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Epilepsy is a global epidemic and 30% of the 60 million patients do not respond to medication treatment. The only treatment options for patients with medically refractory epilepsy are surgical removal or electrical stimulation of the epileptogenic zone (EZ) i.e. the source of their seizures. Despite extensive evaluations with neuroimaging, visual EEG analysis and clinical testing, surgical success rates vary between 30-70%. Currently, no computational methods have been translated into the clinic to assist in localizing the EZ. Here, we applied a dynamical network model that quantifies the fragility of nodes within a patient9s intracranial EEG (iEEG) brain network. Fragility is quantified as the minimal amount of perturbation that must to be applied to a node9s influence on a "balanced" network to cause imbalance. Here, a balanced network is one in which the connectivity between excitatory and inhibitory nodes render a stable system, and an imbalanced network is unstable and hence can generate seizures. Using iEEG data from 91 patients treated across 5 epilepsy centers (44 successes, 47 failures), we demonstrated that nodal fragility is greater in electrodes within the EZ. In addition, we compared fragility of iEEG nodes to 7 frequency-based and 14 graph theoretic features of the EZ in both seizure (n=91) and non-seizure data (n=54). We calculated a confidence statistic, defined as the ratio of the value of a given feature averaged across electrodes in the clinically annotated seizure onset zone to its average across all other electrodes. Fragility has a significantly greater effect size difference between surgical outcomes when compared to other features. This novel feature, outperformed the most popular iEEG features when comparing across surgical outcomes, possibly defining a superior network-based EEG fingerprint for the EZ.

Objective This study aims to evaluate the role of scalp electroencephalography (EEG; ictal and interictal patterns) in predicting resective epilepsy surgery outcomes. We use the data to further develop a nomogram to predict seizure freedom. Methods We retrospectively reviewed the scalp EEG findings and clinical data of patients who underwent surgical resection at three epilepsy centers. Using both EEG and clinical variables categorized into 13 isolated candidate predictors and 6 interaction terms, we built a multivariable Cox proportional hazards model to predict seizure freedom 2 years after surgery. Harrell's step-down procedure was used to sequentially eliminate the least-informative variables from the model until the change in the concordance index (c-index) with variable removal was less than 0.01. We created a separate model using only clinical variables. Discrimination of the two models was compared to evaluate the role of scalp EEG in seizure-freedom prediction. Results Four hundred seventy patient records were analyzed. Following internal validation, the full Clinical + EEG model achieved an optimism-corrected c-index of 0.65, whereas the c-index of the model without EEG data was 0.59. The presence of focal to bilateral tonic-clonic seizures (FBTCS), high preoperative seizure frequency, absence of hippocampal sclerosis, and presence of nonlocalizable seizures predicted worse outcome. The presence of FBTCS had the largest impact for predicting outcome. The analysis of the models' interactions showed that in patients with unilateral interictal epileptiform discharges (IEDs), temporal lobe surgery cases had a better outcome. In cases with bilateral IEDs, abnormal magnetic resonance imaging (MRI) predicted worse outcomes, and in cases without IEDs, patients with extratemporal epilepsy and abnormal MRI had better outcomes. Significance This study highlights the value of scalp EEG, particularly the significance of IEDs, in predicting surgical outcome. The nomogram delivers an individualized prediction of postoperative outcome, and provides a unique assessment of the relationship between the outcome and preoperative findings.

Background:A second antipsychotic is commonly added to clozapine to treat refractory schizophrenia, notwithstanding the limited evidence to support such practice.Methods:The efficacy and adverse effects of this pharmacological strategy were examined in a double-blind, placebo-controlled, 12-week randomized trial of clozapine augmentation with amisulpride, involving 68 adults with treatment-resistant schizophrenia and persistent symptoms despite a predefined trial of clozapine.Results:There were no statistically significant differences between the amisulpride and placebo groups on the primary outcome measure (clinical response defined as a 20% reduction in total Positive and Negative Syndrome Scale score) or other mental state measures. However, the trial under recruited and was therefore underpowered to detect differences in the primary outcome, meaning that acceptance of the null hypothesis carries an increased risk of type II error. The findings suggested that amisulpride-treated participants were more likely to fulfil the clinical response criterion, odds ratio 1.17 (95% confidence interval 0.40–3.42) and have a greater reduction in negative symptoms, but these numerical differences were not statistically significant and only evident at 12 weeks. A significantly higher proportion of participants in the amisulpride group had at least one adverse event compared with the control group ( p = 0.014), and these were more likely to be cardiac symptoms.Conclusions:Treatment for more than 6 weeks may be required for an adequate trial of clozapine augmentation with amisulpride. The greater side-effect burden associated with this treatment strategy highlights the need for safety and tolerability monitoring, including vigilance for indicators of cardiac abnormalities, when it is used in either a clinical or research setting.

BackgroundWhen treatment-refractory schizophrenia shows an insufficient response to a trial of clozapine, clinicians commonly add a second antipsychotic, despite the lack of robust evidence to justify this practice.ObjectivesThe main objectives of the study were to establish the clinical effectiveness and cost-effectiveness of augmentation of clozapine medication with a second antipsychotic, amisulpride, for the management of treatment-resistant schizophrenia.DesignThe study was a multicentre, double-blind, individually randomised, placebo-controlled trial with follow-up at 12 weeks.SettingsThe study was set in NHS multidisciplinary teams in adult psychiatry.ParticipantsEligible participants were people aged 18–65 years with treatment-resistant schizophrenia unresponsive, at a criterion level of persistent symptom severity and impaired social function, to an adequate trial of clozapine monotherapy.InterventionsInterventions comprised clozapine augmentation over 12 weeks with amisulpride or placebo. Participants received 400 mg of amisulpride or two matching placebo capsules for the first 4 weeks, after which there was a clinical option to titrate the dosage of amisulpride up to 800 mg or four matching placebo capsules for the remaining 8 weeks.Main outcome measuresThe primary outcome measure was the proportion of ‘responders’, using a criterion response threshold of a 20% reduction in total score on the Positive and Negative Syndrome Scale.ResultsA total of 68 participants were randomised. Compared with the participants assigned to placebo, those receiving amisulpride had a greater chance of being a responder by the 12-week follow-up (odds ratio 1.17, 95% confidence interval 0.40 to 3.42) and a greater improvement in negative symptoms, although neither finding had been present at 6-week follow-up and neither was statistically significant. Amisulpride was associated with a greater side effect burden, including cardiac side effects. Economic analyses indicated that amisulpride augmentation has the potential to be cost-effective in the short term [net saving of between £329 and £2011; no difference in quality-adjusted life-years (QALYs)] and possibly in the longer term.LimitationsThe trial under-recruited and, therefore, the power of statistical analysis to detect significant differences between the active and placebo groups was limited. The economic analyses indicated high uncertainty because of the short duration and relatively small number of participants.ConclusionsThe risk–benefit of amisulpride augmentation of clozapine for schizophrenia that has shown an insufficient response to a trial of clozapine monotherapy is worthy of further investigation in larger studies. The size and extent of the side effect burden identified for the amisulpride–clozapine combination may partly reflect the comprehensive assessment of side effects in this study. The design of future trials of such a treatment strategy should take into account that a clinical response may be not be evident within the 4- to 6-week follow-up period usually considered adequate in studies of antipsychotic treatment of acute psychotic episodes. Economic evaluation indicated the need for larger, longer-term studies to address uncertainty about the extent of savings because of amisulpride and impact on QALYs. The extent and nature of the side effect burden identified for the amisulpride–clozapine combination has implications for the nature and frequency of safety and tolerability monitoring of clozapine augmentation with a second antipsychotic in both clinical and research settings.Trial registrationEudraCT number 2010-018963-40 and Current Controlled Trials ISRCTN68824876.FundingThis project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full inHealth Technology Assessment; Vol. 21, No. 49. See the NIHR Journals Library website for further project information.

OBJECTIVE To analyze longitudinal seizure outcomes following epilepsy surgery, including reoperations, in patients with intractable focal epilepsy. METHODS Clinicoradiological characteristics of patients who underwent epilepsy surgery from 1995 to 2016 with follow-up of ≥1 year were reviewed. In patients undergoing reoperations, the latest resection was considered the index surgery. The primary outcome was complete seizure freedom (Engel I) at last follow-up. Potentially significant outcome variables were first identified using univariate analyses and then fit in multivariate Cox proportional hazards models. RESULTS Of 898 patients fulfilling study criteria, 110 had reoperations; 92 had one resection prior to the index surgery and 18 patients had two or more prior resective surgeries. Two years after the index surgery, 69% of patients with no prior surgeries had an Engel score of I, as opposed to only 42% of those with one prior surgery, and 33% of those with two or more prior resections (P

Objective This study was designed to investigate if highly epileptic electroencephalogram (EEG) findings in patients with acute brain injury increase the long‐term risk of epilepsy development. Methods Adults patients, lacking epilepsy history, with electrographic seizures or lateralized periodic discharges (LPDs) (cases) were identified and matched based on age, mental status, and etiology with the ones lacking any epileptiform activity (controls) on continuous EEG (cEEG) during hospitalization. The primary outcome of clinical seizures after hospital discharge and their antiepileptic drug (AED) status was determined using a telephonic interview. Logistic regression models using generalized estimating equations to account for the matched nature of the data were performed. Results A total of 70 cases [16 (22.9%) “LPDs only,” 34 (48.6%) “electrographic seizure only,” and 20 (28.6%) “both”] and controls were enrolled. A total of 22 (31.4%) cases developed epilepsy after a mean follow‐up duration of 20.6 ± 5.0 months compared to three (4.3%) controls. After adjusting for cEEG indication and follow‐up duration, the odds of cases developing epilepsy were almost 15 times higher compared to the controls (OR = 14.8, 95% CI = 2.4–92.3, P = 0.004). This elevated risk was despite a 10 times higher likelihood of cases to be taking AEDs at the last follow‐up (OR = 10.34, 95% CI = 3.7–29, P

Sensorimotor control studies have predominantly focused on how motor regions of the brain relay basic movement-related information such as position and velocity. However, motor control is often complex, involving the integration of sensory information, planning, visuomotor tracking, spatial mapping, retrieval and storage of memories, and may even be emotionally driven. This suggests that many more regions in the brain are involved beyond premotor and motor cortices. In this study, we exploited an experimental setup wherein activity from over 87 non-motor structures of the brain were recorded in eight human subjects executing a center-out motor task. The subjects were implanted with depth electrodes for clinical purposes. Using training data, we constructed subject-specific models that related spectral power of neural activity in six different frequency bands as well as a combined model containing the aggregation of multiple frequency bands to movement speed. We then tested the models by evaluating their ability to decode movement speed from neural activity in the test data set. The best models achieved a correlation of 0.38 ± 0.03 (mean ± standard deviation). Further, the decoded speeds matched the categorical representation of the test trials as correct or incorrect with an accuracy of 70 ± 2.75% across subjects. These models included features from regions such as the right hippocampus, left and right middle temporal gyrus, intraparietal sulcus, and left fusiform gyrus across multiple frequency bands. Perhaps more interestingly, we observed that the non-dominant hemisphere (ipsilateral to dominant hand) was most influential in decoding movement speed.

Seizures in patients with medically refractory epilepsy (MRE) cannot be controlled with drugs. For focal MRE, seizures originate in the epileptogenic zone (EZ), which is the minimum amount of cortex that must be treated to be seizure free. Localizing the EZ is often a laborious process wherein clinicians first inspect scalp EEG recordings during several seizure events, and then formulate an implantation plan for subsequent invasive monitoring. The goal of implantation is to place electrodes into the brain region covering the EZ. Then, during invasive monitoring, clinicians visually inspect intracranial EEG recordings to more precisely localize the EZ. Finally, the EZ is then surgically ablated, removed or treated with electrical stimulation. Unfortunately success rates average at 50%. Such grim outcomes call for analytical assistance in creating more accurate implantation plans from scalp EEG. In this paper, we introduce a method that combines imaging data (CT and MRI scans) with scalp EEG to derive an implantation distribution. Specifically, scalp EEG data recorded over a seizure event is converted into a time-gamma frequency map, which is then processed to derive a spectrally annotated implantation distribution (SAID). The SAID represents a distribution of gamma power in each of eight cortical lobe/hemisphere partitions. We applied this method to 4 MRE patients who underwent treatment, and found that the SAID distribution overlapped more with clinical implantations in success cases than in failed cases. These preliminary findings suggest that the SAID may help in improving EZ localization accuracy and surgical outcomes.

Cortical stimulation mapping (CSM) is a common clinical procedure for mapping eloquent cortex in epilepsy patients. Electrical responses to the stimulation, or after-discharges (ADs), that occur in response to stimulation can point to unstable regions of cortex that are more prone to spontaneous seizures. Clinicians are interested in identifying regions that start seizures, i.e., the epileptogenic zone (EZ), so that they can target treatment. However, during CSM, not all regions are stimulated, as it would be time-consuming and potentially harmful to the patient. This limits the clinician's ability to fully explore ADs to reliably localize the EZ. In this paper, we develop a virtual CSM procedure that processes pre-seizure intracranial EEG recordings obtained from epilepsy patients being treated at three different epilepsy centers. First, we identify a linear time varying network (LTVN) model from electrocorticography (ECoG) and stereo-EEG (SEEG) data using sparse least squares estimation for each patient. We then construct an virtual CSM by applying impulse perturbations to each electrode contact in the LTVN model and then measuring the ADs of the network. We summarize the l2-norm of the responses in the form of a heatmap that shows the spatio-temporal evolution of the ADs before, during, and after seizures. Finally we compute an impulse response ratio (IRR) metric from each heatmap, that measures the ratio between the mean norm of ADs of clinically annotated EZ contacts and the mean norm of ADs of the remaining contacts. We find that the IRR is higher in maps derived from patients with successful surgical outcomes and lower in failed surgical outcomes. This suggests that virtual CSM may provide valuable information to clinicians regarding EZ location.

Seizures in patients with medically refractory epilepsy (MRE) epilepsy cannot be controlled with drugs. For focal MRE, seizures originate in the epileptogenic zone (EZ), which is the minimum amount of cortex that must be treated to be seizure free. Localizing the EZ is often a laborious process wherein clinicians first inspect scalp EEG recordings during several seizure events, and then formulate an implantation plan for subsequent invasive monitoring. The goal of implantation is to place electrodes into the brain region covering the EZ. Then, during invasive monitoring, clinicians visually inspect intracranial EEG recordings to more precisely localize the EZ. The EZ is then surgically removed. Unfortunately surgical success rates average at 50%. Such grim outcomes call for analytical assistance in creating more accurate implantation plans from scalp EEG. In this paper, we introduce a method that combines imaging data (CT and MRI scans) with scalp EEG to derive an implantation distribution. Specifically, scalp EEG data recorded over a seizure event is converted into a time-gamma frequency map, which is then processed to derive a spectrally annotated implantation distribution (SAID). The SAID represents a distribution of gamma power in each of the eight cortical lobe/hemisphere partitions. We applied this method to 4 MRE patients who underwent treatment, and found that the SAID distribution overlapped more with clinical implantations in success cases than in failed cases. These preliminary findings suggest that the SAID may help in improving EZ localization accuracy and surgical outcomes.