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

cs.CVMar 10, 2026

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

Authors: Guiliang Guo, Guangqi Wen, Lingwen Liu, Ruoxian Song, Peng Cao, Jinzhu Yang, Fei Wang, Xiaoli Liu, +1 more

Organizations: Computer Science and Engineering, Northeastern University, Shenyang, China · School of Computer Science and Artificial Intelligence, Shandong Normal University, Jinan, China · Graduate School of Information, Production and Systems, Waseda University, Kitakyushu, Fukuoka, Japan · Nanjing Medical University, Nanjing, China · AiShiWeiLai AI Research, Beijing, China · Amii, University of Alberta, Edmonton, Alberta, Canada

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.

Figures & tables

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.

Category	Method	HC vs. MDD		HC vs. BD		HC vs. ASD (NYU center)
Category	Method	ACC(%)	AUC(%)	ACC(%)	AUC(%)	ACC(%)	AUC(%)
Traditional	FC + SVM	$68.4\pm 2.7$	$67.2\pm 3.2$	$65.6\pm 3.1$	$67.3\pm 3.5$	$68.1\pm 2.9$	$69.7\pm 3.6$
	FC + RF	$64.7\pm 2.9$	$61.2\pm 3.4$	$65.2\pm 3.1$	$63.6\pm 4.0$	$66.2\pm 2.8$	$65.1\pm 3.7$
	SVM-RFE [ 3 ]	$70.3\pm 3.8$	$70.1\pm 4.0$	$66.9\pm 3.7$	$67.6\pm 3.9$	$65.2\pm 4.1$	$65.9\pm 4.3$
Static graph	GroupINN [ 23 ]	$67.1\pm 3.9$	$65.7\pm 4.2$	$68.3\pm 3.8$	$65.2\pm 4.0$	$63.3\pm 4.3$	$64.3\pm 4.4$
	BrainGNN [ 10 ]	$65.2\pm 3.6$	$63.7\pm 3.9$	$69.1\pm 3.4$	$68.7\pm 3.6$	$65.2\pm 3.8$	$65.4\pm 4.0$

Table 1: Comparison with state-of-the-art methods on MDD, BD, and ASD diagnosis. The best results are in bold. The average and std of ACC and AUC are reported.

Panel of Figure 2 — 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.

Panel of Figure 3 — 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.

Explore similar work

D-GATNet: Interpretable Temporal Graph Attention Learning for ADHD Identification Using Dynamic Functional Connectivity
Mar 27, 2026Qurat Ul Ain, Alptekin Temizel, Soyiba JawedClassification Neuroimaging
Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan · Graduate School of Informatics, Middle East Technical University, 06800, Ankara, Turkey
BrainLinear: A Linear Model for Brain Network Analysis in Sparse Tangent Subspaces
Aug 15, 2026Sijing Wu, Dongyuan Li, Miaoting Huang +4Brain Network Analysis Functional Connectivity
Guangdong University of Foreign Studies · The University of Tokyo · RIKEN AIP +1
BrainNet Studio: A Unified Toolkit for Brain Network Construction, Intelligent Analysis, and Visualization
Sep 29, 2026Xiwei Zeng, Shengrong Li, Yiheng Liu +3Functional Connectivity Neuroimaging
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, China · School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

Mar 27, 2026J/K move · Enter open · S save

D-GATNet: Interpretable Temporal Graph Attention Learning for ADHD Identification Using Dynamic Functional Connectivity

Qurat Ul Ain, Alptekin Temizel, Soyiba Jawed

Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan · Graduate School of Informatics, Middle East Technical University, 06800, Ankara, Turkey

Classification Neuroimaging Graph Attention Networks

Open paper AskPDF arXiv

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder whose neuroimaging-based diagnosis remains challenging due to complex time-varying disruptions in brain connectivity. Functional MRI (fMRI) provides a powerful non-invasive modality for identifying functional alterations. Existing deep learning (DL) studies employ diverse neuroimaging features; however, static functional connectivity remains widely used, whereas dynamic connectivity modeling is comparatively underexplored. Moreover, many DL models lack interpretability. In this work, we propose D-GATNet, an interpretable temporal graph-based framework for automated ADHD classification using dynamic functional connectivity (dFC). Sliding-window Pearson correlation constructs sequences of functional brain graphs with regions of interest as nodes and connectivity strengths as edges. Spatial dependencies are learned via a multi-layer Graph Attention Network, while temporal dynamics are modeled using 1D convolution followed by temporal attention. Interpretability is achieved through graph attention weights revealing dominant ROI interactions, ROI importance scores identifying influential regions, and temporal attention emphasizing informative connectivity segments. Experiments on the Peking University site of the ADHD-200 dataset using stratified 10-fold cross-validation with a 5-seed ensemble achieved 85.18% +_5.64 balanced accuracy and 0.881 AUC, outperforming state-of-the-art methods. Attention analysis reveals cerebellar and default mode network disruptions, indicating potential neuroimaging biomarkers.

Abstract

Explore similar work

D-GATNet: Interpretable Temporal Graph Attention Learning for ADHD Identification Using Dynamic Functional Connectivity

BrainLinear: A Linear Model for Brain Network Analysis in Sparse Tangent Subspaces

BrainNet Studio: A Unified Toolkit for Brain Network Construction, Intelligent Analysis, and Visualization