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Counterfactual explanations of tree based ensemble models for brain disease analysis with structure function

Shaolong Wei1,2, Zhen Gao3, Hongcheng Yao4

  • 1School of Artificial Intelligence and Computer Science, Nantong University, Nantong, China.

Scientific Reports
|March 13, 2025
PubMed
Summary

This study introduces tree-based models to explain brain connectivity changes in neuropsychiatric disorders. The method aids diagnosis by identifying abnormal structural connectivity-functional connectivity coupling and generating counterfactual examples.

Keywords:
Brain diseasesCounterfactual explanationFunctional connectivitySC-FC couplingStructural connectivity

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Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Machine Learning

Background:

  • Disruptions in brain structural connectivity (SC) and functional connectivity (FC) are linked to neuropsychiatric disorders.
  • SC-FC coupling alterations may offer more sensitive detection of subtle brain abnormalities than single modalities.
  • Existing methods struggle to elucidate the relationship between SC-FC coupling and brain disorders.

Purpose of the Study:

  • To explore tree-based ensemble models for counterfactual explanations of SC-FC coupling.
  • To analyze the predictive role of SC-FC coupling features in diseases.
  • To assist in brain disease diagnosis by generating counterfactual examples for patients.

Main Methods:

  • Constructed SC and FC matrices from diffusion-weighted imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) data.
  • Quantified SC-FC coupling strength per region, converting it into feature vectors.
  • Employed Decision Tree, Random Forest, and Adaptive Boosting models for analysis and counterfactual explanation generation.

Main Results:

  • Validated the proposed method on independent epilepsy and schizophrenia datasets.
  • Identified discriminative brain regions associated with disease-related SC-FC coupling.
  • Generated counterfactual examples to aid in fine-tuning patient-specific connectivity patterns.

Conclusions:

  • Tree-based ensemble models provide effective counterfactual explanations for SC-FC coupling abnormalities.
  • The approach offers new insights into brain disease analysis and diagnosis.
  • Identified brain regions and counterfactuals can potentially improve diagnostic accuracy.