Identifying gene associations with regions of brain activation and neural circuits

Early studies mapped single genes selected based on a measure of candidacy, to an area of brain activation. Examples in addition to COMT mentioned above, include the association of a GRM3 variant with inefficient prefrontal function during a working memory task,39 or the association of the short (S) variant In a variable repeat sequence in the promoter of the serotonin transporter gene, 5-HTTLPR, with altered amygdala activity during an emotionally evocative task.40

Illustrative of genetic vulnerability maps of brain function based on imaging genetics approaches, Rasetti et al reviewed neuroimaging intermediate phenotypes of schizophrenia and the gene variants associated with them, catalogued by cognitive task.41 For example, working memory tasks have revealed abnormal engagement of: dorsolateral prefrontal cortex associated with CACNA1C, COMT, GAD1, GRM3, DRD2, KCNH2, MTHFR, and RGS4; ventrolateral prefrontal cortex associated with COMT, PRODH, and RGS4; thalamus and hippocampus associated with NRG1, DAOA/G72, and DISCI; and parietal lobe associated with PRODH. Cognitive control tasks (designed to challenge executive function of goal-directed behavior in the presence of conflict) have identified abnormal engagement of the anterior cingulate cortex associated with COMT,DRD2, and MAOA; of the dorsolateral prefrontal cortex (DLPFC) associated with DTNBPl,DRD2,MAOA, COMT: and of the parietal cortex associated with DRD2, and MAOA.42 Memory encoding tasks recently identified abnormal engagement of hippocampus parahlppocampus region43 and association of the hippocampus with BDNF, COMT, DISCI, GRM3, and KCNH2. It is worthwhile to note that most association studies of brain function have used single gene variants and risk haplotypes emerging from linkage studies and more recently genome wide association studies, with differing levels of genetic evidence for each candidate gene, though there has been no systematic approach to date to selecting genes for imaging genetics studies.

Imaging genetics approaches have progressed to associating gene variants with multiple regions of activation, with disease-relevant risk circuits and putative distributed functional networks, rather than isolated, single regions. After all, brain information processing does not occur as discrete activation “blobs,” but as activity across distributed neural systems and circuits. Thus, circuit-based phenotypes would be expected to have greater fidelity in showing genetic association at the level of brain function, since in principle, the more realistic the phenotype, the stronger the genetic association. As schizophrenia is an emergent property of neural system function, not isolatable to a singular brain region or localized regional defect, but likely attributable to network-based neurointegrative deficits, neuroimaging and intermediate phenotyplng strategies have progressed to better understand distributed networks associated with Increased genetic risk. To identify a functional network or interregional coupling, functional connectivity between spatially remote regions is inferred based on temporal coherence, by identifying regions of coactivation.44 Statistical analyses used for functional connectivity include mapping based on seed voxel correlations, principal component analysis, independent component analysis, and partial least squares methods.

The functional connectivity literature within schizophrenia research has largely focused on PFC connectivity, especially the DLPFC and anterior cingulate, and DLPFC interaction with the medial temporal lobe, specifically the hippocampal formation (HF), and interaction with the DLPFC-thalamus.45 For the DLPFC, abnormal connectivity has been identified in multiple studies in patients with schizophrenia and in high-risk subjects46-49 and various genetic associations have been established with this putative circuit, during working memory tasks. For example during the n-back working memory task, COMT-associated activation changes mapped onto a prefrontal-parietal-striatal circuit, involving the DLPFC, ventrolateral prefrontal cortex (VLPFC), the posterior parietal regions, and the striatum.50 A PRODH schizophrenia risk haplotype was associated with increased striatal-frontal functional connectivity, while the protective haplotype was associated with decreased striatal-frontal functional connectivity.51 A 7 single-nucleotide polymorphism (SNP) haplotype of PPPIRIB (encoding DARPP-32) was associated with functional coupling and increased activation of the striatum and prefrontal cortex.52 An RGS4 variant was found to impact frontoparietal and frontotemporal coupling.53 A CACNAIC risk SNP was associated with decreased prefrontal-hippocampal connectivity.54 While some functional connectivity studies have employed a classic intermediate phenotype strategy, testing for regions of correlation in affected subjects as well as unaffected relatives, many studies appear to query the association of risk susceptibility gene variants with correlated regions in healthy subjects or affected patients alone, with the putative neural circuit then pending validation as an intermediate phenotype.

Psycho-physiological interaction (PPI) analysis is an alternative approach to estimating connectivity, and measures a regionally specific response in terms of an interaction between a cognitive (or sensorimotor) process and activity in another part of the brain. The supposition is that the remote region is either the source of afferents that confer functional specificity on the target region or is activated by efferents that are specifically active during the task. PPI, therefore, allows for the exploration of the effects of an independent variable (eg, genotype) on taskrelated differences in interregional connectivity.55 As a specific example, combining information about activity in the parietal region, mediating attention to a particular stimulus, and information about the stimulus, PPI aims to identify regions that respond to that stimulus when, and only when, activity in the parietal region is high. If such an interaction exists, then one might infer that the parietal area is modulating responses to the stimulus for which the area is selective. While this approach offers a deeper functional probe of network activity than simple time series correlation analyses, it is still based on a correlation of activity and not on a directional model of activity in one region influencing activity in another.

In one study, during the N-back task, DLPFC-HF coupling was identified in both patients with schizophrenia and their unaffected relatives, and associated with a ZNF804A risk allele, using seeded connectivity as well as PPI approaches.56 PPI analysis showed a reduction in task load-related modulation of coupling between the right DLPFC and bilateral HF, in patients and siblings, compared with controls. Further, subjects homozygous for the risk-associated allele of ZNF804A showed a disruption in task-related modulation of right DLPFC-left HF coupling in the PPI analysis. The seeded connectivity analysis showed similar results to the PPI analysis in DLPFC-HF coupling.

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Overall then, functional connectivity analysis offers some insight into correlation between different brain regions, but is limited in that it does not account for directionality, influence, or causality between putatively interacting regions; it makes no assumptions about the nature of underlying pathways, their structure, nor anatomical connectivity. So while correlative methods provide a way to characterize neural functional networks by temporal coherence of Inter-regional activation patterns, it yields neither an understanding of driving neural origins nor of the directionality of the observed network.

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