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Elucidating Development Trajectories of Brain Functional Abnormalities in Major Depressive Disorder Utilizing a
Yuhong Zheng1, Peng Wang1, Chi Yao1
1Center for Cognition and Brain Disorders/Department of Neurology, The Affiliated Hospital, Hangzhou Normal University, Hangzhou, Zhejiang, China.
Human Brain Mapping
|June 4, 2025
Summary
Major depressive disorder (MDD) patients exhibit distinct progression patterns. A data-driven model identified two subtypes based on brain activity, aiding personalized treatment and prognosis for major depressive disorder.
Area of Science:
- Neuroscience
- Psychiatry
- Medical Imaging
Background:
- Major depressive disorder (MDD) is heterogeneous, with overlooked individual disease progression trajectories.
- This heterogeneity poses challenges for personalized assessment and accurate prognosis in clinical practice.
- Understanding distinct MDD progression pathways is critical for advancing treatment strategies.
Purpose of the Study:
- To identify distinct patient subtypes and disease progression trajectories in MDD using a data-driven approach.
- To investigate the neuroimaging correlates of identified MDD subtypes.
- To enhance personalized medicine and predictive accuracy for MDD.
Main Methods:
- Utilized a data-driven subtype and stage inference (SuStaIn) model.
- Analyzed cross-sectional resting-state functional magnetic resonance imaging (rs-fMRI) data.
- Examined amplitude of low-frequency fluctuations (ALFF) in 833 MDD patients and 834 healthy controls.
Main Results:
- Identified two distinct MDD subtypes based on ALFF trajectories.
- Subtype 1: Declining ALFF from paracentral lobule (PCL) to thalamus to medial orbitofrontal cortex (OFCmed), associated with higher depression scores and gray matter atrophy.
- Subtype 2: Opposing ALFF trajectory, starting with OFCmed decrease and extending to PCL.
Conclusions:
- MDD exhibits significant heterogeneity, with identifiable subtypes based on brain activity patterns.
- The SuStaIn model successfully differentiated MDD subtypes, offering insights into disease progression.
- Findings support the development of precise diagnostic and prognostic tools for major depressive disorder.
