Many studies are currently collecting multiple types of imaging data from the same participants. Each imaging method reports on a limited domain and typically provides both common and unique information about the problem in question. We have developed an ICA-based framework to investigate the integration of data from two modalities. This method identifies components of both modalities and connections between them through enhancing intrinsic interrelationships. We have applied this approach to link functional magnetic resonance imaging (fMRI)/event-related potential (ERP) data and also fMRI and genetic data (single nucleotide polymorphism (SNP) arrays). Results show that parallel ICA (paraICA) provides stable results and can identify the linked components with a relatively high accuracy. In our initial application of paraICA, we defined a genetic independent component as a specific SNP association, i.e., a group of SNPs with various degrees of contribution, which partially determines a specific phenotype or endophenotype. This association can be modeled as a linear combination of SNP genotypes. In our current formulation, the relationship between brain function and the genetic component is calculated as the correlation between the columns of the fMRI and the SNP mixing matrices. Thus, we have a correlation term and the maximization function based upon entropy. In this talk, we provide an overview of the use of ICA to combine or fuse multimodal data, show some simulation results and some intereting findings derived from ERP/SNP and fMRI/SNP data.
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