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Sparsity-guided multiple functional connectivity patterns for classification of schizophrenia via convolutional

Renping Yu1, Cong Pan1, Lingbin Bian2

  • 1School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China.

Human Brain Mapping
|June 15, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for classifying schizophrenia (SZ) using brain functional connectivity networks (FCNs). The method enhances diagnostic accuracy by refining network construction and feature learning for improved identification of aberrant brain connectivity.

Keywords:
convolutional neural networkmultiple sparse patterns learningresting-state functional MRIschizophreniaweighted sparse functional connectivity

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Machine Learning

Background:

  • Resting-state functional magnetic resonance imaging (fMRI) is crucial for analyzing brain functional connectivity networks (FCNs) in neuropsychiatric disorders like schizophrenia (SZ).
  • Traditional methods like Pearson's correlation (PC) may miss complex interactions, while sparse representation methods can oversimplify network structures.
  • Existing approaches often overlook confounding effects from other brain regions, impacting the accuracy of FCN analysis.

Purpose of the Study:

  • To develop a novel framework for schizophrenia classification using advanced FCN analysis.
  • To improve the accuracy of identifying discriminative features in brain networks for SZ diagnosis.
  • To explore potential biomarkers associated with aberrant connectivity in schizophrenia.

Main Methods:

  • A new framework integrating convolutional neural networks (CNNs) with sparsity-guided multiple functional connectivity is proposed.
  • The first component constructs a sparse FCN by combining PC and weighted sparse representation (WSR), accounting for confounding effects.
  • The second component utilizes functional connectivity convolution to learn discriminative features from multiple FCNs, employing an occlusion strategy to identify key regions and connections.

Main Results:

  • The proposed method demonstrates superior performance in schizophrenia identification compared to existing approaches.
  • The framework successfully identifies discriminative features and potential biomarkers for SZ.
  • Experiments validate the effectiveness and advantages of the developed method for SZ classification.

Conclusions:

  • The novel CNN framework with sparsity-guided multiple functional connectivity offers a powerful tool for schizophrenia classification.
  • This approach enhances the understanding of aberrant brain connectivity in SZ.
  • The framework holds potential as a diagnostic tool for various neuropsychiatric disorders.