The model order limit: Deep sparse factorization for resting brain

Debadatta Dash, Vinayak Abrol, Anil Kumar Sao, Bharat Biswal

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

8 Scopus citations

Abstract

The decomposition of resting state Functional Magnetic Resonance Imaging (rs-fMRI) data by Dictionary Learning (DL) using sparsity constraint have been recently shown to be an alternative to traditional consideration of independence criteria to obtain Resting State Networks. However, a single level decomposition is not suitable when applied for group rs-fMRI analysis, as it requires a large model order or an equivalently large number of dictionary atoms to appropriately identify distinct brain networks. This is computationally expensive for group rs-fMRI analysis in resource constraint environments. In this work, we have proposed a deep sparse factorization method for multi-level decomposition of rs-fMRI data. Preliminary results from this study show that a multi-layer framework with very small model order can reveal better spatial maps of resting brain that share equivalent temporal dynamics. In addition, the proposed approach outperforms the existing DL approaches with single level decomposition in the metric of quality as well as computational complexity.

Original languageEnglish (US)
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE Computer Society
Pages1244-1247
Number of pages4
ISBN (Electronic)9781538636367
DOIs
StatePublished - May 23 2018
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: Apr 4 2018Apr 7 2018

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2018-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
CountryUnited States
CityWashington
Period4/4/184/7/18

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Keywords

  • Deep Sparse Factorization (DSF)
  • Dictionary Learning (DL)
  • Functional Connectivity
  • Rs-fMRI

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