Arun Antony

Sensing-enabled hippocampal deep brain stimulation in idiopathic nonhuman primate epilepsy

Epilepsy is a debilitating condition affecting 1% of the population worldwide. Medications fail to control seizures in at least 30% of patients, and deep brain stimulation (DBS) is a promising alternative treatment. A modified clinical DBS hardware platform was recently described (PC+S) allowing long-term recording of electrical brain activity such that effects of DBS on neural networks can be examined. This study reports the first use of this device to characterize idiopathic epilepsy and assess the effects of stimulation in a nonhuman primate (NHP). Clinical DBS electrodes were implanted in the hippocampus of an epileptic NHP bilaterally, and baseline local field potential (LFP) recordings were collected for seizure characterization with the PC+S. Real-time automatic detection of ictal events was demonstrated and validated by concurrent visual observation of seizure behavior. Seizures consisted of large-amplitude 8- to 25-Hz oscillations originating from the right hemisphere and quickly generalizing, with an average occurrence of 0.71 ± 0.15 seizures/day. Various stimulation parameters resulted in suppression of LFP activity or in seizure induction during stimulation under ketamine anesthesia. Chronic stimulation in the awake animal was studied to evaluate how seizure activity was affected by stimulation configurations that suppressed broadband LFPs in acute experiments. This is the first electrophysiological characterization of epilepsy using a next-generation clinical DBS system that offers the ability to record and analyze neural signals from a chronically implanted stimulating electrode. These results will direct further development of this technology and ultimately provide insight into therapeutic mechanisms of DBS for epilepsy.

Seizures After Intracerebral Hemorrhage: Incidence, Risk Factors, and Impact on Mortality and Morbidity

OBJECTIVE Spontaneous intracerebral hemorrhage (ICH) is one of the most frequent causes of epilepsy in the United States. However, reported risk factors for seizure after are inconsistent, and their impact on inpatient morbidity and mortality is unclear. We aimed to study the incidence, risk factors, and impact of seizures after ICH in a nationwide patient sample. METHODS We queried the Nationwide Inpatient Sample for patients admitted to the hospital with a primary diagnosis of ICH between the years 1999 and 2011. Patients were subsequently dichotomized into groups of those with a diagnosis consistent with seizure and those without. Multivariate logistic regression was used to assess risk factors for seizure in this patient sample, and the association between seizures and mortality and morbidity. Logistic regression was then used for trend analysis of incidence of seizure diagnoses over time. RESULTS We identified 220,075 patients admitted with a primary diagnosis of ICH. Of these, 11.87% had a diagnosis consistent with seizure. Factors associated with increased risk of seizure after ICH included higher categorical van Walraven score, encephalopathy, alcohol abuse, solid tumor, and prior stroke. Seizure was independently associated with decreased odds of morbidity (odds ratio [OR], 0.89; 95% confidence interval [CI], 0.86-0.92) and mortality (OR, 0.75; 95% CI, 0.72-0.77) in multivariate models controlling for existing comorbidities. CONCLUSIONS Seizures after were associated with decreased mortality and morbidity despite attempts to correct for existing comorbidities. Continuous monitoring of these patients for seizures may not be necessary in all circumstances, despite their frequency.

Effects of hippocampal low-frequency stimulation in idiopathic non-human primate epilepsy assessed via a remote-sensing-enabled neurostimulator.

&NA; Individuals with pharmacoresistant epilepsy remain a large and under‐treated patient population. Continued technologic advancements in implantable neurostimulators have spurred considerable research efforts directed towards the development of novel antiepileptic stimulation therapies. However, the lack of adequate preclinical experimental platforms has precluded a detailed understanding of the differential effects of stimulation parameters on neuronal activity within seizure networks. In order to chronically monitor seizures and the effects of stimulation in a freely‐behaving non‐human primate with idiopathic epilepsy, we employed a novel simultaneous video‐intracranial EEG recording platform using a state‐of‐the‐art sensing‐enabled, rechargeable clinical neurostimulator with real‐time seizure detection and wireless data streaming capabilities. Using this platform, we were able to characterize the electrographic and semiologic features of the focal‐onset, secondarily generalizing tonic‐clonic seizures stably expressed in this animal. A series of acute experiments exploring low‐frequency (2 Hz) hippocampal stimulation identified a pulse width (150 &mgr;s) and current amplitude (4 mA) combination which maximally suppressed local hippocampal activity. These optimized stimulation parameters were then delivered to the seizure onset‐side hippocampus in a series of chronic experiments. This long‐term testing revealed that the suppressive effects of low‐frequency hippocampal stimulation 1) diminish when delivered continuously but are maintained when stimulation is cycled on and off, 2) are dependent on circadian rhythms, and 3) do not necessarily confer seizure protective effects. HighlightsA novel video‐intracranial EEG implantable telemetry recording system is described.Spontaneous seizures in a primate with idiopathic epilepsy are characterized.Parameter dependent effects of low‐frequency hippocampal stimulation are explored.Factors affecting chronic stimulation effects are modeled using multiple regression.

Short-term neurocognitive outcomes following anterior temporal lobectomy

Changes in cognitive function are a well established risk of anterior temporal lobectomy (ATL). Deficits in verbal memory are a common postoperative finding, though a small proportion of patients may improve. Postoperative evaluation typically occurs after six to 12months. Patients may benefit from earlier evaluation to identify potential needs; however, the results of a formal neuropsychological assessment at an early postoperative stage are not described in the literature. We compared pre- and postoperative cognitive function for 28 right ATL and 23 left ATL patients using repeated measures ANOVA. Changes in cognitive function were compared to ILAE seizure outcome. The mean time to postoperative neuropsychological testing was 11.1weeks (SD=6.7weeks). There was a side×surgery interaction for the verbal tasks: immediate memory recall (F(1,33)=20.68, p<0.001), short delay recall (F(1,29)=4.99, p=0.03), long delay recall (F(1,33)=10.36, p=0.003), recognition (F(1,33)=5.69, p=0.02), and naming (F(1,37)=15.86, p<0.001). This indicated that the left ATL group had a significant decrement in verbal memory following surgery, while the right ATL group experienced a small but significant improvement. For the right ATL group, there was a positive correlation between ILAE outcome and improvement in immediate recall (r=-0.62, p=0.02) and long delay recall (r=-0.57, p=0.03). There was no similar finding for the left ATL group. This study demonstrates that short-interval follow-up is effective in elucidating postoperative cognitive changes. Right ATL was associated with improvement in verbal memory, while left ATL resulted in a decrement in performance. Improvement in the right ATL group was related to improved seizure outcome. Short-interval follow-up may lend itself to the identification of patients who could benefit from early intervention.

Diagnostic accuracy of paroxysmal spells: Clinical history versus observation

Our goal was to evaluate how accurate neurologists are at differentiating between different paroxysmal events based on clinical history versus observation of the spell in question. Forty-seven neurologists reviewed 12 clinical histories and videos of recorded events of patients admitted in the Epilepsy Monitoring Unit (EMU). They were asked to diagnose events as epileptic seizures, non-epileptic behavioral spells (NEBS), or other physiologic events as well as rate their confidence in their diagnosis. The median diagnostic accuracy for all paroxysmal events was 67% for clinical history and 75% for observation (p=.001). This was largely due to the difference in accuracy within the subgroup of patients with NEBS (67% history vs. 83% observation, p<.001). There were trends for higher diagnostic accuracy and increased inter-rater agreement with higher levels of training. Physicians with higher levels of training were more confident with diagnosis based on observation. In summary, reviewing videos of paroxysmal spells may improve diagnostic accuracy and enhance the evaluation of patients. Neurologists at all levels of training should encourage the recording and review of videos of recurrent spells to aid in medical decision-making especially when there is high concern that the spells in question are NEBS.

F90. Efficacy of simultaneous scalp and intracranial EEG in detecting seizure onset zone

Introduction Recently, we showed that three parameters of seizure onset were pertinent in the comparison of scalp and intracranial EEG: latency (La, pattern (Pa, pattern concordance between scalp and intracranial), and location (Lo) concordance. Discrepancy in these three features was associated with inadequate seizure localization and possibly, lack of electrodes in the SOZ. We evaluated La, Pa, and Lo while individual icEEG contacts are (conceptually) omitted from analysis to assess how these parameters may help identify adequacy of the icEEG montage. Methods Seventeen patients who underwent SSIEEG evaluation were reviewed. The first three seizures of patients ( n  = 2) whose SOZ were identified on icEEG and had Engel Class 1 outcome post resective surgery were analyzed. The concordance (or discordance) of the La, Pa, and Lo parameters was assessed before and after IEEG electrodes at SOZ were omitted from analysis. Results With complete scalp and icEEG data, the three features were noted as concordant in 100% of the seizures where SSIEEG had detected the SOZ. When icEEG contacts were removed from the SOZ or from areas of seizure spread, two of the three features were discordant in 67% of seizures. La parameter was noted in 100% of seizures with discordant features while Pa and Lo features were noted in 50%. Conclusion Simultaneous scalp and intracranial EEG appear to be complementary in the analysis of SOZ in this small case series. Assessing the concordance between scalp and icEEG for the latency, pattern, and location of seizure onset may be informative in the assessment of icEEG coverage adequacy.

F123. Ultra-high-density scalp EEG outperforms localized invasive ECoG grids in inferring depth of seizure foci

Introduction During pre-surgical evaluation for medically intractable epilepsy, intracranial electrocorticography (ECoG) is often used to locate seizure foci. However, ECoG grids usually have small spatial coverage because large coverage requires large craniotomies. This may pose difficulties in inferring the depth of the focus, which is important because foci can lie deep inside the brain, e.g. in insular, mesial or basal regions, or within a sulcus. Our team recently demonstrated theoretically [Grover & Venkatesh, Proc. IEEE, ‘17] and experimentally[Robinson et al., Scientific Reports ‘17] that ultra-high-density (UHD) scalp EEG (with sub-centimeter electrode spacing) can recover high spatial resolution information of brain activity. Here, we ask whether UHD-EEG with full scalp coverage can outperform spatially localized ECoG grids in inferring the depth of seizure foci. Methods In our simulation study, ECoG and UHD-EEG electrodes were placed on a template-head MRI. Brainstorm and OpenMEEG were used to generate EEG and ECoG forward models using the Boundary Element Method for the ICBM-152 Brain template with 15,765 dipoles. For 20-electrode ECoG simulations, 8 different locations were considered (over frontal, fronto-parietal, posterior-temporal and occipital regions on left and right hemispheres), with sources at different locations in each case. For 40-electrode ECoG simulations, two adjacent 20-electrode ECoG grids were considered, again for varying source locations. A single radially oriented dipole was manually activated at different depths from each of the 8 locations on the cortex, beneath the 20-electrode ECoG grid. For EEG, 128, 256 and 1378 electrodes (the last being a theoretical UHD EEG system) with whole scalp coverage were considered. Noiseless ECoG and EEG recordings were simulated, and source localization was performed to estimate the source location using the L2-regularized MNE algorithm. Results of source localization were then compared using two standard metrics: the distance of the center of the reconstruction from the true source (bias) and the diffusive extent of the reconstruction (width of Point Spread Function, PSF). Results The reconstruction-bias using UHD-EEG is consistently about 2 cm less than any ECoG reconstruction. PSF widths of deeper sources are observed to be slightly larger for ultra-high-density EEG than other modalities. Nevertheless, visual inference of depth is more accurate using reconstructions of ultra-high-density EEG. Conclusion UHD-EEG can complement other modalities used during pre-surgical evaluation for epilepsy, before and after intracranial electrode implantation, particularly for inferring the presence of a deep seizure focus. ECoG recordings strongly bias reconstructions towards the cortical surface. Future work will similarly compare stereo-EEG and UHD-EEG, for inferring depth of seizure foci, and evaluate how UHD-EEG can inform stereo-EEG electrode placement.

F134. Automated seizure detection using statistical CUSUM detector

Introduction Although automated seizure detection methods using intracranial EEG (iEEG) have achieved high accuracy in previous studies, they acquire many labeled datasets. Also, due to the non-stationarity nature of seizures and the inter and intra-individual variability in signal characteristics, these methods are difficult to implement prospectively in clinical practice. We propose an automated seizure detection method using a cumulative sum (CUSUM) detector that can be used online with fewer training parameters and minimal overall training without the need for labeled datasets. Methods The proposed seizure detector is composed of two main steps, feature extraction followed by detection.The features extracted are a line length (LL), relative energy (RE), coefficient of variation of amplitude (CVA), and the relative amplitude (RA).The main assumption for the extended CUSUM analysis is that the distributions corresponding to normal and seizure EEG are different. Feature vectors are calculated using windows of length N, subdivided into M segments of length n. At each point, the average of each of the M segments is calculated.Assuming that n is large enough, the central limit theorem applies and the sample mean vector of each segment follows a Gaussian distribution, which can be characterized by its mean and variance.A null hypothesis is formed that incoming data will be governed by the same distribution.During training, normal EEG data from the same subject is used to calculate the mean and variance bound distributions, representing the null hypothesis.During detection, for each incoming data segment, the log likelihood cumulative sum needs to be determined for each of these bound distributions.If the null hypothesis is rejected in any case, then a change is assumed to have occurred.Two 24-h long iEEG recordings containing 3 and 9 seizures respectively, were collected (sampling frequency of 2 kHz) from one patient, undergoing right parietal stereo-electroencephalography, (University of Pittsburgh IRB No. PRO15100311). Recordings were labeled by an expert closely familiar with the patients.For each iEEG file, the learning period was chosen to be the first seizure-free hour.The window length used for learning is 2.5 min. Results The CUSUM detector managed to detect the three labeled seizures of the first iEEG recording with a Good Detection Rate of 100%.While the Good Detection Rate results of the second iEEG recording are (LL = 78%, RE = 78%, RA = 88% and CVA = 78%). The number of false detections per hour results for the first EEG recording as follows (LL = 1.6, RE = 1.3, RA = 1.4 and CVA = 1.5). While for the second recording (LL = 1.3, RE = 1, RA = 1.2 and CVA = 1.25). Conclusion Seizure detection using the extended CUSUM test appears to be a promising technique for clinical monitoring purposes.This novel method for automated seizure detection using iEEG is capable of differentiating seizures from normal activity, without the need for highly customizable parameters or previously labeled data.The method also could be applied toward scalp EEG data.

Features of Simultaneous Scalp and Intracranial EEG That Predict Localization of Ictal Onset Zone

Objective. To assess the utility of simultaneous scalp EEG in patients with focal epilepsy undergoing intracranial EEG evaluation after a detailed presurgical testing, including an inpatient scalp video EEG evaluation. Methods. Patients who underwent simultaneous scalp and intracranial EEG (SSIEEG) monitoring were classified into group 1 or 2 depending on whether the seizure onset zone was delineated or not. Seizures were analyzed using the following 3 EEG features at the onset of seizures latency, location, and pattern. Results. The criteria showed at least one of the following features when comparing SSIEEG: prolonged latency, absence of anatomical congruence, lack of concordance of EEG pattern in 11.11% (1/9) of the patients in group 1 and 75 % (3/4) of the patients in group 2. These 3 features were not present in any of the 5 patients who had Engel class I outcome compared with 1 of the 2 patients (50%) who had seizure recurrence after resective surgery. The mean latency of seizure onset in scalp EEG compared with intracranial EEG of patients in group 1 was 17.48 seconds (SD = 16.07) compared with 4.33 seconds (SD = 11.24) in group 2 (P = .03). None of the seizures recorded in patients in group 1 had a discordant EEG pattern in SSIEEG. Conclusion. Concordance in EEG features like latency, location, and EEG pattern, at the onset of seizures in SSIEEG is associated with a favorable outcome after epilepsy surgery in patients with intractable focal epilepsy. Significance. Simultaneous scalp EEG complements intracranial EEG evaluation even after a detailed inpatient scalp video EEG evaluation and could be part of standard intracranial EEG studies in patients with intractable focal epilepsy.