The relationship between the interictal epileptiform discharge source connectivity and cortical structural couplings in temporal lobe epilepsy.

Objective: The objective of this study was to explore the relation between interictal epileptiform discharge (IED) source connectivity and cortical structural couplings (SCs) in temporal lobe epilepsy (TLE). Methods: High-resolution 3D-MRI and 32-sensor EEG data from 59 patients with TLE were collected. Principal component analysis was performed on the morphological data on MRI to obtain the cortical SCs. IEDs were labeled from EEG data and averaged. The standard low-resolution electromagnetic tomography analysis was performed to locate the source of the average IEDs. Phase-locked value was used to evaluate the IED source connectivity. Finally, correlation analysis was used to compare the IED source connectivity and the cortical SCs. Results: The features of the cortical morphology in left and right TLE were similar across four cortical SCs, which could be mainly described as the default mode network, limbic regions, connections bilateral medial temporal, and connections through the ipsilateral insula. The IED source connectivity at the regions of interest was negatively correlated with the corresponding cortical SCs. Significance: The cortical SCs were confirmed to be negatively related to IED source connectivity in patients with TLE as detected with MRI and EEG coregistered data. These findings suggest the important role of intervening IEDs in treating TLE.

Epileptic seizure focus detection from interictal electroencephalogram: a survey.

Electroencephalogram (EEG) is one of most effective clinical diagnosis modalities for the localization of epileptic focus. Most current AI solutions use this modality to analyze the EEG signals in an automated manner to identify the epileptic seizure focus. To develop AI system for identifying the epileptic focus, there are many recently-published AI solutions based on biomarkers or statistic features that utilize interictal EEGs. In this review, we survey these solutions and find that they can be divided into three main categories: (i) those that use of biomarkers in EEG signals, including high-frequency oscillation, phase-amplitude coupling, and interictal epileptiform discharges, (ii) others that utilize feature-extraction methods, and (iii) solutions based upon neural networks (an end-to-end approach). We provide a detailed description of seizure focus with clinical diagnosis methods, a summary of the public datasets that seek to reduce the research gap in epilepsy, recent novel performance evaluation criteria used to evaluate the AI systems, and guidelines on when and how to use them. This review also suggests a number of future research challenges that must be overcome in order to design more efficient computer-aided solutions to epilepsy focus detection.

Improving automated diagnosis of epilepsy from EEGs beyond IEDs.

Objective.Clinical diagnosis of epilepsy relies partially on identifying interictal epileptiform discharges (IEDs) in scalp electroencephalograms (EEGs). This process is expert-biased, tedious, and can delay the diagnosis procedure. Beyond automatically detecting IEDs, there are far fewer studies on automated methods to differentiate epileptic EEGs (potentially without IEDs) from normal EEGs. In addition, the diagnosis of epilepsy based on a single EEG tends to be low. Consequently, there is a strong need for automated systems for EEG interpretation. Traditionally, epilepsy diagnosis relies heavily on IEDs. However, since not all epileptic EEGs exhibit IEDs, it is essential to explore IED-independent EEG measures for epilepsy diagnosis. The main objective is to develop an automated system for detecting epileptic EEGs, both with or without IEDs. In order to detect epileptic EEGs without IEDs, it is crucial to include EEG features in the algorithm that are not directly related to IEDs.Approach.In this study, we explore the background characteristics of interictal EEG for automated and more reliable diagnosis of epilepsy. Specifically, we investigate features based on univariate temporal measures (UTMs), spectral, wavelet, Stockwell, connectivity, and graph metrics of EEGs, besides patient-related information (age and vigilance state). The evaluation is performed on a sizeable cohort of routine scalp EEGs (685 epileptic EEGs and 1229 normal EEGs) from five centers across Singapore, USA, and India.Main results.In comparison with the current literature, we obtained an improved Leave-One-Subject-Out (LOSO) cross-validation (CV) area under the curve (AUC) of 0.871 (Balanced Accuracy (BAC) of 80.9%) with a combination of three features (IED rate, and Daubechies and Morlet wavelets) for the classification of EEGs with IEDs vs. normal EEGs. The IED-independent feature UTM achieved a LOSO CV AUC of 0.809 (BAC of 74.4%). The inclusion of IED-independent features also helps to improve the EEG-level classification of epileptic EEGs with and without IEDs vs. normal EEGs, achieving an AUC of 0.822 (BAC of 77.6%) compared to 0.688 (BAC of 59.6%) for classification only based on the IED rate. Specifically, the addition of IED-independent features improved the BAC by 21% in detecting epileptic EEGs that do not contain IEDs.Significance.These results pave the way towards automated detection of epilepsy. We are one of the first to analyze epileptic EEGs without IEDs, thereby opening up an underexplored option in epilepsy diagnosis.

Connectivity increases during spikes and spike-free periods in self-limited epilepsy with centrotemporal spikes.

OBJECTIVE: To understand the impact of interictal spikes on brain connectivity in patients with Self-Limited Epilepsy with Centrotemporal Spikes (SeLECTS). METHODS: Electroencephalograms from 56 consecutive SeLECTS patients were segmented into periods with and without spikes. Connectivity between electrodes was calculated using the weighted phase lag index. To determine if there are chronic alterations in connectivity in SeLECTS, we compared spike-free connectivity to connectivity in 65 matched controls. To understand the acute impact of spikes, we compared connectivity immediately before, during, and after spikes versus baseline, spike-free connectivity. We explored whether behavioral state, spike laterality, or antiseizure medications affected connectivity. RESULTS: Children with SeLECTS had markedly higher connectivity than controls during sleep but not wakefulness, with greatest difference in the right hemisphere. During spikes, connectivity increased globally; before and after spikes, left frontal and bicentral connectivity increased. Right hemisphere connectivity increased more during right-sided than left-sided spikes; left hemisphere connectivity was equally affected by right and left spikes. CONCLUSIONS: SeLECTS patient have persistent increased connectivity during sleep; connectivity is further elevated during the spike and perispike periods. SIGNIFICANCE: Testing whether increased connectivity impacts cognition or seizure susceptibility in SeLECTS and more severe epilepsies could help determine if spikes should be treated.

Dynamic functional disturbances of brain network in seizure-related cognitive outcomes.

Epilepsy is a prevalent neurologic disorder affecting approximately 50 million people worldwide. Cognitive dysfunction induced by seizures is one of the severe comorbidities of epilepsy and epileptic syndrome, which has a negative impact on epileptic patients' quality of life. Several mechanisms may be associated with cognitive impairment in patients with epilepsy. Here, we review how the dynamic functional alterations of brain network influence seizure-related cognitive outcomes.

Effect of partial drug withdrawal on the lateralization of interictal epileptiform discharges and its relationship to surgical outcome in patients with hippocampal sclerosis.

OBJECTIVE: To assess changes in the relative lateralization of interictal epileptiform discharges (IEDs) and interictal EEG prognostic value in terms of surgical outcome between periods with full medication (FMP) and reduced medication (RMP) in patients with temporal lobe epilepsy (TLE) associated with hippocampal sclerosis (HS). METHODS: Interictal scalp EEGs of 43 patients were evaluated for the presence of IEDs separately in a waking state (WS) and sleeping state (SS) during FMP and RMP. In each period, patients were categorized as having unitemporal or bitemporal IEDs. Surgical outcome was classified at year 1 after surgery and at last follow-up visit as Engel I or Engel II-IV; and alternatively as completely seizure-free or not seizure-free. RESULTS: There were significant changes in relative IED lateralization between FMP and RMP during SS. The representation of patients with unitemporal IEDs declined from 37 (86%) in FMP during SS to 25 (58%) in RMP during SS (p=0.003). At year 1 after surgery, the relative IED lateralization is a predictive factor for surgical outcome defined as Engel I vs. Engel II-IV in both FMP during WS (p=0.037) and during SS (p=0.007), and for surgical outcome defined as completely seizure-free vs. not seizure-free in FMP during SS (p=0.042). At last follow up visit, the relative IED lateralization is a predictor for outcome defined as Engel I vs. Engel II-IV in FMP during SS (p=0.020), and for outcome defined as completely seizure-free vs. not seizure-free in both FMP during WS (p=0.043) and in FMP during SS (p=0.015). When stepwise logistic regression analysis was applied, only FMP during SS was found to be an independent predictor for surgical outcome at year 1 after surgery (completely seizure-free vs. not seizure-free p=0.032, Engel I vs. Engel II-IV p=0.006) and at last follow-up visit (completely seizure-free vs. not seizure-free p=0.024, Engel I vs. Engel II-IV p=0.017). Gender was found to be independent predictor for surgical efficacy at year 1 if the outcome was defined as completely seizure-free vs. not seizure-free (p=0.036). CONCLUSION: The predictive value of relative IED lateralization with respect to surgical outcome in interictal EEG is present only during FMP; the predictive value decreases with the reduction of AEDs caused by the change of relative IED lateralization.