VarCoNet: A variability-aware self-supervised framework for functional connectome extraction from resting-state fMRI

2025-10-02

AI Summary

This study addresses the problem that inter-individual variability in resting-state fMRI functional connectomes (FC) is conventionally mischaracterized as noise during FC estimation. To rectify this, we propose a self-supervised learning framework that explicitly models functional variability as biologically meaningful signal. Methodologically, we introduce a time-series segmentation-based data augmentation strategy for rs-fMRI, design a hybrid 1D-CNN–Transformer architecture, and integrate Bayesian hyperparameter optimization to enable robust and interpretable FC representation learning. Crucially, unlike conventional denoising paradigms, our label-free framework performs end-to-end learning of subject-specific connectivity patterns. Evaluated on the HCP, ABIDE I, and ABIDE II datasets, it achieves state-of-the-art performance in both subject fingerprinting and ASD classification—outperforming 13 leading baselines—while demonstrating strong generalizability, robustness to acquisition variability, and neurobiological interpretability.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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.

Problem

• Extracting functional connectomes from resting-state fMRI data
• Handling inter-individual variability in brain function analysis
• Enabling robust neuroimaging analysis without labeled data

Innovation

• Self-supervised contrastive learning exploits inter-individual variability
• Novel augmentation strategy segments rs-fMRI signals
• 1D-CNN-Transformer encoder with Bayesian hyperparameter optimization