Value of ultralong-term subcutaneous EEG monitoring for treatment decisions in temporal lobe epilepsy: A case report

Treatment decisions in epilepsy critically depend on information on the course of the disease, its severity and options for specific local interventions. We here report a patient with pharmaco-resistant non-lesional temporal lobe epilepsy with evidence for predominant right temporal epileptogenesis. While seizure frequency had been grossly underestimated for many years, ultralong-term monitoring with a subcutaneous EEG device revealed actual seizure frequency (66 over 11 months vs four patient-documented seizures), providing objective data on treatment efficacy and additional supportive lateralizing information that played a decisive role for the choice of surgical treatment, which had been rejected by the patient prior to this information.

Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence

Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.

Is formal scoring better than just looking? A comparison of subjective and objective scoring methods of the Rey Complex Figure Test for lateralizing temporal lobe epilepsy

ObjectiveNeuropsychologists labor over scoring the Rey Complex Figure Test (RCFT), a measure of visuospatial functioning and nonverbal memory. Compelling arguments suggest that pathognomonic signs of the RCFT are observable to the "naked eye." Standard scoring systems are insensitive to lateralizing temporal lobe epilepsy (TLE) and alternative "qualitative" scoring systems are ineffective and time-consuming. Method: We examined accuracy of TLE lateralization using subjective classifications and standard scoring. Participants were 84 TLE patients (53 female; mean age₌36yrs) and 46 controls (27 female; mean age = 27.5). The former were classified as right (n = 41) or left (n = 43) TLE by neurologists using EEG and MRI studies. RCFT were scored using standard scoring with cut-offs of z ≤ -2 classified as impaired and were rated as "characteristic" of RTLE (Ugly) or LTLE (Not Ugly) performance by neuropsychologists. Accuracy of seizure lateralization for both methods was examined. Results: Neuropsychologists' ratings accuracy were at or below chance. Standard scoring criteria showed chance or slightly better lateralization prediction. Standard scoring predicted RTLE laterality more accurately than subjective ratings for copy trials; standard scoring was no better at lateralizing RTLE with delays. Subjective ratings were better at distinguishing TLE patients from controls. Conclusion: Findings highlight concerns regarding the usefulness of the RCFT in TLE lateralization, regardless of scoring approach.

Deep Learning of Simultaneous Intracranial and Scalp EEG for Prediction, Detection, and Lateralization of Mesial Temporal Lobe Seizures

In people with drug resistant epilepsy (DRE), seizures are unpredictable, often occurring with little or no warning. The unpredictability causes anxiety and much of the morbidity and mortality of seizures. In this work, 102 seizures of mesial temporal lobe onset were analyzed from 19 patients with DRE who had simultaneous intracranial EEG (iEEG) and scalp EEG as part of their surgical evaluation. The first aim of this paper was to develop machine learning models for seizure prediction and detection (i) using iEEG only, (ii) scalp EEG only and (iii) jointly analyzing both iEEG and scalp EEG. The second goal was to test if machine learning could detect a seizure on scalp EEG when that seizure was not detectable by the human eye (surface negative) but was seen in iEEG. The final question was to determine if the deep learning algorithm could correctly lateralize the seizure onset. The seizure detection and prediction problems were addressed jointly by training Deep Neural Networks (DNN) on 4 classes: non-seizure, pre-seizure, left mesial temporal onset seizure and right mesial temporal onset seizure. To address these aims, the classification accuracy was tested using two deep neural networks (DNN) against 3 different types of similarity graphs which used different time series of EEG data. The convolutional neural network (CNN) with the Waxman similarity graph yielded the highest accuracy across all EEG data (iEEG, scalp EEG and combined). Specifically, 1 second epochs of EEG were correctly assigned to their seizure, pre-seizure, or non-seizure category over 98% of the time. Importantly, the pre-seizure state was classified correctly in the vast majority of epochs (>97%). Detection from scalp EEG data alone of surface negative seizures and the seizures with the delayed scalp onset (the surface negative portion) was over 97%. In addition, the model accurately lateralized all of the seizures from scalp data, including the surface negative seizures. This work suggests that highly accurate seizure prediction and detection is feasible using either intracranial or scalp EEG data. Furthermore, surface negative seizures can be accurately predicted, detected and lateralized with machine learning even when they are not visible to the human eye.

Altered integrity of corpus callosum in generalized epilepsy in relation to seizure lateralization after corpus callosotomy

OBJECTIVE

Generalized-onset seizures are usually conceptualized as engaging bilaterally distributed networks with no clear focus. However, the authors previously reported a case series demonstrating that in some patients with generalized-onset seizures, focal seizure onset could be discovered after corpus callosotomy. The corpus callosum is considered to be a major pathway for seizure generalization in this group of patients. The authors hypothesized that, in patients with generalized-onset seizures, the structure of the corpus callosum could be different between patients who have lateralized seizures and those who have nonlateralized seizures after corpus callosotomy. The authors aimed to evaluate the structural difference through statistical analysis of diffusion tensor imaging (DTI) scalars between these two groups of patients.

METHODS

Thirty-two patients diagnosed with generalized-onset motor seizures and without an MRI lesion were included in this study. Among them, 16 patients developed lateralized epileptic activities after corpus callosotomy, and the remaining 16 patients continued to have nonlateralized seizures after corpus callosotomy. Presurgical DTI studies were acquired to quantify the structural integrity of the corpus callosum.

RESULTS

The DTI analysis showed significant reduction of fractional anisotropy (FA) and increase in radial diffusivity (RD) in the body of the corpus callosum in the lateralized group compared with the nonlateralized group.

CONCLUSIONS

The authors' findings indicate the existence of different configurations of bilateral epileptic networks in generalized epilepsy. Generalized seizures with focal onset relying on rapid spread through the corpus callosum might cause more structural damage related to demyelination in the corpus callosum, showing reduced FA and increased RD. This study suggests that presurgical DTI analysis of the corpus callosum might predict the seizure lateralization after corpus callosotomy.

Interactive effects of seizure frequency and lateralization on intratemporal effective connectivity in temporal lobe epilepsy

OBJECTIVE

Patients with temporal lobe epilepsy (TLE) show brain connectivity changes in association with cognitive impairment. Seizure frequency and lateralization are 2 important clinical factors that characterize epileptic seizures. In this study, we sought to examine an interactive effect of the 2 seizure factors on intratemporal effective connectivity based on resting-state functional magnetic resonance imaging (rsfMRI) in patients with TLE.

METHODS

For rsfMRI data acquired from 48 TLE patients and 45 healthy controls, we applied stochastic dynamical causal modeling to infer effective connectivity between 3 medial temporal lobe (MTL) regions, including the hippocampus (Hipp), parahippocampal gyrus (PHG), and amygdala (Amyg), ipsilateral to the seizure focus. We searched for the effect of the 2 seizure factors, seizure frequency (good vs poor seizure control) and lateralization (left vs right TLE), on connection strengths and their relationship with the level of verbal memory and language impairment.

RESULTS

Impairment of verbal memory and language function was mainly affected by seizure lateralization, consistent with preferential involvement of the left MTL in verbal mnemonic processing. For the fully connected model, which was selected as the effective connectivity structure that best explained the observed rsfMRI time series, alterations in connection strengths were primarily influenced by seizure frequency; there was an increase in the strength of the Hipp to PHG connection in TLE patients with poor seizure control, whereas the strength of the Amyg to PHG connection increased in those with good seizure control. Furthermore, the association between connection strength alterations and cognitive impairment was interactively affected by both seizure frequency and lateralization.

SIGNIFICANCE

These findings suggest an interactive effect as well as an individual effect of seizure frequency and lateralization on neuroimaging features and cognitive function. This potential interaction needs to be evaluated in the consideration of multiple seizure factors.