title: Local dimension-reduced dynamical spatio-temporal models for resting state network estimation.
abstract: 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.
authors.affiliation: Inter-institutional Grad Program on Bioinformatics, University of São Paulo, São Paulo, Brazil. gilson.vieira@gmail.com.
authors.forename: Gilson
authors.identifier: http://orcid.org/0000-0001-5877-2810
authors.initials: G
authors.lastname: Vieira
authors.affiliation: LIM-44, Department of Radiology, Faculty of Medicine, University of São Paulo, São Paulo, Brazil.
authors.forename: Edson
authors.initials: E
authors.lastname: Amaro
authors.affiliation: Escola Politécnica, University of São Paulo, São Paulo, Brazil.
authors.forename: Luiz A
authors.initials: LA
authors.lastname: Baccalá
keywords: Brain connectivity; Dynamical spatio-temporal models; Resting state fMRI; Sparsity