A Dual-Attention Graph Network for fMRI Data Classification

• URL: http://arxiv.org/abs/2508.13328v1
• Date: Mon, 18 Aug 2025 19:23:18 GMT
• Title: A Dual-Attention Graph Network for fMRI Data Classification
• Authors: Amirali Arbab, Zeinab Davarani, Mehran Safayani,
• Abstract summary: We present a framework that leverages dynamic graph creation and attention mechanisms for Autism Spectrum Disorder(ASD) diagnosis.<n>The approach in this research dynamically infers functional brain connectivity in each time interval using transformer-based attention mechanisms.<n>Our model achieves 63.2 accuracy and 60.0 AUC, outperforming graph static-based approaches.
• Score: 1.3176926720381557
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding the complex neural activity dynamics is crucial for the development of the field of neuroscience. Although current functional MRI classification approaches tend to be based on static functional connectivity or cannot capture spatio-temporal relationships comprehensively, we present a new framework that leverages dynamic graph creation and spatiotemporal attention mechanisms for Autism Spectrum Disorder(ASD) diagnosis. The approach used in this research dynamically infers functional brain connectivity in each time interval using transformer-based attention mechanisms, enabling the model to selectively focus on crucial brain regions and time segments. By constructing time-varying graphs that are then processed with Graph Convolutional Networks (GCNs) and transformers, our method successfully captures both localized interactions and global temporal dependencies. Evaluated on the subset of ABIDE dataset, our model achieves 63.2 accuracy and 60.0 AUC, outperforming static graph-based approaches (e.g., GCN:51.8). This validates the efficacy of joint modeling of dynamic connectivity and spatio-temporal context for fMRI classification. The core novelty arises from (1) attention-driven dynamic graph creation that learns temporal brain region interactions and (2) hierarchical spatio-temporal feature fusion through GCNtransformer fusion.

Related papers

• Spatiotemporal graph neural process for reconstruction, extrapolation, and classification of cardiac trajectories [1.7123388415209657]
  This work introduces a flexible approach for analyzing cardiac motion.<n>It offers a foundation for graph-based learning in structured biomedical time-series data.
  arXiv  Detail & Related papers  (2025-09-16T10:57:51Z)
• BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation [0.33748750222488655]
  We propose BrainATCL, an unsupervised, nonparametric framework for adaptive temporal brain connectivity learning.<n>Our method dynamically adjusts the lookback window for each snapshot based on the rate of newly added edges.<n>Graph sequences are encoded using a GINE-Mamba2 backbone to learn spatial-temporal representations of dynamic functional connectivity in resting-state fMRI data.
  arXiv  Detail & Related papers  (2025-08-09T21:18:25Z)
• Langevin Flows for Modeling Neural Latent Dynamics [81.81271685018284]
  We introduce LangevinFlow, a sequential Variational Auto-Encoder where the time evolution of latent variables is governed by the underdamped Langevin equation.<n>Our approach incorporates physical priors -- such as inertia, damping, a learned potential function, and forces -- to represent both autonomous and non-autonomous processes in neural systems.<n>Our method outperforms state-of-the-art baselines on synthetic neural populations generated by a Lorenz attractor.
  arXiv  Detail & Related papers  (2025-07-15T17:57:48Z)
• 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)
• DSAM: A Deep Learning Framework for Analyzing Temporal and Spatial Dynamics in Brain Networks [4.041732967881764]
  Most rs-fMRI studies compute a single static functional connectivity matrix across brain regions of interest. These approaches are at risk of oversimplifying brain dynamics and lack proper consideration of the goal at hand. We propose a novel interpretable deep learning framework that learns goal-specific functional connectivity matrix directly from time series.
  arXiv  Detail & Related papers  (2024-05-19T23:35:06Z)
• Tri-modal Confluence with Temporal Dynamics for Scene Graph Generation in Operating Rooms [47.31847567531981]
  We propose a Tri-modal (i.e., images, point clouds, and language) confluence with Temporal dynamics framework, termed TriTemp-OR. Our model performs temporal interactions across 2D frames and 3D point clouds, including a scale-adaptive multi-view temporal interaction (ViewTemp) and a geometric-temporal point aggregation (PointTemp) The proposed TriTemp-OR enables the aggregation of tri-modal features through relation-aware unification to predict relations so as to generate scene graphs.
  arXiv  Detail & Related papers  (2024-04-14T12:19:16Z)
• 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)
• DBGDGM: Dynamic Brain Graph Deep Generative Model [63.23390833353625]
  Graphs are a natural representation of brain activity derived from functional magnetic imaging (fMRI) data. It is well known that clusters of anatomical brain regions, known as functional connectivity networks (FCNs), encode temporal relationships which can serve as useful biomarkers for understanding brain function and dysfunction. Previous works, however, ignore the temporal dynamics of the brain and focus on static graphs. We propose a dynamic brain graph deep generative model (DBGDGM) which simultaneously clusters brain regions into temporally evolving communities and learns dynamic unsupervised node embeddings.
  arXiv  Detail & Related papers  (2023-01-26T20:45:30Z)
• DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learning [58.94034282469377]
  We propose DynDepNet, a novel method for learning the optimal time-varying dependency structure of fMRI data induced by downstream prediction tasks. Experiments on real-world fMRI datasets, for the task of sex classification, demonstrate that DynDepNet achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2022-09-27T16:32:11Z)
• Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI Modelling [0.0]
  We propose a dynamic adaptivetemporal graph convolution (DASTGCN) model to overcome the shortcomings of pre-defined static correlation-based graph structures. The proposed approach allows end-to-end inference of dynamic connections between brain regions via layer-wise graph structure learning module. We evaluate our pipeline on the UKBiobank for age and gender classification tasks from resting-state functional scans.
  arXiv  Detail & Related papers  (2021-09-26T07:19:47Z)
• Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention [33.049423523704824]
  We propose STAGIN, a method for learning dynamic graph representation of the brain connectome with temporal attention. Experiments on the HCP-Rest and the HCP-Task datasets demonstrate exceptional performance of our proposed method.
  arXiv  Detail & Related papers  (2021-05-27T23:06:50Z)
• Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis [11.85489505372321]
  We train a-temporal graph convolutional network (ST-GCN) on short sub-sequences of the BOLD time series to model the non-stationary nature of functional connectivity. St-GCN is significantly more accurate than common approaches in predicting gender and age based on BOLD signals.
  arXiv  Detail & Related papers  (2020-03-24T01:56:50Z)

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.