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Posts Tagged ‘Frontal lobe’

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The A-to-T SNP rs7794745 in the CNTNAP2 gene was found to be associated with increased risk of autism (see Arking et al., 2008).  Specifically, the TT genotype, found in about 15% of individuals, increases these folks’ risk by about 1.2-1.7-fold.  Sure enough, when I checked my 23andMe profile, I found that I’m one of these TT risk-bearing individuals.  Interesting, although not alarming since me and my kids are beyond the age where one typically worries about autism.  Still, one can wonder if such a risk factor might have exerted some influence on the development of my brain?

The recent paper by Tan et al., “Normal variation in fronto-occipital circuitry and cerebellar structure with an autism-associated polymorphism of CNTNAP2” [doi:10.1016/j.neuroimage.2010.02.018 ] suggests there may be subtle, but still profound influences of the TT genotype on brain development in healthy individuals.  According to the authors, “homozygotes for the risk allele showed significant reductions in grey and white matter volume and fractional anisotropy in several regions that have already been implicated in ASD, including the cerebellum, fusiform gyrus, occipital and frontal cortices. Male homozygotes for the risk alleles showed greater reductions in grey matter in the right frontal pole and in FA in the right rostral fronto-occipital fasciculus compared to their female counterparts who showed greater reductions in FA of the anterior thalamic radiation.”

The FA (fractional anisotropy – a measurement of white-matter or myelination) results are consistent with a role of CNTNAP2 in the establishment of synaptic contacts and other cell-cell contacts especially at Nodes of Ranvier – which are critical for proper function of white-matter tracts that support rapid, long-range neural transmission.  Indeed, more severe mutations in CNTNAP2  have been associated with cortical dysplasia and focal epilepsy (Strauss et al., 2006).

Subtle changes perhaps influencing long-range information flow in my brain – wow!

More on CNTNAP2 … its evolutionary history and role in language development.

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For a great many reasons, research on mental illness is focused on the frontal cortex.  Its just a small part of the brain, and certainly, many things can go wrong in other places during brain/cognitive development, but, it remains a robust finding, that when the frontal cortex is not working well, individuals have difficulties in regulating thoughts and emotions.  Life is difficult enough to manage, let alone without a well functioning frontal cortex.  So its no surprise that many laboratories look very closely at how this region develops prenatally and during childhood.

One of the more powerful genetic methods is the analysis of gene expression via microarrays (here is a link to a tutorial on this technology).  When this technology is coupled with extremely careful histological analysis and dissection of cortical circuits in the frontal cortex, it begins to become possible to begin to link changes in gene expression with the physiological properties of specific cells and local circuits in the frontal cortex. The reason this is an exciting pursuit is because the mammalian neocortex is organized in a type of layered fashion wherein 6 major layers have different types of connectivity and functionality.  The developmental origins of this functional specificity are thought to lie in a process known as radial migration (here is a video of a neuron as it migrates radially and finds its place in the cortical hierarchy).  As cells are queued out of the ventricular zone, and begin their migration to the cortical surface, they are exposed to all sorts of growth factors and morphogens that help them differentiate and form the proper connectivities.  Thus, the genes that regulate this process are of keen interest to understanding normal and abnormal cognitive development.

Here’s an amazing example of this – 2 papers entitled, “Infragranular gene expression disturbances in the prefrontal cortex in schizophrenia: Signature of altered neural development?” [doi:10.1016/j.nbd.2009.12.013] and “Molecular markers distinguishing supragranular and infragranular layers in the human prefrontal cortex [doi:10.1111/j.1460-9568.2007.05396.x] both by Dominique Arion and colleagues.  In both papers, the authors ask, “what genes are differentially expressed in different layers of the cortex?”.  This is a powerful line of inquiry since the different layers of cortex are functionally different in terms of their connectivity.  For example, layers II-III (the so-called supragranular layers) are known to connect mainly to other cortical neurons – which is different functionally than layers V-VI (the so-called infragranular layers) that connect mainly to the striatum (layer V) and thalamus (layer VI).  Thus, if there are genes whose expression is unique to a layer, then one has a clue as to how that gene might contribute to normal/abnormal information processing.

The authors hail from a laboratory that is well-known for work over many years on fine-scaled histological analysis of the frontal cortex at the University of Pittsburgh and used a method called, laser capture microdissection, where post-mortem sections of human frontal cortex (area 46) were cut to separate the infragraular layer from the supragranular layer.  The mRNA from these tissue sections was then used for DNA microarray hybridization.  Various controls, replicate startegies and in-situ tissue hybridizations were then employed to validate the initial microarray results.

In first paper, the where the authors compare infra vs. supragranular layers, they report that 40 genes were more highly expressed in the supragranular layers (HOP, CUTL2 and MPPE1 were among the most enriched) and 29 genes were highly expressed in the infragranular layers (ZNF312, CHN2, HS3ST2 were among the most enriched).  Other differentially expressed genes included several that have previously been implicated in cortical layer formation such as RLN, TLX-NR2E1, SEMA3E, PCP4, SERPINE2, NR2F2/ARP1, PCDH8, WIF1, JAG1, MBP.  Amazing!! A handful of genes that seem to label subpopulations of projection neurons in the frontal cortex.  Polymorphic markers for these genes would surely be powerful tools for imaging-genetic studies on cognitive development.

In the second paper, the authors compare infra vs. supragranular gene expression in post-mortem brains from patients with schizophrenia and healthy matched controls. Using the same methods, the team reports both supra- and infragranular gene expression changes in schizophrenia (400 & 1200 differences respectively) – more than 70% of the differences appearing to be reductions in gene expression in schizophrenia. Interestingly, the team reports that the genes that were differentially expressed in the infragranular layers provided sufficient information to discriminate between cases and controls, whilst the gene expression differences in the supragranular layers did not.  More to the point, the team finds that 51 genes that were differentially expressed in infra- vs. supragranular expression were also differentially expressed in cases vs. controls  (many of these are also found to be associated in population genetic association studies of schiz vs. control as well!).  Thus, the team has identified layer (function) -specific genes that are associated with schizophrenia.  These genes, the ones enriched in the infragranular layers especially, seem to be at the crux of a poorly functioning frontal cortex.

The authors point to 3 such genes (SEMA3E, SEMA6D, SEMA3C) who happen to members of the same gene family – the semaphorin gene family.  This gene family is very important for the neuronal guidance (during radial migration), morphology, pruning and other processes where cell shape and position are regulated.  The authors propose that the semaphorins might act as “integrators” of various forms of wiring during development and in adulthood.  More broadly, the authors provide a framework to understand how the development of connectivity on the frontal cortex is regulated by genetic factors – indeed, many suspected genetic risk factors play a role in the developmental pathways the authors have focused on.

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In his undergraduate writings while a student at Harvard in the early 1900’s E. E. Cummings quipped that, “Japanese poetry is different from Western poetry in the same way as silence is different from a voice”.  Isabelle Alfandary explores this theme in Cummings’ poetry in her essay, “Voice and Silence in E. E. Cummings’ Poetry“,  giving some context to how the poet explored the meanings and consequences of voice and silence.  Take for example, his poem “silence”

silence

.is
a
looking

bird:the

turn
ing;edge, of
life

(inquiry before snow

e.e. cummings

Lately, it seems that the brain imaging community is similarly beginning to explore the meanings and consequences of the brain when it speaks (activations whilst performing certain tasks) and when it rests quietly.  As Cummings beautifully intuits the profoundness of silence and rest,  I suppose he might have been intrigued by just how very much the human brain is doing when we are not speaking, reading, or engaged in a task. Indeed, a community of brain imagers seem to be finding that the brain at rest has quite a lot to say – moreso in people who carry certain forms of genetic variation (related posts here & here).

A paper by Perrson and colleagues “Altered deactivation in individuals with genetic risk for Alzheimer’s disease” [doi:10.1016/j.neuropsychologia.2008.01.026] asked individuals to do something rather ordinary – to pay attention to words – and later to then respond to the meaning of these words (a semantic categorization task). This simple endeavor, which, in many ways uses the very same thought processes as used when reading poetry, turns out to activate regions of the temporal lobe such as the hippocampus and other connected structures such as the posterior cingulate cortex.  These brain regions are known to lose function over the course of life in some individuals and underlie their age-related difficulties in remembering names and recalling words, etc.  Indeed, some have described Alzheimer’s disease as a tragic descent into a world of silence.

In their recordings of brain activity of subjects (60 healthy participants aged 49-79), the team noticed something extraordinary.  They found that there were differences not in how much the brain activates during the task – but rather in how much the brain de-activates – when participants simply stare into a blank screen at a small point of visual fixation.  The team reports that individuals who carry at least one copy of epsilon-4 alleles of the APOE gene showed less de-activation of their their brain (in at least 6 regions of the so-called default mode network) compared to individuals who do not carry genetic risk for Alzheimer’s disease.  Thus the ability of the brain to rest – or transition in and out of the so-called default mode network – seems impaired in individuals who carry higher genetic risk.

So, I shall embrace the poetic wisdom of E. E. Cummings and focus on the gaps, empty spaces, the vastness around me, the silences, and learn to bring my brain to rest.  And in so doing, perhaps avoid an elderly descent into silence.

***PODCAST ACCOMPANIES THIS POST***

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One of the complexities in beginning to understand how genetic variation relates to cognitive function and behavior is that – unfortunately – there is no gene for “personality”, “anxiety”, “memory” or any other type of “this” or “that” trait.  Most genes are expressed rather broadly across the entire brain’s cortical layers and subcortical systems.  So, just as there is no single brain region for “personality”, “anxiety”, “memory” or any other type of “this” or “that” trait, there can be no such gene.  In order for us to begin to understand how to interpret our genetic make-up, we must learn how to interpret genetic variation via its effects on cells and synapses – that go on to function in circuits and networks.  Easier said than done?  Yes, but perhaps not so intractable.

Here’s an example.  One of the most well studied circuits/networks/systems in the field of cognitive science are so-called basal-ganglia-thalamcortical loops.  These loops have been implicated in a great many forms of cognitive function involving the regulation of everything from movement, emotion and memory to reasoning ability.  Not surprisingly, neuroimaging studies on cognitive function almost always find activations in this circuitry.  In many cases, the data from neuroimaging and other methodologies suggests that one portion of this circuitry – the frontal cortex – plays a role in the representation of such aspects as task rules, relationships between task variables and associations between possible choices and outcomes.  This would be sort of like the “thinking” part of our mental life where we ruminate on all the possible choices we have and the ins and outs of what each choice has to offer.  Have you ever gone into a Burger King and – even though you’ve known for 20 years what’s on the menu – you freeze up and become lost in thought just as its your turn to place your order?  Your frontal cortex is at work!

The other aspect of this circuitry is the subcortical basla ganglia, which seems to play the downstream role of processing all that ruminating activity going on in the frontal cortex and filtering it down into a single action.  This is a simple fact of life – that we can be thinking about dozens of things at a time, but we can only DO 1 thing at a time.  Alas, we must choose something at Burger King and place our order.  Indeed, one of the hallmarks of mental illness seems to be that this circuitry functions poorly – which may be why individuals have difficulty in keeping their thoughts and actions straight – the thinking clearly and acting clearly aspect of healthy mental life.  Certainly, in neurological disorders such as Parkinson’s Disease and Huntington’s Disease, where this circuitry is damaged, the ability to think and move one’s body in a coordinated fashion is disrupted.

Thus, there are at least 2 main components to a complex system/circuits/networks that are involved in many aspects of learning and decision making in everyday life.  Therefore, if we wanted to understand how a gene – that is expressed in both portions of this circuitry – inflenced our mental life, we would have to interpret its function in relation to each specific portion of the circuitry.  In otherwords, the gene might effect the prefrontal (thinking) circuitry in one way and the basla-ganglia (action-selection) circuitry in a different way.  Since we’re all familiar with the experience of walking in to a Burger King and seeing folks perplexed and frozen as they stare at the menu, perhaps its not too difficult to imagine that a gene might differentially influence the ruminating process (hmm, what shall I have today?) and the action selection (I’ll take the #3 combo) aspect of this eveyday occurrance (for me, usually 2 times per week).

Nice idea you say, but does the idea flow from solid science?  Well, check out the recent paper from Cindy M. de Frias and colleagues “Influence of COMT Gene Polymorphism on fMRI-assessed Sustained and Transient Activity during a Working Memory Task.” [PMID: 19642882].  In this paper, the authors probed the function of a single genetic variant (rs4680 is the Methionine/Valine variant of the dopamine metabolizing COMT gene) on cognitive functions that preferentially rely on the prefronal cortex as well as mental operations that rely heavily on the basal-ganglia.  As an added bonus, the team also probed the function of the hippocampus – yet a different set of circuits/networks that are important for healthy mental function.  OK, so here is 1 gene who is functioning  within 3 separable (yet connected) neural networks!

The team focused on a well-studied Methionine/Valine variant of the dopamine metabolizing COMT gene which is broadly expessed across the pre-frontal (thinking) part of the circuitry and the basal-ganglia part of the circuitry (action-selection) as well as the hippocampus.  The team performed a neuroimaging study wherein participants (11 Met/Met and 11 Val/Val) subjects had to view a series of words presented one-at-a-time and respond if they recalled that a word was a match to the word presented 2-trials beforehand  (a so-called “n-back task“).  In this task, each of the 3 networks/circuits (frontal cortex, basal-ganglia and hippocampus) are doing somewhat different computations – and have different needs for dopamine (hence COMT may be doing different things in each network).  In the prefrontal cortex, according to a theory proposed by Robert Bilder and colleagues [doi:10.1038/sj.npp.1300542] the need is for long temporal windows of sustained neuronal firing – known as tonic firing (neuronal correlate with trying to “keep in mind” all the different words that you are seeing).  The authors predicted that under conditions of tonic activity in the frontal cortex, dopamine release promotes extended tonic firing and that Met/Met individuals should produce enhanced tonic activity.  Indeed, when the authors looked at their data and asked, “where in the brain do we see COMT gene associations with extended firing? they found such associations in the frontal cortex (frontal gyrus and cingulate cortex)!

Down below, in the subcortical networks, a differerent type of cognitive operation is taking place.  Here the cells/circuits are involved in the action selection (press a button) of whether the word is a match and in the working memory updating of each new word.  Instead of prolonged, sustained “tonic” neuronal firing, the cells rely on fast, transient “phasic” bursts of activity.  Here, the modulatory role of dopamine is expected to be different and the Bilder et al. theory predicts that COMT Val/Val individuals would be more efficient at modulating the fast, transient form of cell firing required here.   Similarly, when the research team explored their genotype and brain activity data and asked, “where in the brain do we see COMT gene associations with transient firing? they found such associations in the right hippocampus.

Thus, what can someone who carries the Met/Met genotype at rs4680 say to their fellow Val/Val lunch-mate next time they visit a Burger King?  “I have the gene for obesity? or impulsivity? or “this” or “that”?  Perhaps not.  The gene influences different parts of each person’s neural networks in different ways.  The Met/Met having the advantage in pondering (perhaps more prone to annoyingly gaze at the menu forever) whist the Val/Val has the advantage in the action selecting (perhaps ordering promptly but not getting the best burger and fries combo).

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DON’T tell the grant funding agencies, but, in at least one way, the effort to relate genetic variation to individual differences in cognitive function is a totally intractable waste of money.

Let’s say we ask a population of folks to perform a task – perhaps a word memory task – and then we use neuroimaging to identify the areas of the brain that (i) were associated with performance of the task, and (ii) were not only associated with performance, but were also associated with genetic variation in the population.  Indeed, there are already examples of just this type of “imaging-genetic” study in the literature.  Such studies form a crucial translational link in understanding how genes (whose biochemical functions are most often studied in animal models) relate to human brain function (usually studied with cognitive psychology). However, do these genes relate to just this task? What if subjects were recalling objects? or feelings?  What if subjects were recalling objects / experiences / feelings / etc. from their childhoods?  Of course, there are thousands of common cognitive operations one’s brain routinely performs, and, hence, thousands of experimental paradigms that could be used in such “imaging-genetic” gene association studies.  At more than $500/hour (some paradigms last up to 2 hours) in imaging costs, the translational genes-to-cognition endeavor could get expensive!

DO tell the grant funding agencies that this may not be a problem any longer.

The recent paper by Liu and colleagues “Prefrontal-Related Functional Connectivities within the Default Network Are Modulated by COMT val158met in Healthy Young Adults” [doi: 10.1523/jneurosci.3941-09.2010] suggests an approach that may simplify matters.  Their approach still involves genotyping (in this case for rs4680) and neuroimaging.  However, instead of performing a specific cognitive task, the team asks subjects to lay in the scanner – and do nothing.  That’s right – nothing – just lay still with eyes closed and just let the mind wander and not to think about anything in particular – for a mere 10 minutes.  Hunh?  What the heck can you learn from that?

It turns out that one can learn a lot.  This is because the neural pathways that the brain uses when you are actively doing something (a word recall task) are largely intact even when you are doing nothing.  Your brain does not “turn off” when you are laying still with your eyes closed and drifting in thought.  Rather, your brain slips into a kind of default pattern, described in studies of  “default networks” or “resting-state networks” where wide-ranging brain circuits remain dynamically coupled and actively exchange neural information.  One really great paper that describes these networks is a free-and-open article by Hagmann et al., “Mapping the Structural Core of Human Cerebral Cortex” [doi: 10.1371/journal.pbio.0060159] from which I’ve lifted their Figure 1 above.  The work by Hagmann et al., and others show that the brain has a sort of “connectome” where there are thousands of “connector hubs” or nodes that remain actively coupled (meaning that if one node fires, the other node will fire in a synchronized way) when the brain is at rest and when the brain is actively performing cognitive operations.  In a few studies, it seems that the strength of functional coupling in certain brain areas at rest is correlated (positively and negatively) with the activation of these areas when subjects are performing a specific task.

In the genetic study reported by Liu and colleagues, they found that genotype (N=57) at the dopaminergic COMT gene correlated with differences in the functional connectivity (synchronization of firing) of nodes in the prefrontal cortex.  This result is eerily similar to results found for a number of specific tasks (N-back, Wisconsin Card Sorting, Gambling, etc.) where COMT genotype was correlated with the differential activation of the frontal cortex during the task.  So it seems that one imaging paradigm (lay still and rest for 10 minutes) provided comparable insights to several lengthy (and diverse) activation tasks.  Perhaps this is the case. If so, might it provide a more direct route to linking genetic variation with cognitive function?

Liu and colleagues do not comment on this proposition directly nor do they seem to be over-interpreting their results in they way I have editorialized things here.  They very thoughtfully point out the ways in which the networks they’ve identified and similar and different to the published findings of others.  Certainly, this study and the other one like it are the first in what might be a promising new direction!

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In his book, The Beak of the Finch, Jonathan Weiner describes the great diversity of finches on the Galapagos Islands – so much diversity – that Darwin himself initially thought the finch variants to be completely different birds (wrens, mockingbirds, blackbirds and “gross-bills”).  It turns out that one of the pivotal events in Charles Darwin‘s life was his work in 1837 with the great ornithologist John Gould who advised that the birds were actually closely related finches and also specific to separate islands!

Fast-forward to 2009, and we are well on our way to understanding how closely related species can, via natural selection of genetic variation, diverge across space and time. The BMP4 and CaM genes, for example, have been associated with beak morphology in what are now known as Darwin’s Finches.  Wonderful indeed, but now consider, for a moment, the variability – not of finch beaks – but of human cognition.

If you’ve ever been a part of a team or group project at work or school, you know that very few people THINK just like you.  Indeed, variability in human cognition can be the source of a lot of frustration.  Let’s face it, people have different experiences stored away (in a highly distributed fashion) in their memory banks, and each persons brain is extensively wired with trillions of synapses.  Of course! nobody thinks like you.  How could such a complex organ function exactly the same way in 2 separate individuals.

Perhaps then, if you were an alien visitor (as Darwin was to the Galapagos Islands) and you watched 5 separate individuals devise a plan to – oh lets just say, to improve healthcare accessibility and affordability – and you measured individuals based solely on their “thinking patterns” you might conclude (as Darwin did) that you were dealing with 5 separate “species”.  Just flip the TV between FOX, CNN, CNBC, CSPAN and MSNBC if you’re not convinced!

However, if you were to take a more in-depth approach and crack open a current issue of a neuroimaging journal – you might come to the exact opposite conclusion.  That’s right.  If you looked at patterns of brain activity and other indirect measures of neural network dynamics (what I casually meant by “thinking patterns” ) you would mostly see conclusions drawn from studies where many individuals are pooled into large groups and then probed for forms of brain activity that are common rather than different.  Most studies today show that humans use a common set of neural systems to perform mental operations (e.g., recalling events and information).  Brain structures including the hippocampus, frontal cortex, thalamus, parietal cortex are all known to be involved in deciding whether or not you have seen something before.  Thus, if you perform an fMRI brain scanning study on individuals and ask them to complete an episodic memory recall task (show them a list of words before scanning and then – when they are in the scanner – ask them to respond to words they remember seeing), you will likely observe that all or most individuals show some BOLD response activity in these structures.

OK great! But can you imagine where we would be if Charles Darwin returned home from his voyage and said, “Oh, just a bunch of birds out there … you know, the usual common stuff … beaks, wings, etc.”  I’d rather not imagine.

Enter Professor Michael Miller and colleagues and their recent paper, “Unique and persistent individual patterns of brain activity across different memory retrieval tasks” [doi:10.1016/j.neuroimage.2009.06.033].  This paper looks – not just at the common stuff – but the individual differences in BOLD responses among individuals who perform a number of different memory tasks.  The team reports that there are dramatic differences in the patterns of brain activity between individuals.  This can be seen very clearly in Figure 1 which shows left hemisphere activity associated with memory recall.  The group data (N=14) show nice clean frontal parietal activations – but when the data is broken down on an individual-by-individual basis, you might – without knowing that the all subjects were performing the same recall tasks – suspect that each person was doing or “thinking” something quite different.  The research team then re-scanned each subject several months later and asked whether the individual differences were consistent from person to person. Indeed, the team shows that the 2nd brain scan is much more similar to the first (correlations were about 0.5) and that the scan-rescan data for an individual was more similar than the correlation between any single person and the rest of the group (about 0.25).  Hence, as the authors state, “unique patterns of brain activity persist across different tasks”.

Vive la difference!  Yes, the variability is – if you’re interested in using genetics to understand human history and cognitive development – the really exciting part!  Of course, genetics is not the main reason for the stable individual-to-individual differences in brain activity.  There are likely to be many factors that could alter the neural dynamics of broadly distributed neural networks used for memory recall.  Environment, experience, gender are just a few factors that are known to influence the function of these networks.  The authors reveal that individuals may also differ in the strategies and criteria they use to make decisions about whether they can recall or detect a previously viewed item.  Some people will respond only when they are very certain (high criteria) and others will respond even if they feel only slightly sure they’ve seen an item before (low criteria).  The authors show in Figure 5 that the folks who showed similar decision criteria are more likely to have similar patterns of brain activity.

Perhaps then, the genetic differences that (partially) underlie individual differences in brain activity might relate to personality or other aspects of decision making?  I don’t have a clue, but I do know that this approach – of looking carefully at individual differences – is a step forward to doing what Darwin (and don’t forget John Gould!) is so well known for.  Understand where the variation comes from, and you will understand where you come from!

I will follow this literature more closely in the months to come.

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ruler - STUPID INCOMPETENT MANUFACTURERS
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One of the difficult aspects of understanding mental illness, is separating the real causes of the illness from what might be secondary or tertiary consequences of having the illness.  If you think about a car whose engine is not running normally, there may be many observable things going wrong (pinging sound, stalling, smoke, vibration, overheating, loss of power, etc.) – but, what is the real cause of the problem?  What should be done to fix the car? – a faulty sparkplug or timing belt perhaps?  Such is often the problem in medicine, where a fundamental problem can lead to a complex, hard-to-disentangle, etiology of symptoms.  Ideally, you would fix the core problem and then expect the secondary and tertiary consequences to normalize.

This inherent difficulty, particularly in mental illness, is one of the reasons that genetic research is of such interest.  Presumably, the genetic risk factors are deeper and more fundamentally involved in the root causes of the illness – and hence – are preferable targets for treatment.  The recent paper, “Widespread Reductions of Cortical Thickness in Schizophrenia and Spectrum Disorders and Evidence of Heritability” [Arch Gen Psychiatry. 2009;66(5):467-477] seeks to ascertain whether one aspect of schizophrenia – a widespread and well-documented thinning of the neocortex – is due to genetic risk (hence something that is closer to a primary cause) or – rather – if cortical thinning is not due to genetics, and so more of a secondary consequence of things that go wrong earlier in the development of the illness.

To explore this idea, the team of Goldman et al., did something novel.  Rather than examine the differences in cortical thickness between patients and control subjects, the team evaluated the cortical thickness of 59 patients and 72 unaffected siblings as well as 196 unrelated, matched control participants.  If the cortical thickness of the siblings (who share 50% of their genetic variation) was more similar to the patients, then it would suggest that the cortical thinning of the patients was under genetic control and hence – perhaps – a biological trait that is more of a primary cause.  On the other hand, if the cortical thickness of the siblings (who share 0% of their genetic variation) was more similar to that of the healthy control participants, then it would suggest that cortical thinning was – perhaps more of a secondary consequence of some earlier deficit.

The high-resolution structural neuroimaging allowed the team to carefully assess cortical thickness - which is normally between a mere 2 and 4 millimeters – across different areas of the cortex.  The team reports that, for the most part, the cortical thickness measures of the siblings were more similar to the unrelated controls – thus suggesting that cortical thickness may not be a direct component of the genetic risk architecture for schizophrenia.  Still, the paper discusses several candidate mechanisms which could lead to cortical thinning in the illness – some of which might be assessed in the future using other imaging modalities in the context of their patient/sibling/control experimental design.

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