Background: Class I evidence for surgical effectiveness in refractory temporal lobe epilepsy (TLE)

Background and Purpose-The characteristics of intracerebral hemorrhage (ICH) may vary by ICH location because of differences in the distribution of underlying cerebral small vessel diseases. Therefore, we investigated the incidence, characteristics, and outcome of lobar and nonlobar ICH. Methods-In a population-based, prospective inception cohort study of ICH, we used multiple overlapping sources of case ascertainment and follow-up to identify and validate ICH diagnoses in 2010 to 2011 in an adult population of 695 335. Results-There were 128 participants with first-ever primary ICH. The overall incidence of lobar ICH was similar to nonlobar ICH (9.

Summary Objective Epilepsy surgery is the most effective treatment for select patients with drug-resistant epilepsy. In this article, we aim to provide an accurate understanding of the current epidemiologic characteristics of this intervention, as this knowledge is critical for guiding educational, academic, and resource priorities. Methods We profile the practice of epilepsy surgery between 1991 and 2011 in nine major epilepsy surgery centers in the United States, Germany, and Australia. Clinical, imaging, surgical, and histopathologic data were derived from the surgical databases at various centers. Results Although five of the centers performed their highest number of surgeries for mesial temporal sclerosis (MTS) in 1991, and three had their highest number of MTS surgeries in 2001, only one center achieved its peak number of MTS surgeries in 2011. The most productive year for MTS surgeries varied then by center; overall, the nine centers surveyed performed 48% (95% confidence interval [CI] −27.3% to −67.4%) fewer such surgeries in 2011 compared to either 1991 or 2001, whichever was higher. There was a parallel increase in the performance of surgery for nonlesional epilepsy. Further analysis of 5/9 centers showed a yearly increase of 0.6 ± 0.07% in the performance of invasive electroencephalography (EEG) without subsequent resections. Overall, although MTS was the main surgical substrate in 1991 and 2001 (proportion of total surgeries in study centers ranging from 33.3% to 70.2%); it occupied only 33.6% of all resections in 2011 in the context of an overall stable total surgical volume. Significance These findings highlight the major aspects of the evolution of epilepsy surgery across the past two decades in a sample of well-established epilepsy surgery centers, and the critical current challenges of this treatment option in addressing complex epilepsy cases requiring detailed evaluations. Possible causes and implications of these findings are discussed.

No abstract

No abstract

Background This study sought to predict postsurgical seizure freedom from pre-operative diagnostic test results and clinical information using a rapid automated approach, based on supervised learning methods in patients with drug-resistant focal seizures suspected to begin in temporal lobe. Method We applied machine learning, specifically a combination of mutual information-based feature selection and supervised learning classifiers on multimodal data, to predict surgery outcome retrospectively in 20 presurgical patients (13 female; mean age±SD, in years 33±9.7 for females, and 35.3±9.4 for males) who were diagnosed with mesial temporal lobe epilepsy (MTLE) and subsequently underwent standard anteromesial temporal lobectomy. The main advantage of the present work over previous studies is the inclusion of the extent of ipsilateral neocortical gray matter atrophy and spatiotemporal properties of depth electrode-recorded seizures as training features for individual patient surgery planning. Results A maximum relevance minimum redundancy (mRMR) feature selector identified the following features as the most informative predictors of postsurgical seizure freedom in this study's sample of patients: family history of epilepsy, ictal EEG onset pattern (positive correlation with seizure freedom), MRI-based gray matter thickness reduction in the hemisphere ipsilateral to seizure onset, proportion of seizures that first appeared in ipsilateral amygdala to total seizures, age, epilepsy duration, delay in the spread of ipsilateral ictal discharges from site of onset, gender, and number of electrode contacts at seizure onset (negative correlation with seizure freedom). Using these features in combination with a least square support vector machine (LS-SVM) classifier compared to other commonly used classifiers resulted in very high surgical outcome prediction accuracy (95%). Conclusions Supervised machine learning using multimodal compared to unimodal data accurately predicted postsurgical outcome in patients with atypical MTLE.

No abstract

Summary Objective Differentiating pathological and physiological high-frequency oscillations (HFOs) is challenging. In patients with focal epilepsy, HFOs occur during the transitional periods between the up and down state of slow waves. The preferred phase angles of this form of phase-event amplitude coupling are bimodally distributed, and the ripples (80–150 Hz) that occur during the up-down transition more often occur in the seizure onset zone (SOZ). We investigated if bimodal ripple coupling was also evident for faster sleep oscillations, and could identify the SOZ. Methods Using an automated ripple detector, we identified ripple events in 40–60 minute intracranial EEG (iEEG) recordings from 23 patients with medically refractory mesial temporal lobe or neocortical epilepsy. The detector quantified epochs of sleep oscillations and computed instantaneous phase. We utilized a ripple phasor transform, ripple-triggered averaging, and circular statistics to investigate phase event-amplitude coupling. Results We found that at some individual recording sites, ripple event amplitude was coupled with sleep oscillatory phase and the preferred phase angles exhibited two distinct clusters (p<0.05). The distribution of the pooled mean preferred phase angle, defined by combining the means from each cluster at each individual recording site, also exhibited two distinct clusters (p<0.05). Based on the range of preferred phase angles defined by these two clusters, we partitioned each ripple event at each recording site into two groups: depth iEEG peak-trough and trough-peak. The mean ripple rates of the two groups in the SOZ and NSOZ were compared. We found that in the frontal (spindle, p=0.009; theta, p=0.006, slow, p=0.004) and parietal lobe (theta, p=0.007, delta, p=0.002, slow, p=0.001) the SOZ incidence rate for the ripples occurring during the trough-peak transition was significantly increased. Significance Phase-event amplitude coupling between ripples and sleep oscillations may be useful to distinguish pathological and physiological events in patients with frontal and parietal SOZ.