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Record W1994903700 · doi:10.1142/s0218127408020379

ON THE SPATIAL ORGANIZATION OF EPILEPTIFORM ACTIVITY

2008· article· en· W1994903700 on OpenAlexaff
Luís Garcia Dominguez, Ramón Guevara Erra, Richard Wennberg, José Luis Pérez Velázquez

Bibliographic record

VenueInternational Journal of Bifurcation and Chaos · 2008
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsUniversity of TorontoToronto Western HospitalHospital for Sick Children
Fundersnot available
KeywordsIctalNeuroscienceEpilepsyMagnetoencephalographyPremovement neuronal activitySynchronization (alternating current)ElectroencephalographyBrain activity and meditationPsychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The generation and progression of epileptiform activity, especially that associated with ictal paroxysmal neuronal discharges (seizures), is usually studied in terms of its temporal evolution rather than its spatial organization. The characterization of the spatio-temporal dynamics of epileptiform activity represents a major challenge in neuroscience, due to the very intricate nature of brain structure and function. Our study is an initial attempt to reveal the structure hidden under the spatial organization of the synchronization patterns in neuronal activity associated with epilepsy. Analysis of the phase synchronization patterns from magnetoencephalographic recordings in an epileptic patient revealed a decrease in complexity during seizures. Distinct patterns of synchronized activity were observed during interictal and ictal (seizure) activity, and new tools to quantify and visualize the information contained in a synchrony pattern are proposed. The results reported here support previous observations on the high local synchronization in seizures.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.245
Teacher spread0.220 · 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 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

Citations11
Published2008
Admission routes1
Has abstractyes

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