Combining fMRI and SNP data to investigate connections between brain function and genetics using parallel ICA

Jingyu Liu, Godfrey Pearlson, Andreas Windemuth, Gualberto Ruano, Nora I. Perrone-Bizzozero, Vince Calhoun
2009 Human Brain Mapping  
There is current interest in understanding genetic influences on both healthy and disordered brain function. We assessed brain function with functional magnetic resonance imaging (fMRI) data collected during an auditory oddball taskdetecting an infrequent sound within a series of frequent sounds. Then, task-related imaging findings were utilized as potential intermediate phenotypes (endophenotypes) to investigate genomic factors derived from a single nucleotide polymorphism (SNP) array. Our
more » ... et is the linkage of these genomic factors to normal/abnormal brain functionality. We explored parallel independent component analysis (paraICA) as a new method for analyzing multimodal data. The method was aimed to identify simultaneously independent components of each modality and the relationships between them. When 43 healthy controls and 20 schizophrenia patients, all Caucasian, were studied, we found a correlation of 0.38 between one fMRI component and one SNP component. This fMRI component consisted mainly of parietal lobe activations. The relevant SNP component was contributed to significantly by 10 SNPs located in genes, including those coding for the nicotinic -7cholinergic receptor, aromatic amino acid decarboxylase, disrupted in schizophrenia 1, among others. Both fMRI and SNP components showed significant differences in loading parameters between the schizophrenia and control groups (P = 0.0006 for the fMRI component; P = 0.001 for the SNP component). In summary, we constructed a framework to identify interactions between brain functional and genetic information; our findings provide a proof-of-
doi:10.1002/hbm.20508 pmid:18072279 pmcid:PMC2668960 fatcat:p4337yhcpbeunmwbaxzk4x3jv4