Grey matter heterotopia: what EEG-fMRI can tell us about epileptogenicity of neuronal migration disorders
Bibliographic record
Abstract
Grey matter heterotopia are commonly associated with refractory epilepsy. Depth electrodes recordings have shown that epileptiform activity can be generated within these lesions, and also at a distance in the neocortex. Heterotopia seem to be part of a more complex circuitry involving also the surrounding and distant cerebral cortex. Blood oxygenation level-dependent (BOLD) changes to interictal spikes using continuous EEG and functional MRI (EEG-fMRI) can help to understand non-invasively the mechanisms of epileptogenicity in these patients. We studied 14 patients with epilepsy and heterotopia using simultaneous recording of EEG-fMRI. EEG was continuously acquired from inside the scanner during 2 h sessions. Epileptic spikes were visually identified in the filtered EEG and each type of spike determined one EEG-fMRI study. We looked at positive (activation) and negative (deactivation) changes in the BOLD signal. Eleven patients had nodular heterotopia and three band heterotopia. Four patients had more than one type of spikes, with a total of 26 EEG-fMRI studies. We excluded three with less than three spikes, and therefore a total of 23 studies (12 with nodular and 11 with band heterotopia) were analysed. Nodular heterotopia: Activation was present in nine studies, with involvement of the heterotopia or surrounding cortex in six, three of which had concomitant distant activation. Deactivation was also observed in nine studies, with involvement of the heterotopia and surrounding cortex in four, three of which had concomitant distant deactivation. Band heterotopia: Activation was present in all 11 studies, and always involved the heterotopia and surrounding cortex, 9 of which had concomitant distant activation. Deactivation was also observed in all 11 studies, with involvement of both the heterotopia and surrounding cortex, in addition to distant deactivation in 5 studies. EEG-fMRI studies reveal, non-invasively, metabolic responses in the heterotopia despite the fact that spikes are generated in the neocortex. The responses, activation or deactivation, had different correlation with the lesion and surrounding or distant cortex, activation reflecting intense neuronal activity, or excitation, and deactivation a possible distant (extra-lesional) inhibition. EEG-fMRI may become a useful tool to understand the epileptogenicity of such malformations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".