Functional segmentation of the brain cortex using high model order group PICA.

Abstract

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 the blood oxygen level dependent (BOLD) resting state data. In this study, 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 was analyzed using temporal concatenation and a probabilistic independent component analysis algorithm. ICA repeatability testing 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 noninterest 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 intersource connectivity than the RSN (P < 0.0003). We conclude that the high model order ICA of the group BOLD data enables functional segmentation of the brain cortex. The method enables new approaches to causality and connectivity analysis with more specific anatomical details.

DOI: 10.1002/hbm.20813
0100200200920102011201220132014201520162017
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@article{Kiviniemi2009FunctionalSO, title={Functional segmentation of the brain cortex using high model order group PICA.}, author={Vesa Kiviniemi and Tuomo Starck and Jukka J. Remes and Xiangyu Long and Juha Nikkinen and Marianne Haapea and Juha Veijola and Irma Kaarina Moilanen and Matti Isohanni and Yu-feng Zang and Osmo Tervonen}, journal={Human brain mapping}, year={2009}, volume={30 12}, pages={3865-86} }