Local dimension-reduced dynamical spatio-temporal models for resting state network estimation

Brain Informatics - Tập 2 - Trang 53-63 - 2015
Gilson Vieira1, Edson Amaro2, Luiz A. Baccalá3
1Inter-institutional Grad Program on Bioinformatics, University of São Paulo, São Paulo, Brazil
2LIM-44, Department of Radiology, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
3Escola Politécnica, University of São Paulo, São Paulo, Brazil

Tóm tắt

To overcome the limitations of independent component analysis (ICA), today’s most popular analysis tool for investigating whole-brain spatial activation in resting state functional magnetic resonance imaging (fMRI), we present a new class of local dimension-reduced dynamical spatio-temporal model which dispenses the independence assumptions that severely limit deeper connectivity descriptions between spatial components. The new method combines novel concepts of group sparsity with contiguity-constrained clusterization to produce physiologically consistent regions of interest in illustrative fMRI data whose causal interactions may then be easily estimated, something impossible under the usual ICA assumptions.

Tài liệu tham khảo

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