Functional segmentation of the brain cortex using high model order group ICA
Résumé
Baseline activity of resting state brain networks (RSN) in a resting subject has become one of the fastest growing research topics in neuroimaging. It has been shown that up to 12 RSNs can be differentiated using an independent component analysis (ICA) of blood oxygen level dependent (BOLD) resting state data. In this work we investigate how many RSN signal sources can be separated from the entire brain cortex using high dimension ICA analysis from a group dataset. Group data from 55 subjects were analysed using temporal concatenation and a probabilistic ICA (PICA) algorithm. PICA estimation suggested 73 components to be calculated. ICA repeatability testing (ICASSO) verified that 60 of the 70 computed components were robustly detectable. Forty-two independent signal sources were identifiable as RSN, and 28 were related to artifacts or other non-interest sources (non-RSN). The depicted RSNs bore a closer match to functional neuroanatomy than the previously reported RSN components. The non-RSN sources have significantly lower temporal inter-source connectivity than the RSN (p< 0.0003). We conclude that the high model order ICA of group BOLD data enables functional segmentation of the brain cortex. The method enables new analysis approaches to causality and connectivity with more specific anatomical details.
Origine : Fichiers produits par l'(les) auteur(s)
Loading...