A Machine Learning Model for Exploring Aberrant Functional Network Connectivity Transition in Schizophrenia

Mohammad S.E. Sendi, Elaheh Zendehrouh, Zening Fu, Babak Mahmoudi, Robyn L. Miller, Vince D. Calhoun

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

Abstract

Schizophrenia (SZ) is a severe neuropsychiatric disorder with a hallmark of functional dysconnectivity between numerous brain regions. With an implicit assumption of stationary brain interactions during the scanning period, most of the resting-state functional magnetic resonance imaging (fMRI) studies are conducted on static functional network connectivity (sFNC). Dynamic functional network connectivity (dFNC) that explores temporal patterns of functional connectivity (FC) might provide additional information to its static counterpart. In this work, we first estimate latent features (called connectivity states) by applying k-means clustering on dFNC. Next, using the estimated latent features, we trained and tested a classifier, which can differentiate SZ from healthy control (HC) subjects with 71% accuracy. Using a feature selection method embedded in the classifier, we have highlighted the role of transition probabilities between states as potential biomarkers and identified the role of lightly modularized transient connectivity state in pulling healthy subjects out of both highly modularized and very disconnected states. This will offer some new understandings about the way the healthy brain shifts between the most and the least connected states of whole brain connectivity.

Original languageEnglish (US)
Title of host publication2020 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages112-115
Number of pages4
ISBN (Electronic)9781728157450
DOIs
StatePublished - Mar 2020
Externally publishedYes
Event2020 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2020 - Santa Fe, United States
Duration: Mar 29 2020Mar 31 2020

Publication series

NameProceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation
Volume2020-March

Conference

Conference2020 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2020
CountryUnited States
CitySanta Fe
Period3/29/203/31/20

Keywords

  • dynamic functional network connectivity
  • feature learning
  • machine learning
  • resting-state fMRI
  • Schizophrenia

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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  • Cite this

    Sendi, M. S. E., Zendehrouh, E., Fu, Z., Mahmoudi, B., Miller, R. L., & Calhoun, V. D. (2020). A Machine Learning Model for Exploring Aberrant Functional Network Connectivity Transition in Schizophrenia. In 2020 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2020 - Proceedings (pp. 112-115). [9094620] (Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation; Vol. 2020-March). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSIAI49293.2020.9094620