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Understanding the Neurophysiological and Molecular Mechanisms of Exercise-Induced Neuroplasticity in Cortical and Descending Motor Pathways: Where Do We Stand?

2020· review· en· W3116866257 on OpenAlexafffund
Chiara Nicolini, Margaret Fahnestock, Martin J. Gibala, Aimee J. Nelson

Bibliographic record

VenueNeuroscience · 2020
Typereview
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsMcMaster University
FundersCanada Research Chairs
KeywordsNeurorehabilitationNeuroplasticityNeuroscienceNeurophysiologyPsychologyPhysical medicine and rehabilitationMotor learningAerobic exerciseDiseaseRehabilitationMedicinePhysical therapy

Abstract

fetched live from OpenAlex

Exercise is a promising, cost-effective intervention to augment successful aging and neurorehabilitation. Decline of gray and white matter accompanies physiological aging and contributes to motor deficits in older adults. Exercise is believed to reduce atrophy within the motor system and induce neuroplasticity which, in turn, helps preserve motor function during aging and promote re-learning of motor skills, for example after stroke. To fully exploit the benefits of exercise, it is crucial to gain a greater understanding of the neurophysiological and molecular mechanisms underlying exercise-induced brain changes that prime neuroplasticity and thus contribute to postponing, slowing, and ameliorating age- and disease-related impairments in motor function. This knowledge will allow us to develop more effective, personalized exercise protocols that meet individual needs, thereby increasing the utility of exercise strategies in clinical and non-clinical settings. Here, we review findings from studies that investigated neurophysiological and molecular changes associated with acute or long-term exercise in healthy, young adults and in healthy, postmenopausal women.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.868
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.248
GPT teacher head0.322
Teacher spread0.074 · 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.

Study designBench or experimental
Domainnot available
GenreReview

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

Citations50
Published2020
Admission routes2
Has abstractyes

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