functional subdivision of group-ica results of fmri data collected during cinema viewing功能细分group-ica fmri数据收集的结果在电影院观看.pdfVIP

Functional Subdivision of Group-ICA Results of fMRI Data Collected during Cinema Viewing 1 1 1,2 ¨ 1 3 1,2 Siina Pamilo *, Sanna Malinen , Yevhen Hlushchuk , Mika Seppa , Pia Tikka , Riitta Hari 1 Brain Research Unit, O.V. Lounasmaa Laboratory, School of Science, Aalto University, Espoo, Finland, 2 Advanced Magnetic Imaging Centre, O.V. Lounasmaa Laboratory, School of Science, Aalto University, Espoo, Finland, 3 Department of Motion Picture, Television and Production Design, School of Art, Design and Architecture, Aalto University, Helsinki, Finland Abstract Independent component analysis (ICA) can unravel functional brain networks from functional magnetic resonance imaging (fMRI) data. The number of the estimated components affects both the spatial pattern of the identified networks and their time-course estimates. Here group-ICA was applied at four dimensionalities (10, 20, 40, and 58 components) to fMRI data collected from 15 subjects who viewed a 15-min silent film (‘‘At land’’ by Maya Deren). We focused on the dorsal attention network, the default-mode network, and the sensorimotor network. The lowest dimensionalities demonstrated most prominent activity within the dorsal attention network, combined with the visual areas, and in the default-mode network; the sensorimotor network only appeared with ICA comprising at least 20 components. The results suggest that even very low-dimensional ICA can unravel the most prominent functionally-connected brain networks. However, increasing the number of components gives a more detailed picture and functionally feasible subdivision of the major networks. These results improve our understanding of the hierarchical subdivi