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


Journal

medRxiv : the preprint server for health sciences
Titre abrégé: medRxiv
Pays: United States
ID NLM: 101767986

Informations de publication

Date de publication:
12 Jun 2023
Historique:
pubmed: 3 7 2023
medline: 3 7 2023
entrez: 3 7 2023
Statut: epublish

Résumé

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. To investigate how white matter network disruption is related to depression in MS. 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. Single-center academic medical specialty MS clinic. 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 ( Depression diagnosis. 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. 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). 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.

Identifiants

pubmed: 37398183
doi: 10.1101/2023.06.09.23291080
pmc: PMC10312888
pii:
doi:

Types de publication

Preprint

Langues

eng

Subventions

Organisme : NIMH NIH HHS
ID : R01 MH123550
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH120482
Pays : United States
Organisme : NIMH NIH HHS
ID : K23 MH133118
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH113550
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS085211
Pays : United States
Organisme : NIMH NIH HHS
ID : K99 MH127293
Pays : United States
Organisme : NIA NIH HHS
ID : R21 AG070434
Pays : United States
Organisme : NIMH NIH HHS
ID : T32 MH019112
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS112274
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH112847
Pays : United States

Commentaires et corrections

Type : UpdateIn

Déclaration de conflit d'intérêts

Conflict of Interest Disclosures: Dr. Baller reported receiving grants from the National Institutes of Health (NIH) during the conduct of the study. Dr. Shinohara reported receiving grants from the NIH and the Multiple Sclerosis Society during the conduct of the study. Dr. Shinohara receives consulting income from Octave Bioscience, and compensation for scientific reviewing from the American Medical Association. Dr. Satterthwaite reported receiving grants from the NIH during the conduct of the study.

Auteurs

Erica B Baller (EB)

Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA.
Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA.

Elizabeth M Sweeney (EM)

Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA.

Matthew C Cieslak (MC)

Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA.
Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA.

Timothy Robert-Fitzgerald (T)

Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA.

Sydney C Covitz (SC)

Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA.
Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA.

Melissa L Martin (ML)

Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA.

Matthew K Schindler (MK)

Department of Neurology, University of Pennsylvania, Philadelphia, PA USA.
Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, PA USA.

Amit Bar-Or (A)

Department of Neurology, University of Pennsylvania, Philadelphia, PA USA.
Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, PA USA.

Ameena Elahi (A)

Department of Information Services, University of Pennsylvania, Philadelphia, PA USA.

Bart S Larsen (BS)

Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA.
Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA.

Abigail R Manning (AR)

Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA.

Clyde E Markowitz (CE)

Department of Neurology, University of Pennsylvania, Philadelphia, PA USA.
Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, PA USA.

Christopher M Perrone (CM)

Department of Neurology, University of Pennsylvania, Philadelphia, PA USA.
Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, PA USA.

Victoria Rautman (V)

Department of Information Services, University of Pennsylvania, Philadelphia, PA USA.

Madeleine M Seitz (MM)

Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA.
Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA.
Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA.

John A Detre (JA)

Department of Neurology, University of Pennsylvania, Philadelphia, PA USA.

Michael D Fox (MD)

Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women's Hospital, Harvard Medical School.

Russell T Shinohara (RT)

Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA.
Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA USA.

Theodore D Satterthwaite (TD)

Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA.
Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA.
Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA USA.

Classifications MeSH