Evidence map›Paper›PMID 42460432›Full record

ArticleNature. Mental health2026

Generalizable structure-function covariation predictive of antidepressant response revealed by target-oriented multimodal fusion.

Xiaoyu Tong, Kanhao Zhao, Gregory A Fonzo, Hua Xie, Nancy B Carlisle, Corey J Keller, Desmond J Oathes, Yvette Sheline, Charles B Nemeroff, Madhukar Trivedi and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature. Mental health, 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

12 authors.

Xiaoyu TongDepartment of Bioengineering, Lehigh University, Bethlehem, PA, USA.ORCID 0000-0003-2113-5943
Kanhao ZhaoDepartment of Bioengineering, Lehigh University, Bethlehem, PA, USA.ORCID 0000-0002-2955-0917
Gregory A FonzoCenter for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.ORCID 0000-0002-0213-1034
Hua XieCenter for Neuroscience Research, Children's National Hospital, Washington, DC, USA.ORCID 0000-0003-3462-0147
Nancy B CarlisleDepartment of Psychology, Lehigh University, Bethlehem, PA, USA.
Corey J KellerDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0003-0529-3490
Desmond J OathesCenter for Brain Imaging and Stimulation, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.ORCID 0000-0001-7346-2669
Yvette ShelineCenter for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.ORCID 0000-0002-6929-9659
Charles B NemeroffCenter for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.ORCID 0000-0001-7867-1160
Madhukar TrivediThe University of Texas Southwestern Medical Center, Department of Psychiatry, Center for Depression Research and Clinical Care, Dallas, TX, USA.ORCID 0000-0002-2983-1110
Amit EtkinDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0001-8259-3521
Yu ZhangDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0003-4087-6544

Funding

Establishing Multimodal Brain Biomarkers Using Data-driven Analyticsfor Treatment Selection in DepressionR01MH129694 · NIMH · STANFORD UNIVERSITY · PI Yu Zhang · 2023 to 2026
$2.8M
NIMH NIH HHS R01 MH129694
6 · The paper itself

Abstract

Major depressive disorder (MDD) is a prevalent condition that profoundly impairs quality of life across diverse populations. Despite widespread use, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates. Progress in developing effective therapies is hampered by the insufficiently understood heterogeneity of MDD and its elusive underlying mechanisms. Here, to address these challenges, we develop a novel machine learning framework that identifies structure-function covariation through target-oriented fusion of structural and functional connectivity, which robustly predicts individual-level antidepressant response (sertraline,

Identifiers

PMID42460432
PMCPMC13372467

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.