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Record W4406614848 · doi:10.1038/s41531-024-00847-3

Electrophysiological approaches to informing therapeutic interventions with deep brain stimulation

2025· review· en· W4406614848 on OpenAlexafffund
Atefeh Asadi, Alex I. Wiesman, Christoph Wiest, Sylvain Baillet, Huiling Tan, Muthuraman Muthuraman

Bibliographic record

Venuenpj Parkinson s Disease · 2025
Typereview
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsMcGill UniversityMontreal Neurological Institute and HospitalSimon Fraser University
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute for Health and Care ResearchNational Institute of Neurological Disorders and StrokeMedical Research CouncilNatural Sciences and Engineering Research Council of CanadaDeutsche ForschungsgemeinschaftGovernment of CanadaFondazione Grigioni per il Morbo di ParkinsonRosetrees TrustCanadian Institutes of Health ResearchNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsDeep brain stimulationNeuromodulationNeuroscienceElectrophysiologyStimulationSubthalamic nucleusBrain stimulationMedicinePsychological interventionNeuroimagingBrain activity and meditationPsychologyParkinson's diseaseElectroencephalographyPsychiatryInternal medicineDisease

Abstract

fetched live from OpenAlex

Neuromodulation therapy comprises a range of non-destructive and adjustable methods for modulating neural activity using electrical stimulations, chemical agents, or mechanical interventions. Here, we discuss how electrophysiological brain recording and imaging at multiple scales, from cells to large-scale brain networks, contribute to defining the target location and stimulation parameters of neuromodulation, with an emphasis on deep brain stimulation (DBS).

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0000.000

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 head0.163
GPT teacher head0.362
Teacher spread0.199 · 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 designOther design
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

Citations10
Published2025
Admission routes2
Has abstractyes

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