IMPORTANCE A bidirectional brain-computer interface that performs neurostimulation has been shown to improve seizure control in patients with refractory epilepsy, but the therapeutic mechanism is unknown. OBJECTIVE To investigate whether electrographic effects of responsive neurostimulation (RNS), identified in electrocorticographic (ECOG) recordings from the device, are associated with patient outcomes. DESIGN, SETTING, AND PARTICIPANTS Retrospective review of ECOG recordings and accompanying clinical meta-data from 11 consecutive patients with focal epilepsy who were implanted with a neurostimulation system between January 28, 2015, and June 6, 2017, with 22 to 112 weeks of follow-up. Recorded ECOG data were obtained from the manufacturer; additional system-generated meta-data, including recording and detection settings, were collected directly from the manufacturer's management system using an in-house, custom-built platform. Electrographic seizure patterns were identified in RNS recordings and evaluated in the time-frequency domain, which was locked to the onset of the seizure pattern.MAIN OUTCOMES AND MEASURES Patterns of electrophysiological modulation were identified and then classified according to their latency of onset in relation to triggered stimulation events. Seizure control after RNS implantation was assessed by 3 main variables: mean frequency of seizure occurrence, estimated mean severity of seizures, and mean duration of seizures. Overall seizure outcomes were evaluated by the extended Personal Impact of Epilepsy Scale questionnaires, a patient-reported outcome measure of 3 domains (seizure characteristics, medication adverse effects, and quality of life), with a range of possible scores from 0 to 300 in which lower scores indicate worse status, and the Engel scale, which comprises 4 classes (I-IV) in which lower numbers indicate greater improvement. RESULTS Electrocorticographic data from 11 patients (8 female; mean [range] age, 35 [19-65] years; mean [range] duration of epilepsy, 19 [5-37] years) were analyzed. Two main categories of electrophysiological signatures of stimulation-induced modulation of the seizure network were discovered: direct and indirect effects. Direct effects included ictal inhibition and early frequency modulation but were not associated with improved clinical outcomes (odds ratio [OR], 0.67; 95% CI, 0.06-7.35; P > .99). Only indirect effects-those occurring remote from triggered stimulation-were associated with improved clinical outcomes (OR, infinity; 95% CI, -infinity to infinity; P = .02). These indirect effects included spontaneous ictal inhibition, frequency modulation, fragmentation, and ictal duration modulation. CONCLUSIONS AND RELEVANCE These findings suggest that RNS effectiveness may be explained by long-term, stimulation-induced modulation of seizure network activity rather than by direct effects on each detected seizure.

Background: Brain sensing devices are approved today for Parkinson's, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients will benefit from using sensing technology. However, artifacts, such as ECG contamination, can render such treatments unreliable. Therefore, clinicians need to understand how surgical decisions may affect artifact probability. Objectives: Investigate neural signal contamination with ECG activity in sensing enabled neurostimulation systems, and in particular clinical choices such as implant location that impact signal fidelity. Methods: Electric field modeling and empirical signals from 85 patients were used to investigate the relationship between implant location and ECG contamination. Results: The impact on neural recordings depends on the difference between ECG signal and noise floor of the electrophysiological recording. Empirically, we demonstrate that severe ECG contamination was more than 3.2x higher in left-sided subclavicular implants (48.3%), when compared to right-sided implants (15.3%). Cranial implants did not show ECG contamination. Conclusions: Given the relative frequency of corrupted neural signals, we conclude that implant location will impact the ability of brain sensing devices to be used for "closed-loop" algorithms. Clinical adjustments such as implant location can significantly affect signal integrity and need consideration.

Closed-loop brain stimulation is one of the few treatments available for patients who are ineligible for traditional surgical resection of the epileptogenic zone, due to having generalized epilepsy, multifocal epilepsy, or focal epilepsy localized to an eloquent brain region. Due to its clinical efficacy and potential to delivery personalized therapy based on an individual's own intracerebral electrophysiology, this treatment is becoming an important part of clinical practice, despite a limited understanding of how to program detection and stimulation parameters for optimal, patient-specific benefit. To bring this challenge into focus, we review the evolution of neural stimulation for epilepsy, provide a technical overview of the RNS System (the only FDAapproved closed-loop device), and discuss the major challenges of working with a closed-loop device. We then propose an evidence-based solution for individualizing therapy that is driven by a bottom-up informatics approach.