Keywords: Diagnosis/Prediction, fMRI (resting state), functional connectivity, phenotypic prediction, meta-learning, transfer learning
Motivation: Resting-state functional connectivity (RSFC) is widely used to predict phenotypes in individuals. However, predictive models may fail to generalize to new datasets due to differences in population, data collection, and processing across datasets.
Goal(s): To resolve the dataset difference issue, we aimed to generalize knowledge from multiple diverse source datasets and translate the model to new target data.
Approach: Here we proposed Multi-domain and Uni-domain Fusion (MUF) method that combines cross-domain learning and intra-domain learning, to capture both domain-general information and domain-specific information.
Results: The results show that our MUF outperformed 4 strong baseline methods on 6 target datasets.
Impact: Our MUF method is adept at addressing the challenges introduced by different population profiles, fMRI processing pipelines, and prediction tasks. We offer a robust and universal learning strategy for domain-generalization in fMRI-based phenotypic prediction.
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