mlsanityData to 14 Oct 2026

Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion

arXiv 2410.08228 ↗First posted 28 Sep 2024eess.IV cs.CV cs.LGcited by 6

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Abstract (arXiv)

In the realm of neuroscience, identifying distinctive patterns associated with neurological disorders via brain networks is crucial. Resting-state functional magnetic resonance imaging (fMRI) serves as a primary tool for mapping these networks by correlating blood-oxygen-level-dependent (BOLD) signals across different brain regions, defined as regions of interest (ROIs). Constructing these brain networks involves using atlases to parcellate the brain into ROIs based on various hypotheses of brain division. However, there is no standard atlas for brain network classification, leading to limitations in detecting abnormalities in disorders. Some recent methods have proposed utilizing multiple atlases, but they neglect consistency across atlases and lack ROI-level information exchange. To tackle these limitations, we propose an Atlas-Integrated Distillation and Fusion network (AIDFusion) to improve brain network classification using fMRI data. AIDFusion addresses the challenge of utilizing multiple atlases by employing a disentangle Transformer to filter out inconsistent atlas-specific information and distill distinguishable connections across atlases. It also incorporates subject- and population-level consistency constraints to enhance cross-atlas consistency. Additionally, AIDFusion employs an inter-atlas message-passing mechanism to fuse complementary information across brain regions. Experimental results on four datasets of different diseases demonstrate the effectiveness and efficiency of AIDFusion compared to state-of-the-art methods. A case study illustrates AIDFusion extract patterns that are both interpretable and consistent with established neuroscience findings.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 27 of its 89 references that are in the map. First posted on arXiv 28 Sep 2024.

Builds on

27 of 89 references.

References · 27

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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025BrainOOD: Out-of-distribution Generalizable Brain Network Analysiscited by 7
  2. 2025BrainPrompt: Multi-Level Brain Prompt Enhancement for Neurological Condition Identificationcited by 5
  3. 2026Moving Beyond Functional Connectivity: Time-Series Modeling for fMRI-Based Brain Disorder Classificationcited by 2
  4. 2025Improving Brain Disorder Diagnosis with Advanced Brain Function Representation and Kolmogorov-Arnold Networkscited by 1
  5. 2026Toward a Multi-View Brain Network Foundation Model: Cross-View Consistency Learning Across Arbitrary Atlasescited by 0
  6. 2026Learning Cross-Atlas Consistent Brain Disorder Representations via Disentangled Multi-Atlas Functional Connectivity Learningcited by 0