Posts Tagged ‘Neuroimaging’

An historic find has occurred in the quest (gold-rush, if you will) to link genome variation with brain structure-function variation.  This is the publication of the very first genome-wide (GWAS) analysis of individual voxels (voxels are akin to pixels in a photograph, but are rather 3D cubes of brain-image-space about 1mm on each side) of brain structure – Voxelwise genome-wide association study (vGWAS) [doi: 10.1016/j.neuroimage.2010.02.032] by Jason Stein and colleagues under the leadership of Paul M. Thompson, a  leader in the area of neuroimaging and genetics – well-known for his work on brain structure in twin and psychiatric patient populations.

In an effort to discover genes that contribute to individual differences in brain structure, the authors took on the task of statistically analyzing the some 31,622 voxels (per brain) obtained from high-resolution structural brain scans; with 448,293 Illumina SNP genotypes (per person) with minor allele frequencies greater than 0.1 (common variants); in 740 unrelated healthy caucasian adults.  When performed on a voxel-by-voxel basis, this amounts to some 14 billion statistical tests.

Yikes!  A statistical nightmare with plenty of room for false positive results, not to mention the recent disillusionment with the common-variant GWAS approach?  Certainly.  The authors describe these pitfalls and other scenarios wherein false data is likely to arise and most of the paper addresses the pros and cons of different statistical analysis strategies – some which are prohibitive in their computational demands.  Undaunted, the authors describe several approaches for establishing appropriate thresholds and then utilize a ‘winner take all’ analysis strategy wherein a single ‘most-associated winning snp’ is identified for each voxel, which when clustered together in hot spots (at P = 2 x 10e-10), can point to specific brain areas of interest.

Using this analytical approach, the authors report that 8,212 snps were identified as ‘winning, most-associated’ snps across the 31,622 voxels.  They note that there was not as much symmetry with respect to winning snps in the left hemispere and corresponding areas in the right hemisphere, as one might have expected.  The 2 most significant snps across the entire brain and genome were rs2132683 and rs713155 which were associated with white matter near the left posterior lateral ventricle.  Other notable findings were rs2429582 in the synaptic (and possible autism risk factor) CADPS2 gene which was associated with temporal lobe structure and rs9990343 which sits in an intergenic region but is associated with frontal lobe structure.  These and several other notable snps are reported and brain maps are provided that show where in the brain each snp is associated.

As in most genome-wide studies, one can imagine that the authors were initially bewildered by their unexpected findings.  None of the ‘usual suspects’ such as neurotransmitter receptors, transcription factors, etc. etc. that dominate the psychiatric genetics literature.  Bewildered, perhaps, but maybe thats part of the fun and excitement of discovery!  Very exciting stuff to come I’ll bet as this new era unfolds!

Reblog this post [with Zemanta]

Read Full Post »

Darwin's finches or Galapagos finches. Darwin,...
Image via Wikipedia

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.

Reblog this post [with Zemanta]

Read Full Post »

Daniel Weinberger and company have a new installment in-press at Biological Psychiatry in their epic program to untangle the genetic basis of schizophrenia – “Heritability of Brain Morphology Related to Schizophrenia: A Large-Scale Automated Magnetic Resonance Imaging Segmentation Study.” Like all complex illness, schizophrenia is regulated by a variety of environmental sources (perinatal complications, stress & substance abuse are a few) and equally regulated by heritable factors. Although several specific genes for schizophrenia have been painstakingly identified, the genes are expressed widely throughout the brain – making it difficult to pinpoint where in the brain the gene interacts with the environment to exert its detrimental effects. To solve this problem, Weinberger and colleagues pioneered a method known as imaging-genetics where they look at how individual genetic differences correlate with differences in brain structure or functional activity (if you ever have a chance to volunteer for an fMRI brain imaging study – go for it – it’ll be one of the top 10 weirdest experiences of your life). In their latest report, the team pioneers a new “fully-automated whole brain segmentation” technique to show that the genetic factors that put individuals at risk may be functioning vis-a-vis the hippocampus and neocortex. This narrows the search space a lot! and is a major step forward in beginning to localize where in the brain the genetic risk originates.

Reblog this post [with Zemanta]

Read Full Post »