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An ICA framework for integrating fMRI, ERP and genetic data

In this talk, we discuss an ICA-based framework to combine or fuse multimodal data in groups of subjects using features extracted from the single-subject data. Many studies are currently collecting multiple types of imaging data from the same participants. ICA has proven particularly useful for data fusion of multiple tasks or data modalities such as single nucleotide polymorphism (SNP) data or event-related potentials (ERP). For example, in relating SNPs and fMRI data, a genetic independent component is defined as a group of SNPs which partially determines a specific phenotype or endophenotype. The relationship between brain function and the genetic component can be adaptively maximized along with the independence among components. In summary, we hope to motivate the importance of combining multimodal brain imaging data in a unified model and also to show that an ICA-based framework provides a powerful way to identify joint relationships between multimodal data which would have been missed otherwise.