Subclinical epileptiform activity in the Alzheimer continuum: association with disease, cognition and detection method.

BACKGROUND: Epileptic seizures are an established comorbidity of Alzheimer's disease (AD). Subclinical epileptiform activity (SEA) as detected by 24-h electroencephalography (EEG) or magneto-encephalography (MEG) has been reported in temporal regions of clinically diagnosed AD patients. Although epileptic activity in AD probably arises in the mesial temporal lobe, electrical activity within this region might not propagate to EEG scalp electrodes and could remain undetected by standard EEG. However, SEA might lead to faster cognitive decline in AD. AIMS: 1. To estimate the prevalence of SEA and interictal epileptic discharges (IEDs) in a well-defined cohort of participants belonging to the AD continuum, including preclinical AD subjects, as compared with cognitively healthy controls. 2. To evaluate whether long-term-EEG (LTM-EEG), high-density-EEG (hd-EEG) or MEG is superior to detect SEA in AD. 3. To characterise AD patients with SEA based on clinical, neuropsychological and neuroimaging parameters. METHODS: Subjects (n = 49) belonging to the AD continuum were diagnosed according to the 2011 NIA-AA research criteria, with a high likelihood of underlying AD pathophysiology. Healthy volunteers (n = 24) scored normal on neuropsychological testing and were amyloid negative. None of the participants experienced a seizure before. Subjects underwent LTM-EEG and/or 50-min MEG and/or 50-min hd-EEG to detect IEDs. RESULTS: We found an increased prevalence of SEA in AD subjects (31%) as compared to controls (8%) (p = 0.041; Fisher's exact test), with increasing prevalence over the disease course (50% in dementia, 27% in MCI and 25% in preclinical AD). Although MEG (25%) did not withhold a higher prevalence of SEA in AD as compared to LTM-EEG (19%) and hd-EEG (19%), MEG was significantly superior to detect spikes per 50 min (p = 0.002; Kruskall-Wallis test). AD patients with SEA scored worse on the RBANS visuospatial and attention subset (p = 0.009 and p = 0.05, respectively; Mann-Whitney U test) and had higher left frontal, (left) temporal and (left and right) entorhinal cortex volumes than those without. CONCLUSION: We confirmed that SEA is increased in the AD continuum as compared to controls, with increasing prevalence with AD disease stage. In AD patients, SEA is associated with more severe visuospatial and attention deficits and with increased left frontal, (left) temporal and entorhinal cortex volumes. TRIAL REGISTRATION: Clinicaltrials.gov, NCT04131491. 12/02/2020.

Semiautomatic interictal electric source localization based on long-term electroencephalographic monitoring: A prospective study.

OBJECTIVE: Electric source imaging (ESI) of interictal epileptiform discharges (IEDs) has shown significant yield in numerous studies; however, its implementation at most centers is labor- and cost-intensive. Semiautomatic ESI analysis (SAEA) has been proposed as an alternative and has previously shown benefit. Computer-assisted automatic spike cluster retrieval, averaging, and source localization are carried out for each cluster and are then reviewed by an expert neurophysiologist, to determine their relevance for the individual case. Here, we examine its yield in a prospective single center study. METHOD: Between 2017 and 2022, 122 patients underwent SAEA. Inclusion criteria for the current study were unifocal epilepsy disorder, epilepsy surgery with curative purpose, and postoperative follow-up of 2 years or more. All patients (N=40) had continuous video-electroencephalographic (EEG) monitoring with 37 scalp electrodes, which underwent SAEA. Forty patients matched our inclusion criteria. RESULTS: Twenty patients required intracranial monitoring; 13 were magnetic resonance imaging (MRI)-negative. Mean duration of analyzed EEG was 4.3 days (±3.1 days), containing a mean of 12 749 detected IEDs (±22 324). The sensitivity, specificity, and accuracy of SAEA for localizing the epileptogenic focus of the entire group were 74.3%, 80%, and 75%, respectively, leading to an odds ratio (OR) of 11.5 to become seizure-free if the source was included in the resection volume (p < .05). In patients with extratemporal lobe epilepsy, our results indicated an accuracy of 68% (OR=11.7). For MRI-negative patients (n = 13) and patients requiring intracranial EEG (n = 20), we found a similarly high accuracy of 84.6% (OR=19) and 75% (OR = 15.9), respectively. SIGNIFICANCE: In this prospective study of SAEA of long-term video-EEG, spanning several days, we found excellent localizing information and a high yield, even in difficult patient groups. This compares favorably to high-density ESI, most likely due to marked improved signal-to-noise ratio of the averaged IEDs. We propose including ESI, or SAEA, in the workup of all patients who are referred for epilepsy surgery.

Detection of interictal epileptiform discharges in an extended scalp EEG array and high-density EEG-A prospective multicenter study.

OBJECTIVES: High counts of averaged interictal epileptiform discharges (IEDs) are key components of accurate interictal electric source imaging (ESI) in patients with focal epilepsy. Automated detections may be time-efficient, but they need to identify the correct IED types. Thus we compared semiautomated and automated detection of IED types in long-term video-EEG (electroencephalography) monitoring (LTM) using an extended scalp EEG array and short-term high-density EEG (hdEEG) with visual detection of IED types and the seizure-onset zone (SOZ). METHODS: We prospectively recruited consecutive patients from four epilepsy centers who underwent both LTM with 40-electrode scalp EEG and short-term hdEEG with 256 electrodes. Only patients with a single circumscribed SOZ in LTM were included. In LTM and hdEEG, IED types were identified visually, semiautomatically and automatically. Concordances of semiautomated and automated detections in LTM and hdEEG, as well as visual detections in hdEEG, were compared against visually detected IED types and the SOZ in LTM. RESULTS: Fifty-two of 62 patients with LTM and hdEEG were included. The most frequent IED types per patient, detected semiautomatically and automatically in LTM and visually in hdEEG, were significantly concordant with the most frequently visually identified IED type in LTM and the SOZ. Semiautomated and automated detections of IED types in hdEEG were significantly concordant with visually identified IED types in LTM, only when IED types with more than 50 detected single IEDs were selected. The threshold of 50 detected IED in hdEEG was reached in half of the patients. For all IED types per patient, agreement between visual and semiautomated detections in LTM was high. SIGNIFICANCE: Semiautomated and automated detections of IED types in LTM show significant agreement with visually detected IED types and the SOZ. In short-term hdEEG, semiautomated detections of IED types are concordant with visually detected IED types and the SOZ in LTM if high IED counts were detected.

Automated ictal EEG source imaging: A retrospective, blinded clinical validation study.

OBJECTIVE: EEG source imaging (ESI) is a validated tool in the multimodal workup of patients with drug resistant focal epilepsy. However, it requires special expertise and it is underutilized. To circumvent this, automated analysis pipelines have been developed and validated for the interictal discharges. In this study, we present the clinical validation of an automated ESI for ictal EEG signals. METHODS: We have developed an automated analysis pipeline of ictal EEG activity, based on spectral analysis in source space, using an individual head model of six tissues. The analysis was done blinded to all other data. As reference standard, we used the concordance with the resected area and one-year postoperative outcome. RESULTS: We analyzed 50 consecutive patients undergoing epilepsy surgery (34 temporal and 16 extra-temporal). Thirty patients (60%) became seizure-free. The accuracy of the automated ESI was 74% (95% confidence interval: 59.66-85.37%). CONCLUSIONS: Automated ictal ESI has a high accuracy for localizing the seizure onset zone. SIGNIFICANCE: Automating the ESI of the ictal EEG signals will facilitate implementation of this tool in the presurgical evaluation.

Automated interictal source localisation based on high-density EEG.

PURPOSE: To study the accuracy of automated interictal EEG source localisation based on high-density EEG, and to compare it to low-density EEG. METHODS: Thirty patients operated for pharmacoresistant focal epilepsy were retrospectively examined. Twelve months after resective brain surgery, 18 were seizure-free or had 'auras' only, while 12 had persistence of disabling seizures. Presurgical 257-channel EEG lasting 3-20 h was down-sampled to 25, 40, and 204 channels for separate analyses. For each electrode setup, interictal spikes were detected, clustered, and averaged automatically before validation by an expert reviewer. An individual 6-layer finite difference head model and the standardised low-resolution electromagnetic tomography were used to localise the maximum source activity of the most prevalent spike. Sublobar concordance with the resected brain area was visually assessed and related to favourable vs. unfavourable postsurgical outcome. RESULTS: Depending on the EEG setup, epileptic spikes were detected in 21-24 patients (70-80%). The median number of single spikes per average was 470 (range 17-15,066). Diagnostic sensitivity of EEG source localisation was 58-75%, specificity was 50-67%, and overall accuracy was 55-71%. There were no significant differences between low- and high-density EEG setups with 25 to 257 electrodes. CONCLUSION: Automated high-density EEG source localisation provides meaningful information in the majority of cases. With hundreds of single spikes averaged, diagnostic accuracy is similar in high- and low-density EEG. Therefore, low-density EEG may be sufficient for interictal EEG source localisation if high numbers of spikes are available.

Automated electrical source imaging with scalp EEG to define the insular irritative zone: Comparison with simultaneous intracranial EEG.

OBJECTIVE: To evaluate the accuracy of automatedinterictallow-density electrical source imaging (LD-ESI) to define the insular irritative zone (IZ) by comparing the simultaneous interictal ESI localization with the SEEG interictal activity. METHODS: Long-term simultaneous scalp electroencephalography (EEG) and stereo-EEG (SEEG) with at least one depth electrode exploring the operculo-insular region(s) were analyzed. Automated interictal ESI was performed on the scalp EEG using standardized low-resolution brain electromagnetic tomography (sLORETA) and individual head models. A two-step analysis was performed: i) sublobar concordance betweencluster-based ESI localization and SEEG-based IZ; ii) time-locked ESI-/SEEG analysis. Diagnostic accuracy values were calculated using SEEG as reference standard. Subgroup analysis wascarried out, based onthe involvement of insular contacts in the seizure onset and patterns of insular interictal activity. RESULTS: Thirty patients were included in the study. ESI showed an overall accuracy of 53% (C.I. 29-76%). Sensitivity and specificity were calculated as 53% (C.I. 29-76%), 55% (C.I. 23-83%) respectively. Higher accuracy was found in patients with frequent and dominant interictal insular spikes. CONCLUSIONS: LD-ESI defines with good accuracy the insular implication in the IZ, which is not possible with classical interictalscalpEEG interpretation. SIGNIFICANCE: Automated LD-ESI may be a valuable additional tool to characterize the epileptogenic zone in epilepsies with suspected insular involvement.

Ictal EEG source imaging and connectivity to localize the seizure onset zone in extratemporal lobe epilepsy.

PURPOSE: To evaluate the yield of Functional Connectivity (FC) in addition to low-density ictal Electrical Source Imaging (ESI) in extratemporal lobe epilepsy (ETLE), using an automated algorithm for analysis. METHOD: Long-term EEG monitoring of consecutive ETLE patients who underwent surgery was reviewed by epileptologists, and seizure onsets characterized by rhythmical activity were identified. A spectrogram-based algorithm was developed to select objectively the parameters of ESI analysis. Two methods for SOZ localization were compared: i) ESI power, based on LORETA exclusively; ii) ESI + FC, including a Granger causality-based connectivity analysis. Results were determined at a sublobar level. The resection zone, in relation to 1-year follow-up surgical outcome, was considered as reference standard for diagnostic accuracy analyses. RESULTS: Ninety-four seizures from 24 patients were analyzed. At seizure-level, ESI power showed 36 % sensitivity and 72 % specificity (accuracy: 45 %). ESI + FC significantly improved the accuracy, with 52 % sensitivity and 84 % specificity (accuracy: 61 %, p = 0.04). Results of ESI + FC were equally valuable in patients with lateralized or bilateral/generalized visual interpretation of ictal EEG. In a patient level sub-analysis, upon blinded clinical interpretation, ESI + FC showed a correct localization in 67 % of patients and substantial inter-rater agreement (kappa = 0.64), against 27 % achieved by ESI power, with fair inter-rater agreement (kappa = 0.37). CONCLUSION: FC significantly improves SOZ localization compared to ESI solely in ETLE. Ictal ESI + FC could represent a novel option in the armamentarium of presurgical evaluation, aiding also in patients with visually non-localizable scalp ictal EEG. Prospective studies evaluating the clinical added value of automated low-density ictal ESI may be justified.

Automated EEG source imaging: A retrospective, blinded clinical validation study.

OBJECTIVE: To evaluate the accuracy of automated EEG source imaging (ESI) in localizing epileptogenic zone. METHODS: Long-term EEG, recorded with the standard 25-electrode array of the IFCN, from 41 consecutive patients with focal epilepsy who underwent resective surgery, were analyzed blinded to the surgical outcome. The automated analysis comprised spike-detection, clustering and source imaging at the half-rising time and at the peak of each spike-cluster, using individual head-models with six tissue-layers and a distributed source model (sLORETA). The fully automated approach presented ESI of the cluster with the highest number of spikes, at the half-rising time. In addition, a physician involved in the presurgical evaluation of the patients, evaluated the automated ESI results (up to four clusters per patient) in clinical context and selected the dominant cluster and the analysis time-point (semi-automated approach). The reference standard was location of the resected area and outcome one year after operation. RESULTS: Accuracy was 61% (95% CI: 45-76%) for the fully automated approach and 78% (95% CI: 62-89%) for the semi-automated approach. CONCLUSION: Automated ESI has an accuracy similar to previously reported neuroimaging methods. SIGNIFICANCE: Automated ESI will contribute to increased utilization of source imaging in the presurgical evaluation of patients with epilepsy.