Automatic recognition of resting state fMRI networks with dictionary learning

Debadatta Dash, Bharat Biswal, Anil Kumar Sao, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationBrain Informatics - International Conference, BI 2018, Proceedings
EditorsYang Yang, Vicky Yamamoto, Shouyi Wang, Erick Jones, Jianzhong Su, Tom Mitchell, Leon Iasemidis
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783030055868
StatePublished - 2018
EventInternational Conference on Brain Informatics, BI 2018 - Arlington, United States
Duration: Dec 7 2018Dec 9 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11309 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherInternational Conference on Brain Informatics, BI 2018
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


  • Dictionary learning
  • KL divergence
  • Resting state networks
  • fMRI


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