Evidence mapPaperPMID 41275952Full record

ReviewBiological psychiatry2026

Depression as a Disease of White Matter Network Disruption: Learning From Multiple Sclerosis.

Erica B Baller, Elena C Cooper, Matthew K Schindler, Amit Bar-Or, Michael D Fox, Russell T Shinohara, Theodore D Satterthwaite

Abstract readReview
In one paragraph

Review in Biological psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Erica B BallerDepartment of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania. Electronic address: erica.baller@pennmedicine.upenn.edu.
Elena C CooperDepartment of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania.
Matthew K SchindlerDepartment of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, Pennsylvania.
Amit Bar-OrDepartment of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, Pennsylvania.
Michael D FoxCenter for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
Russell T ShinoharaDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Artificial Intelligence and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, Pennsylvania.
Theodore D SatterthwaiteDepartment of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, Pennsylvania.

Funding

Depression as a disease of network disruption: learning from multiple sclerosisK23MH133118 · UNIVERSITY OF PENNSYLVANIA · 2025 to 2025
$195k
NIMH NIH HHS K23 MH133118NIMH NIH HHS L30 MH127652
6 · The paper itself

Abstract

Depression is a common and debilitating psychiatric disorder that is associated with substantial morbidity and mortality. For nearly 40 years, scientists have attempted to localize depression in the brain with neuroimaging. Despite concerted research, many meta-analyses of neuroimaging research in depression have not identified a single brain region that, when lesioned, causes depression. However, recent work using coordinate and lesion network mapping has identified a distributed network from functional magnetic resonance imaging that is relevant for depression. In this review, we propose that multiple sclerosis (MS) is a powerful model to study the relationship between perturbations to white matter brain networks and depression. We highlight recent successes in using lesion network mapping to find associations between depression and MS lesion location and burden in retrospective samples. Next, we describe why prospective, longitudinal studies that track the onset of white matter lesions in MS with the emergence and resolution of depression may provide a way to understand both the pathophysiology of depression in MS and network mechanisms of depression more broadly.

Indexed as

BrainDepressionMultiple SclerosisNerve NetWhite MatterHumansMagnetic Resonance ImagingDepressionDysconnectomeLesion network mappingMRIMultiple sclerosisWhite matter

Identifiers

PMID41275952
PMCPMC13112406

What Socratic holds

Textmetadata
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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.