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Record W2998407795 · doi:10.1097/wnr.0000000000001392

Gender impact on transcranial magnetic stimulation-based cortical excitability and cognition relationship in healthy individuals

2020· article· en· W2998407795 on OpenAlexafffund
Kosalan Akilan, Sanjeev Kumar, Reza Zomorrodi, Daniel M. Blumberger, Zafiris J. Daskalakis, Tarek K. Rajji

Bibliographic record

VenueNeuroreport · 2020
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersCanadian Institutes of Health Research
KeywordsRepeatable Battery for the Assessment of Neuropsychological StatusTranscranial magnetic stimulationCognitionNeuropsychologyEffects of sleep deprivation on cognitive performancePsychologyAssociation (psychology)AudiologyStimulationMedicineClinical psychologyNeuroscience

Abstract

fetched live from OpenAlex

OBJECTIVE: We sought to determine whether cortical excitability, measured via transcranial magnetic stimulation (TMS), is associated with cognition in healthy individuals and whether gender and education have an impact on this relationship. METHODS: Fifty-four healthy individuals (31 males, mean age = 41.94, SD = 21.98; 23 females, mean age 48.57; SD = 22.84) underwent TMS to assess their resting motor threshold (RMT) and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) to assess cognition. Multiple regression analyses were conducted to assess the association between RMT, education, gender and cognition. RESULTS: The multiple regression model revealed a significant association between RBANS Total Index Score and RMT in the female group (B = 0.624, β = 0.602, P = 0.001) and not the male group (B = 0.048, β = 0.034, P = 0.858). CONCLUSION: This study demonstrated that lower cortical excitability is associated with better global cognition in healthy female and not male individuals. RMT could be further studied as a tool to better personalize brain stimulation protocols that aim at enhancing cognition.

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)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.023
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.127
GPT teacher head0.346
Teacher spread0.219 · 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 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

Citations13
Published2020
Admission routes2
Has abstractyes

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