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Updated: Feb 7, 2026

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TMN: Learning multi-timescale functional connectivity for identifying brain disorders.

Jinlong Hu1, Jianmiao Luo1, Jiatong Huang1

  • 1Guangdong Key Lab of Multimodal Big Data Intelligent Analysis, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.

Psychiatry Research. Neuroimaging
|February 5, 2026
PubMed
Summary
This summary is machine-generated.

This study identifies brain disorders using multi-timescale functional connectivity (mFC) and a novel deep learning model (TMN). The findings show mFC outperforms static and dynamic approaches in diagnostic tasks.

Keywords:
Brain disorderDeep multiple instance learningMulti-timescale functional connectivity

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Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Machine Learning

Background:

  • Functional connectivity (FC) is crucial for identifying brain disorders.
  • Existing methods often analyze FC at single timescales, potentially missing complex neural dynamics.

Purpose of the Study:

  • To identify brain disorders by analyzing functional connectivity across multiple timescales.
  • To develop and evaluate a deep learning model for capturing spatio-temporal patterns in multi-timescale functional connectivity (mFC).

Main Methods:

  • Resting-state fMRI data were segmented to compute mFC between brain regions.
  • A deep multiple instance learning (MIL) approach, the Two-stage Multi-stream Network (TMN), was developed to analyze mFC.
  • The TMN model was validated on the ABIDE I and REST-Meta-MDD datasets.

Main Results:

  • The TMN model utilizing mFC achieved superior performance in identifying brain disorders compared to static FC and combined static/dynamic FC methods.
  • Model interpretation using inputXgrad highlighted the contribution of FC across all timescales to disorder identification.
  • Specific important FCs strongly associated with brain disorders were identified.

Conclusions:

  • The proposed TMN with mFC effectively identifies brain disorders.
  • Multi-timescale analysis of functional connectivity offers a more comprehensive approach to understanding brain disorders.
  • Further validation with larger cohorts and diverse brain disorders is warranted.