invasive techniques. This is why the recent paper describing a method to activate neurons with a magnetic field is so exciting. Arnd Pralle and colleagues have devised a fiendishly clever combination of nanoscience and neurogenetics to accomplish this feat.

There aren’t any known proteins that are endogenously sensitive to magnetic fields (at least not known to me) so a proxy was required – in this case, the trick was to use the magnetic field to cause an increase in temperature and to make the neurons responsive to this change. To target this effect to specific neurons involved several components, some of which the Borg themselves would have been proud of. The first is a manganese-ferrite (MnFe₂O₄ for those of you keeping score) nanoparticle, which have the property that they rapidly heat up in a magnetic field. (For a similar reason, people with heavy tattooing are usually advised not to go in a magnetic resonance scanner as the iron in the ink can heat up to painful levels). These nanoparticles were attached to a small chemical tag called streptavidin. When presented in an aqueous solution these nanoparticles will stick to another chemical tag, biotin, which can be attached to a specific protein, which can be expressed in just some cells, using the kinds of transgenic technologies described above. The final component is another channel protein that is sensitive to heat – called TRPV1. These proteins are normally expressed in sensory neurons in the skin that respond to heat and pain. Incidentally, they also are expressed in the tongue and respond to capsaicin – the spicy ingredient in chili peppers, explaining why chili tastes “hot” and painful. By itself, the TRPV1 channel simply lets ions into the cell – the interpretation of that signal depends on which part of the brain the message is sent to, so the protein can be used in other cells without inducing a painful sensation.

Putting it all together: two transgenes are used, one to express a biotin-tagged protein which will capture the nanoparticles on the surface of the cell and the other a TRPV1 channel - both in the same specific set of cells. When a radio frequency magnetic field is applied, the nanoparticles heat up – very locally and not enough to cause any damage but enough to activate the nearby TRPV1 channel, which then opens, causing the neurons to fire. Nice!

This technology still needs some work to be ready for prime time but something along these lines should succeed and will provide a very useful complement to optogenetic approaches, where the requirement to get light into the tissue is too limiting.

Lee, J., Durand, R., Gradinaru, V., Zhang, F., Goshen, I., Kim, D., Fenno, L., Ramakrishnan, C., & Deisseroth, K. (2010). Global and local fMRI signals driven by neurons defined optogenetically by type and wiring Nature, 465 (7299), 788-792 DOI: 10.1038/nature09108

Busskamp, V., Duebel, J., Balya, D., Fradot, M., Viney, T., Siegert, S., Groner, A., Cabuy, E., Forster, V., Seeliger, M., Biel, M., Humphries, P., Paques, M., Mohand-Said, S., Trono, D., Deisseroth, K., Sahel, J., Picaud, S., & Roska, B. (2010). Genetic Reactivation of Cone Photoreceptors Restores Visual Responses in Retinitis Pigmentosa Science, 329 (5990), 413-417 DOI: 10.1126/science.1190897

Kravitz, A., Freeze, B., Parker, P., Kay, K., Thwin, M., Deisseroth, K., & Kreitzer, A. (2010). Regulation of parkinsonian motor behaviours by optogenetic control of basal ganglia circuitry Nature, 466 (7306), 622-626 DOI: 10.1038/nature09159

Huang, H., Delikanli, S., Zeng, H., Ferkey, D., & Pralle, A. (2010). Remote control of ion channels and neurons through magnetic-field heating of nanoparticles Nature Nanotechnology DOI: 10.1038/nnano.2010.125

Sexual orientation – wired that way

2010-07-09T06:30:00.000-07:00

In a recent post, I presented the evidence that sexual preference is strongly influenced by genetic variation. Here, I discuss the neurobiological evidence that shows that the brains of homosexual men and women are wired differently from those of their heterosexual counterparts.

First, we must consider the differences between the brains of heterosexual males and females. These differences are extensive and arise mainly due to the influence of testosterone during a critical period of early development (see Wired for Sex). They include, not surprisingly, differences in the number of neurons in specific regions of the brain involved in reproductive or sexual behaviours as well as differences in the number of nerve fibres connecting these areas. But they also involve areas not dedicated to these types of behaviours, such as the cerebellum, for example, which is involved in motor control among other things, and which shows a very large difference between men and women. Another area that shows prominent differences is the corpus callosum, the very large sheet of fibres that connects the two cerebral hemispheres, which is larger in females, despite lower overall brain size. Indeed, females show greater and more efficient connectivity in cortical networks than males, on average.

It should be emphasized that all of these differences are apparent only in group averages and there is very substantial overlap in the distributions of the measures of different brain regions in males and females. This is a similar situation to height, where, while it is true that men tend to be taller than women, on average, the distributions overlap and the average difference is not diagnostic – if the only thing you know about someone is their height then you have little predictive power as to which sex they are. That is because height is affected by many other variables besides sex and so sex simply shifts the mean of a wide distribution. Similarly, it is not possible to tell from one measurement from a brain scan what sex a person is, because brain structure is also affected by many other variables (primarily the rest of the genome). Nevertheless, just as with height, the group sex differences in brain structure are very robust and reproducible. They are also correlated with average differences in many different aspects of cognition, perception, emotion and any number of other psychological domains as well as large sex differences in susceptibility to various psychiatric diseases.

With that as background, what have studies of the brains of homosexual men and women found? The naïve hypothesis would be that the brains of homosexual men might look more like heterosexual women and vice versa for homosexual women. In fact, this is exactly what has been found, for the most part, not just in structural measures but also in measures of brain activity. Starting in the early 1990’s, a number of studies by Swaab, Le Vay and others found differences in the size of specific regions of the hypothalamus between homosexual men and heterosexual men, with homosexual men showing a more female pattern. As the hypothalamus is involved in regulating many sexual and reproductive behaviours, and given that brain activity feeds back onto the organization of brain circuits, it was possible that such differences arose due to differences in behaviour, rather than the other way around. Experiments in animals argue strongly against that conclusion, however, given that similar differences can be induced by the manipulation of sex hormones during a critical period of early development.

A number of other studies have found similar results in other areas of the brain where sexual dimorphism is observed, including the size of the corpus callosum and also of the anterior commissure, another tract connecting the two sides of the brain. Both of these are larger, on average, in homosexual than in heterosexual men, mirroring the difference between heterosexual women and men. Conversely, various regions in homosexual women tend to show a more masculine pattern.

Interestingly, there is also strong sexual dimorphism in the degree of lateralization of brain structures and of brain activity – in general, men show greater lateralization than women (e.g., for language or face-processing areas or indeed, for the overall size of the cerebral hemispheres), and this trend is reversed in homosexuals. This is not limited to the brain itself but also extends to facial symmetry– males tend to show greater asymmetry in facial features than females, but the opposite is true for homosexual males and females.

Differences in brain activation have also been observed, for example in response to pheromones or to visual presentation of male or female faces. In both cases, homosexual men respond in a way that is more similar to heterosexual women, and homosexual women show responses more like heterosexual men. That may not be a surprise, you say – in fact it may seem obvious that that must be true and is not necessarily evidence for innate differences.

The study by Savic and Lindstrom referred to below extends these observations to another brain system, the amygdala – a region involved in emotional processing, but not directly linked to sexual behaviour. In heterosexual men and homosexual women, the right amygdala tends to be slightly larger than the left, while the opposite was found in homosexual men and heterosexual women. Differences in resting state functional connectivity were also observed (this refers to which areas are active in synchrony with the region of interest while the subject is at rest – not performing any specific task). In the first group there were more connections from the right amygdala and they were stronger to a different set of brain regions (including prefrontal cortex, caudate and putamen) than in the second group (which showed connections with the contralateral amygdala and cingulate). These results show that differences in brain wiring and functional activation between homosexuals and heterosexuals are not restricted to brain regions directly linked to reproductive behaviours or to responses to sexual cues.

Taking the genetic and neurobiological evidence together thus provides a clear picture of the biological basis of sexual orientation, though the details remain unknown. It should not be long however before some genetic variants are discovered that are associated with sexual orientation and these should give clues to the genesis of brain wiring differences between the sexes and how they control sexual preference.

Savic I, & Lindström P (2008). PET and MRI show differences in cerebral asymmetry and functional connectivity between homo- and heterosexual subjects. Proceedings of the National Academy of Sciences of the United States of America, 105 (27), 9403-8 PMID: 18559854

Swaab DF (2008). Sexual orientation and its basis in brain structure and function. Proceedings of the National Academy of Sciences of the United States of America, 105 (30), 10273-4 PMID: 18653758

What is a “neurodevelopmental disorder”?

2010-06-11T01:18:00.000-07:00

This question arose at the recent, excellent meeting of the International Society for Developmental Neuroscience in Estoril, Portugal. The question came up due to some very exciting and very unexpected successes in reversing in adult animals the effects of mutations causing neurodevelopmental disorders, including neurofibromatosis, Down syndrome, Rett syndrome and tuberous sclerosis.

All of these disorders are caused by specific genetic lesions and characterised by very early deficits, variously including intellectual disability, autism, epilepsy and other psychological and neurological phenotypes. They are also associated with some degree of neuropathology, usually involving differences in the elaboration of neuronal morphology, branching and connectivity. Because of the early onset of symptoms, these disorders have traditionally been considered as being due to defects in neurodevelopment that have led to a permanently structurally compromised brain. The last thing most people would have expected is that many of the cognitive deficits in these disorders would be reversible in adults, by either restoring normal gene function or by compensating for the effects of the genetic lesion.

These lesions can be modeled in mice, often very directly, by knocking out the homologous gene, by introducing the human disease-causing mutation into the mouse gene or, for Down syndrome, by transgenically increasing the copy number of all the genes corresponding to human chromosome 21. For all these syndromes, genetically engineered mice display a range of cognitive phenotypes, correlated with alterations in the physiology of individual neurons and circuits. They thus provide invaluable models to investigate the mechanisms of disease and develop and test possible therapeutic approaches.

Rett syndrome is caused by mutations in MeCP2, which encodes a protein that binds to methylated DNA and controls gene expression. Such mutations are lethal in males, who have only one copy of this X-chromosomal gene, while girls inheriting the mutation develop a syndrome of intellectual disability, autism and sometimes epilepsy. MeCP2 mutations are also lethal in male mice, though they survive for some time and show severe behavioural defects, while females show a milder phenotype. These defects were assumed to be due to the absence of MeCP2 function during neurodevelopment and were therefore expected to be irreversible by subsequent restoration of MeCP2 activity.

However, reversal was exactly what was observed when the MeCP2 gene was turned on again from a transgene in adult mice. Lethality in males was completely rescued (not, as Adrian Bird said, after the fact), and defects in both sexes in synaptic plasticity underlying memory (long-term potentiation) and in behavioural measures of memory were all essentially completely reversed. Even more remarkable, reductions in size of particular neurons (parvalbumin-positive interneurons), in the thickness of the cortex and in the density of dendritic spines (reflecting the number of synaptic connections) were all also reversed. These results argue that Rett syndrome is less a disorder of neurodevelopment and more a disorder of aberrant neuronal maintenance.

Results for neurofibromatosis, tuberous sclerosis and Down syndrome were all similar, though in these cases, reversal was achieved in adults by pharmacologically compensating for the effects of the genetic lesions. By mutating the gene responsible for neurofibromatosis, NF1, specifically in distinct classes of neurons in mice, Alcino Silva and colleagues had found that the effects of this mutation on physiology and behaviour were only observed when NF1 was deleted in inhibitory interneurons. This led to an increase in the activity of these neurons, and a concomitant decrease in the activity of excitatory neurons. By blocking the receptors of inhibitory neurotransmission using picrotoxin, it was possible to restore the correct balance of inhibition and excitation in adults and to reverse the effects on physiology and behaviour. As with Rett syndrome, the effects of the NF1 mutation were thus not as permanent as expected.

Down syndrome model mice showed a similar effect: an increase in inhibitory neurotransmission, correlated with defects in long-term potentiation and performance in cognitive tasks. As with neurofibromatosis, blocking inhibitory receptors in adults caused remarkable rescue of these defects. Working out the nature of the biochemical defect in tuberous sclerosis also suggested a mechanism by which to reverse the effects in adults, which worked equally well.

These successes have been remarkable not just for how unexpected they were but also for how substantial the improvement is in adult animals when normal gene function is restored or the biochemical or physiological defect is compensated for. The results obviously offer great promise for effective therapeutic intervention in patients and some of the approaches are already in small-scale clinical trials. As in all such situations, however, translation to humans will be slow and difficult and miracle cures are unlikely to emerge in the near future.

Do these principles apply to all neurodevelopmental disorders? It is unlikely. Indeed, many other disorders not previously classified as neurodevelopmental may have to be re-evaluated as such. One of these is schizophrenia. While the symptoms of this disease do not usually emerge until late adolescence, there is very good evidence that they stem from earlier insults in neurodevelopment. This is supported by additional modeling of schizophrenia-associated mutations in mice, including mutations in the gene DISC1. DISC1 mutations cause a range of schizophrenia-related behavioural phenotypes in mice. Remarkably, disruption of the activity of DISC1 just during development is sufficient to induce these phenotypes in adults, even though normal gene function was subsequently restored. In this case, defects in neurodevelopment can thus not be reversed in adults.

Perhaps neurodevelopmental disorders should be defined as such not so much on when the symptoms arise but on the timing of the requirement for gene function.

EHNINGER, D., LI, W., FOX, K., STRYKER, M., & SILVA, A. (2008). Reversing Neurodevelopmental Disorders in Adults Neuron, 60 (6), 950-960 DOI: 10.1016/j.neuron.2008.12.007

Sexual orientation – in the genes?

2010-05-31T03:39:00.000-07:00

Is homosexuality a lifestyle choice or an innate biological disposition? The idea that it is a choice is certainly widespread – a part of several mainstream religious doctrines and political ideologies – and is used to condone significant discrimination against homosexuals and the criminalization of homosexual behaviour. But what does the science say?

The broad conclusions are that sexual orientation is an innate disposition – no different from whether you are left or right-handed – that it is affected by genetic influences and that it reflects differences in brain structure and function. I will consider the evidence of genetic effects on sexual orientation here, including some recent additions – a later blog will look at the neurobiological findings.

A number of family and twin studies of the heritability of sexual orientation, starting in the 1950’s, found significant genetic influences: the statistical likelihood of an individual being homosexual increased somewhat if they had a homosexual dizygotic (fraternal) twin and dramatically if they had a homosexual monozygotic (identical) twin. However, these studies have generally suffered from some methodological limitations, including small sample sizes, the possibility of ascertainment bias due to the methods of recruitment of participants and an assumption that homosexuality in males and females is likely caused by the same mechanisms.

This assumption reflects a common idea that heterosexuality represents the same default state in both males and females – that it is the “normal”, baseline condition, one that requires no active processes. In fact, there is not a single rule: “be attracted to members of the opposite sex” – there are two rules: either be attracted to males or be attracted to females. These “rules” are embodied in anatomical and physiological differences in neural circuitry controlling sexual desire and behaviour, which differ between heterosexual males and females. (See: Wired for Sex for more on these processes in male brains). Understanding that both these behavioural rules require active and possibly distinct neurodevelopmental processes to establish makes it much easier to appreciate how alterations to those processes can lead to exceptions to how those rules are expressed.

Three recent twin studies have largely overcome previous methodological issues, demonstrate clear genetic influences on sexual orientation and argue strongly that homosexuality in males and females is due to distinct mechanisms. These studies all used large, population-based samples – that is, the subjects were not recruited to the study based on sexual orientation – in Sweden, Finland and Australia. In each study, rates of homosexuality were compared between pairs of monozygotic or dizygotic (same-sex or opposite-sex) twins. Each study had several thousand participants and several hundred twin pairs, making them well-powered statistically to detect genetic or environmental effects on sexual orientation.

These studies differed significantly in how they assessed sexual orientation, however, which may be reflected in their results. Zietsch and colleagues in the Australian study used a questionnaire to assess sexual attraction on a seven-point “Kinsey” scale, from exclusive heterosexual attraction to some degree of homosexual attraction. In response to this question, 11% of men and 13% of women, were rated as non-heterosexual. Alanko and colleagues in the Finnish study used a composite measure of same-sex attraction and same-sex sexual contact. In this survey, 6.1% of men and 6.6% of women reported a homosexual orientation. Interestingly, the quantitative measures used demonstrated a much more bimodal distribution in males than in females, mirroring previous observations that bisexuality is much more common in females than in males (males tending to be either strongly heterosexual or strongly homosexual). Langstrom and colleagues used a direct question about lifetime number of same-sex sexual partners, with 5.6% of men and 7.8% of women reporting at least one – among those reporting at least one, men reported significantly more same-sex partners.

Estimates of genetic influences were high across all three studies. The Australian study found heritability of 48% for sexual orientation across males and females together. The Finnish study estimated genetic influences on sexual orientation of 45% and 50% for men and women, respectively. Neither study found any evidence of an effect of shared environment. The Swedish study gave somewhat different results – the heritability for male heterosexuality was quite high, 39%, with no effect of a shared environment. However, the estimated heritability for female heterosexuality was lower in this study, around 18-19%, and a significant contribution from the shared environment was found for females in this study (16-17%). These differences could reflect sampling effects, population genetic or cultural differences, or the differences in how sexual orientation was assessed (based on actual same-sex sexual behaviour in the Swedish study). It is important to note that a shared family environment for dizygotic twins includes a shared uterine environment, which may impact on neural development.

Importantly, both the Australian and the Finnish studies found zero correlation of homosexuality across opposite-sex dizygotic twin pairs, while same-sex dizygotic twin pairs showed substantial correlations. So, if a male has a fraternal twin brother who is homosexual, there is a significantly increased likelihood that he will also be. This is not the case if his twin sister is homosexual (and vice versa).

The major conclusion from these studies corroborates previous findings: sexual orientation is strongly influenced by genetics. Whatever the underlying biological processes, they are likely different for males and females, as reflected in differences in reports of same-sex attraction and expression of sexual behaviour, with males showing a more bimodal distribution. The potential neurobiological processes involved will be the subject of a later post.

Those are the scientific conclusions. My personal interpretation is that dispositional homosexuality is no more a choice than left or right-handedness. Most heterosexuals certainly can not point to the time when they “chose” to be straight no more than someone can say they chose to be right-handed. Natural left-handers can certainly learn to write right-handed but that will not change the inherent disposition, nor is there any good reason to try and change it. At the other extreme, these findings do not suggest that homosexuality is a biological “disorder”. Conditions are only defined as a disorder if they have a negative impact on someone’s life – by this definition, homosexuality is only a disorder if society’s reaction makes it one.

Långström N, Rahman Q, Carlström E, & Lichtenstein P (2010). Genetic and environmental effects on same-sex sexual behavior: a population study of twins in Sweden. Archives of sexual behavior, 39 (1), 75-80 PMID: 18536986

Alanko K, Santtila P, Harlaar N, Witting K, Varjonen M, Jern P, Johansson A, von der Pahlen B, & Sandnabba NK (2010). Common genetic effects of gender atypical behavior in childhood and sexual orientation in adulthood: a study of Finnish twins. Archives of sexual behavior, 39 (1), 81-92 PMID: 19172387

Zietsch BP, Verweij KJ, Bailey JM, Wright MJ, & Martin NG (2009). Sexual Orientation and Psychiatric Vulnerability: A Twin Study of Neuroticism and Psychoticism. Archives of sexual behavior PMID: 19588238

Blue bananas and pink elephants

2010-05-24T02:53:00.000-07:00

Most people know that strawberries are red, lemons are yellow and grass is green. And practically all adults can correctly identify the name of a colour when visually presented with it (allowing for some disagreements based on retinal pigment gene variants – more on that in a later post – and, yes, your wife is right, it’s green, not blue – just accept it).

The ability to recognise colours and remember which one goes with which object seems so trivial that it is hard to appreciate how specialised a skill it is – one that requires a lot of practice and which involves dedicated brain circuits. Children are able to visually discriminate between colours from a very young age and will readily separate objects by colour. However, they learn the names of colours with great difficulty, usually starting around the age of 3. At this stage, they will still frequently misidentify quite dissimilar colours, like red and blue. As they learn more colour names, they will only mix up ones that are more similar, like red and pink. They also have trouble picking out appropriately from inappropriately coloured objects – like picking which banana is the correct colour if shown one blue and one yellow one.

Part of the difficulty with colour is that it is completely unisensory and unlinked to any other information. Colour can’t be cross-checked with another sense in the way that form or texture can, for example. (This may be one reason why colour is such a common part of the “extra” perception in synaesthesia – it can be added to a sound or an odour without conflicting with the primary sensory information). In a way it’s remarkable that we can learn to so accurately categorise light of different wavelengths into specific colours, without any external reference point – after all, those of us without perfect pitch (the vast majority of the population) are not able to do that for musical notes of different frequency.

A rare condition called colour agnosia (or lack of knowledge of colours) sheds some light on how the brain categorises colours and how typical colours come to be attached to objects in our minds. As with other types of agnosia (including prosopagnosia, the lack of knowledge of faces), colour agnosia is characterised by normal processing of sensory information but an inability to categorise and assimilate this information – in essence, people with this condition have lost the concept of colour. For example, they will typically be perfectly able to separate objects by colour but be unable to name the colours, to pick out an example of a particular colour or to group distinct colours into related categories – hues of red, for example. They may, for example, be unable to remember what colour their car is – not just to name it, but also to pick it out of a colour palette. They do not readily incorporate colour into their “schema” of objects – though they may know, semantically, that the word grass and the word green are associated they will not associate the concept of green with the concept of grass.

This condition is all the more amazing for how specific it is – naming and knowledge of other types of stimuli or categories is typically unimpaired and the overall neuropsychological profile is unremarkable. Until a few years ago, only acquired cases of colour agnosia were known – caused by damage to a specific region of the brain, the left occipito-temporal region. This region is in the “ventral stream” of the visual system, where colour information is processed. It sits at a higher level in the hierarchy of processing than regions such as V4, where lesions cause the inability to perceive colour at all.

In 2007, Edward de Haan and colleagues reported a case of developmental colour agnosia. This was a man who showed all the classic deficits of colour agnosia but who claimed to have always had the condition. While he was referred to a neurology clinic following a stroke, this affected an area not involved in colour processing (in the cerebellum). He had otherwise no history of neurological insult or other abnormalities on an MRI scan.

Interestingly, this patient reported that his mother and daughter had the same problem. A follow-up study by the same authors confirmed this – both mother and daughter performed very poorly on selective colour knowledge tasks, while they were perfectly able to distinguish different colours. Now, I know what you’re thinking and you’re right – that might just mean that they never learned their colours in this family. Apart from the inherent implausibility of that idea (given that learning colours is also a part of early formal education) and the fact that the subjects had a full colour term vocabulary, the observation that the subject’s other daughter performed normally on all tasks argues strongly against that explanation. This suggests instead a genetic cause of this developmental form of colour agnosia. The authors speculate that it might involve the wiring of the colour knowledge area in the visual ventral stream.

This has yet to be tested, but if true, would be another example of a situation where altered wiring is thought to explain highly specific differences in the subjective representation of perceptual parameters.

van Zandvoort MJ, Nijboer TC, & de Haan E (2007). Developmental colour agnosia. Cortex; a journal devoted to the study of the nervous system and behavior, 43 (6), 750-7 PMID: 17710826

Nijboer TC, van Zandvoort MJ, & de Haan EH (2007). A familial factor in the development of colour agnosia. Neuropsychologia, 45 (8), 1961-5 PMID: 17337019

Hub neurons spotted in the wild

2010-05-14T09:42:00.000-07:00

The prevailing model for how the network of the brain is organized is the “small-world” network. In such a network, most units, or nodes, are very sparsely and only locally connected. However, a very small proportion of nodes, called hubs, are very highly connected, and over longer distances. These hubs thus provide an indirect but short pathway of connectivity between any two nodes in the network (like people with thousands of “friends” on Facebook). This overall architecture is highly efficient and robust and can be observed not just at the level of networks of neurons but also at a higher level of brain organization, in the pattern of connectivity of cortical areas. Indeed, it is also typical of genetic, social and many other networks, including the internet.

In the brain, the existence of hub neurons had thus been hypothesised, but these beasts had not actually been observed until a recent study by Rosa Cossart and colleagues. They were analysing the activity patterns of very large numbers of neurons in the developing hippocampus. At this stage, network activity in the hippocampus consists of fairly simple, large and rhythmic depolarisations, which are easily detected. (These oscillations are known to be crucial for the normal maturation of the network).

By observing the activity of large numbers of neurons over time, these researchers were able to examine which neurons in the network fired in synchrony with each other – these were deemed to be “functionally connected”. Most neurons were functionally connected with only a small number of other neurons in the network. However, a small subset was very highly connected – these neurons behaved like hubs in the network. The overall architecture fit the small-world model very well.

As well as recording the activity of the neurons they were also able to directly stimulate individual cells. Stimulating the sparsely connected neurons did not have much effect on the activity of the rest of the network. In contrast, stimulating the hub neurons had dramatic effects, directly activating many other neurons in the network and also affecting the synchrony of firing – in some cases greatly increasing it and in others completely abolishing it.

The hub neurons have several interesting properties: first, they are GABAergic – i.e., when they synapse on another cell they release the neurotransmitter GABA. In adults this tends to inhibit the activity of the recipient neuron, though in developing networks, GABA has excitatory effects. They also have very extensive axonal arborisations – they project over larger distances and make a greater number of and stronger synaptic connections than non-hub neurons. Finally, they are also more responsive to inputs and quicker to fire action potentials themselves, placing them in a position to orchestrate the responses of the entire network.

Though hub neurons have so far only been observed in the hippocampus it seems almost certain that they will also be found in the cortex, where their effects may be fundamental for the information processing capabilities of the brain.

Bonifazi, P., Goldin, M., Picardo, M., Jorquera, I., Cattani, A., Bianconi, G., Represa, A., Ben-Ari, Y., & Cossart, R. (2009). GABAergic Hub Neurons Orchestrate Synchrony in Developing Hippocampal Networks Science, 326 (5958), 1419-1424 DOI: 10.1126/science.1175509