Evidence map›Paper›PMID 38359694›Full record

ArticleMultiple sclerosis and related disorders2024

Diffusion basis spectrum imaging and diffusion tensor imaging predict persistent black hole formation in multiple sclerosis.

Lindsey Wooliscroft, Amber Salter, Gautam Adusumilli, Victoria A Levasseur, Peng Sun, Samantha Lancia, Dana C Perantie, Kathryn Trinkaus, Robert T Naismith, Sheng-Kwei Song and 1 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Multiple sclerosis and related disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.3field-weighted citation impact, top 23% of its field
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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
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

11 authors at 3 institutions in 1 country.

Lindsey WooliscroftDepartment of Neurology, Oregon Health & Science University, Portland, OR, USA; Department of Neurology, VA Portland Health Care System, Portland, OR, USA; Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA. Electronic address: wooliscr@ohsu.edu.
Amber SalterDepartment of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA; Department of Biostatistics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Gautam AdusumilliDepartment of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
Victoria A LevasseurDepartment of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Minneapolis Clinic of Neurology, Coon Rapids, MN, USA.
Peng SunDepartment of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
Samantha LanciaDepartment of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Department of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA; Department of Biostatistics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Dana C PerantieDepartment of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
Kathryn TrinkausBiostatistics Shared Resource, Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA.
Robert T NaismithDepartment of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
Sheng-Kwei SongDepartment of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
Anne H CrossDepartment of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
Washington University in St. Louis · USOregon Health & Science University · USThe University of Texas MD Anderson Cancer Center · US

Funding

Validating diffusion MRI biomarkers of inflammation and axon pathologies in EAEP01NS059560 · NINDS · WASHINGTON UNIVERSITY · PI KLEIN, ROBYN S. · 2008 to 2018
$11.3M
PREDICTIVE VALUE OF DIFFUSION MRI IN CERVICAL SPONDYLOTIC MYELOPATHYR01NS047592 · NINDS · WASHINGTON UNIVERSITY · PI RAY, WILSON Z, SONG, SHENG-KWEI · 2005 to 2021
$5.8M
IMAGING OPTIC NERVE FUNCTION AND PATHOLOGYU01EY025500 · NEI · WASHINGTON UNIVERSITY · PI SONG, SHENG-KWEI · 2015 to 2019
$3.1M
Virtual Histology for Assessing MS PathologiesR01NS116091 · NINDS · WASHINGTON UNIVERSITY · PI SONG, SHENG-KWEI · 2021 to 2025
$2.2M
Aerobic exercise to improve mobility in multiple sclerosis: optimizing design and execution for a full-scale multimodal remyelination clinical trialK23HD101667 · NICHD · OREGON HEALTH & SCIENCE UNIVERSITY · PI WOOLISCROFT, LINDSEY BRIANNA · 2020 to 2024
$799k
NEI NIH HHS U01 EY025500NICHD NIH HHS K23 HD101667NINDS NIH HHS P01 NS059560NINDS NIH HHS R01 NS047592NINDS NIH HHS R01 NS116091
6 · The paper itself

Abstract

BACKGROUND AND

objectivesDiffusion basis spectrum imaging (DBSI) extracts multiple anisotropic and isotropic diffusion tensors, providing greater histopathologic specificity than diffusion tensor imaging (DTI). Persistent black holes (PBH) represent areas of severe tissue damage in multiple sclerosis (MS), and a high PBH burden is associated with worse MS disability. This study evaluated the ability of DBSI and DTI to predict which acute contrast-enhancing lesions (CELs) would persist as T

methodsIn this prospective cohort study, relapsing MS patients with ≥1 CEL(s) underwent monthly MRI scans for 4 to 6 months until gadolinium resolution. DBSI and DTI metrics were quantified when the CEL was most conspicuous during the monthly scans. To determine whether the CEL became a PBH, a follow-up MRI was performed at least 12 months after the final monthly scan.

resultsThe cohort included 20 MS participants (median age 33 years; 13 women) with 164 CELs. Of these, 59 (36 %) CELs evolved into PBHs. At Gd-max, DTI RD and AD of all CELs increased, and both metrics were significantly elevated for CELs which became PBHs, as compared to non-black holes (NBHs). DTI RD above 0.74 conferred an odds ratio (OR) of 7.76 (CI 3.77-15.98) for a CEL becoming a PBH (AUC 0.80, CI 0.73-0.87); DTI axial diffusivity (AD) above 1.22 conferred an OR of 7.32 (CI 3.38-15.86) for becoming a PBH (AUC 0.75, CI 0.66-0.83). DBSI RD and AD did not predict PBH development in a multivariable model. At Gd-max, DBSI restricted fraction decreased and DBSI non-restricted fraction increased in all CELs, and both metrics were significantly different for CELs which became PBHs, as compared to NBHs. A CEL with a DBSI non-restricted fraction above 0.45 had an OR of 4.77 (CI 2.35-9.66) for becoming a PBH (AUC 0.74, CI 0.66-0.81); a CEL with a DBSI restricted fraction below 0.07 had an OR of 9.58 (CI 4.59-20.02) for becoming a PBH (AUC 0.80, 0.72-0.87).

conclusionOur findings suggest that greater degree of edema/extracellular space in a CEL is a predictor of tissue destruction, as evidenced by PBH evolution.

Indexed as

Multiple SclerosisAdultBrainDiffusion Tensor ImagingEdemaFemaleHumansProspective StudiesDiffusion basis spectrum imagingDiffusion tensor imagingEnhancing lesionsMultiple sclerosisPersistent black holes

Identifiers

PMID38359694
PMCPMC10978237
OpenAlexW4391735807

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Registered trials

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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.