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

• URL: http://arxiv.org/abs/2603.09825v1
• Date: Tue, 10 Mar 2026 15:47:09 GMT
• Title: BrainSTR: Spatio-Temporal Contrastive Learning for Interpretable Dynamic Brain Network Modeling
• Abstract summary: BrainSTR learns a distributed Brain-temporal contrastive learning framework for interpretable dynamic brain network modeling.<n>Tests on ASD, BD, and MDD provide interpretable evidence consistent with prior findings.
• Score: 11.472484296820225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Brain-Atlas-Guided Generative Counterfactual Attention for Explainable Cognitive Decline Diagnosis Using Multimodal Connectomes [29.55834943593375]
  This paper proposes an atlas-knowledge-guided Generative Counterfactual Attention-guided Network (GCAN) for explainable cognitive decline diagnosis.<n>GCAN formulates diagnosis as a source-to-target counterfactual generation problem.<n>An Atlas-aware Bidirectional Transformer (AABT) performs network-level token encoding and decoding under brain-atlas constraints.
  arXiv  Detail & Related papers  (2026-05-31T13:39:11Z)
• Distance-Aware Joint Spatio-Temporal Graph Contrastive Learning for Major Depressive Disorder Diagnosis [14.96995835279229]
  Major depressive disorder (MDD) is a common condition whose accurate diagnosis from resting-state functional magnetic resonance imaging (rs-fMRI) remains difficult.<n> Dynamic functional connectivity (DFC) captures time-varying interactions among brain regions and provides rich-temporal correlation, yet DFC-based methods face three limitations.
  arXiv  Detail & Related papers  (2026-05-22T06:12:31Z)
• DiGAN: Diffusion-Guided Attention Network for Early Alzheimer's Disease Detection [1.6471330810152984]
  Early diagnosis of Alzheimer's disease (AD) remains a major challenge due to the subtle and temporally irregular progression of structural brain changes.<n>Existing deep learning approaches require large longitudinal datasets and often fail to model the temporal continuity and modality irregularities inherent in real-world clinical data.<n>We propose the Diffusion-Guided Attention Network (DiGAN), which integrates latent diffusion modelling with an attention-guided convolutional network.
  arXiv  Detail & Related papers  (2026-02-02T21:59:23Z)
• Interpretable Neuropsychiatric Diagnosis via Concept-Guided Graph Neural Networks [56.75602443936853]
  One in five adolescents currently live with a diagnosed mental or behavioral health condition, such as anxiety, depression, or conduct disorder.<n>While prior works use graph neural network (GNN) approaches for disorder prediction, they remain black-boxes, limiting their reliability and clinical translation.<n>In this work, we propose a concept-based diagnosis framework that that encodes interpretable functional connectivity concepts.<n>Our design ensures predictions through clinically meaningful connectivity patterns, enabling both interpretability and strong predictive performance.
  arXiv  Detail & Related papers  (2025-10-02T19:38:46Z)
• CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding [57.90382885533593]
  We propose a Cross-scale Spatiotemporal Brain foundation model for generalized decoding EEG signals.<n>We show that CSBrain consistently outperforms task-specific and foundation model baselines.<n>These results establish cross-scale modeling as a key inductive bias and position CSBrain as a robust backbone for future brain-AI research.
  arXiv  Detail & Related papers  (2025-06-29T03:29:34Z)
• Detecting Neurocognitive Disorders through Analyses of Topic Evolution and Cross-modal Consistency in Visual-Stimulated Narratives [83.15653194899126]
  Early detection of neurocognitive disorders (NCDs) is crucial for timely intervention and disease management.<n>Current VSN-based NCD detection methods primarily focus on linguistic microstructures closely tied to bottom-up, stimulus-driven cognitive processes.<n>We propose two novel macrostructural approaches: a Dynamic Topic Model (DTM) to track topic evolution over time, and a Text-Image Temporal Alignment Network (TITAN) to measure cross-modal consistency between narrative and visual stimuli.
  arXiv  Detail & Related papers  (2025-01-07T12:16:26Z)
• Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
  In this study, we first construct the brain-effective network via the dynamic causal model. We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE) This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
  arXiv  Detail & Related papers  (2024-05-21T20:37:07Z)
• Spatial-Temporal DAG Convolutional Networks for End-to-End Joint Effective Connectivity Learning and Resting-State fMRI Classification [42.82118108887965]
  Building comprehensive brain connectomes has proved to be fundamental importance in resting-state fMRI (rs-fMRI) analysis. We model the brain network as a directed acyclic graph (DAG) to discover direct causal connections between brain regions. We propose Spatial-Temporal DAG Convolutional Network (ST-DAGCN) to jointly infer effective connectivity and classify rs-fMRI time series.
  arXiv  Detail & Related papers  (2023-12-16T04:31:51Z)
• TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling [54.97005925277638]
  The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. It remains a challenging task for state-of-the-art spiking neural networks (SNNs) to establish long-term temporal dependency between distant cues. We propose a novel biologically inspired Two-Compartment Leaky Integrate-and-Fire spiking neuron model, dubbed TC-LIF.
  arXiv  Detail & Related papers  (2023-08-25T08:54:41Z)
• A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification [5.563162319586206]
  Recent applications of pattern recognition techniques on brain connectome classification using functional connectivity (FC) are shifting towards acknowledging aspects of brain connectivity across time. In this paper, a deep non-temporalal variation Bayes framework is proposed to learn to identify autism spectrum disorder (ASD) in human participants. The framework incorporates a spatial-aware recurrent neural network with an attention-based message passing scheme to capture richtemporal patterns across dynamic FC networks.
  arXiv  Detail & Related papers  (2023-02-14T18:42:17Z)
• Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [102.02672176520382]
  Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. We develop deep diffusion processes to model "dynamic comorbidity networks"
  arXiv  Detail & Related papers  (2020-01-08T15:47:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.