Citations

ABSTRACT BACKGROUND AND PURPOSE SISCOM and STATISCOM were clinically proved to be effective for ictal/inter‐ictal single‐photon emission computed tomography (SPECT) analysis coregistered with magnetic resonance imaging (MRI) for seizure localization. Recently, a software package also became available for this analysis. This study aimed to investigate and compare the performance of these analysis methods for seizure localization. METHODS A total of 378 patients who underwent 99m Tc‐ethyl cysteinate dimer (ECD) SPECT scans were retrospectively reviewed and 28 remained after applying exclusion criteria. Their SPECT and MRI images were analyzed with SISCOM (with z ‐score of 1.5 and 2), STATISCOM, and MIMneuro, resulting in a total of 112 image data sets. Two experienced radiologists participated in the blind review process using a custom tool and they can mark up to two hyper‐ and/or hypoperfusion regions. Their review results were analyzed using the Jackknife Free Response Receiver‐Operating Characteristics (JAFROC) test and the JAFROC figure‐of‐merit (FoM) was reported for each method. The interobserver agreement was also assessed using Cohen's kappa test. RESULTS Based on the readers’ two choices, averaged FoM was 85.7%, 83.9%, 66.1%, and 51.8% for STATISCOM, MIMneuro, SISCOM ( z ‐score = 2), and SISCOM ( z ‐score = 1.5), respectively. The average confidence rating was 2.5, 2.3, 1.6, and 1.1 for STATISCOM, MIMneuro, SISCOM ( z ‐score = 2), and SISCOM ( z ‐score = 1.5), respectively. For interobserver agreement, kappa was .742 for STATISCOM, .816 for MIMneuro, .517 for SISCOM ( z ‐score = 2), and .441 for SISCOM ( z ‐score = 1.5; all P < .001). CONCLUSION Our study demonstrated that STATISCOM showed the best performance for seizure localization, which was closely followed by MIMneuro. In addition, MIMneuro was not inferior to SISCOM with either z ‐score.

Glucagon-like peptide-1 (GLP-1) relaxes the stomach during fasting but decreases hunger and food consumption and retards gastric emptying. The interrelationships between volume, emptying, and postprandial symptoms in response to GLP-1 are unclear. We performed, in healthy human volunteers, a placebo-controlled study of the effects of intravenous GLP-1 on gastric volume using99mTc-single photon emission computed tomography imaging, gastric emptying of a nutrient liquid meal (Ensure) using scintigraphy, maximum tolerated volume (MTV) of Ensure, and postprandial symptoms 30 min after MTV. The role of vagal cholinergic function in the effects of GLP-1 was assessed by human pancreatic polypeptide (HPP) response to the Ensure meal. GLP-1 increased fasting and postprandial gastric volumes and retarded gastric emptying; MTV and postprandial symptoms were not different compared with controls. Effects on postprandial gastric function were associated with reduced postprandial HPP levels. GLP-1 does not induce postprandial symptoms despite significant inhibition of gastric emptying and vagal function; this may be partly explained by the increase in postprandial gastric volume.

Postprandial symptoms of bloating, distension, early satiety, and nausea are associated with impaired postprandial gastric accommodation, which is detectable by means of an intragastric, barostatically controlled balloon in the proximal stomach and by ultrasound in the distal stomach. Our aim was to develop a noninvasive method to measure the entire gastric accommodation reflex. In 10 healthy volunteers, we used single photon emission computed tomography (SPECT) to measure fasting and postprandial gastric volumes. This method involved intravenous injection of99mTc pertechnetate and gastric reconstruction of tomographic images with Analyze software. SPECT-Analyze imaging detects the postprandial gastric accommodation reflex in vivo. Mean fasting gastric volume was 182 ± 11 (SE) ml and mean postprandial volume was 690 ± 32 ml ( P < 0.001). Both proximal and distal segments of stomach showed a two- to almost fourfold difference in volumes postprandially. Intraobserver coefficients of variation in estimated fasting and postprandial volumes were 9 and 8%; interobserver variations were 13 and 12%, respectively. SPECT-Analyze noninvasively measures postprandial gastric (total, proximal, and distal) accommodation in humans. This method appears promising to compare the accommodation response in health and disease and to perform mechanistic studies of the accommodation response.

OBJECTIVEEpilepsy surgery is effective for lesional epilepsy, but it can be associated with significant morbidity when seizures originate from eloquent cortex that is resected. Here, the objective was to describe chronic subthreshold cortical stimulation and evaluate its early surgical safety profile in adult patients with epilepsy originating from seizure foci in cortex that is not amenable to resection.METHODSAdult patients with focal drug-resistant epilepsy underwent intracranial electroencephalography monitoring for evaluation of resection. Those with seizure foci in eloquent cortex were not candidates for resection and were offered a short therapeutic trial of continuous subthreshold cortical stimulation via intracranial monitoring electrodes. After a successful trial, electrodes were explanted and permanent stimulation hardware was implanted.RESULTSTen patients (6 males) who underwent chronic subthreshold cortical stimulation between 2014 and 2016 were included. Based on radiographic imaging, intracranial pathologies included cortical dysplasia (n = 3), encephalomalacia (n = 3), cortical tubers (n = 1), Rasmussen encephalitis (n = 1), and linear migrational anomaly (n = 1). The duration of intracranial monitoring ranged from 3 to 20 days. All patients experienced an uneventful postoperative course and were discharged home with a median length of stay of 10 days. No postoperative surgical complications developed (median follow-up length 7.7 months). Seizure severity and seizure frequency improved in all patients.CONCLUSIONSThe authors’ institutional experience with this small group shows that chronic subthreshold cortical stimulation can be safely and effectively performed in appropriately selected patients without postoperative complications. Future investigation will provide further insight to recently published results regarding mechanism and efficacy of this novel and promising intervention.

Abstract 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 34 ± 12 , 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.

The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but machine learning methods using intracranial electroencephalographic (iEEG) measures have shown promise. A machine-learning-based pipeline was developed to process iEEG recordings and generate seizure warnings. Results support the ability to forecast seizures at rates greater than a Poisson random predictor for all feature sets and machine learning algorithms tested. In addition, subject-specific neurophysiological changes in multiple features are reported preceding lead seizures, providing evidence supporting the existence of a distinct and identifiable preictal state.