DOI: 10.1101/2023.05.21.541570

New vistas for epileptogenic zone (EZ) localization and interpretation

S. H.Wang G. Arnulfo L. Nobili V. Myrov P. Ferrari P. Ciuciu S. Palva J. M. Palva

S. H.Wang G. Arnulfo L. Nobili ...+4 J. M. Palva

摘要

Drug resistant epilepsy (DRE) accounts for 30-40% of all diagnosed cases of epilepsy. To date, surgical disruption of the epileptogenic zone (EZ) is the most effective treatment for seizure control in DRE. The epileptogenic zone (EZ) refers to a pathological brain network that is necessary and sufficient for seizures to emerge. Conventional EZ-localization markers, however, localize EZ inconsistently and incompletely, which leads to only 30-80% of patients achieving long-term seizure freedom after the surgery. Epileptic seizures are episodes of catastrophic hypersynchrony originating from EZ and may synchronize and spread to other brain areas. We hypothesize that in between seizures, the EZ operates in a dynamic state that mechanistically predisposes it to having seizures. This brain state leads to aberrant spontaneous brain dynamics, which can be identified with electrophysiological recordings and thereby used to delineate the EZ. To test the aberrant dynamics hypothesis for epileptogenic mechanisms, we built a generative model for cortical oscillations. When the model was controlled by strong positive feedback, it produced highly bistable oscillations in a critical-like regime of scale-free dynamics. The anomalies in this dynamic regime included inhibition-dominance and hyper-irritability thereby a minuscule increase in connectivity can lead to abrupt seizure-like hypersynchrony onset, i.e., an increased seizure risk. Clinical experts identified the "EpiNet " brain areas where ictal activity emerged and propagated. We assessed local dynamics and phase synchrony using inter-ictal resting-state SEEG recorded from 64 DRE patients. We then used these assessments to train supervised classifiers to identify EpiNet from "NonEpiNet" that did not show seizures. Combining local and synchrony assessments yielded optimal classification on cohort and individual level. To complement supervised classification, a cohort-level tentative pathological cluster was identified using unsupervised classification. In this cluster, EpiNet and a sizable number of NonEpiNet showed elevated 2-5.4 Hz synchrony with concurring excessive bistability and inhibition-dominance in 45-225 Hz oscillations. Combining all features yielded better EpiNet-classification than using single features (area under the curve reaching 0.85 vs 0.6-0.7, respectively). This explains why previous studies reported inaccurate EZ-localization with a single marker and also indicated that the aberrant dynamics of EZ indeed have a local and a large-scale aspect. The contacts forming the pathological cluster globally engaged in elevated synchrony and locally showed striking resemblance to our model in the increased seizure risk state. The NonEpiNet from this cluster did not engage in seizures and thus conventionally would have been regarded as healthy. The EpiNet contacts partitioned into the tentative healthy clusters, however, did not bear significant features of aberrant dynamics, and we postulated that these atypical EpiNet contacts could be non-essential to the EZ network. Our findings thus offer novel evidence to support the multi-component hypothesis for EZ. We advanced here a promising novel approach for EZ-localization, and more future efforts should be directed into investigating the pathological-like NonEpiNet revealed here and with individual specificity and pathological substrates considered.

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