Worrell, Gregory A.

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.

AbstractObjectiveMost seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors, such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient‐specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts.MethodsThis study performed post hoc analysis on data from eight patients with a total of 12.2 years of continuous intracranial electroencephalographic recordings (average = 1.5 years, range = 1.0–2.1 years), originally collected in a prospective trial. Patients also had sleep scoring and location‐specific weather data. Histograms of future seizure likelihood were generated for each feature. The predictive utility of individual features was measured using a Bayesian approach to combine different features into an overall forecast of seizure likelihood. Performance of different feature combinations was compared using the area under the receiver operating curve. Performance evaluation was pseudoprospective.ResultsFor the eight patients studied, seizures could be predicted above chance accuracy using sleep (five patients), weather (two patients), and temporal features (six patients). Forecasts using combined features performed significantly better than chance in six patients. For four of these patients, combined forecasts outperformed any individual feature.SignificanceEnvironmental and physiological data, including sleep, weather, and temporal features, provide significant predictive information on upcoming seizures. Although forecasts did not perform as well as algorithms that use invasive intracranial electroencephalography, the results were significantly above chance. Complementary signal features derived from an individual's historic seizure records may provide useful prior information to augment traditional seizure detection or forecasting algorithms. Importantly, many predictive features used in this study can be measured noninvasively.

AbstractBackgroundPast research has demonstrated that clinical EEG is sensitive to dementia associated with Alzheimer's (AD) and Parkinson’s diseases (PD), and nonfluent/agrammatic Primary Progressive Aphasia (agPPA), but not Primary Progressive Apraxia of Speech (PPAOS). However, clinical EEG studies do not quantify interactions of multiple brain areas, or network activity. Graph theory (GT) simplifies analyzing and comparing complex brain networks. Studies have shown changes in EEG GT measures in AD and PD, but have not been studied in agPPA or PPAOS. The goal of this study was to use GT to characterize network‐level changes and associations with speech and language functioning in patients with agPPA and PPAOS.MethodsScalp EEGs of 14 agPPA and 7 PPAOS patients were collected during relaxed wakefulness (10‐20 positions, 256Hz sampling, 1–200Hz bandpass filter). Eight artifact‐free epochs were selected from non‐overlapping 1024‐point epochs. Via Brainwave software, GT weighted connectivity and minimum spanning tree (MST) measures were calculated for theta and alpha frequency bands. Differences in theta MST measures between agPPA and PPAOS were assessed with Wilcoxon rank‐sum tests. Spearman correlations were computed between behavioral and all network measures across all patients.ResultsTheta frequency MST diameter and eccentricity were significantly lower and leaf was significantly higher in agPPA compared to PPAOS patients; betweenness centrality did not differ. This indicates agPPA networks are more integrated (star‐like) than PPAOS. There were significant correlations between several network measures (e.g. kappa, lambda, PLI) and indices of speech‐language functioning.ConclusionsThe results provide EEG evidence of network alteration in agPPA and PPAOS patients. The GT measures and associations with behavioral measures provide support for reduced global efficiency, or network specificity, in these patients. The correlation of language measures with lambda (path length) in the theta frequency band suggests agPPA is associated with an inefficient path length and/or increased integration. The correlation between apraxia of speech (AOS) measures and MST eccentricity in the alpha frequency band suggests that more severe AOS is associated with reduced distance between two nodes. This study demonstrates potential for EEG GT measures to quantify network changes and distinguish network changes between related but different clinical syndromes.

Abstract Scientists debate where, when, and how different visual, orthographic, lexical, and semantic features are involved in visual word recognition. In this study, we investigate intracranial neurophysiology data from 151 patients engaged in reading single words. Using representational similarity analysis, we characterize the neural representation of a hierarchy of word features across the entire cerebral cortex. We find evidence of both feed-forward and feedback processing, with early representation of visual and lexical information in lingual gyrus followed by lexical representation in fusiform gyrus and semantic sensitivity in inferior frontal gyrus, with letter representation emerging later in fusiform gyrus. Furthermore, we observed a variety of anatomically heterogeneous temporal response shapes, and these functional populations had significant feature sensitivity. Taken together, our results demonstrate the early influence of lexical, phonological, and semantic features in visual word recognition and reveal feed-forward, feed-back, and anatomically distributed processing mechanisms that contribute to visual word recognition.

AbstractEpilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.