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Dynamic functional network connectivity analysis in schizophrenia based on a spatiotemporal CPD framework.

Li-Dan Kuang1, He-Qiang Li1, Jianming Zhang1

  • 1School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, People's Republic of China.

Journal of Neural Engineering
|February 9, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel sparse and low-rank canonical polyadic decomposition (SLRCPD) method to identify dynamic brain connectivity changes in schizophrenia. The approach effectively distinguishes aberrant functional connectivity patterns and temporal states between patients and healthy controls.

Keywords:
Canonical polyadic decomposition (CPD)Schizophreniadynamic functional network connectivity (dFNC)dynamic moduleslow-rank constraint

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Computational Biology

Background:

  • Dynamic functional network connectivity (dFNC) is crucial for understanding brain diseases like schizophrenia.
  • Independent component (IC) analysis and canonical polyadic decomposition (CPD) offer frameworks for analyzing complex brain data.
  • Existing methods may not fully capture the intricate spatiotemporal dynamics in schizophrenia.

Purpose of the Study:

  • To develop and validate an innovative sparse and low-rank CPD (SLRCPD) method for analyzing three-way dFNC tensors.
  • To identify significant dynamic spatiotemporal aberrant changes in schizophrenia using SLRCPD.
  • To enhance the characterization of dynamic spatial and temporal fluctuations in brain connectivity.

Main Methods:

  • Proposed a sparse and low-rank CPD (SLRCPD) approach for three-way dFNC tensors.
  • Applied L1 regularization for sparse spatial modules and low-rank constraints for temporal state clustering.
  • Utilized K-means clustering and classification to analyze group differences in time-varying weights.

Main Results:

  • Identified three typical dFNC patterns across two independent datasets (82 subjects and COBRE schizophrenia dataset).
  • Detected aberrant connections in auditory, somatomotor, visual, cognitive control, and cerebellar networks in schizophrenia patients (SZs) compared to healthy controls (HCs).
  • Found significant differences in four temporal states between SZs and HCs, achieving classification accuracy >0.96.

Conclusions:

  • SLRCPD effectively excavates significant spatio-temporal patterns in schizophrenia.
  • The method provides a robust framework for identifying aberrant brain connectivity in neurological disorders.
  • This approach holds promise for improving diagnostic and therapeutic strategies for schizophrenia.