Automatic recognition of resting state fMRI networks with dictionary learning

Document Type

Conference Proceeding

Publication Date

1-1-2018

Abstract

Resting state functional magnetic resonance imaging (rs-fMRI) is a functional neuroimaging technique that investigates the spatially remote yet functionally linked neuronal coactivation patterns of the brain at rest. Non-invasiveness and task-free characteristics of rs-fMRI make it particularly suitable for aging, pediatric and clinical population. Researchers typically follow a source separation strategy to efficiently reconstruct the concurrent interacting resting state networks (RSN) from a myriad of whole brain fMRI signals. RSNs are currently identified by visual inspection with prior knowledge of spatial clustering of RSNs, as the variability and spatial overlapping nature of RSNs combined with presence of various sources of noise make automatic identification of RSNs a challenging task. In this study, we have developed an automated recognition algorithm to classify all the distinct RSNs. First, in contrast to traditional single level decomposition, a multi-level deep sparse matrix factorization-based dictionary leaning strategy was used to extract hierarchical features from the data at each level. Then we used maximum likelihood estimates of these spatial features using Kullback-Leibler divergence to perform the recognition of RSNs. Experimental results confirmed the effectiveness of our proposed approach in accurately classifying all the RSNs.

Identifier

85058525895 (Scopus)

ISBN

[9783030055868]

Publication Title

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

External Full Text Location

https://doi.org/10.1007/978-3-030-05587-5_24

e-ISSN

16113349

ISSN

03029743

First Page

249

Last Page

259

Volume

11309 LNAI

Grant

R03 DC013990

Fund Ref

National Institutes of Health

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