Multi-Scale Factor Analysis of High-Dimensional Brain Signals

Handle URI:
http://hdl.handle.net/10754/626521
Title:
Multi-Scale Factor Analysis of High-Dimensional Brain Signals
Authors:
Ting, Chee-Ming; Ombao, Hernando; Salleh, Sh-Hussain
Abstract:
In this paper, we develop an approach to modeling high-dimensional networks with a large number of nodes arranged in a hierarchical and modular structure. We propose a novel multi-scale factor analysis (MSFA) model which partitions the massive spatio-temporal data defined over the complex networks into a finite set of regional clusters. To achieve further dimension reduction, we represent the signals in each cluster by a small number of latent factors. The correlation matrix for all nodes in the network are approximated by lower-dimensional sub-structures derived from the cluster-specific factors. To estimate regional connectivity between numerous nodes (within each cluster), we apply principal components analysis (PCA) to produce factors which are derived as the optimal reconstruction of the observed signals under the squared loss. Then, we estimate global connectivity (between clusters or sub-networks) based on the factors across regions using the RV-coefficient as the cross-dependence measure. This gives a reliable and computationally efficient multi-scale analysis of both regional and global dependencies of the large networks. The proposed novel approach is applied to estimate brain connectivity networks using functional magnetic resonance imaging (fMRI) data. Results on resting-state fMRI reveal interesting modular and hierarchical organization of human brain networks during rest.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
arXiv
Issue Date:
18-May-2017
ARXIV:
arXiv:1705.06502
Type:
Preprint
Additional Links:
http://arxiv.org/abs/1705.06502v1; http://arxiv.org/pdf/1705.06502v1
Appears in Collections:
Other/General Submission; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorTing, Chee-Mingen
dc.contributor.authorOmbao, Hernandoen
dc.contributor.authorSalleh, Sh-Hussainen
dc.date.accessioned2017-12-28T07:32:14Z-
dc.date.available2017-12-28T07:32:14Z-
dc.date.issued2017-05-18en
dc.identifier.urihttp://hdl.handle.net/10754/626521-
dc.description.abstractIn this paper, we develop an approach to modeling high-dimensional networks with a large number of nodes arranged in a hierarchical and modular structure. We propose a novel multi-scale factor analysis (MSFA) model which partitions the massive spatio-temporal data defined over the complex networks into a finite set of regional clusters. To achieve further dimension reduction, we represent the signals in each cluster by a small number of latent factors. The correlation matrix for all nodes in the network are approximated by lower-dimensional sub-structures derived from the cluster-specific factors. To estimate regional connectivity between numerous nodes (within each cluster), we apply principal components analysis (PCA) to produce factors which are derived as the optimal reconstruction of the observed signals under the squared loss. Then, we estimate global connectivity (between clusters or sub-networks) based on the factors across regions using the RV-coefficient as the cross-dependence measure. This gives a reliable and computationally efficient multi-scale analysis of both regional and global dependencies of the large networks. The proposed novel approach is applied to estimate brain connectivity networks using functional magnetic resonance imaging (fMRI) data. Results on resting-state fMRI reveal interesting modular and hierarchical organization of human brain networks during rest.en
dc.publisherarXiven
dc.relation.urlhttp://arxiv.org/abs/1705.06502v1en
dc.relation.urlhttp://arxiv.org/pdf/1705.06502v1en
dc.rightsArchived with thanks to arXiven
dc.titleMulti-Scale Factor Analysis of High-Dimensional Brain Signalsen
dc.typePreprinten
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.eprint.versionPre-printen
dc.contributor.institutionCenter for Biomedical Engineering, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, Malaysiaen
dc.contributor.institutionDepartment of Statistics, University of California, Irvine CA 92697, USAen
dc.identifier.arxividarXiv:1705.06502en
kaust.authorOmbao, Hernandoen
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