Interpretable Cross-Network Attention for Resting-State fMRI Representation Learning

• URL: http://arxiv.org/abs/2603.00786v1
• Date: Sat, 28 Feb 2026 19:15:52 GMT
• Title: Interpretable Cross-Network Attention for Resting-State fMRI Representation Learning
• Authors: Karanpartap Singh, Adam Turnbull, Mohammad Abbasi, Kilian Pohl, Feng Vankee Lin, Ehsan Adeli,
• Abstract summary: BrainInterNet is a network-aware self-supervised framework based on masked reconstruction with cross-attention.<n>We train BrainInterNet on multi-cohort fMRI data and evaluate on the Alzheimer's Disease Neuroimaging Initiative dataset.<n>Our method reveals systematic alterations in the brain's network interactions under Alzheimer's disease.
• Score: 5.79619045482301
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding how large-scale functional brain networks reorganize during cognitive decline remains a central challenge in neuroimaging. While recent self-supervised models have shown promise for learning representations from resting-state fMRI, their internal mechanisms are difficult to interpret, limiting mechanistic insight. We propose BrainInterNet, a network-aware self-supervised framework based on masked reconstruction with cross-attention that explicitly models inter-network dependencies in rs-fMRI. By selectively masking predefined functional networks and reconstructing them from remaining context, our approach enables direct quantification of network predictability and interpretable analysis of cross-network interactions. We train BrainInterNet on multi-cohort fMRI data (from the ABCD, HCP Development, HCP Young Adults, and HCP Aging datasets) and evaluate on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, in total comprising 5,582 recordings. Our method reveals systematic alterations in the brain's network interactions under AD, including in the default mode, limbic, and attention networks. In parallel, the learned representations support accurate Alzheimer's-spectrum classification and yield a compact summary marker that tracks disease severity longitudinally. Together, these results demonstrate that network-guided masked modeling with cross-attention provides an interpretable and effective framework for characterizing functional reorganization in neurodegeneration.

Related papers

• Multimodal Graph Neural Networks for Prognostic Modeling of Brain Network Reorganization [0.0]
  Understanding the dynamic reorganization of brain networks is critical for predicting cognitive decline, neurological progression, and individual variability in clinical outcomes.<n>This work proposes a multimodal graph neural network neuroimaging framework that integrates structural diffusion tensor imaging, and MRI to model brain network reorganization.
  arXiv  Detail & Related papers  (2025-12-06T05:11:49Z)
• Moving Beyond Diffusion: Hierarchy-to-Hierarchy Autoregression for fMRI-to-Image Reconstruction [65.67001243986981]
  We propose MindHier, a coarse-to-fine fMRI-to-image reconstruction framework built on scale-wise autoregressive modeling.<n>MindHier achieves superior semantic fidelity, 4.67x faster inference, and more deterministic results than the diffusion-based baselines.
  arXiv  Detail & Related papers  (2025-10-25T15:40:07Z)
• Concept-Guided Interpretability via Neural Chunking [64.6429903327095]
  We show that neural networks exhibit patterns in their raw population activity that mirror regularities in the training data.<n>We propose three methods to extract recurring chunks on a neural population level.<n>Our work points to a new direction for interpretability, one that harnesses both cognitive principles and the structure of naturalistic data.
  arXiv  Detail & Related papers  (2025-05-16T13:49:43Z)
• Discovering Chunks in Neural Embeddings for Interpretability [53.80157905839065]
  We propose leveraging the principle of chunking to interpret artificial neural population activities.<n>We first demonstrate this concept in recurrent neural networks (RNNs) trained on artificial sequences with imposed regularities.<n>We identify similar recurring embedding states corresponding to concepts in the input, with perturbations to these states activating or inhibiting the associated concepts.
  arXiv  Detail & Related papers  (2025-02-03T20:30:46Z)
• Generative forecasting of brain activity enhances Alzheimer's classification and interpretation [16.09844316281377]
  Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive method to monitor neural activity. Deep learning has shown promise in capturing these representations. In this study, we focus on time series forecasting of independent component networks derived from rs-fMRI as a form of data augmentation.
  arXiv  Detail & Related papers  (2024-10-30T23:51:31Z)
• 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)
• 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)
• Functional2Structural: Cross-Modality Brain Networks Representation Learning [55.24969686433101]
  Graph mining on brain networks may facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. We propose a novel graph learning framework, known as Deep Signed Brain Networks (DSBN), with a signed graph encoder. We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets.
  arXiv  Detail & Related papers  (2022-05-06T03:45:36Z)
• Cross-Frequency Coupling Increases Memory Capacity in Oscillatory Neural Networks [69.42260428921436]
  Cross-frequency coupling (CFC) is associated with information integration across populations of neurons. We construct a model of CFC which predicts a computational role for observed $theta - gamma$ oscillatory circuits in the hippocampus and cortex. We show that the presence of CFC increases the memory capacity of a population of neurons connected by plastic synapses.
  arXiv  Detail & Related papers  (2022-04-05T17:13:36Z)
• Ensembling complex network 'perspectives' for mild cognitive impairment detection with artificial neural networks [5.194561180498554]
  We propose a novel method for mild cognitive impairment detection based on jointly exploiting the complex network and the neural network paradigm. In particular, the method is based on ensembling different brain structural "perspectives" with artificial neural networks.
  arXiv  Detail & Related papers  (2021-01-26T08:38:11Z)

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.