Multiscale Contextual Mamba: Advancing Psychiatric Disorder Detection across Multisite Functional Magnetic Resonance...

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 approach. MSC-Mamba has achieved state-of-the-art performance, with an accuracy of 69.91% for MDD detection and 73.08% for ASD detection. The results demonstrate the model's robust generalization across imaging sites and its sensitivity to intricate brain network dynamics. Conclusions: This paper demonstrates the potential of state-space models in advancing psychiatric neuroimaging research. The findings suggest that such models can significantly enhance detection accuracy for MDD and ASD, pointing toward more reliable, data-driven diagnostic tools in psychiatric disorder detection.