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Record W4380049206 · doi:10.3389/fneur.2023.1188124

Processing speed and memory test performance are associated with different brain region volumes in Veterans and others with progressive multiple sclerosis

2023· article· en· W4380049206 on OpenAlexafffund
Rebecca Spain, Andrea Hildebrand, Carin S. Waslo, William D. Rooney, Joshua Emmons, Daniel L. Schwartz, Mark S. Freedman, M. Mateo Paz Soldán, Pavle Repovic, Andrew Solomon, John R. Rinker, Mitchell T. Wallin, Jodie K. Haselkorn, Olaf Stüve, Robert Gross, Aaron P. Turner

Bibliographic record

VenueFrontiers in Neurology · 2023
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersNational Center for Advancing Translational SciencesMultiple Sclerosis Society of CanadaNational Multiple Sclerosis SocietyU.S. Department of Veterans Affairs
KeywordsExpanded Disability Status ScaleMedicineEffects of sleep deprivation on cognitive performanceCognitionInterquartile rangePhysical therapyPhysical medicine and rehabilitationBrain sizeMultiple sclerosisInternal medicineMagnetic resonance imagingPsychiatry

Abstract

fetched live from OpenAlex

Background Cognitive dysfunction and brain atrophy are both common in progressive multiple sclerosis (MS) but are seldom examined comprehensively in clinical trials. Antioxidant treatment may affect the neurodegeneration characteristic of progressive MS and slow its symptomatic and radiographic correlates. Objectives This study aims to evaluate cross-sectional associations between cognitive battery components of the Brief International Cognitive Assessment for Multiple Sclerosis with whole and segmented brain volumes and to determine if associations differ between secondary progressive (SPMS) and primary progressive (PPMS) MS subtypes. Design The study was based on a baseline analysis from a multi-site randomized controlled trial of the antioxidant lipoic acid in veterans and other people with progressive MS (NCT03161028). Methods Cognitive batteries were conducted by trained research personnel. MRIs were processed at a central processing site for maximum harmonization. Semi-partial Pearson's adjustments evaluated associations between cognitive tests and MRI volumes. Regression analyses evaluated differences in association patterns between SPMS and PPMS cohorts. Results Of the 114 participants, 70% had SPMS. Veterans with MS made up 26% (n = 30) of the total sample and 73% had SPMS. Participants had a mean age of 59.2 and sd 8.5 years, and 54% of them were women, had a disease duration of 22.4 (sd 11.3) years, and had a median Expanded Disability Status Scale of 6.0 (with an interquartile range of 4.0–6.0, moderate disability). The Symbol Digit Modalities Test (processing speed) correlated with whole brain volume (R = 0.29, p = 0.01) and total white matter volume (R = 0.33, p < 0.01). Both the California Verbal Learning Test (verbal memory) and Brief Visuospatial Memory Test-Revised (visual memory) correlated with mean cortical thickness (R = 0.27, p = 0.02 and R = 0.35, p < 0.01, respectively). Correlation patterns were similar in subgroup analyses. Conclusion Brain volumes showed differing patterns of correlation across cognitive tasks in progressive MS. Similar results between SPMS and PPMS cohorts suggest combining progressive MS subtypes in studies involving cognition and brain atrophy in these populations. Longitudinal assessment will determine the therapeutic effects of lipoic acid on cognitive tasks, brain atrophy, and their associations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.252
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2023
Admission routes2
Has abstractyes

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