Evidence map›Paper›PMID 40755356›Full record

ArticleCNS neuroscience & therapeutics2025

Disruptions in Cognitive-Affective Circuitry in Major Depression Disorder: Insights From REST-Meta-MDD and Its Implication for Predicting TMS Treatment Efficacy.

Na Zhao, Liang Li, Matthew Lock, Yi-Fan Ai, Jian Liu, Chun-Ying Zhu, Yu-Feng Zang, Hua-Ning Wang, Bao-Juan Li

Abstract read
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

9 authors.

Na ZhaoCenter for Cognition and Brain Disorders/Department of Neurology, The Affiliated Hospital of Hangzhou Normal University, Zhejiang, Hangzhou, China.ORCID 0000-0002-0994-8733
Liang LiThe Brain Science Center, Beijing Institute of Basic Medical Sciences, Beijing, China.
Matthew LockCenter for Psychological Sciences, Zhejiang University, Hangzhou, China.
Yi-Fan AiCenter for Cognition and Brain Disorders/Department of Neurology, The Affiliated Hospital of Hangzhou Normal University, Zhejiang, Hangzhou, China.
Jian LiuTeaching and Research Support Center, Air Force Medical University, Xi'an, Shaanxi, China.
Chun-Ying ZhuThe Department of Psychology, The Affiliated Hospital of Hangzhou Normal University, Zhejiang, China.
Yu-Feng ZangCenter for Cognition and Brain Disorders/Department of Neurology, The Affiliated Hospital of Hangzhou Normal University, Zhejiang, Hangzhou, China.
Hua-Ning WangDepartment of Psychiatry, Xijing Hospital, Air Force Medical University, Xi'an, China.ORCID 0000-0003-1981-4293
Bao-Juan LiSchool of Biomedical Engineering, Air Force Medical University, Xi'an, Shaanxi, China.

Funding

Beijing Natural Science Foundation 4234087Joint Funding Project of Innovation Research Institute, Xijing Hospital LHJJ24YG0Key Research and Development Projects of Shaanxi Province 2023-YBGY-011National Natural Science Foundation of China 61976248National Natural Science Foundation of China 82301742National Natural Science Foundation of China 82330043the Construction Fund of Key Medical Disciplines of Hang Zhou 2025HZGF02
6 · The paper itself

Abstract

aimsMajor depressive disorder (MDD) is a common psychiatric disorder whose causes and manifestations are diverse and numerous. To facilitate targeted therapeutic interventions, we characterized the abnormalities in effective connectivity within the cognitive-affective (CCN-AN) circuits to identify predictive biomarkers of TMS efficacy based on a large multicenter dataset and an independent dataset from patients receiving TMS.

methodsBoth functional and effective connectivity (FC, EC) were analyzed. As there was only one significant connection observed in FC, classification based on the differences in EC was performed using REST-meta-MDD. Furthermore, correlations between these abnormal connectivity and depression severity, as well as depression and suicidality alleviation, were calculated to determine their predictive implications for TMS efficacy using an independent dataset.

resultsOverall increased connectivity from the AN to the CCN and decreased connectivity from the CCN to the AN in MDD were observed using EC. These disruptions drove the classification accuracy up to 79.1%. Furthermore, the connection from the right inferior parietal lobule (IPL. R) to the right amygdala (AMYG.R) was negatively correlated with depression scores. Notably, the IPL connectivity to the anterior cingulate cortex (ACC) and the AMYG.R were closely correlated with depression and suicidal ideation alleviation following TMS treatment.

conclusionsThese findings suggest that MDD is characterized by disruptions in both top-down and bottom-up emotion regulation systems. Notably, the key abnormal connectivities, particularly those from the IPL to ACC and AMYG, could predict the efficacy of TMS treatment. This insight refines MDD diagnosis and paves the way for more precise targeted interventions in the future.

Indexed as

BrainMajor Depressive DisorderTranscranial Magnetic StimulationAdultFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedNeural PathwaysRestTreatment Outcomeamygdalaanterior cingulate cortexcognitive‐affective circuitryeffective connectivitymajor depressive disorderpredicative powerspectral dynamic causal model

Identifiers

PMID40755356
PMCPMC12319422

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 OpenQuestion graph.