{"ID":2861545,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.02120","arxiv_id":"2510.02120","title":"VarCoNet: A variability-aware self-supervised framework for functional connectome extraction from resting-state fMRI","abstract":"Accounting for inter-individual variability in brain function is key to precision medicine. Here, by considering functional inter-individual variability as meaningful data rather than noise, we introduce VarCoNet, an enhanced self-supervised framework for robust functional connectome (FC) extraction from resting-state fMRI (rs-fMRI) data. VarCoNet employs self-supervised contrastive learning to exploit inherent functional inter-individual variability, serving as a brain function encoder that generates FC embeddings readily applicable to downstream tasks even in the absence of labeled data. Contrastive learning is facilitated by a novel augmentation strategy based on segmenting rs-fMRI signals. At its core, VarCoNet integrates a 1D-CNN-Transformer encoder for advanced time-series processing, enhanced with a robust Bayesian hyperparameter optimization. Our VarCoNet framework is evaluated on two downstream tasks: (i) subject fingerprinting, using rs-fMRI data from the Human Connectome Project, and (ii) autism spectrum disorder (ASD) classification, using rs-fMRI data from the ABIDE I and ABIDE II datasets. Using different brain parcellations, our extensive testing against state-of-the-art methods, including 13 deep learning methods, demonstrates VarCoNet's superiority, robustness, interpretability, and generalizability. Overall, VarCoNet provides a versatile and robust framework for FC analysis in rs-fMRI.","short_abstract":"Accounting for inter-individual variability in brain function is key to precision medicine. Here, by considering functional inter-individual variability as meaningful data rather than noise, we introduce VarCoNet, an enhanced self-supervised framework for robust functional connectome (FC) extraction from resting-state...","url_abs":"https://arxiv.org/abs/2510.02120","url_pdf":"https://arxiv.org/pdf/2510.02120v2","authors":"[\"Charalampos Lamprou\",\"Aamna Alshehhi\",\"Leontios J. Hadjileontiadis\",\"Mohamed L. Seghier\"]","published":"2025-10-02T15:29:17Z","proceeding":"cs.NE","tasks":"[\"cs.NE\",\"cs.AI\",\"cs.LG\",\"q-bio.NC\"]","methods":"[\"Transformer\",\"Convolutional Neural Network\"]","has_code":false}