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Record W2171358966 · doi:10.1152/jn.00977.2014

Modulation of cerebello-cerebral resting state networks by site-specific stimulation

2015· letter· en· W2171358966 on OpenAlexaff
Anuj Rastogi, Ayda Ghahremani, Robin Cash

Bibliographic record

VenueJournal of Neurophysiology · 2015
Typeletter
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNeuroscienceTranscranial magnetic stimulationNeuromodulationResting state fMRICerebellumNeuroimagingPsychologyStimulationBrain stimulationFunctional connectivityCognition

Abstract

fetched live from OpenAlex

Converging evidence from neuroimaging and neuromodulation literature suggests that the cerebellum plays a broad role in motor as well as cognitive processes through its participation in resting-state networks. A recent study by Halko et al. (J Neurosci 34: 12049-12056, 2014) demonstrates, for the first time, the ability to modulate functional connectivity of some of these distinct resting-state networks using site-specific repetitive transcranial magnetic stimulation (rTMS) of the cerebellum. In this Neuro Forum, we discuss and critically analyze this study, emphasizing important findings, potential therapeutic relevance, and areas worthy of further inquiry.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.054
GPT teacher head0.266
Teacher spread0.212 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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