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.

翻译：将大脑功能的个体间变异性纳入考量是精准医学的关键。本文通过将功能个体间变异性视为有意义的数据而非噪声，提出了VarCoNet——一种增强型自监督框架，用于从静息态功能磁共振成像（rs-fMRI）数据中稳健地提取功能连接组（FC）。VarCoNet采用自监督对比学习来利用固有的功能个体间变异性，作为一个大脑功能编码器，即使在缺乏标注数据的情况下，也能生成可直接应用于下游任务的FC嵌入表示。对比学习通过一种基于rs-fMRI信号分割的新型数据增强策略得以实现。其核心在于，VarCoNet集成了一个1D-CNN-Transformer编码器用于高级时间序列处理，并辅以稳健的贝叶斯超参数优化进行增强。我们在两个下游任务上评估了VarCoNet框架：（i）受试者指纹识别，使用来自人类连接组计划的rs-fMRI数据；（ii）自闭症谱系障碍（ASD）分类，使用来自ABIDE I和ABIDE II数据集的rs-fMRI数据。通过使用不同的大脑分区图谱，我们与包括13种深度学习方法在内的先进方法进行了广泛的对比测试，结果证明了VarCoNet的优越性、稳健性、可解释性和泛化能力。总体而言，VarCoNet为rs-fMRI中的FC分析提供了一个通用且稳健的框架。