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2015 Parapan American Games: Does Physical Activity Participation Improve Cerebrovascular Function after High‐Level Spinal Cord Injury?

2016· article· en· W4389024508 on OpenAlexafffundabout
Aaron A. Phillips, Jordan W. Squair, Katharine D. Currie, Shieak Yc Tzeng, Philip N. Ainslie, Andrei V. Krassioukov

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversity of British Columbia, Okanagan CampusKelowna General HospitalInternational Collaboration On Repair Discoveries
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsCerebral autoregulationSpinal cord injuryCerebral blood flowMedicineTranscranial DopplerCardiologyCognitionInternal medicineSpinal cordAutoregulationBlood pressurePhysical medicine and rehabilitationStroke (engine)AnesthesiaNeurosciencePsychology

Abstract

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Introduction The risk of stroke is elevated 3–4 fold after spinal cord injury (SCI), and cognitive dysfunction is widespread. Using human and experimental animal models, we have previously established that cerebrovascular function is impaired after SCI, and is related to cognitive decline. Lower physical activity is thought to be associated with cerebrovascular complications in a number of clinical populations, however no link to cerebrovascular function has been established in those with SCI. Neurovascular coupling (NVC) and dynamic cerebral autoregulation (dCA) are established metrics for evaluating cerebrovascular function in humans. While NVC describes the efficacious matching of cerebral blood flow (CBF) to changes in neuronal activation, dCA reflects the capacity of the cerebrovasculature to buffer transient changes in blood pressure (BP). The appropriate control of CBF in the face of rapidly changing metabolic requirements and BP is thought to underlie cognitive performance as well as protect the delicate cerebral microvessels. Objective Examine the role physical activity plays in cerebrovascular function after SCI. Methods Through a field study at the 2015 Parapan American Games, we compared some of the most physically active SCI individuals in the world (consisting of 9 wheelchair rugby national teams [SCIa; n=19]), relatively inactive SCI controls (SCIb; n=9), and age‐matched able‐bodied individuals (AB; n=11). Both SCI groups suffered motor complete SCI (>T6 spinal segment). In the seated position, blood velocities in the middle and posterior cerebral arteries (MCA, PCA; transcranial Doppler and beat‐by‐beat BP (photoplethymography)) were recorded continuously. Thereafter, validated tests of NVC (visual stimulation), dCA (transfer function analysis), attention/concentration (Stroop test) and moderate to vigorous physical activity (MVPA; Godin‐Shephard questionnaire) were completed. Results The SCIa group was dramatically more physically active (62±3 au) than both SCIb (22±4 au, p<0.05) and AB (34±7 au, p <0.05). NVC was reduced 31% and 23% in SCIa and SCIb compared to AB, respectively (both p <0.01, ), however NVC was similar between the SCI groups. For the PCA but not the MCA, over a range of frequencies, SCIa consistency demonstrated improved buffering of the cerebrovasculature. These changes were reflected in a 27–37% (all p <0.01, ) reduced coherence‐weighted normalized gain, indicative of a reduced influence of BP on posterior cerebral circulation. Stroop performance was reduced 27% in SCIb (P3‐P2 time, p <0.05) but was preserved in SCIa when compared to AB. Conclusions Our data demonstrate that physically active individuals with SCI exhibit improved dCA and cognitive function; these changes may help compensate for the persistent impairment in NVC and thereby prevent vascular cognitive decline. The present findings have important implications in furthering our understanding of the favorable impact of exercise on cerebrovascular health in SCI. Support or Funding Information HSF‐Canada, CNF, CIHR, MSFHR

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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.310
Teacher spread0.282 · 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".

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Citations1
Published2016
Admission routes3
Has abstractyes

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