mlsanityData to 14 Oct 2026

Continual Learning for fMRI-Based Brain Disorder Diagnosis via Functional Connectivity Matrices Generative Replay

arXiv 2604.14259 ↗First posted 15 Apr 2026q-bio.TO cs.LG eess.IVcited by 0

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Abstract (arXiv)

Functional magnetic resonance imaging (fMRI) is widely used for studying and diagnosing brain disorders, with functional connectivity (FC) matrices providing powerful representations of large-scale neural interactions. However, existing diagnostic models are trained either on a single site or under full multi-site access, making them unsuitable for real-world scenarios where clinical data arrive sequentially from different institutions. This results in limited generalization and severe catastrophic forgetting. This paper presents the first continual learning framework specifically designed for fMRI-based diagnosis across heterogeneous clinical sites. Our framework introduces a structure-aware variational autoencoder that synthesizes realistic FC matrices for both patient and control groups. Built on this generative backbone, we develop a multi-level knowledge distillation strategy that aligns predictions and graph representations between new-site data and replayed samples. To further enhance efficiency, we incorporate a hierarchical contextual bandit scheme for adaptive replay sampling. Experiments on multi-site datasets for major depressive disorder (MDD), schizophrenia (SZ), and autism spectrum disorder (ASD) show that the proposed generative model enhances data augmentation quality, and the overall continual learning framework substantially outperforms existing methods in mitigating catastrophic forgetting. Our code is available at https://github.com/4me808/FORGE.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 56 references that are in the map. First posted on arXiv 15 Apr 2026.

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32 of 56 references.

References · 32

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20 more
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  2. 2017Lifelong Learning with Dynamically Expandable Networkscited by 694
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  7. 2024SDDGR: Stable Diffusion-based Deep Generative Replay for Class Incremental Object Detectioncited by 44
  8. 2021Hierarchical Prototype Networks for Continual Graph Representation Learningcited by 28
  9. 2022BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneckcited by 17
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  12. 2024Continual Learning on Graphs: A Surveycited by 12
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  20. 2025A Generative Adaptive Replay Continual Learning Model for Temporal Knowledge Graph Reasoningcited by 3

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