Citations

AbstractThere is a paucity of data to guide anterior nucleus of the thalamus (ANT) deep brain stimulation (DBS) with brain sensing. The clinical Medtronic Percept DBS device provides constrained brain sensing power within a frequency band (power‐in‐band [PIB]), recorded in 10‐min averaged increments. Here, four patients with temporal lobe epilepsy were implanted with an investigational device providing full bandwidth chronic intracranial electroencephalogram (cEEG) from bilateral ANT and hippocampus (Hc). ANT PIB‐based seizure detection was assessed. Detection parameters were cEEG PIB center frequency, bandwidth, and epoch duration. Performance was evaluated against epileptologist‐confirmed Hc seizures, and assessed by area under the precision‐recall curve (PR‐AUC). Data included 99 days of cEEG, and 20, 278, 3, and 18 Hc seizures for Subjects 1–4. The best detector had 7‐Hz center frequency, 5‐Hz band width, and 10‐s epoch duration (group PR‐AUC = .90), with 75% sensitivity and .38 false alarms per day for Subject 1, and 100% and .0 for Subjects 3 and 4. Hc seizures in Subject 2 did not propagate to ANT. The relative change of ANT PIB was maximal ipsilateral to seizure onset for all detected seizures. Chronic ANT and Hc recordings provide direct guidance for ANT DBS with brain sensing.

Abstract Objective This study aims to evaluate the role of scalp electroencephalography (EEG; ictal and interictal patterns) in predicting resective epilepsy surgery outcomes. We use the data to further develop a nomogram to predict seizure freedom. Methods We retrospectively reviewed the scalp EEG findings and clinical data of patients who underwent surgical resection at three epilepsy centers. Using both EEG and clinical variables categorized into 13 isolated candidate predictors and 6 interaction terms, we built a multivariable Cox proportional hazards model to predict seizure freedom 2 years after surgery. Harrell's step‐down procedure was used to sequentially eliminate the least‐informative variables from the model until the change in the concordance index (c‐index) with variable removal was less than 0.01. We created a separate model using only clinical variables. Discrimination of the two models was compared to evaluate the role of scalp EEG in seizure‐freedom prediction. Results Four hundred seventy patient records were analyzed. Following internal validation, the full Clinical + EEG model achieved an optimism‐corrected c‐index of 0.65, whereas the c‐index of the model without EEG data was 0.59. The presence of focal to bilateral tonic‐clonic seizures (FBTCS), high preoperative seizure frequency, absence of hippocampal sclerosis, and presence of nonlocalizable seizures predicted worse outcome. The presence of FBTCS had the largest impact for predicting outcome. The analysis of the models’ interactions showed that in patients with unilateral interictal epileptiform discharges (IEDs), temporal lobe surgery cases had a better outcome. In cases with bilateral IEDs, abnormal magnetic resonance imaging (MRI) predicted worse outcomes, and in cases without IEDs, patients with extratemporal epilepsy and abnormal MRI had better outcomes. Significance This study highlights the value of scalp EEG, particularly the significance of IEDs, in predicting surgical outcome. The nomogram delivers an individualized prediction of postoperative outcome, and provides a unique assessment of the relationship between the outcome and preoperative findings.

Abstract Quantitative volumetric brain MRI measurement is important in research applications, but translating it into patient care is challenging. We explore the incorporation of clinical automated quantitative MRI measurements in statistical models predicting outcomes of surgery for temporal lobe epilepsy. Four hundred and thirty-five patients with drug-resistant epilepsy who underwent temporal lobe surgery at Cleveland Clinic, Mayo Clinic and University of Campinas were studied. We obtained volumetric measurements from the pre-operative T1-weighted MRI using NeuroQuant, a Food and Drug Administration approved software package. We created sets of statistical models to predict the probability of complete seizure-freedom or an Engel score of I at the last follow-up. The cohort was randomly split into training and testing sets, with a ratio of 7:3. Model discrimination was assessed using the concordance statistic (C-statistic). We compared four sets of models and selected the one with the highest concordance index. Volumetric differences in pre-surgical MRI located predominantly in the frontocentral and temporal regions were associated with poorer outcomes. The addition of volumetric measurements to the model with clinical variables alone increased the model’s C-statistic from 0.58 to 0.70 (right-sided surgery) and from 0.61 to 0.66 (left-sided surgery) for complete seizure freedom and from 0.62 to 0.67 (right-sided surgery) and from 0.68 to 0.73 (left-sided surgery) for an Engel I outcome score. 57% of patients with extra-temporal abnormalities were seizure-free at last follow-up, compared to 68% of those with no such abnormalities (P-value = 0.02). Adding quantitative MRI data increases the performance of a model developed to predict post-operative seizure outcomes. The distribution of the regions of interest included in the final model supports the notion that focal epilepsies are network disorders and that subtle cortical volume loss outside the surgical site influences seizure outcome.

Summary: Electrical brain stimulation is an established therapy for movement disorders, epilepsy, obsessive compulsive disorder, and a potential therapy for many other neurologic and psychiatric disorders. Despite significant progress and FDA approvals, there remain significant clinical gaps that can be addressed with next generation systems. Integrating wearable sensors and implantable brain devices with off-the-body computing resources (smart phones and cloud resources) opens a new vista for dense behavioral and physiological signal tracking coupled with adaptive stimulation therapy that should have applications for a range of brain and mind disorders. Here, we briefly review some history and current electrical brain stimulation applications for epilepsy, deep brain stimulation and responsive neurostimulation, and emerging applications for next generation devices and systems.

Abstract BACKGROUND The Food and Drug Administration approved the deep brain stimulation of the anterior nucleus of the thalamus (ANT-DBS) as an adjunctive therapy for drug-resistant epilepsy (DRE) in the United States in 2018. The DBS Therapy for Epilepsy Post-Approval Study is further evaluating the safety and effectiveness of ANT-DBS among different patients’ groups. For this study, devices for vagus nerve stimulation (VNS) must be removed prior to enrolment. OBJECTIVE To investigate the outcomes of concomitant ANT-DBS and VNS treatment for DRE. METHODS A retrospective analysis was performed for 33 patients who underwent ANT-DBS using previous VNS to define distinct subgroups: standard ANT-DBS (9 subjects), ANT-DBS with functional VNS (12 subjects), and ANT-DBS with the VNS implantable pulse generator explanted or turned off at the time of the DBS (12 subjects). Effectiveness and safety data were analyzed across the whole population and among subgroups. RESULTS A mean decrease in seizure frequency of 55% was observed after a mean follow-up of 25.5 mo. Approximately 67% of patients experienced ≥50% reduction in seizure frequency. Seizure reduction percentage was not significantly different among groups. Approximately 50% of subjects with no appreciable improvement and 75% of those who showed benefit after VNS (including improvement in seizure frequency, seizure severity, and seizure duration or quality of life) achieved a seizure reduction ≥50% after ANT-DBS surgery. There were no complications related to concomitant VNS and ANT-DBS. CONCLUSION ANT-DBS for DRE provides excellent results despite previous and ongoing VNS therapy. Removal of VNS does not appear to be necessary before ANT-DBS.

Abstract Routine scalp EEG is essential in the clinical diagnosis and management of epilepsy. However, a normal scalp EEG (based on expert visual review) recorded from a patient with epilepsy can cause delays in diagnosis and clinical care delivery. Here, we investigated whether normal EEGs might contain subtle electrophysiological clues of epilepsy. Specifically, we investigated (i) whether there are indicators of abnormal brain electrophysiology in normal EEGs of epilepsy patients, and (ii) whether such abnormalities are modulated by the side of the brain generating seizures in focal epilepsy. We analysed awake scalp EEG recordings of age-matched groups of 144 healthy individuals and 48 individuals with drug-resistant focal epilepsy who had normal scalp EEGs. After preprocessing, using a bipolar montage of eight channels, we extracted the fraction of spectral power in the alpha band (8–13 Hz) relative to a wide band of 0.5–40 Hz within 10-s windows. We analysed the extracted features for (i) the extent to which people with drug-resistant focal epilepsy differed from healthy subjects, and (ii) whether differences within the drug-resistant focal epilepsy patients were related to the hemisphere generating seizures. We then used those differences to classify whether an EEG is likely to have been recorded from a person with drug-resistant focal epilepsy, and if so, the epileptogenic hemisphere. Furthermore, we tested the significance of these differences while controlling for confounders, such as acquisition system, age and medications. We found that the fraction of alpha power is generally reduced (i) in drug-resistant focal epilepsy compared to healthy controls, and (ii) in right-handed drug-resistant focal epilepsy subjects with left hemispheric seizures compared to those with right hemispheric seizures, and that the differences are most prominent in the frontal and temporal regions. The fraction of alpha power yielded area under curve values of 0.83 in distinguishing drug-resistant focal epilepsy from healthy and 0.77 in identifying the epileptic hemisphere in drug-resistant focal epilepsy patients. Furthermore, our results suggest that the differences in alpha power are greater when compared with differences attributable to acquisition system differences, age and medications. Our findings support that EEG-based measures of normal brain function, such as the normalized spectral power of alpha activity, may help identify patients with epilepsy even when an EEG does not contain any epileptiform activity, recorded seizures or other abnormalities. Although alpha abnormalities are unlikely to be disease-specific, we propose that such abnormalities may provide a higher pre-test probability for epilepsy when an individual being screened for epilepsy has a normal EEG on visual assessment.

Implantable devices for controlling medically intractable seizures nondestructively are rapidly advancing. These offer reversible, potentially, restorative options beyond traditional, surgical procedures, which rely, largely on resection or ablation of selected brain sites. Several lines of, investigation aimed at improving efficacy of these devices are discussed, ranging from identifying novel subcortical, white matter, or cell-type specific targets to engineering advances for adaptive techniques based- on continuous, dynamic system analysis.

Significance Millions of people affected by epilepsy may undergo surgical resection of the epileptic tissues to stop seizures if such epileptic foci can be accurately delineated. High-frequency oscillations (HFOs), existing in electroencephalography, are highly correlated with epileptic brain, which is promising for guiding successful neurosurgery. However, it is unclear whether and how pathological HFOs can be differentiated to localize the epileptogenic tissues given the presence of various nonepileptic high-frequency activities. Here, we show morphological and source imaging evidence that pathological HFOs can be identified by the concurrence of epileptiform spikes. We describe a framework to delineate the underlying epileptogenicity using this biomarker. Our work may offer translational tools to improve treatments by noninvasively demarking pathological activities and hence epileptic foci.