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  1. Home
  3. Machine Learning On Dynamic Functional Connectivity: Promise, Pitfalls, And Interpretations.
  1. Home
  3. Machine Learning On Dynamic Functional Connectivity: Promise, Pitfalls, And Interpretations.

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Machine Learning on Dynamic Functional Connectivity: Promise, Pitfalls, and Interpretations.

Jiaqi Ding1, Tingting Dan2, Ziquan Wei1

  • 1Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, 27599, North Carolina, USA.

Information Sciences
|February 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

No single deep learning model excels across all functional neuroimaging tasks. Model performance in decoding cognitive states and diagnosing diseases varies based on demographics, task type, and disease stage.

Keywords:
BOLD SignalDisease DiagnosisMachine LearningTask RecognitionfMRI Analysis

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Area of Science:

  • Neuroimaging
  • Machine Learning
  • Cognitive Neuroscience

Background:

  • Large-scale functional Magnetic Resonance Imaging (fMRI) data offers opportunities to link brain activity to cognition using data-driven methods.
  • Current deep learning models for decoding cognitive states from fMRI data show inconsistent performance across different settings.

Purpose of the Study:

  • Establish empirical guidelines for designing deep learning models in neuroimaging.
  • Evaluate model performance in cognitive task recognition and disease diagnosis.
  • Identify limitations and provide selection criteria for machine learning backbones in neuroimaging.

Main Methods:

  • Utilized a large dataset of 39,784 fMRI samples from seven databases.
  • Conducted comprehensive evaluations and statistical analyses across cognitive and clinical scenarios.
  • Applied an attention-based interpretability method to analyze brain activation patterns.

    • Main Results:

    • No single deep learning model universally outperforms others in neuroimaging applications.
    • Model effectiveness is contingent upon factors including demographics, task type, and disease stage.
    • Identified key limitations and trade-offs of current deep learning models.

    Conclusions:

    • Model selection for neuroimaging requires careful consideration of specific application factors.
    • Findings provide a foundation for developing more robust and interpretable deep learning models in neuroscience.
    • Attention-based interpretability reveals task- and disorder-specific brain activation patterns.