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Record W4399663873 · doi:10.1093/brain/awae192

Explaining slow seizure propagation with white matter tractography

2024· article· en· W4399663873 on OpenAlexafffund
Abdullah Azeem, Chifaou Abdallah, Nicolás von Ellenrieder, Charbel El Kosseifi, Birgit Frauscher, Jean Gotman

Bibliographic record

VenueBrain · 2024
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersCanadian Institutes of Health ResearchMcGill University
KeywordsStereoelectroencephalographyTractographyWhite matterEpilepsyEpileptic seizureNeuroscienceElectroencephalographyConnectomePsychologyMedicineEpilepsy surgeryFunctional connectivityMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

Epileptic seizures recorded with stereo-EEG can take a fraction of a second or several seconds to propagate from one region to another. What explains such propagation patterns? We combine tractography and stereo-EEG to determine the relationship between seizure propagation and the white matter architecture and to describe seizure propagation mechanisms. Patient-specific spatiotemporal seizure propagation maps were combined with tractography from diffusion imaging of matched subjects from the Human Connectome Project. The onset of seizure activity was marked on a channel-by-channel basis by two board-certified neurologists for all channels involved in the seizure. We measured the tract connectivity (number of tracts) between regions-of-interest pairs among the seizure onset zone, regions of seizure spread and non-involved regions. We also investigated how tract-connected the seizure onset zone is to regions of early seizure spread compared with regions of late spread. Comparisons were made after correcting for differences in distance. Sixty-nine seizures were marked across 26 patients with drug-resistant epilepsy; 11 were seizure free after surgery (Engel IA) and 15 were not (Engel IB-Engel IV). The seizure onset zone was more tract-connected to regions of seizure spread than to non-involved regions (P < 0.0001); however, regions of seizure spread were not differentially tract-connected to other regions of seizure spread compared with non-involved regions. In seizure-free patients only, regions of seizure spread were more tract-connected to the seizure onset zone than to other regions of spread (P < 0.0001). Over the temporal evolution of a seizure, the seizure onset zone was significantly more tract-connected to regions of early spread compared with regions of late spread in seizure-free patients only (P < 0.0001). By integrating information on structure, we demonstrate that seizure propagation is likely to be mediated by white matter tracts. The pattern of connectivity between seizure onset zone, regions of spread and non-involved regions demonstrates that the onset zone might be largely responsible for seizures propagating throughout the brain, rather than seizures propagating to intermediate points, from which further propagation takes place. Our findings also suggest that seizure propagation over seconds might be the result of a continuous bombardment of action potentials from the seizure onset zone to regions of spread. In non-seizure-free patients, the paucity of tracts from the presumed seizure onset zone to regions of spread suggests that the onset zone was missed. Fully understanding the structure-propagation relationship might eventually provide insight into selecting the correct targets for epilepsy surgery.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.810
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

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

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 head0.032
GPT teacher head0.321
Teacher spread0.289 · 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 designNot applicable
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

Citations26
Published2024
Admission routes2
Has abstractyes

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