Phase-Amplitude Coupling Localizes Pathologic Brain with Aid of Behavioral Staging in Sleep.

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.

Epilepsy Personal Assistant Device-A Mobile Platform for Brain State, Dense Behavioral and Physiology Tracking and Controlling Adaptive Stimulation.

Epilepsy is one of the most common neurological disorders, and it affects almost 1% of the population worldwide. Many people living with epilepsy continue to have seizures despite anti-epileptic medication therapy, surgical treatments, and neuromodulation therapy. The unpredictability of seizures is one of the most disabling aspects of epilepsy. Furthermore, epilepsy is associated with sleep, cognitive, and psychiatric comorbidities, which significantly impact the quality of life. Seizure predictions could potentially be used to adjust neuromodulation therapy to prevent the onset of a seizure and empower patients to avoid sensitive activities during high-risk periods. Long-term objective data is needed to provide a clearer view of brain electrical activity and an objective measure of the efficacy of therapeutic measures for optimal epilepsy care. While neuromodulation devices offer the potential for acquiring long-term data, available devices provide very little information regarding brain activity and therapy effectiveness. Also, seizure diaries kept by patients or caregivers are subjective and have been shown to be unreliable, in particular for patients with memory-impairing seizures. This paper describes the design, architecture, and development of the Mayo Epilepsy Personal Assistant Device (EPAD). The EPAD has bi-directional connectivity to the implanted investigational Medtronic Summit RC+STM device to implement intracranial EEG and physiological monitoring, processing, and control of the overall system and wearable devices streaming physiological time-series signals. In order to mitigate risk and comply with regulatory requirements, we developed a Quality Management System (QMS) to define the development process of the EPAD system, including Risk Analysis, Verification, Validation, and protocol mitigations. Extensive verification and validation testing were performed on thirteen canines and benchtop systems. The system is now under a first-in-human trial as part of the US FDA Investigational Device Exemption given in 2018 to study modulated responsive and predictive stimulation using the Mayo EPAD system and investigational Medtronic Summit RC+STM in ten patients with non-resectable dominant or bilateral mesial temporal lobe epilepsy. The EPAD system coupled with an implanted device capable of EEG telemetry represents a next-generation solution to optimizing neuromodulation therapy.

Pre-motor versus motor cerebral cortex neuromodulation for chronic neuropathic pain.

Electrical stimulation of the cerebral cortex (ESCC) has been used to treat intractable neuropathic pain for nearly two decades, however, no standardized approach for this technique has been developed. In order to optimize targeting and validate the effect of ESCC before placing the permanent grid, we introduced initial assessment with trial stimulation, using a temporary grid of subdural electrodes. In this retrospective study we evaluate the role of electrode location on cerebral cortex in control of neuropathic pain and the role of trial stimulation in target-optimization for ESCC. Location of the temporary grid electrodes and location of permanent electrodes were evaluated in correlation with the long-term efficacy of ESCC. The results of this study demonstrate that the long-term effect of subdural pre-motor cortex stimulation is at least the same or higher compare to effect of subdural motor or combined pre-motor and motor cortex stimulation. These results also demonstrate that the initial trial stimulation helps to optimize permanent electrode positions in relation to the optimal functional target that is critical in cases when brain shift is expected. Proposed methodology and novel results open a new direction for development of neuromodulation techniques to control chronic neuropathic pain.

Centromedian thalamic nucleus with or without anterior thalamic nucleus deep brain stimulation for epilepsy in children and adults: A retrospective case series.

The centromedian (CM) and anterior nucleus of the thalamus (ANT) are deep brain stimulation (DBS) targets for management of generalized, and focal drug resistant epilepsy (DRE), respectively. We report on a single center retrospective case series of 16 children and adults with DRE who underwent CM with simultaneous ANT (69 %) or CM without simultaneous ANT DBS (31 %). Seizure frequency, epilepsy severity, life satisfaction, and quality of sleep before and after DBS were compared. Baseline median seizure frequency was 323 seizures per month (IQR, 71-563 sz/mo). Median follow up time was 80 months (IQR 37-97 mo). Median seizure frequency reduction was 58 % (IQR 13-87 %, p = 0.002). Ten patients (63 %) reported ≥50 % seizure frequency reduction. Median seizure frequency reduction and responder rate were not significantly different for CM + ANT versus CM only. Seizure severity and life satisfaction were significantly improved. Three patients (19 %) developed device-related side effects, 2 of them (12.5 %) required surgical intervention. In a heterogenous population of children and adults with generalized, multifocal, posterior onset, and poorly localized DRE, CM with or without ANT DBS is feasible, relatively safe and is associated with reduced seizure frequency and severity, as well as improved life satisfaction.

Cortical and thalamic electrode implant followed by temporary continuous subthreshold stimulation yields long-term seizure freedom: A case report.

Neuromodulation strategies that target the epileptogenic network are options for treating focal drug-resistant epilepsy. These brain stimulation approaches include responsive neurostimulation and more recently, chronic subthreshold stimulation. Long-term seizure freedom with neuromodulation is uncommon. Seizure control typically requires ongoing froms of electrical stimulation. Here, we present the case of a patient implanted with three cortical electrodes targeting the inferior frontal lobe, insula, and one subcortical electrode targeting the ipsilateral anterior thalamic nucleus. This patient received continuous subthreshold electrical stimulation to the frontal electrodes for 7 months, at which time stimulation was inadvertently stopped. He has now been free of seizures for 42 months. This case suggests the possibility that neuromodulation can alter epileptogenic networks and lead to seizure freedom without ongoing electrical stimulation.

Probing circuit of Papez with stimulation of anterior nucleus of the thalamus and hippocampal evoked potentials.

PURPOSE: Despite documented clinical effectiveness, deep brain stimulation (DBS) therapy for drug-resistant epilepsy rarely yields long-term seizure free outcomes. METHODS: This pilot study in five patients investigated circuit of Papez evoked potentials (EPs) using hippocampal sensing during anterior nucleus of the thalamus (ANT) electrical stimulation. We hypothesize that hippocampal EP is a potential biomarker that could be useful for ANT electrode targeting and improving seizure reduction. We obtained bilateral circuit of Papez EPs in five patients with bilateral temporal lobe epilepsy (TLE). The circuit of Papez EPs were measured and assessed by signal amplitude. Volumetric analysis of relevant mesial temporal structures and ANT stimulation analysis was performed on immediate post-implantation images. RESULTS: The patient with the most favorable seizure outcome, which meant long-term seizure reduction greater than 50 % compared to baseline, had strong bilateral EPs and normal hippocampal structure. Conversely, those without clinical benefit with ANT DBS had absent or weak bilateral EPs as well as MRI findings consistent with mesial temporal sclerosis (MTS). CONCLUSION: The data support the hypothesis that hippocampal EPs with ANT stimulation may be used to as a surrogate marker to probe circuit of Papez and predict ANT DBS efficacy.

Chronic subthreshold cortical stimulation and stimulation-related EEG biomarkers for focal epilepsy.

Brain stimulation offers an alternative to focal resection for the treatment of focal drug-resistant epilepsy. Chronic subthreshold cortical stimulation is an individualized biomarker-informed open-loop continuous electrical stimulation approach targeting the seizure onset zone and surrounding areas. Before permanent implantation, trial stimulation is performed during invasive monitoring to assess stimulation efficacy as well as to optimize stimulation location and parameters by modifying interictal EEG biomarkers. We present clinical and neurophysiological results from a retrospective analysis of 21 patients, showing a median percent reduction in seizure frequency of 100% and responder rate of 89% with a median follow-up of 27 months. About 40% of patients were free of disabling seizures for a 12-month period or longer. We find that stimulation-induced decreases in delta (1-4 Hz) power and increases in alpha and beta (8-20 Hz) power during trial stimulation correlate with improved long-term clinical outcomes. These results suggest chronic subthreshold cortical stimulation may be an effective alternative approach to treating focal drug-resistant epilepsy and that short-term stimulation-related changes in spectral power may be a useful interictal biomarker and relate to long-term clinical outcome.

Targeting analysis of a novel parietal approach for deep brain stimulation of the anterior nucleus of the thalamus for epilepsy.

PURPOSE: Deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) is a promising treatment for refractory epilepsy; however, it remains challenging to successfully target the ANT. The results of Medtronic Registry for Epilepsy (MORE) supported a frontal transventricular(TV) compared to frontal extraventricular (EV) lead trajectory for ANT DBS may have better coverage of the ANT. Here we report the safety and targeting efficacy of a novel, posterior parietal extraventricular (PEV) approach to the ANT. METHODS: We conducted a retrospective analysis of ten patients who underwent bilateral ANT DBS (20 total trajectories) for medically-refractory epilepsy. Similar targeting methodology as the MORE trial was used, and the DBS Intrinsic Template Atlas (DISTAL) was utilized for ANT localization and contact position relative to ANT. Clinical data were assessed for DBS targeting efficacy and surgical complications. RESULTS: The demonstrated PEV trajectory showed a successful ANT targeting rate of 90% bilaterally. Two or more contacts within ANT were presented in 75% of all leads. Mean contact number in ANT was 2.2+ 1.2. There were no intracranial hemorrhages, cerebrospinal fluid leakage, or permanent neurologic deficits. CONCLUSION: In this small series, the novel PEV for ANT DBS is feasible with good targeting accuracy and potential safety advantages. The high accuracy of the PEV trajectory suggests that it is a reasonable alternative trajectory for ANT DBS. Larger studies will be needed to assess this trajectory on clinical outcome of DBS treatment to epilepsy.

Automated unsupervised behavioral state classification using intracranial electrophysiology.

OBJECTIVE: Automated behavioral state classification in intracranial EEG (iEEG) recordings may be beneficial for iEEG interpretation and quantifying sleep patterns to enable behavioral state dependent neuromodulation therapy in next generation implantable brain stimulation devices. Here, we introduce a fully automated unsupervised framework to differentiate between awake (AW), sleep (N2), and slow wave sleep (N3) using intracranial EEG (iEEG) only and validated with expert scored polysomnography. APPROACH: Data from eight patients undergoing evaluation for epilepsy surgery (age [Formula: see text], three female) with intracranial depth electrodes for iEEG monitoring were included. Spectral power features (0.1-235 Hz) spanning several frequency bands from a single electrode were used to classify behavioral states of patients into AW, N2, and N3. MAIN RESULTS: Overall, classification accuracy of 94%, with 94% sensitivity and 93% specificity across eight subjects using multiple spectral power features from a single electrode was achieved. Classification performance of N3 sleep was significantly better (95%, sensitivity 95%, specificity 93%) than that of the N2 sleep phase (87%, sensitivity 78%, specificity 96%). SIGNIFICANCE: Automated, unsupervised, and robust classification of behavioral states based on iEEG data is possible, and it is feasible to incorporate these algorithms into future implantable devices with limited computational power, memory, and number of electrodes for brain monitoring and stimulation.

Integrating Brain Implants With Local and Distributed Computing Devices: A Next Generation Epilepsy Management System.

Brain stimulation has emerged as an effective treatment for a wide range of neurological and psychiatric diseases. Parkinson's disease, epilepsy, and essential tremor have FDA indications for electrical brain stimulation using intracranially implanted electrodes. Interfacing implantable brain devices with local and cloud computing resources have the potential to improve electrical stimulation efficacy, disease tracking, and management. Epilepsy, in particular, is a neurological disease that might benefit from the integration of brain implants with off-the-body computing for tracking disease and therapy. Recent clinical trials have demonstrated seizure forecasting, seizure detection, and therapeutic electrical stimulation in patients with drug-resistant focal epilepsy. In this paper, we describe a next-generation epilepsy management system that integrates local handheld and cloud-computing resources wirelessly coupled to an implanted device with embedded payloads (sensors, intracranial EEG telemetry, electrical stimulation, classifiers, and control policy implementation). The handheld device and cloud computing resources can provide a seamless interface between patients and physicians, and realtime intracranial EEG can be used to classify brain state (wake/sleep, preseizure, and seizure), implement control policies for electrical stimulation, and track patient health. This system creates a flexible platform in which low demand analytics requiring fast response times are embedded in the implanted device and more complex algorithms are implemented in offthebody local and distributed cloud computing environments. The system enables tracking and management of epileptic neural networks operating over time scales ranging from milliseconds to months.

Physiological and pathological high frequency oscillations in focal epilepsy.

OBJECTIVE: This study investigates high-frequency oscillations (HFOs; 65-600 Hz) as a biomarker of epileptogenic brain and explores three barriers to their clinical translation: (1) Distinguishing pathological HFOs (pathHFO) from physiological HFOs (physHFO). (2) Classifying tissue under individual electrodes as epileptogenic (3) Reproducing results across laboratories. METHODS: We recorded HFOs using intracranial EEG (iEEG) in 90 patients with focal epilepsy and 11 patients without epilepsy. In nine patients with epilepsy putative physHFOs were induced by cognitive or motor tasks. HFOs were identified using validated detectors. A support vector machine (SVM) using HFO features was developed to classify tissue under individual electrodes as normal or epileptogenic. RESULTS: There was significant overlap in the amplitude, frequency, and duration distributions for spontaneous physHFO, task induced physHFO, and pathHFO, but the amplitudes of the pathHFO were higher (P < 0.0001). High gamma pathHFO had the strongest association with seizure onset zone (SOZ), and were elevated on SOZ electrodes in 70% of epilepsy patients (P < 0.0001). Failure to resect tissue generating high gamma pathHFO was associated with poor outcomes (P < 0.0001). A SVM classified individual electrodes as epileptogenic with 63.9% sensitivity and 73.7% specificity using SOZ as the target. INTERPRETATION: A broader range of interictal pathHFO (65-600 Hz) than previously recognized are biomarkers of epileptogenic brain, and are associated with SOZ and surgical outcome. Classification of HFOs into physiological or pathological remains challenging. Classification of tissue under individual electrodes was demonstrated to be feasible. The open source data and algorithms provide a resource for future studies.

Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy.

OBJECTIVE: An ability to map seizure-generating brain tissue, i.e. the seizure onset zone (SOZ), without recording actual seizures could reduce the duration of invasive EEG monitoring for patients with drug-resistant epilepsy. A widely-adopted practice in the literature is to compare the incidence (events/time) of putative pathological electrophysiological biomarkers associated with epileptic brain tissue with the SOZ determined from spontaneous seizures recorded with intracranial EEG, primarily using a single biomarker. Clinical translation of the previous efforts suffers from their inability to generalize across multiple patients because of (a) the inter-patient variability and (b) the temporal variability in the epileptogenic activity. APPROACH: Here, we report an artificial intelligence-based approach for combining multiple interictal electrophysiological biomarkers and their temporal characteristics as a way of accounting for the above barriers and show that it can reliably identify seizure onset zones in a study cohort of 82 patients who underwent evaluation for drug-resistant epilepsy. MAIN RESULTS: Our investigation provides evidence that utilizing the complementary information provided by multiple electrophysiological biomarkers and their temporal characteristics can significantly improve the localization potential compared to previously published single-biomarker incidence-based approaches, resulting in an average area under ROC curve (AUC) value of 0.73 in a cohort of 82 patients. Our results also suggest that recording durations between 90 min and 2 h are sufficient to localize SOZs with accuracies that may prove clinically relevant. SIGNIFICANCE: The successful validation of our approach on a large cohort of 82 patients warrants future investigation on the feasibility of utilizing intra-operative EEG monitoring and artificial intelligence to localize epileptogenic brain tissue. Broadly, our study demonstrates the use of artificial intelligence coupled with careful feature engineering in augmenting clinical decision making.

Comparing spiking and slow wave activity from invasive electroencephalography in patients with and without seizures.

OBJECTIVES: To develop quantitative measures for estimating seizure probability, we examine intracranial EEG data from patient groups with three qualitative seizure probabilities: patients with drug resistant focal epilepsy (high), these patients during cortical stimulation (intermediate), and patients who have no history of seizures (low). METHODS: Patients with focal epilepsy were implanted with subdural electrodes during presurgical evaluation. Patients without seizures were implanted during treatment with motor cortex stimulation for atypical facial pain. RESULTS: The rate and amplitude of spikes correlate with qualitative seizure probability across patient groups and with proximity to the seizure onset zone in focal epilepsy patients. Spikes occur earlier during the negative oscillation of underlying slow activity (0.5-2 Hz) when seizure probability is increased. Similarly, coupling between slow and fast activity is increased. CONCLUSIONS: There is likely a continuum of sharply contoured activity between non-epileptiform and epileptiform. Characteristics of spiking and how spikes relate to slow activity can be combined to predict seizure onset zones. SIGNIFICANCE: Intracranial EEG data from patients without seizures represent a unique comparison group and highlight changes seen in spiking and slow wave activity with increased seizure probability. Slow wave activity and related physiology are an important potential biomarker for estimating seizure probability.

Spatial variation in high-frequency oscillation rates and amplitudes in intracranial EEG.

OBJECTIVE: To assess the variation in baseline and seizure onset zone interictal high-frequency oscillation (HFO) rates and amplitudes across different anatomic brain regions in a large cohort of patients. METHODS: Seventy patients who had wide-bandwidth (5 kHz) intracranial EEG (iEEG) recordings during surgical evaluation for drug-resistant epilepsy between 2005 and 2014 who had high-resolution MRI and CT imaging were identified. Discrete HFOs were identified in 2-hour segments of high-quality interictal iEEG data with an automated detector. Electrode locations were determined by coregistering the patient's preoperative MRI with an X-ray CT scan acquired immediately after electrode implantation and correcting electrode locations for postimplant brain shift. The anatomic locations of electrodes were determined using the Desikan-Killiany brain atlas via FreeSurfer. HFO rates and mean amplitudes were measured in seizure onset zone (SOZ) and non-SOZ electrodes, as determined by the clinical iEEG seizure recordings. To promote reproducible research, imaging and iEEG data are made freely available (msel.mayo.edu). RESULTS: Baseline (non-SOZ) HFO rates and amplitudes vary significantly in different brain structures, and between homologous structures in left and right hemispheres. While HFO rates and amplitudes were significantly higher in SOZ than non-SOZ electrodes when analyzed regardless of contact location, SOZ and non-SOZ HFO rates and amplitudes were not separable in some lobes and structures (e.g., frontal and temporal neocortex). CONCLUSIONS: The anatomic variation in SOZ and non-SOZ HFO rates and amplitudes suggests the need to assess interictal HFO activity relative to anatomically accurate normative standards when using HFOs for presurgical planning.

The CS algorithm: A novel method for high frequency oscillation detection in EEG.

BACKGROUND: High frequency oscillations (HFOs) are emerging as potentially clinically important biomarkers for localizing seizure generating regions in epileptic brain. These events, however, are too frequent, and occur on too small a time scale to be identified quickly or reliably by human reviewers. Many of the deficiencies of the HFO detection algorithms published to date are addressed by the CS algorithm presented here. NEW METHOD: The algorithm employs novel methods for: 1) normalization; 2) storage of parameters to model human expertise; 3) differentiating highly localized oscillations from filtering phenomena; and 4) defining temporal extents of detected events. RESULTS: Receiver-operator characteristic curves demonstrate very low false positive rates with concomitantly high true positive rates over a large range of detector thresholds. The temporal resolution is shown to be +/-∼5ms for event boundaries. Computational efficiency is sufficient for use in a clinical setting. COMPARISON WITH EXISTING METHODS: The algorithm performance is directly compared to two established algorithms by Staba (2002) and Gardner (2007). Comparison with all published algorithms is beyond the scope of this work, but the features of all are discussed. All code and example data sets are freely available. CONCLUSIONS: The algorithm is shown to have high sensitivity and specificity for HFOs, be robust to common forms of artifact in EEG, and have performance adequate for use in a clinical setting.

Chronic subthreshold cortical stimulation: a therapeutic and potentially restorative therapy for focal epilepsy.

INTRODUCTION: Approximately one third of patients with focal epilepsy continue to have ongoing seizures despite adequate trials of anti-seizure medications. Surgery to remove the epileptogenic zone remains the most efficacious treatment option for focal drug-resistant epilepsy. However, when cortical areas are eloquent or there are multiple epileptogenic zones, surgical resection is not an ideal approach. Cortical stimulation provides an attractive alternative. Area covered: Here, the authors describe Chronic Subthreshold Cortical Stimulation (CSCS), which uses continuous intracranial electrical stimulation applied near the epileptogenic zone to lower seizure probability. The authors review literature related to CSCS. One challenge is finding the most efficacious set of stimulation parameters for each patient. Expert commentary: Data supporting CSCS are limited but promising for the treatment of patients with focal drug resistant epilepsy who are not surgical candidates. Additional electrophysiological biomarkers to estimate cortical excitability are needed.

Behavioral state classification in epileptic brain using intracranial electrophysiology.

OBJECTIVE: Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. APPROACH: Data from seven patients (age [Formula: see text], 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1-600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier. MAIN RESULTS: Classification accuracy of 97.8 ± 0.3% (normal tissue) and 89.4 ± 0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8 ± 0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1 ± 1.6%). Spectral power in high frequency band features (Ripple (80-250 Hz), Fast Ripple (250-600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy ⩾90% using a single electrode contact and single spectral feature. SIGNIFICANCE: Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.

Reactivation of seizure-related changes to interictal spike shape and synchrony during postseizure sleep in patients.

OBJECTIVE: Local field potentials (LFPs) arise from synchronous activation of millions of neurons, producing seemingly consistent waveform shapes and relative synchrony across electrodes. Interictal spikes (IISs) are LFPs associated with epilepsy that are commonly used to guide surgical resection. Recently, changes in neuronal firing patterns observed in the minutes preceding seizure onset were found to be reactivated during postseizure sleep, a process called seizure-related consolidation (SRC), due to similarities with learning-related consolidation. Because IISs arise from summed neural activity, we hypothesized that changes in IIS shape and relative synchrony would be observed in the minutes preceding seizure onset and would be reactivated preferentially during postseizure slow-wave sleep (SWS). METHODS: Scalp and intracranial recordings were obtained continuously across multiple days from clinical macroelectrodes implanted in patients undergoing treatment for intractable epilepsy. Data from scalp electrodes were used to stage sleep. Data from intracranial electrodes were used to detect IISs using a previously established algorithm. Partial correlations were computed for sleep and wake periods before and after seizures as a function of correlations observed in the minutes preceding seizures. Magnetic resonance imaging (MRI) and computed tomography (CT) scans were co-registered with electroencephalography (EEG) to determine the location of the seizure-onset zone (SOZ). RESULTS: Changes in IIS shape and relative synchrony were observed on a subset of macroelectrodes minutes before seizure onset, and these changes were reactivated preferentially during postseizure SWS. Changes in synchrony were greatest for pairs of electrodes where at least one electrode was located in the SOZ. SIGNIFICANCE: These data suggest preseizure changes in neural activity and their subsequent reactivation occur across a broad spatiotemporal scale: from single neurons to LFPs, both within and outside the SOZ. The preferential reactivation of seizure-related changes in IISs during postseizure SWS adds to a growing body of literature suggesting that pathologic neural processes may utilize physiologic mechanisms of synaptic plasticity.