Evidence map›Paper›PMID 40888362›Full record

ArticlePsychological medicine2025

Two subtypes of major depressive disorder are identified from individualized gray matter morphological abnormalities in a large multi-site dataset.

Keke Fang, Baohong Wen, Liang Liu, Ya Tian, Huiting Yang, Shaoqiang Han, Xianfu Sun, Lianjie Niu

Abstract readMulticenter Study
In one paragraph

Article in Psychological medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Keke FangDepartment of Pharmacy, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital.
Baohong Wenhttps://ror.org/04ypx8c21Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China.
Liang Liuhttps://ror.org/04ypx8c21Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China.
Ya Tianhttps://ror.org/04ypx8c21Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China.
Huiting Yanghttps://ror.org/04ypx8c21Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China.
Shaoqiang Hanhttps://ror.org/04ypx8c21Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China.ORCID 0000-0001-7331-0315
Xianfu SunDepartment of Breast Disease, Henan Breast Cancer Center, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital.
Lianjie NiuDepartment of Breast Disease, Henan Breast Cancer Center, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNeuroimaging studies provide compelling evidence that major depressive disorder (MDD) is associated with widespread gray matter morphological abnormalities. However, significant interindividual variability complicates the interpretation of group-level findings, highlighting the need for investigating potential MDD subtypes.

methodsIn this study, we aimed to identify subtypes of MDD based on individualized deviations from normative gray matter volumes (GMVs), as estimated using a normative model derived from healthy controls (HCs). We leveraged a large, multi-site dataset of high-resolution structural MRI scans, comprising 1,276 MDD patients and 1,104 matched HCs. To explore the transcriptional and molecular mechanisms underlying the observed structural abnormalities, we examined the relationships between GMV deviations, transcriptomic similarities (as measured by the correlated gene expression [CGE] connectome), and the distribution of neurotransmitter receptors/transporters.

resultsOur results revealed two reproducible MDD subtypes, each exhibiting distinct patterns of GMV abnormalities across study sites. Subtype 1 displayed increased GMVs in cerebral regions and decreased GMVs in cerebellar regions, whereas subtype 2 showed the opposite pattern, with decreased GMVs in cerebral regions and increased GMVs in cerebellar areas. The identified GMV abnormalities were differentially associated with neurotransmitter receptor/transporter distributions. Furthermore, these abnormalities were linked to transcriptionally connected gene networks, suggesting genetic underpinnings for both subtypes. Notably, the two subtypes exhibited distinct CGE-informed disease epicenters.

conclusionsThis study identifies two robust MDD subtypes, providing new insights into the neurobiological and genetic bases of MDD and offering a potential advancement in the nosology of the disorder.

Indexed as

Gray MatterMajor Depressive DisorderAdultConnectomeFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedNeuroimagingYoung Adultgray matter volumeindividualizedMDD subtypenormative modelling

Identifiers

PMID40888362
PMCPMC13054913

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