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- July 8, 2009
- 02:37 AM
- 633 views
Hot News in The Genetics of Schizophrenia
by Kevin Mitchell in Wiring the Brain
Schizophrenia is a common and devastating disorder, involving stable impairments in a wide range of cognitive, sensory and motor domains, as well as fluctuating episodes of psychosis, characterised by disordered thoughts, hallucinations and delusions. Though it tends to emerge as a full-blown disorder in late adolescence or early adulthood, a wealth of evidence supports the model that it is caused by disturbances in neural development at much earlier time-points, including prenatally. Recent neuroimaging analyses have supported psychological theories of schizophrenia as a “disconnection syndrome”, showing altered structural and functional connectivity between (and also within) many regions of the brain. Schizophrenia can thus be thought of as the result of alterations in brain wiring, and these alterations are, in turn, caused by mutations. There is strong and consistent evidence from twin, adoption and family studies that schizophrenia is highly heritable. Though this fact is now widely accepted there is far less agreement on exactly how it is inherited. Risks to family members are clearly much higher than to the general population (approximately ten percent in first-degree relatives, versus 0.5-1% prevalence in the general population). And concordance between monozygotic twins is much higher (averaging 0.48) than between dizygotic twins (about 0.17). On the other hand, a majority of cases of schizophrenia are sporadic and do not have an affected first-degree relative. In addition, looking across families with multiple affected individuals, no clear pattern emerges that suggests a simple Mendelian mode of inheritance, or at least not a consistent one. Various models have been proposed to explain the genetic architecture of schizophrenia. Early researchers suggested Mendelian inheritance, either recessive or dominant, with partial penetrance (i.e., not everyone who inherits the putative causative mutation develops the disorder). Based on the fact that any one of these modes of inheritance could not explain all cases of schizophrenia, and on a rejection of the notion that different cases might follow different modes of inheritance (i.e., genetic heterogeneity), these models have been almost completely replaced by a polygenic model. This states that schizophrenia arises due to the inheritance of a large number of genetic variants in any individual. Any one of these variants alone would have a small effect on risk, but collectively, a “toxic combination” of such variants could lead to disease. To explain the prevalence of the disorder, such variants must be common in the population. The alternative model, which has been dubbed the multiple rare variants model, proposes that schizophrenia is caused in each individual by a single mutation and that such mutations are rare because they are rapidly selected against. To explain the prevalence of the disorder under this model requires a high mutation rate and a large target of genes that can result in schizophrenia when mutated. A recent set of papers has directly tested the common variants, polygenic model (cited below). These papers describe very large genome-wide association studies (GWAS) of schizophrenia, carried out in unprecedented collaborations on huge samples by large numbers of researchers in different facilities across the globe. The goal of these studies is to find alleles of common variants that are significantly enriched in people with schizophrenia (cases) versus those without (controls). Under the common variants model, each such variant is likely to increase risk only very slightly itself and is therefore likely to be at only slightly higher frequency in cases. However, comparing frequencies of a set of common variants across the entire genome in large samples offers enough statistical power to detect even very modest effects. The hope is that identifying such “risk genes” will lead to insights of the pathogenic mechanisms of the disorder or offer the means to predict level of risk in individuals. The main finding of these three studies, consistent with several smaller forerunners, is that there are no common variants of even modest effect size. None of these studies alone detected a single such variant. When combined in a meta-analysis, a few regions emerged with very small effect sizes. These explain only a tiny fraction of the total heritability of the disorder, however. Considering the demonstrable power of these studies to have detected variants of modest effect if they existed, these negative results provide the strongest evidence yet that the common variants, polygenic model is incorrect. (Note that a further analysis does suggest some polygenic contribution to risk, but based on the combined effects of thousands of variants. The simulations used to estimate the magnitude of this effect are far from conclusive, however. Regardless of its overall contribution to risk, this finding could be consistent with a “genetic background” effect, which modifies the penetrance and expressivity of rare, causal mutations.)Are these disappointing results cause for dismay? Quite the opposite, I would say. They provide additional support for the multiple rare variants model, which is now gaining traction with the recent discoveries of many such rare, causal mutations. This should encourage geneticists to re-focus their efforts on families and individuals and move away from an epidemiological approach that focuses on risk across the population. Schizophrenia liability is not a quantitative trait and should not be treated as one. Happily, the technologies to detect rare, causal variants are now available, most obviously whole-genome sequencing. The upside of this model being true is that the effects of such mutations in single genes can be very directly modeled in animals, to help elucidate the pathogenic mechanisms, pathophysiology and etiology of the disorder. For more discussion see: http://www.schizophreniaforum.org/new/detail.asp?id=1532Purcell, S., Wray, N., Stone, J., Visscher, P., O'Donovan, M., Sullivan, P., Sklar, P., Purcell (Leader), S., Stone, J., Sullivan, P., Ruderfer, D., McQuillin, A., Morris, D., O’Dushlaine, C., Corvin,... Read more »
Purcell, S., Wray, N., Stone, J., Visscher, P., O'Donovan, M., Sullivan, P., Sklar, P., Purcell (Leader), S., Stone, J., Sullivan, P.... (2009) Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. DOI: 10.1038/nature08185
Shi, J., Levinson, D., Duan, J., Sanders, A., Zheng, Y., Pe’er, I., Dudbridge, F., Holmans, P., Whittemore, A., Mowry, B.... (2009) Common variants on chromosome 6p22.1 are associated with schizophrenia. Nature. DOI: 10.1038/nature08192
Stefansson, H., Ophoff, R., Steinberg, S., Andreassen, O., Cichon, S., Rujescu, D., Werge, T., Pietiläinen, O., Mors, O., Mortensen, P.... (2009) Common variants conferring risk of schizophrenia. Nature. DOI: 10.1038/nature08186
- August 20, 2010
- 07:45 AM
- 626 views
When to blame your parents, and for what
by Kevin Mitchell in Wiring the Brain
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.2010.01.002Rice, 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... Read more »
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
- March 26, 2010
- 04:42 AM
- 617 views
Intelligence a matter of the right connections
by Kevin Mitchell in Wiring the Brain
What makes some people smarter than others? Is intelligence innate? Is it under genetic control? Is there something different about the brains of people with high versus low intelligence, and if so, what is the nature of the difference? Some answers to these important questions on this often touchy subject are emerging. Many would bristle at the very notion that some people are “smarter” than others, if that is meant to imply an innate difference in ability. There is however a wealth of evidence that that is precisely the case, though it is important to define exactly what is meant by intelligence. When people are examined on a variety of tests, spanning different cognitive abilities – verbal ability, spatial reasoning, abstract logic, memory – it is found that people who do well on one of these tests tend to also do well on the others. Psychologists use the term “g”, for general intelligence, to denote a statistical construct which captures this correlation and which is thought to reflect some underlying characteristic that contributes to success on all these measures. Results from a large number of twin, family and adoption studies agree that the heritability of g is very high – at least 50% and perhaps as high as 80%. (This means that 50-80% of the variance in g across the population is due to differences in genes). Some effects of a shared family environment are seen at early ages but these tend to disappear when examined in older individuals. Whatever the effect of the family environment on IQ measures when an individual is within that environment, these effects seem to be temporary and diminish in later life. (An important aside: Note that the heritability within populations does not tell us anything about what might cause a difference in the mean of a trait between two populations. This is a common misinterpretation – differences in mean between populations may be entirely due to differences in environment, even if the trait is very highly heritable within each population, where environmental variance is low). Presumably, some parameter of brain structure or function that correlates with intelligence is being affected by these genetic differences. While a correlation with overall brain size has been repeatedly noted (“Check out the big brain on Brett!”), this leaves a lot of the variance in intelligence unexplained (and is also not particularly informative). Is intelligence localized to a certain brain region or is it a distributed property of the entire network? A number of recent studies, taking very different approaches, arrive at the same conclusion – it is the connectivity between areas of the brain that best correlates with intelligence. Paul Thompson and colleagues analysed brain connectivity and intelligence in a large twin study (with 92 pairs of twins). Using diffusion tensor imaging, they were able to assess the size, organization and “integrity” of axonal tracts connecting all areas of the brain. They found, when comparing these measures across pairs of either monozygotic or dizygotic twins, that these parameters were more heritable for some tracts than for others. Most importantly, they also found that intelligence was correlated with connectivity measures across numerous tracts in the brain. They could also show a substantial shared genetic effect – the genes affecting structural connectivity were also affecting intelligence. Ralph Adolphs and colleagues used a very different approach – they analysed a large collection of patients with lesions in different parts of the brain. By looking across this collection for sites where lesions were consistently correlated with reduced intelligence they were able to map a network of important regions. The most striking finding is that many of the “regions” thus defined were actually within the white matter – they were not restricted to specific cortical areas but rather reflected the connections between areas of the brain. Again, the implication is that it is the efficiency and effectiveness of brain connectivity which are the major parameters affecting intelligence. While the overall genetic effects are very robust, only a few specific genes have been identified that seem to influence intelligence. For now, the neurodevelopmental processes which mediate the effects of these and other genes on the parameters of brain connectivity remain a mystery. Chiang, M., Barysheva, M., Shattuck, D., Lee, A., Madsen, S., Avedissian, C., Klunder, A., Toga, A., McMahon, K., de Zubicaray, G., Wright, M., Srivastava, A., Balov, N., & Thompson, P. (2009). Genetics of Brain Fiber Architecture and Intellectual Performance Journal of Neuroscience, 29 (7), 2212-2224 DOI: 10.1523/JNEUROSCI.4184-08.2009 Glascher, J., Rudrauf, D., Colom, R., Paul, L., Tranel, D., Damasio, H., & Adolphs, R. (2010). Distributed neural system for general intelligence revealed by lesion mapping Proceedings of the National Academy of Sciences, 107 (10), 4705-4709 DOI: 10.1073/pnas.0910397107 ... Read more »
Chiang, M., Barysheva, M., Shattuck, D., Lee, A., Madsen, S., Avedissian, C., Klunder, A., Toga, A., McMahon, K., de Zubicaray, G.... (2009) Genetics of Brain Fiber Architecture and Intellectual Performance. Journal of Neuroscience, 29(7), 2212-2224. DOI: 10.1523/JNEUROSCI.4184-08.2009
Glascher, J., Rudrauf, D., Colom, R., Paul, L., Tranel, D., Damasio, H., & Adolphs, R. (2010) Distributed neural system for general intelligence revealed by lesion mapping. Proceedings of the National Academy of Sciences, 107(10), 4705-4709. DOI: 10.1073/pnas.0910397107
- July 20, 2009
- 04:47 AM
- 603 views
Cell Fate and Connectivity Intertwined
by Kevin Mitchell in Wiring the Brain
The traditional view of neural development is linear. First, the embryo and neurectoderm are patterned by secreted factors, which establish cell fates among progenitors and then differentiated neurons, encoded by combinations of transcription factors. The fate or phenotype of each neuron includes the expression of the specific set of ion channels, neurotransmitters and receptors that determine its physiological function. It also includes expression of a particular repertoire of guidance receptors and surface molecules regulating connectivity, which enable axonal pathfinding and target selection. The processes that establish connectivity are usually thought of as happening after the fate of neurons and their targets have been established. This linear paradigm, from patterning to differentiation to connection, has been increasingly challenged by studies from both invertebrate and vertebrate systems.A number of studies have shown that incoming axons can regulate the proliferation and differentiation of their synaptic target cells. In many cases in fact, the target cells do not even exist at the time that the incoming axons are making their targeting decisions. In the fly visual system, for example, photoreceptor axons target the developing optic lobe and secrete the morphogen hedgehog, which induces optic lobe progenitor cells to complete a final cell division and undergo neuronal differentiation (Huang and Kunes, 1996). In addition, secretion of additional signaling molecules induces expression in the optic lobe neurons of adhesion molecules and guidance factors necessary for retinal axons to recognize them as appropriate synaptic targets (Bazigou et al., 2007). Thus, the final differentiation of cells in the optic lobe requires the prior pathfinding of retinal axons to this area. A similar situation has been demonstrated in the mammalian brain, where axons from the visual thalamus induce the proliferation and differentiation of the primary visual cortex (Dehay et al., 2001). Significant patterning of the cortical sheet occurs prior to thalamic axon invasion and directs the guidance of visual thalamic axons to the appropriate part of the cortex (Little et al., 2009). However, ultimate elaboration of the mature cytoarchitectonic characteristics of primary visual cortex, including its pattern of connectivity with other cortical areas, requires correct innervation by visual thalamic axons. Though it has not been shown, it seems likely that this kind of hierarchical dependence on afferent innervation might also be crucial in the elaboration of later-maturing higher-order cortical areas, which receive inputs from earlier-maturing areas (Bargary and Mitchell, 2008). The linear developmental paradigm must thus be substantially modified to include a highly dynamic interplay between differentiation and establishment of connectivity. Importantly, a recent study suggests that the influence of this interplay also extends to the maintenance of cell fate in the adult nervous system. It is well known that many neurons require retrograde neurotrophic support from their target cells to stay alive. A study from Drosophila (Eade and Allan, 2009) suggests that retrograde signals, in this case involving bone morphogenetic protein (BMP) signaling, may also be required to maintain expression of neuronal phenotype in connecting cells, demonstrated through an effect on expression of a specific neuropeptide. This signaling was shown to require active axonal transport mechanisms. If this mechanism holds in vertebrates it has several important implications. First, neuronal phenotypes in the adult nervous system may be more plastic than previously recognised and more actively maintained by regulators of gene expression in response to ongoing retrograde (and possibly anterograde?) signaling. Second, neurodegenerative disorders involving defects in axonal transport, such as Huntington’s disease, may have their primary effects on neuronal phenotype and physiological function, inducing partial de-differentiation prior to overt degeneration. Therapeutics aimed at preventing this process may thus be able to target the earliest stages of such diseases.HUANG, Z., & KUNES, S. (1996). Hedgehog, Transmitted along Retinal Axons, Triggers Neurogenesis in the Developing Visual Centers of the Drosophila Brain Cell, 86 (3), 411-422 DOI: 10.1016/S0092-8674(00)80114-2BAZIGOU, E., APITZ, H., JOHANSSON, J., LOREN, C., HIRST, E., CHEN, P., PALMER, R., & SALECKER, I. (2007). Anterograde Jelly belly and Alk Receptor Tyrosine Kinase Signaling Mediates Retinal Axon Targeting in Drosophila Cell, 128 (5), 961-975 DOI: 10.1016/j.cell.2007.02.024Cell-cycle kinetics of neocortical precursors are influenced by embryonic thalamic axons.Dehay C, Savatier P, Cortay V, Kennedy H.J Neurosci. 2001 Jan 1;21(1):201-14.Little, G., López-Bendito, G., Rünker, A., García, N., Piñon, M., Chédotal, A., Molnár, Z., & Mitchell, K. (2009). Specificity and Plasticity of Thalamocortical Connections in Sema6A Mutant Mice PLoS Biology, 7 (4) DOI: 10.1371/journal.pbio.1000098Bargary, G., & Mitchell, K. (2008). Synaesthesia and cortical connectivity Trends in Neurosciences, 31 (7), 335-342 DOI: 10.1016/j.tins.2008.03.007Eade, K., & All... Read more »
HUANG, Z., & KUNES, S. (1996) Hedgehog, Transmitted along Retinal Axons, Triggers Neurogenesis in the Developing Visual Centers of the Drosophila Brain. Cell, 86(3), 411-422. DOI: 10.1016/S0092-8674(00)80114-2
BAZIGOU, E., APITZ, H., JOHANSSON, J., LOREN, C., HIRST, E., CHEN, P., PALMER, R., & SALECKER, I. (2007) Anterograde Jelly belly and Alk Receptor Tyrosine Kinase Signaling Mediates Retinal Axon Targeting in Drosophila. Cell, 128(5), 961-975. DOI: 10.1016/j.cell.2007.02.024
Little, G., López-Bendito, G., Rünker, A., García, N., Piñon, M., Chédotal, A., Molnár, Z., & Mitchell, K. (2009) Specificity and Plasticity of Thalamocortical Connections in Sema6A Mutant Mice. PLoS Biology, 7(4). DOI: 10.1371/journal.pbio.1000098
Bargary, G., & Mitchell, K. (2008) Synaesthesia and cortical connectivity. Trends in Neurosciences, 31(7), 335-342. DOI: 10.1016/j.tins.2008.03.007
Eade, K., & Allan, D. (2009) Neuronal Phenotype in the Mature Nervous System Is Maintained by Persistent Retrograde Bone Morphogenetic Protein Signaling. Journal of Neuroscience, 29(12), 3852-3864. DOI: 10.1523/JNEUROSCI.0213-09.2009
- February 19, 2010
- 04:11 AM
- 578 views
Noisy genes and the limits of genetic determinism
by Kevin Mitchell in Wiring the Brain
Why are genetically identical monozygotic twins not phenotypically identical? They are obviously much more similar than people who do not share all their DNA, but even in outward physical appearance are not really identical. And when it comes to psychological traits or psychiatric disorders, they can be quite divergent (concordance between monozygotic twins for schizophrenia for example is only around 50%). What is the source of this phenotypic variance? Why are the effects of a mutation often variable, even across genetically identical organisms?“Nurture” has been the answer proffered by many, but there is good evidence that environmental or experience-dependent effects can not explain all the extra phenotypic variance and in most cases contribute very little to it. (See post on “Nature, nurture and noise” on June 24th, 2009 for more on this: http://wiringthebrain.blogspot.com/2009/06/nature-nurture-and-noise.html). An alternative source of variation is intrinsic to the developmental programme itself. In particular, small, random fluctuations in the expression of genes at various times during development can have large effects on the phenotypic outcome. A new study in Nature by Raj and colleagues directly illustrates this point for the first time and highlights several important principles of developmental systems. They studied the effects of mutations in components of a genetic network involved in the specification of a small number of intestinal cells in the nematode, Caenorhabditis elegans. This is the perfect organism for such studies, as the cells in question are individually identifiable and generated in an invariant pattern in wild-type animals. Mutations in one of the components led to an incompletely penetrant mutant phenotype: some animals made intestinal cells and others did not (even though all had the identical geneotype). To determine whether noise in gene expression could explain this diversity the authors directly measured the precise number of messenger RNA molecules being transcribed from the genes encoding other components of this developmental pathway in particular cells of each embryo and correlated these measurements with phenotypic outcome. They showed that the expression of one of these genes in particular became highly variable in the mutant background. If, by chance, the level of expression crossed a particular threshold it turned on the master gene responsible for intestinal cell specification and these cells were generated. If the levels did not cross the threshold then the cells were not generated. In this way, a bimodal phenotypic distribution can arise from an identical starting genotype. This study illustrates several important principles of complex regulatory systems that apply not just to developmental and genetic networks but also to neuronal networks. First, a certain amount of noise is a normal part of the system – a feature, not a bug – that increases robustness to external variation. Developmental systems are normally buffered, however, to reduce noise in gene expression and to absorb its effects. This buffering can be disrupted when individual components of a regulatory system are removed; this is why when genes are mutated, one expects (and always sees) not just a change in phenotype but an increase in phenotypic variability. The effects of stochastic fluctuations in expression levels of various genes can lead to a continuous distribution of phenotypic outcomes or, as in this case, dramatically different phenotypes. Interlocking positive and negative feedback loops can generate extremely discrete thresholds, where once a certain level of a component is reached it will reinforce its own expression and shift the network into a different state. Such bistability is a common feature of complex systems and is sometimes taken advantage of to generate phenotypic diversity or plasticity. This study elucidates a molecular mechanism of intrinsic variation in developmental systems and shows that it can have a large effect on the eventual phenotype, even in genetically identical organisms. No matter how precise the recipe, you can’t bake the same cake twice. Raj, A. (2010). Variability in Gene Expression Underlies Incomplete Penetrance in C. Elegans: Using Single Molecules To Study the Development of Single Cells Biophysical Journal, 98 (3), 14-14 DOI: 10.1016/j.bpj.2009.12.087... Read more »
Raj, A. (2010) Variability in Gene Expression Underlies Incomplete Penetrance in C. Elegans: Using Single Molecules To Study the Development of Single Cells. Biophysical Journal, 98(3), 14-14. DOI: 10.1016/j.bpj.2009.12.087
- September 1, 2009
- 02:40 AM
- 564 views
Visualising Connections in the Human Brain
by Kevin Mitchell in Wiring the Brain
Most of us are familiar with pictures from magnetic resonance imaging (MRI) of the human brain; indeed, these black-and-white images have achieved almost iconic status at this stage. From popular television programmes, and regrettably from common experience, the use of these images to detect lesions, such as tumours or the effects of stroke, is well known. Classic MRI can distinguish grey and white matter based on their different cellular composition but cannot go very far beyond that, because all the white matter has effectively the same contrast. This makes traditional MRI of limited use in examining connectivity between areas of the brain, except at a very gross level (such as whether the corpus callosum exists, for example). However, with a few modifications, MRI can be applied to non-invasively interrogate connectivity in the living human brain, with ever-increasing sensitivity. These new techniques are opening up avenues of investigation that have not just tremendous clinical importance but that also promise to make humans a powerful model organism for the study of axon guidance and related neurodevelopmental processes. The modifications rely on the fact that the diffusion of water molecules gives off a magnetic resonance signal. Within the brain, the diffusion of water is affected by the local cytoarchitecture; in particular, within axonal bundles the direction of diffusion is constrained by the direction of the axons and their myelin sheaths. By determining the bias in direction of diffusion within a voxel of white matter (the “diffusion tensor”) it is possible to infer the dominant direction of axonal projections in that voxel. Diffusion tensor imaging (DTI) can be applied across the brain, so that, by following the direction of the tensors from voxel to voxel, axonal tracts projecting across large areas of the brain can be derived. DTI has been extremely powerful, though it does have limitations. Foremost among these is difficulty in distinguishing fibres that cross within a single voxel. More recent refinements, including q-ball imaging and diffusion spectrum imaging (DSI) apply higher-resolution scan acquisition and different statistical approaches to largely resolve this issue. These approaches have yielded dramatic images of fibre tracts within the brain, revealing three-dimensional connectivity patterns across the entire brain that would be impossible to obtain with traditional anatomical tracing or histology, even in post mortem tissue. It is important to realize, however, that the “fibres” being drawn are really three-dimensional plots of statistical values that may vary depending on method of acquisition, scan parameters, software used, statistical approach as well as subjective thresholding criteria. Comparison of imaging methods and validation using data derived by classical techniques was thus crucial. These kinds of comparisons have now shown that, in the case of DSI at least, the congruence with known or classically-derived tractography is actually very good (Schmahmann et al., 2007; Wedeen et al. , 2008). This is not to say that the approach does not still have limitations – tracking fibres through sharp bends, as they de-fasciculate into small bundles or as they project into grey matter are all still problematic, for example – but these limitations are likely to be overcome by more sophisticated algorithms. Given the recent pace of improvements in tractography approaches, we can thus expect in the very near future that this technique will allow routine examination of patterns of connectivity in the human brain. These approaches are already being applied to investigate structural connectivity in various disorders with a neurodevelopmental etiology, including schizophrenia, autism, dyslexia and several less well-known conditions, such as prosopagnosia (the inability to recognize faces) and synaesthesia (where sensory stimuli in one modality can cross-activate another). Tractography can also be used to define a connectivity profile or fingerprint of a particular part of the brain, and thus to automatically parcellate the brain into units of likely functional distinction. This kind of approach is especially useful to delineate functional areas of the neocortex, which are often not obviously anatomically distinct. By defining a connectivity profile of each voxel to all other voxels in the brain, and constructing a matrix of such profiles, it is possible, using a clustering algorithm similar to those applied to microarray data, to cluster voxels with similar connectivity profiles and thus to distinguish regions where the profile suddenly changes, thus delineating the border between two presumptive areas. Exactly this approach has been used by several groups and validated with functional imaging and other anatomical data (Klein et al., 2007; Perrin et al., 2008).The development of these techniques finally gives us the means to see, literally, differences in how each of our brains is wired, which may affect many aspects of our personality, cognitive abilities and style and other psychological traits that make us who we are. Wedeen, V., Wang, R., Schmahmann, J., Benner, T., Tseng, W., Dai, G., Pandya, D., Hagmann, P., D'Arceuil, H., & de Crespigny, A. (2008). Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers NeuroImage, 41 (4), 1267-1277 DOI: 10.1016/j.neuroimage.2008.03.036Schmahmann, J., Pandya, D., Wang, R., Dai, G., D'Arceuil, H., de Crespigny, A., & Wedeen, V. (2007). Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography Brain, 130 (3), 630-653 DOI: 10.1093/brain/awl359Perrin, M., Cointepas, Y... Read more »
Wedeen, V., Wang, R., Schmahmann, J., Benner, T., Tseng, W., Dai, G., Pandya, D., Hagmann, P., D'Arceuil, H., & de Crespigny, A. (2008) Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers. NeuroImage, 41(4), 1267-1277. DOI: 10.1016/j.neuroimage.2008.03.036
Schmahmann, J., Pandya, D., Wang, R., Dai, G., D'Arceuil, H., de Crespigny, A., & Wedeen, V. (2007) Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography. Brain, 130(3), 630-653. DOI: 10.1093/brain/awl359
Perrin, M., Cointepas, Y., Cachia, A., Poupon, C., Thirion, B., Rivière, D., Cathier, P., El Kouby, V., Constantinesco, A., Le Bihan, D.... (2008) Connectivity-Based Parcellation of the Cortical Mantle Using q-Ball Diffusion Imaging. International Journal of Biomedical Imaging, 1-19. DOI: 10.1155/2008/368406
Klein, J., Behrens, T., Robson, M., Mackay, C., Higham, D., & Johansen-Berg, H. (2007) Connectivity-based parcellation of human cortex using diffusion MRI: Establishing reproducibility, validity and observer independence in BA 44/45 and SMA/pre-SMA. NeuroImage, 34(1), 204-211. DOI: 10.1016/j.neuroimage.2006.08.022
- July 8, 2011
- 08:56 AM
- 564 views
Environmental influences on autism - splashy headlines from dodgy data
by Kevin Mitchell in Wiring the Brain
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: 0.21For females: MZ: 0.60, DZ: 0.27Those 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: 0.31Females: 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... Read more »
Hallmayer J, Cleveland S, Torres A, Phillips J, Cohen B, Torigoe T, Miller J, Fedele A, Collins J, Smith K.... (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
- August 3, 2011
- 05:14 AM
- 552 views
Welcome to your genome
by Kevin Mitchell in Wiring the Brain
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: ... Read more »
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
- June 21, 2011
- 08:10 AM
- 548 views
Synaesthesia and savantism
by Kevin Mitchell in Wiring the Brain
“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 fo... Read more »
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
- July 9, 2010
- 09:30 AM
- 544 views
Sexual orientation – wired that way
by Kevin Mitchell in Wiring the Brain
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. ... Read more »
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
- May 7, 2010
- 10:31 AM
- 539 views
Connecting Left and Right
by Kevin Mitchell in Wiring the Brain
Organisms with a bilaterally symmetric nervous system face a problem – how to integrate functions across the two sides so that behavioural outputs can be coordinated for the entire body. In the brain this is important to allow integration of the large number of cognitive “modules” which are differentially lateralised, such as language. (The importance of this communication is dramatically illustrated by so-called “split-brain” patients, who have had the majority of the connections between the two cerebral hemispheres severed in order to treat otherwise intractable epilepsy. These patients, first studied by Roger Sperry and colleagues, end up in essence with two brains inside the same skull, and it could be argued, two largely independent minds). The importance of bilateral integration is also evident and very well understood in the control of movement, where motor commands have to be tightly and dynamically coordinated across the two sides of the body. The integration of the two sides of the nervous system is mediated by nerve fibres that project from one side to the other. Some neurons project axons across the midline and others do not – the binary nature of this choice has made it a favourite model system of developmental neurobiologists to investigate how growing axons are guided along specific pathways to their appropriate targets. As a result, a great deal is known about the cellular and molecular processes that control whether an axon will cross the midline. The process is mediated by attractive and repulsive signals made by specialised cells which reside at the midline of the nervous system. (Interestingly, the study of such “chemotropic” molecules was also pioneered by Sperry in a set of seminal experiments in the frog visual system). These signals are detected by dedicated receptor proteins expressed on the surfaces of growing axons. The signals at the midline include two major families of secreted proteins: Netrins, which function to attract some neurons towards and across the midline, while repelling others, and Slits, which repel axons that normally do not cross the midline and which also prevent axons from re-crossing the midline multiple times. How each neuron responds to these cues depends on which receptors it expresses: DCC is a receptor protein for Netrins, while Robo proteins are receptors for Slits. Amazingly, all these proteins are very highly conserved, as are their functions in controlling axonal projections across the midline, which were discovered and elucidated in great detail in nematodes, fruit flies and mice. A number of studies have shown that their functions are also conserved and equally crucial in humans. Elizabeth Engle and her colleagues found that mutations in the ROBO3 gene can lead to a condition with the unwieldy name of horizontal gaze palsy with progressive scoliosis (HGPPS). This syndrome is characterised by an inability to coordinate the lateral movement of the eyes in the horizontal plane. Lateral eye movements are controlled by the abducens-oculomotor nerves, one on each side of the head. The coordination of these movements of the two eyes is mediated by a set of interneurons which normally project across the midline of the hindbrain, where the cell bodies that form these nerves are located, and coordinate the activity of the nerves on the two sides. As the ROBO3 gene is known to be required for axons to cross the midline, the implication was that the defect resulted from a failure to connect these cranial nerve nuclei on the two sides. A recent study by Alain Chedotal and colleagues modeling the effects of Robo3 mutations in mice strongly supports this explanation – deletion of the gene in just that part of the hindbrain did indeed disrupt connectivity between the two sides and resulted in similar defects in horizontal eye movements in the mice. (If you’ve been paying attention you may be surprised that mutations in a Robo gene should cause a failure of axons to cross the midline – if the normal role of the Slit signals to which they respond is to keep axons on their own side, then mutation of a Robo gene should cause extra axons to cross the midline. This is true for the other two Robo genes – Robo3 performs a different role, down-regulating the responses of the other Robo proteins and so mutating it has the opposite effect). Another study just published by Guy Rouleau and colleagues shows that the function of DCC in establishing trans-midline connectivity is also essential in humans. They found mutations in this gene in patients with Congenital Mirror Movements. These are involuntary movements of one side of the body that occur in response to voluntary movement of the other side – i.e., a failure to independently control and coordinate the two sides. A similar effect has been seen in mice with mutations in this gene, which are called “Kanga” mice because of their unusual hopping gait, caused by moving both hindlimbs at the same time. The mirror movements could be caused by a failure to project across the midline of interneurons in the spinal cord that normally inhibit movement of one side when the other side is moving. These studies provide a dramatic example of the importance of bilateral integration in the control of movement. It will be interesting to investigate whether patients with these disorders also show any differences in cognitive domains which might relate to subtly altered connectivity of the two cerebral hemispheres. Srour M, Rivière JB, Pham JM, Dubé MP, Girard S, Morin S, Dion PA, Asselin G, Rochefort D, Hince P, Diab S, Sharafaddinzadeh N, Chouinard S, Théoret H, Charron F, & Rouleau GA (2010). Mutations in DCC cause congenital mirror movements. Science (New York, N.Y.), 328 (5978) PMID: ... Read more »
Srour M, Rivière JB, Pham JM, Dubé MP, Girard S, Morin S, Dion PA, Asselin G, Rochefort D, Hince P.... (2010) Mutations in DCC cause congenital mirror movements. Science (New York, N.Y.), 328(5978), 592. PMID: 20431009
Jen, J. (2004) Mutations in a Human ROBO Gene Disrupt Hindbrain Axon Pathway Crossing and Morphogenesis. Science, 304(5676), 1509-1513. DOI: 10.1126/science.1096437
Renier N, Schonewille M, Giraudet F, Badura A, Tessier-Lavigne M, Avan P, De Zeeuw CI, & Chédotal A. (2010) Genetic dissection of the function of hindbrain axonal commissures. PLoS biology, 8(3). PMID: 20231872
- August 27, 2010
- 06:48 AM
- 534 views
Coloured hearing in Williams syndrome
by Kevin Mitchell in Wiring the Brain
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: 10.1352/1944-7588-115.172Marenco, 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... Read more »
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), 172. 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.... (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
- September 8, 2010
- 06:48 AM
- 531 views
Wild-type humans
by Kevin Mitchell in Wiring the Brain
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.... Read more »
Ng, S., Turner, E., Robertson, P., Flygare, S., Bigham, A., Lee, C., Shaffer, T., Wong, M., Bhattacharjee, A., Eichler, E.... (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.... (2010) Analysis of Genetic Inheritance in a Family Quartet by Whole-Genome Sequencing. Science, 328(5978), 636-639. DOI: 10.1126/science.1186802
- May 14, 2010
- 12:42 PM
- 530 views
Hub neurons spotted in the wild
by Kevin Mitchell in Wiring the Brain
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 ... Read more »
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
- October 18, 2010
- 09:21 AM
- 530 views
Colour my world
by Kevin Mitchell in Wiring the Brain
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). ... Read more »
Field GD, Gauthier JL, Sher A, Greschner M, Machado TA, Jepson LH, Shlens J, Gunning DE, Mathieson K, Dabrowski W.... (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 , 8(2), 244-61. PMID: 11495112
- April 29, 2010
- 03:54 PM
- 522 views
Wired for Sex
by Kevin Mitchell in Wiring the Brain
Male and female brains are wired differently. That is not intended metaphorically – they literally have different amounts and/or patterns of axonal connections between a variety of brain regions, as well as differences in the size or number of cells in various regions. This is true in mammals, birds, fish, even insects and correlates with hard-wired, innate differences in behaviour between the sexes across in species across all these phyla. This is as true for humans as for any other species. The behaviours that show the most robust and innate differences between the sexes are involved in mating, reproduction, parental behaviour, territoriality and aggression and it is the brain areas that control these behaviours that are the most obviously sexually dimorphic (showing a difference in size or morphology between the sexes). In mammals, these include areas in the limbic system, including parts of the hypothalamus, amygdala, preoptic area and bed nucleus of the stria terminalis. How do these differences come about? Sex in mammals is determined by the presence of a specific gene, Sry, on the Y chromosome – this gene sets in motion a cascade of gene expression and biochemical changes which lead to the conversion of the undifferentiated gonads into testes in male embryos. (In female embryos the gonads follow a default differentiation pathway to form ovaries). Testes make testosterone, of course, and testosterone is essential to masculinise the developing embryo, so that it develops male external genitalia and other physical sexual characteristics. Testosterone is also essential to masculinise the brain, but exactly how it does this is very surprising. This phenomenon has been well studied in rodents but the principles apply across mammalian species. In male mice and rats there is a surge in the production of testosterone shortly after birth, which lasts for a couple of days. This surge is precisely timed with a critical period of brain development , during which it is susceptible to the effects of testosterone. If male rats are castrated at this age (I know, sorry), then their brain will develop in the female pattern and they will not display typical male behaviours in adulthood, even when supplemented with testosterone. Conversely, if female rats are given a single dose of testosterone a day or two after birth, their brains develop a male morphological pattern and they will show male-typical behaviours (mounting other females and decreased receptivity to being mounted). Crucially, the same manipulations carried out a week or two later have no effect on either brain morphology or behaviour. These effects of testosterone are called “organisational”, for obvious reasons, and are distinguished from the later effects in acutely stimulating male behaviours, which are called “activational”. Now for the surprise. It was also found that a single dose of estrogen, given to postnatal female rats, was just as effective as the testosterone in masculinising their brains – even more effective, in fact! How could this be? How can estrogen have the same effects on the developing brain as testosterone? As it turns out, testosterone is actively converted into estrogen in the brain, through the action of an enzyme, aromatase. This enzyme is specifically enriched in brain regions that show sexual dimorphism. The estrogen then acts through two estrogen receptors and this activity has been shown to be required for the masculinising effects of testosterone. In particular, mutations in aromatase or in the estrogen receptors block the effects of testosterone and result in male animals with female brain morphology and behaviour, despite normal levels of circulating testosterone. So, why don’t females have masculinised brains? They should have loads of estrogen, shouldn’t they? In fact female rodents have very low levels of circulating estrogen at this early postnatal stage, coinciding with the critical period. The surprising findings implicating aromatase and estrogen receptors have left a mystery surrounding the role of the androgen receptor – the protein traditionally associated with direct responses to testosterone. Mice with mutations in the gene for the androgen receptor also show feminised behaviours, suggesting it is also important in the process of masculinisation. However, this interpretation is complicated by the fact that these mutants also show testicular atrophy (sorry again) and consequently have very low levels of circulating testosterone. A new study by Nirao Shah and colleagues has now resolved the role of the androgen receptor in controlling sexual behaviour. By knocking out this gene just in the brain, they managed to get around the requirements for testicular function and so cleanly address the possible functions in the brain. They clearly show that the brains of these conditional mutants are still masculinised, morphologically. These mice also generally show a male pattern of behaviour. However, they do not express all of these behaviours to the same extent as wild-type males. In particular, the frequency of mating behaviours in the presence of an estrus female is reduced, though when they do engage in the behaviour the routine is effectively normal. The mutant males also mark their territory less than wild-types and spend less time fighting with other “intruder” males. Thus, while the developmental effects of testosterone appear to be fully explained by its conversion to estrogen, the activational effects in adults which are required for the full expression of male behaviours depend at least in part on its direct action through the androgen receptor. Wu, M., Manoli, D., Fraser, E., Coats, J., Tollkuhn, J., Honda, S., Harada, N., & Shah, N. (2009). Estrogen Masculinizes Neural Pathways and Sex-Specific Behaviors Cell, 139 (1), 61-72 DOI: 10.1016/j.cell.2009.07.036 ... Read more »
Wu, M., Manoli, D., Fraser, E., Coats, J., Tollkuhn, J., Honda, S., Harada, N., & Shah, N. (2009) Estrogen Masculinizes Neural Pathways and Sex-Specific Behaviors. Cell, 139(1), 61-72. DOI: 10.1016/j.cell.2009.07.036
Scott A. Juntti, Jessica Tollkuhn, Melody V. Wu, Eleanor J. Fraser, Taylor Soderborg, Stella Tan,, & Shin-Ichiro Honda, Nobuhiro Harada, and Nirao M. Shah. (2010) The Androgen Receptor Governs the Execution, but Not Programming, of Male Sexual and Territorial Behaviors. Neuron, 260-272. info:/DOI 10.1016/j.neuron.2010.03.024
- June 28, 2011
- 04:15 AM
- 519 views
Complex interactions among epilepsy genes
by Kevin Mitchell in Wiring the Brain
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 gene... Read more »
Klassen T, Davis C, Goldman A, Burgess D, Chen T, Wheeler D, McPherson J, Bourquin T, Lewis L, Villasana D.... (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.... (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
- August 13, 2010
- 05:03 AM
- 516 views
Defining developmental disorders through genetics
by Kevin Mitchell in Wiring the Brain
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. ... Read more »
Shen, Y., Dies, K., Holm, I., Bridgemohan, C., Sobeih, M., Caronna, E., Miller, K., Frazier, J., Silverstein, I., Picker, J.... (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.... (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
- December 21, 2010
- 04:13 AM
- 506 views
Self-organising principles in the nervous system
by Kevin Mitchell in Wiring the Brain
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. ... Read more »
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
- March 12, 2010
- 07:51 AM
- 501 views
Wired for Music
by Kevin Mitchell in Wiring the Brain
Music has a bizarre power to engage and affect us – to move us emotionally or literally, whether it’s foot-tapping, finger-drumming or booty-shaking. It seems to have properties that make it automatically and powerfully salient for human beings. An obvious question is whether this reflects some innate properties of the human brain or whether it emerges over time due to experience with types of music. Put another way, does the brain shape the music or the other way around? Does music show particular structures because those are inherently salient and pleasant to humans or is this reaction caused by the brain’s tendency to specialise in processing stimuli that occur with some statistical regularity in its environment? A new study by Perani and colleagues demonstrates very convincingly that the human newborn brain already shows strong functional specialisation for music processing. By performing functional magnetic resonance imaging on newborns, all under 3 days old, they found a strongly lateralized pattern of activations in response to music. These responses were much stronger in the right hemisphere, as is observed in adult humans. More interestingly, they found that modifications to the music, which introduced infrequent key shifts or dissonance through shifting one component a half-tone higher, resulted in a very different response pattern. This type of altered music did not engage the right hemisphere network as efficiently as the original music, and did engage regions in the left hemisphere that were not responsive to the original music. Thus, the right hemisphere auditory network is not just specialized for music, it is specialized for music with the appropriate structure (consonance) which most listeners agree is most pleasant.Previous studies have shown that the left hemisphere is preferentially activated by language stimuli even in newborn infants. The authors speculate that there may be a general division of labour between the two hemispheres, with the left more specialized for processing temporal characteristics of stimuli and the right for processing spectral characteristics (including frequency or pitch). Interestingly, the right hemisphere is also involved in processing prosody – the melodic components of natural speech – modulations in emphasis and inflection that communicate emotional content and tone. It seems likely that the apparent specialisation “for” music reflects the fact that these circuits are pre-tuned to be most responsive to stimuli with specific acoustic characteristics. We did not evolve to enjoy music – music evolved (or was actively designed) to best stimulate our natural preferences.Newborns thus arrive in the world pre-wired to process different types of acoustic stimuli in specialized circuits, localized to either hemisphere, one for detecting and distinguishing sequences of sounds in time and the other for decoding oscillatory components of the stimuli, including tone, pitch, timbre, rhythm, etc. How this specialisation arises during development is a fascinating topic, and one that is poorly understood. The mechanisms underlying the initial establishment of left-right differences in early embryos are fairly well-established but how these affect the developmental programmes in the brain is far less clear, though a number of genes have been found that are differentially expressed in the two hemispheres of developing human brains (see Sun et al, below). The fact that functional lateralisation depends upon a genetic programme also suggests that variation in the responsible genes might lead to differences in the degree or direction of lateralisation in different people. This is known to occur for language lateralisation (which can vary with handedness, itself under genetic influence). Lateralisation is also known to be affected in a range of psychatric disorders, most notably schizophrenia. How the kinds of mutations that result in these disorders affect lateralisation and how this contributes to psychiatric symptoms are important questions for the future. Perani, D., Saccuman, M., Scifo, P., Spada, D., Andreolli, G., Rovelli, R., Baldoli, C., & Koelsch, S. (2010). Functional specializations for music processing in the human newborn brain Proceedings of the National Academy of Sciences, 107 (10), 4758-4763 DOI: 10.1073/pnas.0909074107Sun, T. (2005). Early Asymmetry of Gene Transcription in Embryonic Human Left and Right Cerebral Cortex Science, 308 (5729), 1794-1798 DOI: 10.1126/science.1110324 ... Read more »
Perani, D., Saccuman, M., Scifo, P., Spada, D., Andreolli, G., Rovelli, R., Baldoli, C., & Koelsch, S. (2010) Functional specializations for music processing in the human newborn brain. Proceedings of the National Academy of Sciences, 107(10), 4758-4763. DOI: 10.1073/pnas.0909074107
Sun, T. (2005) Early Asymmetry of Gene Transcription in Embryonic Human Left and Right Cerebral Cortex. Science, 308(5729), 1794-1798. DOI: 10.1126/science.1110324
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