Citations

AbstractArtificial intelligence (AI) allows data analysis and integration at an unprecedented granularity and scale. Here we review the technological advances, challenges, and future perspectives of using AI for electro‐clinical phenotyping of animal models and patients with epilepsy. In translational research, AI models accurately identify behavioral states in animal models of epilepsy, allowing identification of correlations between neural activity and interictal and ictal behavior. Clinical applications of AI‐based automated and semi‐automated analysis of audio and video recordings of people with epilepsy, allow significant data reduction and reliable detection and classification of major motor seizures. AI models can accurately identify electrographic biomarkers of epilepsy, such as spikes, high‐frequency oscillations, and seizure patterns. Integrating AI analysis of electroencephalographic, clinical, and behavioral data will contribute to optimizing therapy for patients with epilepsy.

Abstract Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulation’s behavioral and physiological effects depend on the stimulation target’s anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered the stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brain’s memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulation’s behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.

BACKGROUND AND OBJECTIVES: The anterior nucleus of the thalamus (ANT) is a common target for deep brain stimulation (DBS) for drug-resistant epilepsy (DRE). However, the surgical approach to the ANT remains challenging because of its unique anatomy. This study aims to summarize our experience with the posterior temporo-parietal extraventricular (TPEV) approach targeting the ANT for DBS in DRE. METHODS: We performed a retrospective analysis of patients with DRE who underwent ANT-DBS using the TPEV approach between January 2011 and February 2021. Subjects with at least 6-month follow-up were eligible. The final lead position and number of active contacts targeting the anteroventral nucleus (AV) of the ANT were assessed using Lead-DBS. Mean seizure frequency reduction percentage and responder rate (≥50% decrease in seizure frequency) were determined. RESULTS: Thirty-one patients (mean age: 32.9 years; 52% female patients) were included. The mean follow-up period was 27.6 months ± 13.9 (29, 16-36). The mean seizure frequency reduction percentage was 65% ± 26 (75, 50-82). Twenty-six of 31 participants (83%) were responders, P < .001. Two subjects (6%) were seizure-free for at least 6 months at the last evaluation. Antiepileptic drugs dose and/or number decreased in 17/31 subjects (55%). The success rate for placing at least 1 contact at AV was 87% (27/31 patients) bilaterally. The number of active contacts at the AV was significantly greater in the responder group, 3.1 ± 1.3 (3, 2-4) vs 1.8 ± 1.1 (2, 1-2.5); P = .041 with a positive correlation between the number of active contacts and seizure reduction percentage; r = 0.445, R 2 = 0.198, P = .012. CONCLUSION: The TPEV trajectory is a safe and effective approach to target the ANT for DBS. Future studies are needed to compare the clinical outcomes and target accuracy with the standard approaches.

Abstract Objective Previous studies suggested that patients with epilepsy might be able to forecast their own seizures. This study aimed to assess the relationships between premonitory symptoms, perceived seizure risk, and future and recent self‐reported and electroencephalographically (EEG)‐confirmed seizures in ambulatory patients with epilepsy in their natural home environments. Methods Long‐term e‐surveys were collected from patients with and without concurrent EEG recordings. Information obtained from the e‐surveys included medication adherence, sleep quality, mood, stress, perceived seizure risk, and seizure occurrences preceding the survey. EEG seizures were identified. Univariate and multivariate generalized linear mixed‐effect regression models were used to estimate odds ratios (ORs) for the assessment of the relationships. Results were compared with the seizure forecasting classifiers and device forecasting literature using a mathematical formula converting OR to equivalent area under the curve (AUC). Results Fifty‐four subjects returned 10 269 e‐survey entries, with four subjects acquiring concurrent EEG recordings. Univariate analysis revealed that increased stress (OR = 2.01, 95% confidence interval [CI] = 1.12–3.61, AUC = .61, p = .02) was associated with increased relative odds of future self‐reported seizures. Multivariate analysis showed that previous self‐reported seizures (OR = 5.37, 95% CI = 3.53–8.16, AUC = .76, p < .001) were most strongly associated with future self‐reported seizures, and high perceived seizure risk (OR = 3.34, 95% CI = 1.87–5.95, AUC = .69, p < .001) remained significant when prior self‐reported seizures were added to the model. No correlation with medication adherence was found. No significant association was found between e‐survey responses and subsequent EEG seizures. Significance Our results suggest that patients may tend to self‐forecast seizures that occur in sequential groupings and that low mood and increased stress may be the result of previous seizures rather than independent premonitory symptoms. Patients in the small cohort with concurrent EEG showed no ability to self‐predict EEG seizures. The conversion from OR to AUC values facilitates direct comparison of performance between survey and device studies involving survey premonition and forecasting.

AbstractDirect cortical stimulation has been applied in epilepsy for nearly a century and has experienced a renaissance, given unprecedented opportunities to probe, excite, and inhibit the human brain. Evidence suggests stimulation can increase diagnostic and therapeutic utility in patients with drug‐resistant epilepsies. However, choosing appropriate stimulation parameters is not a trivial issue, and is further complicated by epilepsy being characterized by complex brain state dynamics. In this article derived from discussions at the ICTALS 2022 Conference (International Conference on Technology and Analysis for Seizures), we succinctly review the literature on cortical stimulation applied acutely and chronically to the epileptic brain for localization, monitoring, and therapeutic purposes. In particular, we discuss how stimulation is used to probe brain excitability, discuss evidence on the usefulness of stimulation to trigger and stop seizures, review therapeutic applications of stimulation, and finally discuss how stimulation parameters are impacted by brain dynamics. Although research has advanced considerably over the past decade, there are still significant hurdles to optimizing use of this technique. For example, it remains unclear to what extent short timescale diagnostic biomarkers can predict long‐term outcomes and to what extent these biomarkers add information to already existing biomarkers from passive electroencephalographic recordings. Further questions include the extent to which closed loop stimulation offers advantages over open loop stimulation, what the optimal closed loop timescales may be, and whether biomarker‐informed stimulation can lead to seizure freedom. The ultimate goal of bioelectronic medicine remains not just to stop seizures but rather to cure epilepsy and its comorbidities.

Low frequency brain rhythms facilitate communication across large spatial regions in the brain and high frequency rhythms are thought to signify local processing among nearby assemblies. A heavily investigated mode by which these low frequency and high frequency phenomenon interact is phase-amplitude coupling (PAC). This phenomenon has recently shown promise as a novel electrophysiologic biomarker, in a number of neurologic diseases including human epilepsy. In 17 medically refractory epilepsy patients undergoing phase-2 monitoring for the evaluation of surgical resection and in whom temporal depth electrodes were implanted, we investigated the electrophysiologic relationships of PAC in epileptogenic (seizure onset zone or SOZ) and non-epileptogenic tissue (non-SOZ). That this biomarker can differentiate seizure onset zone from non-seizure onset zone has been established with ictal and pre-ictal data, but less so with interictal data. Here we show that this biomarker can differentiate SOZ from non-SOZ interictally and is also a function of interictal epileptiform discharges. We also show a differential level of PAC in slow-wave-sleep relative to NREM1-2 and awake states. Lastly, we show AUROC evaluation of the localization of SOZ is optimal when utilizing beta or alpha phase onto high-gamma or ripple band. The results suggest an elevated PAC may reflect an electrophysiology-based biomarker for abnormal/epileptogenic brain regions.