Tae-Hun Kang  Sung-Bae Cho

10.5626/JOK.2025.52.11.954

functional MRI brain disorder diagnosis multi-task pretraining domain alignment

Functional magnetic resonance imaging (fMRI) is a crucial tool for diagnosing brain disorders and understanding their pathophysiology. However, reliably identifying abnormal brain patterns is challenging due to the high-dimensional inter-individual variability of fMRI data. In particular, variability in acquisition protocols introduces batch effects that lead to negative transfer, which degrades model performance. To address this issue, we propose a cross-domain alignment method for multi-task learning that manages heterogeneity between data sources and aligns essential normal and abnormal brain patterns. By aligning the common and discriminative features observed across multiple domains, our method effectively learns both normal and abnormal patterns. This approach facilitates large-scale pretraining on diverse datasets that include various brain disorders and healthy controls. Experiments conducted on clinical data from 2,424 subjects across four real-world disease datasets demonstrate that our proposed method achieves greater generalization than conventional single-disease training while reducing negative transfer, thereby validating its superior performance in diagnosing brain diseases.