Evidence map›Paper›PMID 41527472›Full record

ArticlePsychological medicine2026

Controllability of morphometric network colocalize with underlying neurobiology in major depression.

Jinpeng Niu, Jie Xia, Yaohui He, Wei Li, Kangjia Chen, Qingjin Liu, Wenxia Li, Jiang Qiu, Huafu Chen, Jiao Li and 1 more

Abstract read
In one paragraph

Article in Psychological medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Jinpeng NiuThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, https://ror.org/04qr3zq92University of Electronic Science and Technology of China, Chengdu611731, P.R. China.ORCID https://orcid.org/0000-0002-6448-6615
Jie XiaThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, https://ror.org/04qr3zq92University of Electronic Science and Technology of China, Chengdu611731, P.R. China.
Yaohui HeThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, https://ror.org/04qr3zq92University of Electronic Science and Technology of China, Chengdu611731, P.R. China.
Wei LiThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, https://ror.org/04qr3zq92University of Electronic Science and Technology of China, Chengdu611731, P.R. China.
Kangjia ChenThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, https://ror.org/04qr3zq92University of Electronic Science and Technology of China, Chengdu611731, P.R. China.
Qingjin LiuThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, https://ror.org/04qr3zq92University of Electronic Science and Technology of China, Chengdu611731, P.R. China.
Wenxia LiThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, https://ror.org/04qr3zq92University of Electronic Science and Technology of China, Chengdu611731, P.R. China.
Jiang QiuKey Laboratory of Cognition and Personality, Faculty of Psychology, https://ror.org/01kj4z117Southwest University, Chongqing400715, P.R. China.
Huafu ChenThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, https://ror.org/04qr3zq92University of Electronic Science and Technology of China, Chengdu611731, P.R. China.
Jiao LiThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, https://ror.org/04qr3zq92University of Electronic Science and Technology of China, Chengdu611731, P.R. China.
Wei LiaoThe Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, https://ror.org/04qr3zq92University of Electronic Science and Technology of China, Chengdu611731, P.R. China.ORCID https://orcid.org/0000-0001-7406-7193

Funding

Fundamental Research Funds for the Central Universities ZYGX2022YGRH008, ZYGX2024XJ054National Natural Science Foundation of China 62473082, 62571105, 82121003, 62036003, 62333003
6 · The paper itself

Abstract

backgroundCognitive and behavioral symptoms of major depressive disorder (MDD) are linked to aberrant changes in the controllability of brain networks. However, previous studies examined network controllability using white matter tractography, neglecting the contributions of gray matter. We aimed to examine differences in the controllability of morphometric networks between patients with MDD and demographic-matched healthy controls and identify the associated neurobiological signatures.

methodsBased on the structural and diffusion MRI data from two independent cohorts, we calculated the controllability of morphometric similarity networks for each participant. A generalized additive model was used to investigate the case-control differences in regional controllability and their cognitive and behavioral associations. We investigated the associations between imaging-derived controllability and neurotransmitters, brain metabolism, and gene transcription profiles using multivariate linear regression and partial least squares regression analyses.

resultsIn both cohorts, depression-related abnormalities of morphometric network controllability were primarily located in the prefrontal, cingulate, and visual cortices, contributing to memory, sensation, and perception processes. These abnormalities in network controllability were spatially aligned with the distributions of serotonergic transmission pathways as well as with altered oxygen and glucose metabolism. In addition, these abnormalities spatially overlapped with differentially expressed genes enriched in annotations related to protein catabolism and mitochondria in neuronal cells and were disproportionately located on chromosome 22.

conclusionsCollectively, neuroimaging evidence revealed aberrant morphometric network controllability underlying MDD-related cognitive and behavioral deficits, and the associated genetic and molecular signatures may help identify the neurobiological mechanisms underlying MDD and provide feasible therapeutic targets.

Indexed as

BrainMajor Depressive DisorderNerve NetAdultCase-Control StudiesDiffusion Magnetic Resonance ImagingFemaleHumansMaleMiddle Agedcontrollabilitymajor depressive disordermorphometric networkneurotransmittertranscriptomic signature

Identifiers

PMID41527472
PMCPMC12885345

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.