A three-way parallel ICA approach to analyze links among genetics, brain structure and brain function.

ABSTRACT: Multi-modal data analysis techniques, such as the Parallel Independent Component Analysis (pICA), are essential in neuroscience, medical imaging and genetic studies. The pICA algorithm allows the simultaneous decomposition of up to two data modalities achieving better performance than separate ICA decompositions and enabling the discovery of links between modalities. However, advances in data acquisition techniques facilitate the collection of more than two data modalities from each subject. Examples of commonly measured modalities include genetic information, structural magnetic resonance imaging (MRI) and functional MRI. In order to take full advantage of the available data, this work extends the pICA approach to incorporate three modalities in one comprehensive analysis. Simulations dem

SUBMITTER: Vergara VM 

PROVIDER: S-EPMC4141686 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature