VarCoNet: A variability-aware self-supervised framework for functional connectome extraction from resting-state fMRI
This work addresses the need for robust and interpretable functional connectome analysis in neuroscience, particularly for precision medicine applications, though it appears incremental as it builds on existing self-supervised and contrastive learning techniques.
The paper tackles the problem of extracting functional connectomes from resting-state fMRI data by accounting for inter-individual variability as meaningful information, introducing VarCoNet, a self-supervised framework that outperforms 13 state-of-the-art deep learning methods in tasks like subject fingerprinting and autism spectrum disorder classification.
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.