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A two-center radiomic analysis for differentiating major depressive disorder using multi-modality MRI data under

Kai Sun1, Zhenyu Liu2, Guanmao Chen3

  • 1Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China.

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Summary

This study used multi-modal MRI data to identify brain differences between major depressive disorder (MDD) patients and healthy controls. Combining functional connectivity, ALFF, and gray matter volume showed high accuracy in distinguishing MDD patients.

Keywords:
ClassificationMajor depressive disorderRadiomicsVBMrs-fMRI

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Medical Imaging

Background:

  • Major Depressive Disorder (MDD) is a prevalent mental health condition with complex pathophysiology.
  • Understanding the neurobiological underpinnings of MDD is crucial for developing effective treatments.

Purpose of the Study:

  • To investigate differences in brain function and structure between MDD patients and healthy controls (HCs).
  • To explore the utility of multi-modal magnetic resonance imaging (MRI) data for discriminating MDD patients.
  • To identify potential core brain regions involved in the pathophysiology of MDD.

Main Methods:

  • Utilized a two-center, multi-modal MRI dataset including functional connectivity (FC), amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and gray matter volume (GMV).
  • Developed and compared classifiers using combinations of these MRI features to distinguish MDD patients from HCs.
  • Evaluated classifier performance using different anatomical templates, including the AAL template.

Main Results:

  • A classifier combining FC, ALFF, and GMV achieved the highest diagnostic performance (AUC=0.916, ACC=84.8%) under the AAL template.
  • Key brain regions identified were primarily located within the default mode network, affective network, and prefrontal cortex.
  • Multi-modal MRI data significantly improved classification accuracy compared to single modalities.

Conclusions:

  • Multi-modal MRI analysis enhances the ability to differentiate MDD patients from healthy individuals.
  • Specific brain networks and regions, including the default mode and affective networks, are critical targets for understanding MDD.
  • The findings provide valuable insights into the neurobiological basis of MDD and potential biomarkers.