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BrainSTR: Spatio-Temporal Contrastive Learning for Interpretable Dynamic Brain Network Modeling

Guiliang Guo, Guangqi Wen, Lingwen Liu, Ruoxian Song, Peng Cao, Jinzhu Yang, Fei Wang, Xiaoli Liu, Osmar R. Zaiane

TL;DR

A spatio-temporal supervised contrastive learning approach that leverages diagnosis-relevant spatio-temporal patterns to refine the similarity metric between samples and capture more discriminative spatio-temporal features, thereby constructing a well-structured semantic space for coherent and interpretable representations.

Abstract

Dynamic functional connectivity captures time-varying brain states for better neuropsychiatric diagnosis and spatio-temporal interpretability, i.e., identifying when discriminative disease signatures emerge and where they reside in the connectivity topology. Reliable interpretability faces major challenges: diagnostic signals are often subtle and sparsely distributed across both time and topology, while nuisance fluctuations and non-diagnostic connectivities are pervasive. To address these issues, we propose BrainSTR, a spatio-temporal contrastive learning framework for interpretable dynamic brain network modeling. BrainSTR learns state-consistent phase boundaries via a data-driven Adaptive Phase Partition module, identifies diagnostically critical phases with attention, and extracts disease-related connectivity within each phase using an Incremental Graph Structure Generator regularized by binarization, temporal smoothness, and sparsity. Then, we introduce a spatio-temporal supervised contrastive learning approach that leverages diagnosis-relevant spatio-temporal patterns to refine the similarity metric between samples and capture more discriminative spatio-temporal features, thereby constructing a well-structured semantic space for coherent and interpretable representations. Experiments on ASD, BD, and MDD validate the effectiveness of BrainSTR, and the discovered critical phases and subnetworks provide interpretable evidence consistent with prior neuroimaging findings. Our code: https://anonymous.4open.science/r/BrainSTR1.

BrainSTR: Spatio-Temporal Contrastive Learning for Interpretable Dynamic Brain Network Modeling

TL;DR

A spatio-temporal supervised contrastive learning approach that leverages diagnosis-relevant spatio-temporal patterns to refine the similarity metric between samples and capture more discriminative spatio-temporal features, thereby constructing a well-structured semantic space for coherent and interpretable representations.

Abstract

Dynamic functional connectivity captures time-varying brain states for better neuropsychiatric diagnosis and spatio-temporal interpretability, i.e., identifying when discriminative disease signatures emerge and where they reside in the connectivity topology. Reliable interpretability faces major challenges: diagnostic signals are often subtle and sparsely distributed across both time and topology, while nuisance fluctuations and non-diagnostic connectivities are pervasive. To address these issues, we propose BrainSTR, a spatio-temporal contrastive learning framework for interpretable dynamic brain network modeling. BrainSTR learns state-consistent phase boundaries via a data-driven Adaptive Phase Partition module, identifies diagnostically critical phases with attention, and extracts disease-related connectivity within each phase using an Incremental Graph Structure Generator regularized by binarization, temporal smoothness, and sparsity. Then, we introduce a spatio-temporal supervised contrastive learning approach that leverages diagnosis-relevant spatio-temporal patterns to refine the similarity metric between samples and capture more discriminative spatio-temporal features, thereby constructing a well-structured semantic space for coherent and interpretable representations. Experiments on ASD, BD, and MDD validate the effectiveness of BrainSTR, and the discovered critical phases and subnetworks provide interpretable evidence consistent with prior neuroimaging findings. Our code: https://anonymous.4open.science/r/BrainSTR1.
Paper Structure (18 sections, 5 equations, 3 figures, 1 table)

This paper contains 18 sections, 5 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Method
  3. Adaptive Phase Partition via Time-Invariant Representation
  4. Incremental Graph Structure Generator
  5. Base and incremental structure.
  6. Differentiable structure learning.
  7. Structure regularization.
  8. Structure-Aware Encoder
  9. Spatio-Temporal Contrastive Learning
  10. Attention and aggregation.
  11. Spatio-temporal contrastive loss.
  12. Loss and Training
  13. Experiments and Results
  14. Dataset and Experimental Details
  15. Classification Results
  16. ...and 3 more sections

Figures (3)

  • Figure 1: Overview of the proposed BrainSTR framework. Given a subject's BOLD signal $X\in\mathbb{R}^{T\times N}$, where $T$ and $N$ denote the numbers of time points and ROIs, respectively, Adaptive Phase Partition (APP) infers state-consistent phase boundaries to construct phase-wise FCs $\{A_t\}_{t=1}^{W}$, where $W$ is the number of partitioned phases, with each $A_t\in\mathbb{R}^{N\times N}$. Each $A_t$ is decomposed into $A_t^{+}$/$A_t^{-}$ via an Incremental Graph Structure Generator that learns a phase-wise structure $S_t$, and then encoded by a shared Structure-Aware Encoder. Attention then weights informative phases to form subject embeddings, while contrastive supervision makes them diagnosis-discriminative relative to original-graph embeddings.
  • Figure 2: Case-level temporal--topological interpretability of BrainSTR on representative subjects. Top: phase-wise structures, together with the overall retained-connectivity ratio $R$ of each phase. Bottom: normalized connectivity strength of subnetworks.
  • Figure 3: Group-level DMN-centered structure retention. The top row (Imp) and bottom row (Non) visualize the group-aggregated retained connectivities in important and non-important phases, respectively, shown from three brain views.