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Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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

Shusheng Li1, Yang Bo2, Yuchu Chen3

  • 1International Research Institute for Artificial Intelligence, Harbin Institute of Technology (Shenzhen), Shenzhen, China.

Health Data Science
|August 6, 2025
PubMed
Summary

A new model, Multiscale Contextual Mamba (MSC-Mamba), improves diagnosis for Major Depressive Disorder (MDD) and Autism Spectrum Disorder (ASD) using brain imaging. This data-driven approach enhances accuracy in detecting these complex conditions.

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Area of Science:

  • Neuroscience
  • Computer Science
  • Psychiatry

Background:

  • Major depressive disorder (MDD) and autism spectrum disorder (ASD) are complex neuropsychiatric conditions with overlapping symptoms, complicating accurate diagnosis.
  • Functional neuroimaging data offers a promising avenue for developing data-driven diagnostic tools.
  • Existing methods often fail to capture long-term dependencies and dynamic patterns in neuroimaging data, especially across different sites.

Purpose of the Study:

  • To introduce Multiscale Contextual Mamba (MSC-Mamba), a novel Mamba-based model for analyzing multivariate time-series data from functional neuroimaging.
  • To enhance the capture of long-term dependencies and dynamic patterns in brain functional networks for improved psychiatric disorder detection.
  • To validate the model's performance on large-scale, multisite neuroimaging datasets for MDD and ASD detection.

Main Methods:

  • Developed MSC-Mamba, a Mamba-based model designed for linear scalability and capturing long-term dependencies in time-series data.
  • Utilized MSC-Mamba's ability to generate contextual information across multiple scales, addressing both channel-mixing and channel-independence scenarios.
  • Applied the model to two large-scale multisite fMRI datasets: REST-meta-MDD (n=1,642) and ABIDE (n=1,022).

Main Results:

  • MSC-Mamba achieved state-of-the-art accuracy: 69.91% for MDD detection and 73.08% for ASD detection.
  • Demonstrated robust generalization performance across diverse neuroimaging sites.
  • Showcased sensitivity to intricate brain network dynamics, highlighting the model's effectiveness in capturing subtle patterns.

Conclusions:

  • State-space models, like MSC-Mamba, hold significant potential for advancing psychiatric neuroimaging research.
  • The proposed model substantially enhances detection accuracy for MDD and ASD.
  • Findings suggest MSC-Mamba can contribute to the development of more reliable, data-driven diagnostic tools for psychiatric disorders.