Scalp electroencephalographic biomarkers in epilepsy patients with focal cortical dysplasia

Epitashvili, Nino; Antonio‐Arce, Victoria San; Brandt, Armin; Schulze‐Bonhage, Andreas

doi:10.1002/ana.25322

Cited by 29 publications

(31 citation statements)

References 42 publications

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“…Recruiting theta activity, delta slowing, or fast activity is all described ictal EEG features in patients with EE. 25 Neuroimaging MRI abnormalities have been noted in 30% of subjects with EE. Most lesions are described in the posterior temporal-centroparietal-occipital regions.…”

Section: Electroencephalographic Featuresmentioning

confidence: 99%

“…17 There are several surface EEG biomarkers associated with an underlying cortical dysplasia: rhythmic epileptiform discharges, focal fast continuous spikes/polyspikes, continuous irregular slowing, and "brushes," which could be present even in patients with a negative brain MRI. 23,25 Ictal onset could be focal or generalized, and apparently generalized ictal events could be recorded even in the group of patients with structural epilepsy. 4,6 Focal ictal changes may vary depending on the localization and nature of brain lesion or malformation.…”

Section: Electroencephalographic Featuresmentioning

confidence: 99%

“…27 Several antiepileptic drugs have been used for EE depending on the age of onset and the underlying etiology. 8,17,19,24,25 Use of sodium channel blockers in patients with mesial temporal sclerosis and valproic acid or lamotrigine in patients with negative MRI has been described. 12,17 Clobazam intake before meals was shown to be effective to prevent some cases of eatingprovoked seizures.…”

Section: Treatmentmentioning

confidence: 99%

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Eating Epilepsy: Pathophysiologic Models, Electroclinical Phenotypes, and Outcome

Iodice

2019

J Pediatr Epilepsy

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Eating epilepsy (EE) is a rare form of reflex epilepsy precipitated by food. Ictal semiology may vary depending on the etiology, age at onset, and cerebral areas involved in the epileptogenic network. In childhood, EE could manifest as tonic head drop seizures, generalized seizures, or late-onset epileptic spasms. However, in teenagers or adults, seizures are often preceded by aura and commonly manifest as focal seizures with or without impaired awareness. Brain magnetic resonance imaging abnormalities are seen in less than half of the reported cases. Posterior or multifocal interictal discharges can be seen in patients with EE. No randomized data about treatment are available. Structural epilepsies or coexistent unprovoked seizures other than eating seizures are poor prognostic factors for pharmacological outcome.

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Section: Electroencephalographic Featuresmentioning

confidence: 99%

Section: Electroencephalographic Featuresmentioning

confidence: 99%

Section: Treatmentmentioning

confidence: 99%

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Eating Epilepsy: Pathophysiologic Models, Electroclinical Phenotypes, and Outcome

Iodice

2019

J Pediatr Epilepsy

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“…To date, numerous studies have investigated the electrophysiological characteristics of cortical epileptogenic tissue. A variety of cortical interictal patterns including trains of repetitive, rhythmic and periodic spikes/sharp waves, bursting epileptiform activity, “brushes,” and high‐frequency oscillations (HFOs) have been described over the few decades besides classical interictal discharges manifesting as a spike, spike, and wave or sharp wave 16,17 . Also, distinct ictal patterns of epileptic tissue were identified during seizures arising from cortical lesions 18,19 .…”

Section: Introductionmentioning

confidence: 99%

Electrophysiological properties and seizure networks in hypothalamic hamartoma

Wang

Shan

Bartolomei

et al. 2020

Ann Clin Transl Neurol

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Objective: Little is known about the intrinsic electrophysiological properties of hypothalamic hamartoma (HH) in vivo and seizure network since only few cases using stereoelectroencephalography (SEEG) electrodes exploring both cortex and HH have been published. To elucidate these issues, we analyzed simultaneous SEEG recordings in HH and cortex systematically. Methods: We retrospectively investigated data from 15 consecutive patients with SEEG electrodes into the HH for the treatment purpose of radiofrequency thermocoagulation treatment. Additional SEEG electrodes were placed into the cortex in 11 patients to assess extra-HH involvement. Interictal discharges within the HH and anatomo-electroclinical correlations during seizures of each patient were qualitatively and quantitatively analyzed. Results: Overall, 77 electrodes with 719 contacts were implanted, and 33 spontaneous seizures were recorded during long-term SEEG monitoring. Interictally, distinct electrophysiological patterns, including isolated intermittent spikes/sharp waves, burst spike and wave trains, paroxysmal fast discharges, periodic discharges, and high-frequency oscillations, were identified within the HH. Notably, synchronized or independent interictal discharges in the cortex were observed. Regarding the ictal discharges, the electrical onset pattern within the HH always started with abrupt giant shifts superimposed on low-voltage fast activity across patients. The gelastic seizure network mainly involved the HH, orbitofrontal areas, and cingulate gyrus. Seizures with automatisms and impaired awareness primarily propagated to mesial temporal lobes. Moreover, independent ictal discharges arising from the mesial temporal lobe were detected in three out of nine patients. Interpretation: This study comprehensively reveals intrinsic electrophysiological patterns and epileptogenic networks in vivo, providing new insights into the mechanisms underlying cortical and subcortical epileptogenesis.

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“…Rácz's et al study indicated that age at epilepsy onset was younger in patients with FCD compared to those with GNTs (9). Though 18 F-FDG PET can't contribute to the differentiation of FCD and GNTs, 11 Cmethionine PET identified a significant difference between them (10). But, up to now, 11 C-methionine PET was unavailable in most hospitals worldwide.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing Focal Cortical Dysplasia From Glioneuronal Tumors in Patients With Epilepsy by Machine Learning

et al. 2020

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Purpose: We are aiming to build a supervised machine learning-based classifier, in order to preoperatively distinguish focal cortical dysplasia (FCD) from glioneuronal tumors (GNTs) in patients with epilepsy.Methods: This retrospective study was comprised of 96 patients who underwent epilepsy surgery, with the final neuropathologic diagnosis of either an FCD or GNTs. Seven classical machine learning algorithms (i.e., Random Forest, SVM, Decision Tree, Logistic Regression, XGBoost, LightGBM, and CatBoost) were employed and trained by our dataset to get the classification model. Ten features [i.e., Gender, Past history, Age at seizure onset, Course of disease, Seizure type, Seizure frequency, Scalp EEG biomarkers, MRI features, Lesion location, Number of antiepileptic drug (AEDs)] were analyzed in our study.Results: We enrolled 56 patients with FCD and 40 patients with GNTs, which included 29 with gangliogliomas (GGs) and 11 with dysembryoplasic neuroepithelial tumors (DNTs). Our study demonstrated that the Random Forest-based machine learning model offered the best predictive performance on distinguishing the diagnosis of FCD from GNTs, with an F1-score of 0.9180 and AUC value of 0.9340. Furthermore, the most discriminative factor between FCD and GNTs was the feature “age at seizure onset” with the Chi-square value of 1,213.0, suggesting that patients who had a younger age at seizure onset were more likely to be diagnosed as FCD.Conclusion: The Random Forest-based machine learning classifier can accurately differentiate FCD from GNTs in patients with epilepsy before surgery. This might lead to improved clinician confidence in appropriate surgical planning and treatment outcomes.

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Scalp electroencephalographic biomarkers in epilepsy patients with focal cortical dysplasia

Cited by 29 publications

References 42 publications

Eating Epilepsy: Pathophysiologic Models, Electroclinical Phenotypes, and Outcome

Eating Epilepsy: Pathophysiologic Models, Electroclinical Phenotypes, and Outcome

Electrophysiological properties and seizure networks in hypothalamic hamartoma

Distinguishing Focal Cortical Dysplasia From Glioneuronal Tumors in Patients With Epilepsy by Machine Learning

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