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Combining EEG and fMRI in Epilepsy: Methodological Challenges and Clinical Results

2004· article· en· W1982231590 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Clinical Neurophysiology · 2004
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsEEG-fMRIElectroencephalographyFunctional magnetic resonance imagingEpilepsyNeuroscienceComputer scienceBrain functionMagnetic resonance imagingPsychologyPattern recognition (psychology)Artificial intelligenceMedicineRadiology

Abstract

fetched live from OpenAlex

It is now possible to combine continuous recording of the EEG and continuous functional MRI scanning. This makes it possible to determine the regions of the brain showing changes in the fMRI signal in response to epileptic spikes occurring in the EEG. This article reviews the experience with this method in more than 100 studies performed over the last 4 years at the Montreal Neurological Institute. The technique is complex, and the authors review the various issues related to obtaining a good-quality EEG in the hostile environment of the magnetic resonance scanner, the statistical analysis of magnetic resonance images, in particular the issue of knowing what is the hemodynamic response function appropriate for the analysis of epileptic spikes, and the combination of EEG and fMRI results. The difficult theoretical issues raised by the interpretation of activation and deactivation, both frequently seen in response to spikes, are discussed. Finally, the authors give examples of fMRI responses seen with focal spikes and with generalized spike and wave discharges.

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.

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)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.325
GPT teacher head0.520
Teacher spread0.195 · 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