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Record W3106578606 · doi:10.1016/j.clinph.2020.11.012

Influence of post-stroke fatigue on reaction times and corticospinal excitability during movement preparation

2020· article· en· W3106578606 on OpenAlexaff
William De Doncker, Katlyn E. Brown, Annapoorna Kuppuswamy

Bibliographic record

VenueClinical Neurophysiology · 2020
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsUniversity of Waterloo
FundersWellcome Trust
KeywordsPhysical medicine and rehabilitationMovement (music)Stroke (engine)NeuroscienceMedicinePsychologyPhysics

Abstract

fetched live from OpenAlex

OBJECTIVES: Reduced corticospinal excitability at rest is associated with post-stroke fatigue (PSF). However, it is not known if corticospinal excitability prior to a movement is also altered in fatigue which may then influence subsequent behaviour. We hypothesized that the levels of PSF can be explained by differences in modulation of corticospinal excitability during movement preparation. METHODS: 73 stroke survivors performed an auditory reaction time task. Corticospinal excitability was measured using transcranial magnetic stimulation. Fatigue was quantified using the fatigue severity scale. The effect of time and fatigue on corticospinal excitability and reaction time was analysed using a mixed effects model. RESULTS: Those with greater levels of PSF showed reduced suppression of corticospinal excitability during movement preparation and increased facilitation immediately prior to movement onset (β = -0.0066, t = -2.22, p = 0.0263). Greater the fatigue, slower the reaction times the closer the stimulation time to movement onset (β = 0.0024, t = 2.47, p = 0.0159). CONCLUSIONS: Lack of pre-movement modulation of corticospinal excitability in high fatigue may indicate poor sensory processing supporting the sensory attenuation model of fatigue. SIGNIFICANCE: We take a systems-based approach and investigate the motor system and its role in pathological fatigue allowing us to move towards gaining a mechanistic understanding of chronic pathological fatigue.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.978
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.076
GPT teacher head0.359
Teacher spread0.283 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations28
Published2020
Admission routes1
Has abstractyes

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