Parallel group ICA for multimodal biomedical data analyses

Multiple types of signals or images are often collected from the same participants in biomedical research. Multimodal analyses have been shown to better capture the joint information. We propose a new method named parallel group independent component analysis (para-GICA) to address a special need for parallel processing of multimodal brain images or signals where it is desirable to partition into groups, for example to stratify by age. Para-GICA is designed to identify associated components between two modalities based on their loading variations in participants, while allowing components to show group specificity. Simulation using synthetic MRI and genetic data demonstrates that para-GICA is able to recover group specific brain networks and the connection between brain networks and genetic factors. A real data application on brain gray matter concentration and whiter matter fractional anisotropy images extracts associated gray matter and white matter components, and ageing induced spatial differences of the components.