Fused estimation of sparse connectivity patterns from rest fMRI

Pascal Zille, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson, Yu Ping Wang

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

2 Scopus citations

Abstract

Functional magnetic resonance imaging (fMRI) is a powerful tool to analyze brain development and neuronal activity. Identifying discriminative brain regions between various groups within a population has generated great interest in recent years. In this work, we consider the problem of estimating multiple sparse, co-activated brain regions from fMRI observations belonging to different classes. More precisely, we propose a method to analyze functional connectivity differences between children and young adults. Often, analysis is conducted on each class separately. Here, we propose to rely on a generalized fused Lasso penalty to extract both class-specific and shared co-expressed regions. In order to validate our method, experiments are performed on an fMRI dataset comprised of normally developing children from 8 to 21. The results demonstrate that the proposed method is able to properly extract meaningful sub-networks, which results in improved classification accuracy between the two classes.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6160-6164
Number of pages5
ISBN (Electronic)9781509041176
DOIs
StatePublished - Jun 16 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: Mar 5 2017Mar 9 2017

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period3/5/173/9/17

Keywords

  • Brain Development
  • Classification
  • Joint Lasso
  • Sparse Models

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Zille, P., Calhoun, V. D., Stephen, J. M., Wilson, T. W., & Wang, Y. P. (2017). Fused estimation of sparse connectivity patterns from rest fMRI. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings (pp. 6160-6164). [7953340] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2017.7953340