Mapping subtype-specific disease epicenters and brain aging characteristics in major depressive disorder through normative model-driven analysis of brain structural alterations

Major depressive disorder (MDD), a prevalent mental health condition, manifests intricate alterations in brain structure that evolve gradually over time and across various brain regions. Despite significant research efforts, two fundamental questions remain unsettled: the precise brain origins of MDD and whether MDD contributes to accelerates brain aging. To this end, we conducted a comprehensive investigation leveraging data from 830 MDD patients and 853 matched healthy controls (HC). Normative models, established on HC gray matter volume (GMV) data, were utilized to quantify individual deviations in GMV among MDD patients. Employing k-means clustering to these deviation profiles, we successfully discerned two clinically distinct subtypes. Subtype 1 is characterized by GMV atrophy, coupled with indications of accelerated brain aging processes. In contrast, subtype 2 exhibits increased GMV without significant acceleration of aging phenomena. Intriguingly, both subtypes converge on the default mode network as a common disease epicenter, highlighting a shared neurophysiological underpinning. However, subtype-specific epicenters diverge, with subtype 1 featuring unique foci primarily in the hippocampus and amygdala, whereas subtype 2 distinguishes itself with epicenters primarily located in the accumbens. This nuanced examination of subtype-specific brain alterations, incorporating their intricate spatiotemporal dynamics, provides profound insights into the heterogeneity and complexity inherent in MDD.

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Publication Details

Journal
NeuroImage
Published
2025-08-05
DOI
https://doi.org/10.1016/j.neuroimage.2025.121409
Citations
2
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
2.86
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article

Mapping subtype-specific disease epicenters and brain aging characteristics in major depressive disorder through normative model-driven analysis of brain structural alterations

Yuhong Zheng, Xuelian Zang, Li Sun, Yang Xiao et al.
2 citations
NeuroImage
Functional Brain Connectivity Studies
2.86
article

Mapping subtype-specific disease epicenters and brain aging characteristics in major depressive disorder through normative model-driven analysis of brain structural alterations

Yuhong Zheng, Xuelian Zang, Li Sun, Yang Xiao, Shao‐Wei Xue, Jinhui Wang, Peng Wang, Jinghua Wang
article en
2 citations

Abstract

Major depressive disorder (MDD), a prevalent mental health condition, manifests intricate alterations in brain structure that evolve gradually over time and across various brain regions. Despite significant research efforts, two fundamental questions remain unsettled: the precise brain origins of MDD and whether MDD contributes to accelerates brain aging. To this end, we conducted a comprehensive investigation leveraging data from 830 MDD patients and 853 matched healthy controls (HC). Normative models, established on HC gray matter volume (GMV) data, were utilized to quantify individual deviations in GMV among MDD patients. Employing k-means clustering to these deviation profiles, we successfully discerned two clinically distinct subtypes. Subtype 1 is characterized by GMV atrophy, coupled with indications of accelerated brain aging processes. In contrast, subtype 2 exhibits increased GMV without significant acceleration of aging phenomena. Intriguingly, both subtypes converge on the default mode network as a common disease epicenter, highlighting a shared neurophysiological underpinning. However, subtype-specific epicenters diverge, with subtype 1 featuring unique foci primarily in the hippocampus and amygdala, whereas subtype 2 distinguishes itself with epicenters primarily located in the accumbens. This nuanced examination of subtype-specific brain alterations, incorporating their intricate spatiotemporal dynamics, provides profound insights into the heterogeneity and complexity inherent in MDD.

NeuroImageVol. 318
South China Normal University (CN), Peking University (CN), Affiliated Hospital of Hangzhou Normal University (CN), Peking University Sixth Hospital (CN)
Good health and well-being
Openalex Percentile: Top 9%
Functional Brain Connectivity Studies
2.86
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