Resting-state functional MRI (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis. Existing studies usually suffer from significant cross-site/domain data heterogeneity caused by site effects such as differences in scanners/protocols. Many methods have been proposed to reduce fMRI heterogeneity between source and target domains, heavily relying on the availability of source data. But acquiring source data is challenging due to privacy concerns and/or data storage burdens in multi-site studies. To this end, we design a source-free collaborative domain adaptation (SCDA) framework for fMRI analysis, where only a pretrained source model and unlabeled target data are accessible. Specifically, a multi-perspective feature enrichment method (MFE) is developed for target fMRI analysis, consisting of multiple collaborative branches to dynamically capture fMRI features of unlabeled target data from multiple views. Each branch has a data-feeding module, a spatiotemporal feature encoder, and a class predictor. A mutual-consistency constraint is designed to encourage pair-wise consistency of latent features of the same input generated from these branches for robust representation learning. To facilitate efficient cross-domain knowledge transfer without source data, we initialize MFE using parameters of a pretrained source model. We also introduce an unsupervised pretraining strategy using 3,806 unlabeled fMRIs from three large-scale auxiliary databases, aiming to obtain a general feature encoder. Experimental results on three public datasets and one private dataset demonstrate the efficacy of our method in cross-scanner and cross-study prediction tasks. The model pretrained on large-scale rs-fMRI data has been released to the public.

翻译：静息态功能性磁共振成像（rs-fMRI）在多站点研究中被越来越多地用于辅助神经疾病分析。现有研究常因扫描仪/协议差异等站点效应导致的跨站点/域数据异质性而面临挑战。为减少源域与目标域之间的fMRI异质性，诸多方法被提出，它们严重依赖源数据的可用性。然而，在多站点研究中，由于隐私问题和/或数据存储负担，获取源数据存在困难。为此，我们设计了一种用于fMRI分析的无源协同域适应（SCDA）框架，该框架仅需一个预训练的源模型及无标签的目标数据即可运行。具体地，我们提出一种多视角特征增强方法（MFE）用于目标fMRI分析，该方法包含多个协同分支，可动态从多视角捕捉无标签目标数据的fMRI特征。每个分支包含数据供给模块、时空特征编码器和类别预测器。我们设计了互一致性约束，以鼓励这些分支针对同一输入生成的潜在特征之间保持两两一致性，从而实现稳健的表征学习。为在无源数据情况下促进高效的跨域知识迁移，我们采用预训练源模型的参数初始化MFE。此外，我们引入了一种无监督预训练策略，利用来自三个大型辅助数据库的3,806个无标签fMRI数据，旨在获取通用特征编码器。在三个公开数据集和一个私有数据集上的实验结果证明了该方法在跨扫描仪和跨研究预测任务中的有效性。基于大规模rs-fMRI数据预训练的模型已向公众开放。