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

• Published in: Human brain mapping Vol. 30; no. 12; pp. 3865 - 3886
• Main Authors: Kiviniemi, Vesa, Starck, Tuomo, Remes, Jukka, Long, Xiangyu, Nikkinen, Juha, Haapea, Marianne, Veijola, Juha, Moilanen, Irma, Isohanni, Matti, Zang, Yu-Feng, Tervonen, Osmo
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.12.2009; Wiley-Liss
• Subjects: Adult; Biological and medical sciences; Brain - physiology; brain cortex; Brain Mapping - methods; Electrodiagnosis. Electric activity recording; Female; fMRI; Humans; ICA; Image Processing, Computer-Assisted - methods; Investigative techniques, diagnostic techniques (general aspects); Magnetic Resonance Imaging; Male; Medical sciences; Nervous system; Principal Component Analysis; Radiodiagnosis. Nmr imagery. Nmr spectrometry; resting state; fMRI; Nervous system diseases; resting state; brain cortex; Radiodiagnosis; Segmentation; ICA; Central nervous system; Models; Nuclear magnetic resonance imaging; Encephalon
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.20813

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. Hum Brain Mapp, 2009. © 2009 Wiley‐Liss, Inc.