Evidence map›Paper›PMID 42292915›Full record

ArticleDepression and anxiety2026

Distinct Trajectories of Amygdala Connectivity Patterns Characterize Remission vs. Non-Remission in Patients With Major Depressive Disorder.

Shudong Zhang, Zhifang Zhang, Xiaoya Li, Jingjing Zhou, Ruinan Li, Rui Liu, Yun Wang, Xiongying Chen, Yuan Feng, Jian Cui and 3 more

Abstract read
In one paragraph

Article in Depression and anxiety, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

13 authors.

Shudong ZhangBeijing Key Laboratory of Intelligent Drug Research and Development for Mental Disorders, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China, ccmu.edu.cn.ORCID https://orcid.org/0009-0001-7484-5570
Zhifang ZhangBeijing Key Laboratory of Intelligent Drug Research and Development for Mental Disorders, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China, ccmu.edu.cn.ORCID https://orcid.org/0000-0002-3806-2093
Xiaoya LiBeijing Key Laboratory of Intelligent Drug Research and Development for Mental Disorders, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China, ccmu.edu.cn.ORCID https://orcid.org/0009-0004-7999-709X
Jingjing ZhouBeijing Key Laboratory of Intelligent Drug Research and Development for Mental Disorders, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China, ccmu.edu.cn.ORCID https://orcid.org/0000-0001-7855-3615
Ruinan LiBeijing Key Laboratory of Intelligent Drug Research and Development for Mental Disorders, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China, ccmu.edu.cn.ORCID https://orcid.org/0000-0001-6937-4324
Rui LiuBeijing Key Laboratory of Intelligent Drug Research and Development for Mental Disorders, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China, ccmu.edu.cn.ORCID https://orcid.org/0000-0001-7613-1144
Yun WangBeijing Key Laboratory of Intelligent Drug Research and Development for Mental Disorders, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China, ccmu.edu.cn.ORCID https://orcid.org/0000-0001-8803-6777
Xiongying ChenBeijing Key Laboratory of Intelligent Drug Research and Development for Mental Disorders, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China, ccmu.edu.cn.ORCID https://orcid.org/0000-0001-8361-7847
Yuan FengBeijing Key Laboratory of Intelligent Drug Research and Development for Mental Disorders, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China, ccmu.edu.cn.ORCID https://orcid.org/0000-0003-4172-4024
Jian CuiDepartment of Psychiatry, Shandong Daizhuang Hospital, Jining, China.ORCID https://orcid.org/0009-0008-5956-8086
Ling ZhangBeijing Key Laboratory of Intelligent Drug Research and Development for Mental Disorders, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China, ccmu.edu.cn.ORCID https://orcid.org/0000-0002-9713-1458
Yuan ZhouState Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China, cas.cn.ORCID https://orcid.org/0000-0001-6378-1711
Gang WangBeijing Key Laboratory of Intelligent Drug Research and Development for Mental Disorders, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China, ccmu.edu.cn.ORCID https://orcid.org/0000-0003-3949-1188

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This study aimed to investigate the neural basis of individual differences in antidepressant efficacy using an 8-week longitudinal multitime point resting-state functional magnetic resonance imaging (fMRI) design. Methods: Forty-eight patients with major depressive disorder (MDD) completed two or three scans, and 44 healthy controls (HCs) underwent a baseline scan. Patients were categorized into remission (MDDr) and nonremission (MDDnr) groups based on treatment outcomes. Group differences in resting-state functional connectivity (rsFC) of the amygdala subregions at baseline were examined among MDDr, MDDnr, and HCs. Longitudinal changes in the identified rsFC were compared between the MDDr and MDDnr groups. Correlation analyses were conducted to explore the relationship between baseline rsFC or its longitudinal changes and depressive symptom severity or improvement. Results: At baseline, rsFC between the right basolateral (BL) amygdala and the right supplementary motor area (SMA) was lower in the MDDr group but higher in the MDDnr group compared with HCs, although this effect did not survive multiple comparisons correction across amygdala subregions. The trajectory of this rsFC differed between the two patient groups during treatment, with normalization observed at 2 and 8 weeks posttreatment. Correlation analyses indicated that baseline rsFC was associated with treatment response and that longitudinal changes in rsFC were aligned with symptom improvement, although some associations did not survive multiple comparisons correction. Conclusions: Our findings provide novel and valuable insights into the neural mechanisms underlying antidepressant response and highlight the role of amygdala subregional connectivity in explaining interindividual variability in treatment efficacy. Trial Registration: ClinicalTrials.gov identifier: ChiCTR2400093823.

Indexed as

AmygdalaAntidepressive AgentsConnectomeMajor Depressive DisorderMotor CortexAdultFemaleHumansLongitudinal StudiesMagnetic Resonance ImagingMaleMiddle AgedRemission InductionTreatment Effect HeterogeneityAntidepressive Agentsamygdalaantidepressantlongitudinal studymajor depressive disorderrest-state functional connectivity

Identifiers

PMID42292915
PMCPMC13254815

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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.