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Record W4387409102 · doi:10.21203/rs.3.rs-3263385/v1

The future of MEG: Improved task-related responses using optically-pumped magnetometers compared to a conventional system

2023· preprint· en· W4387409102 on OpenAlexafffund
Kristina Safar, Marlee M. Vandewouw, Julie Sato, Jasen Devasagayam, Ryan M. Hill, Molly Rea, Matthew J. Brookes, Margot J. Taylor

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsSickKids FoundationHospital for Sick Children
FundersCanadian Institutes of Health ResearchHospital for Sick ChildrenSimons Foundation Autism Research Initiative
KeywordsMagnetoencephalographyMagnetometerTask (project management)Computer scienceNoise (video)Artificial intelligencePsychologyNeuroscienceElectroencephalographyPhysicsEngineeringMagnetic field

Abstract

fetched live from OpenAlex

Abstract Optically-pumped magnetometers (OPMs) offer a new wearable means to measure magnetoencephalography (MEG) signals, with many advantages compared to conventional systems. However, OPMs are an emerging technology, thus characterizing and replicating MEG recordings is essential. Using OPM and cryogenic MEG, this study investigated evoked responses, oscillatory power, and functional connectivity during emotion processing in 21 adults, to establish replicability across the two technologies. Five participants with dental fixtures were included to assess the validity of OPM recordings in those with irremovable metal. Replicable task-related evoked responses were observed in both modalities, with the OPMs demonstrating higher peak amplitude and improved signal-to-noise. Similar patterns of oscillatory power to faces were observed in both systems. Increased connectivity was found in cryogenic versus OPM MEG in an occipital and parietal anchored network. Notably, high quality OPM data were retained in participants with metallic fixtures, from whom no useable data was collected using cryogenic MEG.

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)0.004
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0000.002
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 head0.074
GPT teacher head0.404
Teacher spread0.330 · 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 designSimulation or modeling
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

Citations2
Published2023
Admission routes2
Has abstractyes

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