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Record W2791091921 · doi:10.3233/rnn-170790

Sensorimotor integration in chronic stroke: Baseline differences and response to sensory training

2018· article· en· W2791091921 on OpenAlexafffund
Katlyn E. Brown, Jason L. Neva, Samantha J. Feldman, Richard Staines, Lara A. Boyd

Bibliographic record

VenueRestorative Neurology and Neuroscience · 2018
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsUniversity of WaterlooUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsSensory systemStroke (engine)Baseline (sea)Physical medicine and rehabilitationChronic strokePsychologyTraining (meteorology)MedicineNeuroscienceRehabilitationBiology

Abstract

fetched live from OpenAlex

BACKGROUND: The integration of somatosensory information from the environment into the motor cortex to inform movement is essential for motor function. As motor deficits commonly persist into the chronic phase of stroke recovery, it is important to understand potential contributing factors to these deficits, as well as their relationship with motor function. To date the impact of chronic stroke on sensorimotor integration has not been thoroughly investigated. OBJECTIVES: The current study aimed to comprehensively examine the influence of chronic stroke on sensorimotor integration, and determine whether sensorimotor integration can be modified with an intervention. Further, it determined the relationship between neurophysiological measures of sensorimotor integration and motor deficits post-stroke. METHODS: Fourteen individuals with chronic stroke and twelve older healthy controls participated. Motor impairment and function were quantified in individuals with chronic stroke. Baseline neurophysiology was assessed using nerve-based measures (short- and long-latency afferent inhibition, afferent facilitation) and vibration-based measures of sensorimotor integration, which paired vibration with single and paired-pulse TMS techniques. Neurophysiological assessment was performed before and after a vibration-based sensory training paradigm to assess changes within these circuits. RESULTS: Vibration-based, but not nerve-based measures of sensorimotor integration were different in individuals with chronic stroke, as compared to older healthy controls, suggesting that stroke differentially impacts integration of specific types of somatosensory information. Sensorimotor integration was behaviourally relevant in that it related to both motor function and impairment post-stroke. Finally, sensory training modulated sensorimotor integration in individuals with chronic stroke and controls. CONCLUSION: Sensorimotor integration is differentially impacted by chronic stroke based on the type of afferent feedback. However, both nerve-based and vibration-based measures relate to motor impairment and function in individuals with chronic stroke.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.092
GPT teacher head0.329
Teacher spread0.237 · 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

Citations20
Published2018
Admission routes2
Has abstractyes

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