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Background: Structural neuroimaging studies have consistently shown a pattern of extra-hippocampal atrophy in patients with left and right drug-refractory medial temporal lobe epilepsy (MTLE). However, it is not yet completely understood how extra-hippocampal atrophy is related to hippocampal atrophy. Moreover, patients with left MTLE often exhibit more intense cognitive impairment, and subtle brain asymmetries have been reported in patients with left MTLE versus right MTLE but have not been explored in a controlled study. Objectives: To investigate the association between extra-hippocampal and hippocampal atrophy in patients with MTLE, and the effect of side of hippocampal atrophy on extra-hippocampal atrophy. Methods: Voxel-based morphometry analyses of magnetic resonance images of the brain were performed to determine the correlation between regional extra-hippocampal grey matter volume and hippocampal grey matter volume. The results from 36 patients with right and left MTLE were compared, and results from the two groups were compared with those from 49 healthy controls. Results: Compared with controls, patients with MTLE showed a more intense correlation between hippocampal grey matter volume and regional grey matter volume in locations such as the contralateral hippocampus, bilateral parahippocampal gyri and frontal and parietal areas. Compared with right MTLE, patients with left MTLE exhibited a wider area of atrophy related to hippocampal grey matter loss, encompassing both the contralateral and ipsilateral hemispheres, particularly affecting the contralateral hippocampus. Conclusions: Our results suggest that left hippocampal atrophy is associated with a larger degree of extrahippocampal atrophy. This may help to explain the more intense cognitive impairment usually observed in these patients.

discharges (IEDs) in electroencephalograms (EEGs) are a biomarker of epilepsy, seizure risk, and clinical decline. However, there is a scarcity of experts qualified to interpret EEG results. Prior attempts to automate IED detection have been limited by small samples and have not demonstrated expert-level performance. There is a need for a validated automated method to detect IEDs with expert-level reliability.OBJECTIVE To develop and validate a computer algorithm with the ability to identify IEDs as reliably as experts and classify an EEG recording as containing IEDs vs no IEDs.DESIGN, SETTING, AND PARTICIPANTS A total of 9571 scalp EEG records with and without IEDs were used to train a deep neural network (SpikeNet) to perform IED detection. Independent training and testing data sets were generated from 13 262 IED candidates, independently annotated by 8 fellowship-trained clinical neurophysiologists, and 8520 EEG records containing no IEDs based on clinical EEG reports. Using the estimated spike probability, a classifier designating the whole EEG recording as positive or negative was also built.MAIN OUTCOMES AND MEASURES SpikeNet accuracy, sensitivity, and specificity compared with fellowship-trained neurophysiology experts for identifying IEDs and classifying EEGs as positive or negative or negative for IEDs. Statistical performance was assessed via calibration error and area under the receiver operating characteristic curve (AUC). All performance statistics were estimated using 10-fold cross-validation.RESULTS SpikeNet surpassed both expert interpretation and an industry standard commercial IED detector, based on calibration error (SpikeNet, 0.041; 95% CI, 0.033-0.049; vs industry standard, 0.066; 95% CI, 0.060-0.078; vs experts, mean, 0.183; range, 0.081-0.364) and binary classification performance based on AUC (SpikeNet, 0.980; 95% CI, 0.977-0.984; vs industry standard, 0.882; 95% CI, 0.872-0.893). Whole EEG classification had a mean calibration error of 0.126 (range, 0.109-0.1444) vs experts (mean, 0.197; range, 0.099-0.372) and AUC of 0.847 (95% CI, 0.830-0.865). CONCLUSIONS AND RELEVANCEIn this study, SpikeNet automatically detected IEDs and classified whole EEGs as IED-positive or IED-negative. This may be the first time an algorithm has been shown to exceed expert performance for IED detection in a representative sample of EEGs and may thus be a valuable tool for expedited review of EEGs.

SUMMARYPurpose: Medial temporal epilepsy (MTLE) is associated with extrahippocampal brain atrophy. The mechanisms underlying brain damage in MTLE are unknown. Seizures may lead to neuronal damage, but another possible explanation is deafferentation from loss of hippocampal connections. This study aimed to investigate the relationship between hippocampal deafferentation and brain atrophy in MTLE. Methods: Three different MRI studies were performed involving 23 patients with unilateral MTLE (8 left and 15 right) and 34 healthy controls: (1) voxel-based morphometry (VBM), (2) diffusion tensor imaging (DTI) and (3) probabilistic tractography (PT). VBM was employed to define differences in regional gray matter volume (GMV) between controls and patients. Voxel-wise analyses of DTI evaluated differences in fractional anisotropy (FA), mean diffusivity (MD) and hippocampal PT. Z-scores were computed for regions-of-interest (ROI) GMV and perihippocampal FA and MD (to quantify hippocampal fiber integrity). The relationship between hippocampal deafferentation and regional GMV was investigated through the association between ROI Z scores and hippocampal fiber integrity. Results: Patients with MTLE exhibited a significant reduction in GMV and FA in perihippocampal and limbic areas. There was a decrease in hippocampal PT in patients with MTLE in limbic areas. A significant relationship between loss of hippocampal connections and regional GMV atrophy was found involving the putamen, pallidum, middle and inferior temporal areas, amygdala and ceberellar hemisphere. Discussion: There is a relationship between hippocampal disconnection and regional brain atrophy in MTLE. These results indicate that hippocampal deafferentation plays a contributory role in extrahippocampal brain damage in MTLE.

SummaryObjectivePatients with Lennox‐Gastaut syndrome (LGS) who completed 1 of 2 randomized, double‐blind, placebo‐controlled trials of add‐on cannabidiol (CBD) (GWPCARE3, NCT02224560 or GWPCARE4, NCT02224690) were invited to enroll in an open‐label extension (OLE) study evaluating the long‐term safety and efficacy of CBD (GWPCARE5, NCT02224573). Herein we present an interim analysis of the safety, efficacy, and patient‐reported outcomes from this trial.MethodsPatients received a pharmaceutical formulation of highly purified CBD oral solution (Epidiolex; 100 mg/mL), titrated from 2.5 to 20 mg/kg/d over a 2‐week titration period, in addition to their existing medications. Doses could be reduced if not tolerated or increased up to 30 mg/kg/d if thought to be of benefit.ResultsThis interim analysis was based on a November 2016 data cut. Of 368 patients who completed treatment in GWPCARE3 and GWPCARE4, 366 (99.5%) enrolled in the OLE study (GWPCARE5). Median treatment duration was 38 weeks at a mean modal dose of 23 mg/kg/d. Most patients (92.1%) experienced adverse events (AEs), primarily of mild (32.5%) or moderate (43.4%) severity. The most common AEs were diarrhea (26.8%), somnolence (23.5%), and convulsion (21.3%). Thirty‐five patients (9.6%) discontinued treatment due to AEs. Liver transaminase elevations were reported in 37 patients (10.1%), of whom 29 were receiving concomitant valproic acid; 34 cases resolved spontaneously or with dose modification of CBD or concomitant medication. Median reduction from baseline in drop seizure frequency (quantified monthly over 12‐week periods) ranged from 48% to 60% through week 48. Median reduction in monthly total seizure frequency ranged from 48% to 57% across all 12‐week periods through week 48. Eighty‐eight percent of patients/caregivers reported an improvement in the patient's overall condition per the Subject/Caregiver Global Impression of Change scale.SignificanceIn this study, long‐term add‐on CBD treatment had an acceptable safety profile in patients with LGS and led to sustained reductions in seizures.

SummaryObjectiveA prospective multicenter phase III trial was undertaken to evaluate the performance and tolerability in the epilepsy monitoring unit (EMU) of an investigational wearable surface electromyographic (sEMG) monitoring system for the detection of generalized tonic–clonic seizures (GTCSs).MethodsOne hundred ninety‐nine patients with a history of GTCSs who were admitted to the EMU in 11 level IV epilepsy centers for clinically indicated video‐electroencephalographic monitoring also received sEMG monitoring with a wearable device that was worn on the arm over the biceps muscle. All recorded sEMG data were processed at a central site using a previously developed detection algorithm. Detected GTCSs were compared to events verified by a majority of three expert reviewers.ResultsFor all subjects, the detection algorithm detected 35 of 46 (76%, 95% confidence interval [CI] = 0.61–0.87) of the GTCSs, with a positive predictive value (PPV) of 0.03 and a mean false alarm rate (FAR) of 2.52 per 24 h. For data recorded while the device was placed over the midline of the biceps muscle, the system detected 29 of 29 GTCSs (100%, 95% CI = 0.88–1.00), with a detection delay averaging 7.70 s, a PPV of 6.2%, and a mean FAR of 1.44 per 24 h. Mild to moderate adverse events were reported in 28% (55 of 199) of subjects and led to study withdrawal in 9% (17 of 199). These adverse events consisted mostly of skin irritation caused by the electrode patch that resolved without treatment. No serious adverse events were reported.SignificanceDetection of GTCSs using an sEMG monitoring device on the biceps is feasible. Proper positioning of this device is important for accuracy, and for some patients, minimizing the number of false positives may be challenging.

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