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Record W4379375570 · doi:10.1016/j.brs.2023.06.001

Design of coil holder for the improved maneuvering in concurrent TMS-MRI

2023· article· en· W4379375570 on OpenAlexafffund
Hsin-Ju Lee, KJ Woudsma, Mohammed F. Ishraq, Fa‐Hsuan Lin

Bibliographic record

VenueBrain stimulation · 2023
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsOntario Institute for Cancer ResearchSunnybrook HospitalUniversity of TorontoSunnybrook Health Science Centre
FundersCanadian Institutes of Health ResearchOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaMitacsCanada Foundation for Innovation
KeywordsTranscranial magnetic stimulationElectromagnetic coilOrientation (vector space)Rotation (mathematics)Magnetic resonance imagingVoice coilComputer scienceAcousticsPhysicsNeuroscienceArtificial intelligenceStimulationEngineeringPsychologyElectrical engineeringMedicineMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: Concurrent transcranial magnetic stimulation (TMS) and magnetic resonance imaging (MRI) is time-consuming because of the limited space in the MRI bore and the sophisticated placement and orientation of the TMS coil to elicit the desired brain activities and behaviors. OBJECTIVE: We developed a TMS coil holder capable of quick adjustment of the TMS coil position and orientation. The holder can also hold an MRI receiver coil array. METHODS: A holder with one controlling knob, two omni-direction rotation joints, and two in-plane rotation joints was developed. RESULTS: Different TMS coil positions and orientations can be arranged and fixed in seconds. The holder can also accommodate two TMS coils to allow for multi-coil TMS-MRI. CONCLUSION: Our development significantly improves the workflow of the concurrent TMS-MRI in new neuroscience studies and clinical applications.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.126
GPT teacher head0.338
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 designBench or experimental
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
Published2023
Admission routes2
Has abstractyes

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