VarCoNet: A Variability-Aware Self-Supervised Framework for Functional Connectome Extraction From Resting-State fMRI.

Accounting for interindividual variability in brain function is key to precision medicine. Here, by considering functional interindividual 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 interindividual 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-convolutional neural network (CNN) with a Transformer encoder for advanced time-series processing, enhanced with 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 (2117 recordings), and (ii) autism spectrum disorder (ASD) classification, using rs-fMRI data from the Autism Brain Imaging Data Exchange (ABIDE) I (995 recordings) and II (730 recordings) 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, achieving up to 98% subject fingerprinting accuracy and an area under the curve (AUC) of 72.6% for ASD classification. Overall, VarCoNet provides a versatile and robust framework for FC analysis in rs-fMRI.