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Detecting abnormal connectivity in schizophrenia via a joint directed acyclic graph estimation model.

Gemeng Zhang1, Biao Cai1, Aiying Zhang2

  • 1Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118, USA.

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Summary

This study introduces a novel joint directed functional connectivity (FC) model to analyze brain networks. The model accurately identifies brain differences in schizophrenia and improves patient classification, highlighting the importance of directed interactions.

Keywords:
BrainConnectivity analysisFunctional imagingProbabilistic and statistical methodsfMRI analysis

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Functional connectivity (FC) analysis using fMRI BOLD signals reveals brain region interactions but lacks directional information.
  • Existing directed acyclic graph (DAG) methods for directed FC estimation face limitations with high-dimensional data and small sample sizes.

Purpose of the Study:

  • To propose and validate a joint DAG model for estimating directed functional connectivity, addressing limitations of previous methods.
  • To apply the directed FC model to a case-control study of schizophrenia (SZ) and assess its utility in brain network analysis and classification.

Main Methods:

  • Developed a joint DAG model incorporating group regularization and samples from both case and control groups to estimate directed FC.
  • Validated the model's efficiency and accuracy through simulation studies.
  • Applied the model to schizophrenia data from the MIND Clinical Imaging Consortium (MCIC).

Main Results:

  • The joint DAG model successfully identified decreased functional integration, disrupted hub structures, and characteristic edges (CtEs) in SZ patients.
  • Findings in SZ patients were consistent with previous studies, with some potential markers identified.
  • Directed FC analysis revealed significant differences compared to undirected FC, improving SZ patient classification accuracy.

Conclusions:

  • The proposed joint DAG model effectively estimates directed functional connectivity, offering advantages over undirected approaches.
  • Directed FC analysis provides valuable insights into brain network alterations in schizophrenia and enhances diagnostic classification.
  • This approach advances brain network analysis by incorporating directional information for a more comprehensive understanding of brain function.