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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pointer to:
It was a delight today to chat with Monica Coenraads, Executive Director of the 
OK, now, mutations in the gene that encode the MecP2 gene – such as those found at Arginine residue 133 and Serine residue 134 impair the ability of the protein to bind to these 5-Methylcyctosine residues. 
The 
suggests that neural networks with too many synapses may not be appropriately connected and may be locked-in to sub-optimal architectures. 
Genes 2 Brains 2 Mind 2 Me 