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

Evidence based noninvasive presurgical evaluation for patients with drug resistant epilepsies

PURPOSE OF REVIEW

To review the current practices and evidence for the diagnostic accuracy and the benefits of presurgical evaluation.

RECENT FINDINGS

Preoperative evaluation of patients with drug-resistant focal epilepsies and subsequent epilepsy surgery leads to a significant proportion of seizure-free patients. Even those who are not completely seizure free postoperatively often experience improved quality of life with better social integration. Systematic reviews and meta-analysis on the diagnostic accuracy are available for Video-electroencephalographic (EEG) monitoring, magnetic resonance imaging (MRI), electric and magnetic source imaging, and functional MRI for lateralization of language and memory. There are currently no evidence-based international guidelines for presurgical evaluation and epilepsy surgery.

SUMMARY

Presurgical evaluation is a complex multidisciplinary and multiprofessional clinical pathway. We rely on limited consensus-based recommendations regarding the required staffing or methodological expertise in epilepsy centers.

Neurostimulation targeting the epileptic focus: Current understanding and perspectives for treatment

For the one third of people with epilepsy whose seizures are not controlled with medications, targeting the seizure focus with neurostimulation can be an effective therapeutic strategy. In this focused review, we summarize a discussion of targeted neurostimulation modalities during a workshop held in Frankfurt, Germany in September 2023. Topics covered include: available devices for seizure focus stimulation; alternating current (AC) and direct current (DC) stimulation to reduce focal cortical excitability; modeling approaches to simulate DC stimulation; reconciling the efficacy of focal stimulation with the network theory of epilepsy; and the emerging concept of 'neurostimulation zones,' which are defined as cortical regions where focal stimulation is most effective for reducing seizures and which may or may not directly involve the seizure onset zone. By combining experimental data, modeling results, and clinical outcome analysis, rational selection of target regions and stimulation parameters is increasingly feasible, paving the way for a broader use of neurostimulation for epilepsy in the future.

An artificial intelligence-based pipeline for automated detection and localisation of epileptic sources from magnetoencephalography

Objective.Magnetoencephalography (MEG) is a powerful non-invasive diagnostic modality for presurgical epilepsy evaluation. However, the clinical utility of MEG mapping for localising epileptic foci is limited by its low efficiency, high labour requirements, and considerable interoperator variability. To address these obstacles, we proposed a novel artificial intelligence-based automated magnetic source imaging (AMSI) pipeline for automated detection and localisation of epileptic sources from MEG data.Approach.To expedite the analysis of clinical MEG data from patients with epilepsy and reduce human bias, we developed an autolabelling method, a deep-learning model based on convolutional neural networks and a hierarchical clustering method based on a perceptual hash algorithm, to enable the coregistration of MEG and magnetic resonance imaging, the detection and clustering of epileptic activity, and the localisation of epileptic sources in a highly automated manner. We tested the capability of the AMSI pipeline by assessing MEG data from 48 epilepsy patients.Main results.The AMSI pipeline was able to rapidly detect interictal epileptiform discharges with 93.31% ± 3.87% precision based on a 35-patient dataset (with sevenfold patientwise cross-validation) and robustly rendered accurate localisation of epileptic activity with a lobar concordance of 87.18% against interictal and ictal stereo-electroencephalography findings in a 13-patient dataset. We also showed that the AMSI pipeline accomplishes the necessary processes and delivers objective results within a much shorter time frame (∼12 min) than traditional manual processes (∼4 h).Significance.The AMSI pipeline promises to facilitate increased utilisation of MEG data in the clinical analysis of patients with epilepsy.

Clinical added value of interictal automated electrical source imaging in the presurgical evaluation of MRI-negative epilepsy: A real-life experience in 29 consecutive patients

OBJECTIVE

During the presurgical evaluation, manual electrical source imaging (ESI) provides clinically useful information in one-third of the patients but it is time-consuming and requires specific expertise. This prospective study aims to assess the clinical added value of a fully automated ESI analysis in a cohort of patients with MRI-negative epilepsy and describe its diagnostic performance, by evaluating sublobar concordance with stereo-electroencephalography (SEEG) results and surgical resection and outcome.

METHODS

All consecutive patients referred to the Center for Refractory Epilepsy (CRE) of St-Luc University Hospital (Brussels, Belgium) for presurgical evaluation between 15/01/2019 and 31/12/2020 meeting the inclusion criteria, were recruited to the study. Interictal ESI was realized on low-density long-term EEG monitoring (LD-ESI) and, whenever available, high-density EEG (HD-ESI), using a fully automated analysis (Epilog PreOp, Epilog NV, Ghent, Belgium). The multidisciplinary team (MDT) was asked to formulate hypotheses about the epileptogenic zone (EZ) location at sublobar level and make a decision on further management for each patient at two distinct moments: i) blinded to ESI and ii) after the presentation and clinical interpretation of ESI. Results leading to a change in clinical management were considered contributive. Patients were followed up to assess whether these changes lead to concordant results on stereo-EEG (SEEG) or successful epilepsy surgery.

RESULTS

Data from all included 29 patients were analyzed. ESI led to a change in the management plan in 12/29 patients (41%). In 9/12 (75%), modifications were related to a change in the plan of the invasive recording. In 8/9 patients, invasive recording was performed. In 6/8 (75%), the intracranial EEG recording confirmed the localization of the ESI at a sublobar level. So far, 5/12 patients, for whom the management plan was changed after ESI, were operated on and have at least one-year postoperative follow-up. In all cases, the EZ identified by ESI was included in the resection zone. Among these patients, 4/5 (80%) are seizure-free (ILAE 1) and one patient experienced a seizure reduction of more than 50% (ILAE 4).

CONCLUSIONS

In this single-center prospective study, we demonstrated the added value of automated ESI in the presurgical evaluation of MRI-negative cases, especially in helping to plan the implantation of depth electrodes for SEEG, provided that ESI results are integrated into the whole multimodal evaluation and clinically interpreted.

Source localization of ictal SEEG to predict postoperative seizure outcome

OBJECTIVE

Stereo-electroencephalography (SEEG) is inherently-three-dimensional and can be modeled using source localization. This study aimed to assess the validity of ictal SEEG source localization.

METHODS

The dominant frequency at ictal onset was used for source localization in the time and frequency domains using rotating dipoles and current density maps. Validity was assessed by concordance with the epileptologist-defined seizure onset zone (conventional SOZ) and the surgical treatment volume (TV) of seizure-free versus non-seizure-free patients.

RESULTS

Source localization was performed on 68 seizures from 27 patients. Median distance to nearest contact in the conventional SOZ was 7 (IQR 6-12) mm for time-domain dipoles. Current density predicted ictal activity with up to 86 % (60-87 %) accuracy. Distance from time-domain dipoles to the TV was smaller (P = 0.045) in seizure-free (2 [0-4] mm) versus non-seizure-free (12 [2-17] mm) patients, and predicted surgical outcome with 91 % sensitivity and 63 % specificity. Removing near-field data from contacts within the TV negated outcome prediction (P = 0.51).

CONCLUSIONS

Source localization of SEEG accurately mapped ictal onset compared with conventional interpretation. Proximity of dipoles to the TV predicted seizure outcome when near-field recordings were analyzed.

SIGNIFICANCE

Ictal SEEG source localization is useful in corroborating the epileptogenic zone, assuming near-field recordings are obtained.

Implementation of a Morphological Filter for Removing Spikes from the Epileptic Brain Signals to Improve Identification Ripples

Epilepsy is a very common disease affecting at least 1% of the population, comprising a number of over 50 million people. As many patients suffer from the drug-resistant version, the number of potential treatment methods is very small. However, since not only the treatment of epilepsy, but also its proper diagnosis or observation of brain signals from recordings are important research areas, in this paper, we address this very problem by developing a reliable technique for removing spikes and sharp transients from the baseline of the brain signal using a morphological filter. This allows much more precise identification of the so-called epileptic zone, which can then be resected, which is one of the methods of epilepsy treatment. We used eight patients with 5 KHz data set and depended upon the Staba 2002 algorithm as a reference to detect the ripples. We found that the average sensitivity and false detection rate of our technique are significant, and they are ∼94% and ∼14%, respectively.