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Record W2083961471 · doi:10.1007/s12576-009-0036-8

Typical dipole locations can be estimated using averaged somatosensory-evoked potentials and a standard brain model

2009· article· en· W2083961471 on OpenAlexaboutno aff
Yuri Masaoka, Hiroyoshi Yajima, Miho Takayama, Akiko Kawase, Nobuari Takakura, Ikuo Homma

Bibliographic record

VenueThe Journal of Physiological Sciences · 2009
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsnot available
Fundersnot available
KeywordsDipoleSomatosensory evoked potentialSomatosensory systemPhysicsScalpElectroencephalographyHead (geology)NeuroscienceNuclear magnetic resonancePsychologyGeologyMedicineAnatomy

Abstract

fetched live from OpenAlex

It is reasonable to hypothesize that dipoles estimated from grand averaged event-related potentials based on summed-up data obtained from multiple subjects and standard head models could correspond to typical brain regions associated to a particular event. Six healthy subjects were enrolled in a study to test this hypothesis. We estimated dipoles from somatosensory-evoked potentials (SEP) elicited by electrical stimulation to the left median nerve. We also created individual three-layered (scalp, skull, and brain) head models from each subject's magnetic resonance imaging scan, and dipoles were estimated from the individual averaged SEP with each individual head model. We then estimated dipoles using grand averaged SEP across all subjects on the standard head model created from the Montreal Neurological Institute (MNI) standard coordinate system brain template to compare the estimated dipoles located on our own head model and those on the MNI. The dipoles in the post-central gyrus were estimated from negative potentials at 20 ms from the grand averaged data incorporated with the MNI head model, corresponding to a typical location related to SEP stimulation. The results suggest the validity of estimating the dipole location from the grand averaged potential of all subjects with the MNI model if we focus on typical regions related to the task.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.115
GPT teacher head0.349
Teacher spread0.233 · 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 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

Citations5
Published2009
Admission routes1
Has abstractyes

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