The Role of Diversity in Complex ICA Algorithms for fMRIAnalysis

摘要

BackgroundThe widespread use of data-driven methods, such as independent component analysis (ICA), for the analysis of functional magnetic resonance imaging data (fMRI) has enabled deeper understanding of neural function. However, most popular ICA algorithms for fMRI analysis make several simplifying assumptions, thus ignoring sources of statistical information, types of diversity, and limiting their performance.