Evidence map›Paper›PMID 37398183›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Mapping the relationship of white matter lesions to depression in multiple sclerosis.

Erica B Baller, Elizabeth M Sweeney, Matthew C Cieslak, Timothy Robert-Fitzgerald, Sydney C Covitz, Melissa L Martin, Matthew K Schindler, Amit Bar-Or, Ameena Elahi, Bart S Larsen and 9 more

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. 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, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors at 5 institutions in 2 countries.

Erica B BallerPenn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA.
Elizabeth M SweeneyPenn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA.
Matthew C CieslakPenn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA.
Timothy Robert-FitzgeraldPenn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA.
Sydney C CovitzPenn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA.
Melissa L MartinPenn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA.
Matthew K SchindlerDepartment of Neurology, University of Pennsylvania, Philadelphia, PA USA.
Amit Bar-OrDepartment of Neurology, University of Pennsylvania, Philadelphia, PA USA.
Ameena ElahiDepartment of Information Services, University of Pennsylvania, Philadelphia, PA USA.
Bart S LarsenPenn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA.
Abigail R ManningPenn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA.
Clyde E MarkowitzDepartment of Neurology, University of Pennsylvania, Philadelphia, PA USA.
Christopher M PerroneDepartment of Neurology, University of Pennsylvania, Philadelphia, PA USA.
Victoria RautmanDepartment of Information Services, University of Pennsylvania, Philadelphia, PA USA.
Madeleine M SeitzPenn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA.
John A DetreDepartment of Neurology, University of Pennsylvania, Philadelphia, PA USA.
Michael D FoxCenter for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women's Hospital, Harvard Medical School.
Russell T ShinoharaPenn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA.
Theodore D SatterthwaitePenn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA.
Penn Center for AIDS Research · USScience History Institute · USUniversity of Pennsylvania · USLaboratoire d’Imagerie Biomédicale · FRBrigham and Women's Hospital · US

Funding

Longitudinal Mapping of Network Development Underlying Executive Dysfunction in AdolescenceR01MH113550 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI Danielle Smith Bassett, Theodore Satterthwaite · 2018 to 2026
$6.9M
SCHIZOPHRENIA: A NEUROPSYCHIATRIC PERSPECTIVET32MH019112 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI GUR, RAQUEL E · 1990 to 2024
$6.2M
Inter-modal Coupling Image AnalyticsR01MH112847 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI Theodore Satterthwaite, Russell Takeshi Shinohara · 2017 to 2026
$5.9M
Harmonization of Multi-Site Neuroimaging Data from Complex Study DesignsR01MH123550 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI SHINOHARA, RUSSELL TAKESHI · 2020 to 2025
$4.0M
The Neuroimaging Brain Chart Software SuiteU24NS130411 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI Christos Davatzikos, Yong Fan · 2023 to 2026
$3.7M
Reproducible imaging-based brain growth charts for psychiatryR01MH120482 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI MILHAM, MICHAEL PETER, SATTERTHWAITE, THEODORE · 2019 to 2023
$3.5M
Advanced Statistical Analytics of MRI in MSR01NS112274 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI SHINOHARA, RUSSELL TAKESHI · 2020 to 2024
$2.8M
Statistical methods for large and complex databases of ultra-high-dimensionalR01NS085211 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI SHINOHARA, RUSSELL TAKESHI · 2013 to 2017
$1.8M
Depression as a disease of network disruption: learning from multiple sclerosisK23MH133118 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI Erica Berlin Baller · 2023 to 2026
$780k
Impact of white matter hyperintensities on structural connectivity and cognitionR21AG070434 · NIA · UNIVERSITY OF PENNSYLVANIA · PI DETRE, JOHN A · 2020 to 2020
$446k
Brain iron as a neurodevelopmental mechanism for transdianostic executive dysfunctionK99MH127293 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI LARSEN, BART · 2022 to 2023
$168k
NIA NIH HHS R21 AG070434NIMH NIH HHS K23 MH133118NIMH NIH HHS K99 MH127293NIMH NIH HHS R01 MH112847NIMH NIH HHS R01 MH113550NIMH NIH HHS R01 MH120482NIMH NIH HHS R01 MH123550NIMH NIH HHS T32 MH019112NINDS NIH HHS R01 NS085211NINDS NIH HHS R01 NS112274NINDS NIH HHS U24 NS130411
6 · The paper itself

Abstract

Importance: Multiple sclerosis (MS) is an immune-mediated neurological disorder that affects nearly one million people in the United States. Up to 50% of patients with MS experience depression. Objective: To investigate how white matter network disruption is related to depression in MS. Design: Retrospective case-control study of participants who received research-quality 3-tesla neuroimaging as part of MS clinical care from 2010-2018. Analyses were performed from May 1 to September 30, 2022. Setting: Single-center academic medical specialty MS clinic. Participants: Participants with MS were identified via the electronic health record (EHR). All participants were diagnosed by an MS specialist and completed research-quality MRI at 3T. After excluding participants with poor image quality, 783 were included. Inclusion in the depression group ( Exposure: Depression diagnosis. Main Outcomes and Measures: We first evaluated if lesions were preferentially located within the depression network compared to other brain regions. Next, we examined if MS+Depression patients had greater lesion burden, and if this was driven by lesions specifically in the depression network. Outcome measures were the burden of lesions (e.g., impacted fascicles) within a network and across the brain. Secondary measures included between-diagnosis lesion burden, stratified by brain network. Linear mixed-effects models were employed. Results: Three hundred-eighty participants met inclusion criteria, (232 MS+Depression: age[SD]=49[12], %females=86; 148 MS-Depression: age[SD]=47[13], %females=79). MS lesions preferentially affected fascicles within versus outside the depression network (β=0.09, 95% CI=0.08-0.10, P<0.001). MS+Depression had more white matter lesion burden (β=0.06, 95% CI=0.01-0.10, P=0.015); this was driven by lesions within the depression network (β=0.02, 95% CI 0.003-0.040, P=0.020). Conclusions and Relevance: We provide new evidence supporting a relationship between white matter lesions and depression in MS. MS lesions disproportionately impacted fascicles in the depression network. MS+Depression had more disease than MS-Depression, which was driven by disease within the depression network. Future studies relating lesion location to personalized depression interventions are warranted.

Identifiers

PMID37398183
PMCPMC10312888
OpenAlexW4380356902

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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.