Swamy, Chandra Prakash

Abstract High-frequency oscillations (HFOs) are clinically established biomarkers of epileptogenic tissue, yet their translation from hospital-based recording systems to implantable neural interfaces remains a major barrier to their utilization in adaptive neuromodulation in epilepsy. Here, we established a bedside translational framework using a benchtop implementation of a wireless implantable neural interface (Brain Interchange, BIC) to evaluate whether clinically meaningful HFO can be preserved under implant-grade sensing constraints. Ten patients with drug resistant epilepsy underwent simultaneous 24-hour intracranial EEG recordings using synchronized BICs and a clinical-grade amplifier. A sensitive detector identified candidate HFOs, while sparse signal processing and machine learning methods removed artifact related pseudo-HFOs. Despite its lower sampling rate, narrower analog bandwidth, and higher noise floor, the integrated sensing and computational intelligence framework preserved approximately 82% of clinical amplifier HFOs and maintained nearly identical spatial distribution of pathological activity. After pseudo-HFO elimination, BIC derived HFOs localized the seizure onset zone with 84% sensitivity and 90% specificity, comparable to the clinical amplifier despite lower fast ripple detection. These findings demonstrate that implant-grade neural sensing, when integrated with artifact resilient computational intelligence, can preserve clinically relevant HFO biomarkers and their spatial fidelity without reproducing every individual electrophysiological event. More broadly, this work establishes a translational framework for chronic tracking of evolving epileptogenic networks and future biomarker-guided adaptive neuromodulation.

Abstract Objective Interictal epileptiform spikes (IESs) are widely used biomarkers of epileptogenic tissue in presurgical epilepsy evaluation. However, their reliability for seizure onset zone (SOZ) localization in high‐density intracranial electroencephalography (iEEG) remains inconsistent. It is underexplored whether this variability reflects methodological differences across detection algorithms or a limitation of IESs as a biomarker. This study evaluates whether IES detection alone provides sufficient spatial specificity for accurate SOZ localization in iEEG. Methods We systematically benchmarked eight established IES detection algorithms using iEEG recordings from 35 patients with drug‐resistant focal epilepsy. To provide a clinically meaningful reference, algorithm performance was evaluated against the clinically defined SOZ. To refine detection outputs and move beyond only IES detection, we applied masking strategies to isolate informative IES subsets based on rate, amplitude, frequency, and co‐occurrence with high‐frequency oscillations (HFOs). Conceptually, masking acts as a spatial refinement strategy that progressively restricts diffuse IES activity to more focal, pathologically relevant event populations. Results Despite substantial variability in detection characteristics, SOZ localization performance (ratio of events originating from SOZ divided by total number of events) converged within a narrow range (.23–.51), suggesting that IESs exhibit an intrinsic ceiling in SOZ localization performance when used alone. Amplitude‐based masking yielded modest improvements, whereas frequency‐based masking reduced performance. In contrast, IES‐HFO co‐occurrence markedly improved localization, particularly when combined with rate‐based masking, achieving a median SOZ localization ratio of .89, exceeding IESs (.37) and HFOs (.63) evaluated independently. Significance These findings demonstrate that IES‐based SOZ localization may require moving beyond detection‐only approaches. The integration of informed, biomarker‐driven masking strategies provides a more robust framework for identifying the activity most relevant to the epileptogenic network and paves the way for future incorporation into the clinical decision‐making pipeline.

Abstract General anesthesia increasingly facilitates asleep deep brain stimulation (DBS) surgery but suppresses spontaneous electrophysiological signals used to guide lead placement. Here, we investigated whether evoked resonant neural activity (ERNA), a stimulation-evoked biomarker of basal ganglia circuitry, remains informative during asleep DBS. We analyzed intraoperative recordings from a multisite cohort of 45 patients (86 hemispheres) undergoing subthalamic nucleus or globus pallidus internus DBS under awake and anesthetized conditions. Anesthesia markedly attenuated spontaneous beta and high-frequency oscillatory activity. Although ERNA amplitude was reduced at matched stimulation intensities, increasing stimulation current restored high-amplitude, spatially localized responses while preserving anatomical specificity. Across both targets, ERNA spatial distributions remained stable across awake and anesthetized states, co-localized with established motor-network biomarkers, and retrospectively identified electrode levels associated with the greatest postoperative therapeutic window. These retrospective findings position stimulation-evoked circuit interrogation as a complement to imaging for physiology-guided asleep DBS and as a candidate signal for contact prioritization. Prospective studies are warranted to determine whether ERNA-guided decisions improve placement, programming efficiency and clinical outcomes.

Abstract For implantable closed loop neuromodulation systems, real-time neural sensing, biomarker detection, and targeted stimulation are essential components for delivering personalized therapy. Current limitations in sampling rate and the number of available recording channels in implantable systems may pose significant challenges, especially in epilepsy research where high-frequency biomarkers and multi-site monitoring are essential. The Brain Interchange (BIC) system of CorTec is an externally powered, wireless implantable system that enables sensing and stimulation up to 32 channels at 1 kHz addressing the current limitations in the field. In this study, we present a modular closed loop neuromodulation environment developed for the BIC, enabling real-time data streaming, event detection, and flexible stimulation control. The framework integrates a Simulink-based processing pipeline, real-time communication, and a graphical user interface supporting both open and closed loop paradigms. System performance was evaluated through impedance measurement, latency assessment across three stimulation modes, and real-time closed loop stimulation using electrocardiogram (ECG) for in-lab demonstration. This modular environment provides a flexible platform for developing and validating neuromodulation algorithms and can be extended to incorporate advanced biomarker detection and multi-channel neural interfaces for future clinical research.