Multiscale Contextual Mamba: Advancing Psychiatric Disorder Detection across Multisite Functional Magnetic Resonance Imaging Datasets via State Space Modeling

作者：Shusheng Li, Bo Yang, Yuchu Chen, Jianfeng Cao, Bo Bi, Ting Ma, Chenfei Ye · 发表于：Health Data Science · 年份：2025 · DOI：10.34133/hds.0224 · 被引用次数：4 · 研究领域：Functional Brain Connectivity Studies、EEG and Brain-Computer Interfaces、Neural dynamics and brain function

Background: Major depressive disorder (MDD) and autism spectrum disorder (ASD) are complex and heterogeneous neuropsychiatric disorders with overlapping symptoms, presenting remarkable challenges for accurate diagnosis. Leveraging functional neuroimaging data offers an opportunity to develop more robust, data-driven approach for psychiatric disorder detection. However, existing methods often struggle to capture the long-term dependencies and dynamic patterns inherent in such data, particularly across diverse imaging sites. Methods: We propose Multiscale Contextual Mamba (MSC-Mamba), a Mamba-based model designed for capturing long-term dependencies in multivariate time-series data while maintaining linear scalability, allowing us to account for long-range interactions and subtle dynamic patterns within the brain’s functional networks. One of the main advantages of MSC-Mamba is its ability to leverage the distinct characteristics of time-series data, allowing it to generate meaningful contextual information across various scales. This method effectively addresses both channel-mixing and channel-independence scenarios, facilitating the selection of relevant features for prediction by considering both global and local contexts at multiple scales. Results: Two large-scale multisite functional magnetic resonance imaging datasets, including REST-meta-MDD ( n = 1,642) and Autism Brain Imaging Data Exchange (ABIDE) ( n = 1,022), were used to validate the performance of our proposed ap...