While investigations of epidemiology and genetics based on symptom-related phenotypes has yielded disappointing results in many polygenetically determined diseases, including functional gastrointestinal disorders, the efforts to combine these lines of investigations with neuroimaging approaches have resulted in provocative insights and major advances in the understanding of common polygenic disorders. In the causal chain from gene to protein to mental function, brain network activity is likely to be a key intermediate phenotype that can bridge the gap between genes and clinical phenotype. Parallel independent components analysis has recently been shown to be useful for fusing neuroimaging data with genetic data from large SNP arrays. The advantage of this technique is that it is model free, offering a powerful and versatile data-driven approach for studying relationships between genetics and brain network activity.
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