PI-FC: Pre-training Individual-specific Functional Connectome through State-invariant Contrastive Learning

作者：Yingjie Peng, Xiaohan Tian, Yini He, Shangzheng Huang, Tongyu Zhang, Junxing Xian, Tian Gao, Qi Wang, Changsheng Dong, Xiya Liu, Kaixin Li, Yao Ge, Xianchang Zhang, Lei G. Wang, Yiheng Tu, Bing Liu, Meiyun Wang, Yan Yan, Ang Li · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2025 · DOI：10.1101/2025.09.21.677570 · 被引用次数：1 · 研究领域：Functional Brain Connectivity Studies、Advanced MRI Techniques and Applications、Domain Adaptation and Few-Shot Learning

Functional MRI enables non-invasive mapping of brain connectivity, yet its clinical translation remains hindered by uncontrolled state-dependent variability that obscures individual-specific signatures during routine scanning. Here we introduce PI-FC - a deep learning framework leveraging state-invariant contrastive learning to extract stable individual brain signatures across diverse arousal levels, cognitive states, and temporal scales spanning tens of seconds to hours. PI-FC achieves equivalent phenotypic prediction accuracy using substantially reduced scanning time, and eliminates state-dependent effects varying task demands and brain states. Trained on 36,119 subjects across 8 independent datasets, our model demonstrates superior cross-site generalization and outperforms traditional functional connectome (FC) in predicting neuropsychiatric conditions including schizophrenia, autism, depression, and anxiety. Furthermore, PI-FC enables zero-shot inference of brain age, biological sex, and cognitive ability without site-specific retraining. Overall, PI-FC represents a robust, clinically scalable framework that overcomes fundamental barriers to real-world deployment of precision functional neuroimaging.