Faculty Bibliography

PURPOSE: We investigated the cumulative probability of seizure remission and relapse in an adult population with drug-resistant epilepsy and frequent seizures. In addition, we determined clinical predictors of remission and relapse in this population. METHODS: IN 2003, we identified 246 patients at a single center with drug-resistant epilepsy defined as at least one seizure per month and failure of at least two antiepileptic drugs. These patients were followed prospectively (cohort design). We examined the cumulative probability of seizure remission and relapse in this population using Kaplan-Meier methodology. Clinical predictors of remission and relapse were also evaluated using Cox regression analysis. KEY FINDINGS: The estimated cumulative probability of 12-month seizure remission was 34.6% at 7 years in the entire population and 33.4% when limited to those without surgery. The risk for relapse after a 12-month period of seizure remission was 71.2% at 5 years. Negative predictors of seizure remission included developmental delay, symptomatic generalized epilepsy syndrome, duration of intractability, and number of antiepileptic drugs failed. Localization-related epilepsy was the only negative predictor of relapse. SIGNIFICANCE: Among patients with drug-resistant epilepsy, 5% per year enter seizure remission even with a follow-up of 6 years. However, a substantial proportion of these patients relapse after the first year following a remission. The large proportion of patients entering a significant remission gives these patients hope; however, caution should be advised when discussing the likelihood of future seizures

Magnetic resonance imaging has revolutionized the detection of structural abnormalities in patients with epilepsy. However, many focal abnormalities remain undetected in routine visual inspection. Here we use an automated, surface-based method for quantifying morphometric features related to epileptogenic cortical malformations to detect abnormal cortical thickness and blurred gray-white matter boundaries. Using MRI morphometry at 3T with surface-based spherical averaging techniques that precisely align anatomical structures between individual brains, we compared single patients with known lesions to a large normal control group to detect clusters of abnormal cortical thickness, gray-white matter contrast, local gyrification, sulcal depth, jacobian distance and curvature. To assess the effects of threshold and smoothing on detection sensitivity and specificity, we systematically varied these parameters with different thresholds and smoothing levels. To test the effectiveness of the technique to detect lesions of epileptogenic character, we compared the detected structural abnormalities to expert-tracings, intracranial EEG, pathology and surgical outcome in a homogeneous patient sample. With optimal parameters and by combining thickness and GWC, the surface-based detection method identified 92% of cortical lesions (sensitivity) with few false positives (96% specificity), successfully discriminating patients from controls 94% of the time. The detected structural abnormalities were related to the seizure onset zones, abnormal histology and positive outcome in all surgical patients. However, the method failed to adequately describe lesion extent in most cases. Automated surface-based MRI morphometry, if used with optimized parameters, may be a valuable additional clinical tool to improve the detection of subtle or previously occult malformations and therefore could improve identification of patients with intractable focal epilepsy who may benefit from surgery

Epilepsy is the third most common chronic brain disorder, and is characterized by an enduring predisposition to generate seizures. Despite progress in pharmacological and surgical treatments of epilepsy, relatively little is known about the processes leading to the generation of individual seizures, and about the mechanisms whereby a healthy brain is rendered epileptic. These gaps in our knowledge hamper the development of better preventive treatments and cures for the approximately 30% of epilepsy cases that prove resistant to current therapies. Here, we focus on the rapidly growing body of evidence that supports the involvement of inflammatory mediators-released by brain cells and peripheral immune cells-in both the origin of individual seizures and the epileptogenic process. We first describe aspects of brain inflammation and immunity, before exploring the evidence from clinical and experimental studies for a relationship between inflammation and epilepsy. Subsequently, we discuss how seizures cause inflammation, and whether such inflammation, in turn, influences the occurrence and severity of seizures, and seizure-related neuronal death. Further insight into the complex role of inflammation in the generation and exacerbation of epilepsy should yield new molecular targets for the design of antiepileptic drugs, which might not only inhibit the symptoms of this disorder, but also prevent or abrogate disease pathogenesis

Rationale: To identify genetic influences on human epilepsy we performed a genome wide association study (GWAS) on DNA samples from unrelated patients with either idiopathic generalized (IGE) or cryptogenic focal (CFE) seizures compared to unrelated healthy controls. Methods: The Illumina HumanHap550 Bead Chip was used to genotype over 500,000 single nucleotide polymorphisms (SNPs) across the entire genome in a cohort of cryptogenic focal patients (n=295) and healthy controls (n=2,282). A second cohort of idiopathic generalized patients (n=412) and separate controls (n=3,876) was then genotyped on the same platform. Differences between SNP minor allele frequencies were compared between patients and controls using contingency analysis. Copy number variation (CNV) was identified using the Penn CNV software program. All subjects were of European ancestry and all studies approved by the local institutional review boards at each participating site. Results: SNP rs9572727 on Chr 13q22 near the 5' end of Dachshund 1 (DACH1) was the marker that exhibited the largest statistical difference between cases and controls: focal cohort p=0.001, OR 1.93; IGE cohort p= 3.46x10-13, OR 2.89; combined cohort p=1.71x10-14, OR 2.48. Other candidate genes include MYH11 and MMP8 for the combined cohort (p=1.3x10-9 and p=6.3x10-7 respectively) ZNF695 and VGLL3 for the focal cohort and C6orf103, ENPP2 and C7orf41 for the IGE cohort. In addition, preliminary CNV analysis identified two IGE patients that carry a 1.5 Mb deletion at 15q13.3 and two separate IGE patients that carry a 1.2 Mb deletion on 16p13.11, both CNV regions were previously associated with epilepsy. Conclusions: Our GWAS results identify several novel candidate genes for further analysis to identify potential epilepsy susceptibility alleles. These preliminary data await replication in an independent cohort and suggest that variations in genes related to developmental biology and control of gene expression may be associated with epilepsy susceptibility. In addition, we have identified CNVs on 15q13.3 and 16p13.11 in our cohort previously reported as a susceptibility factors for epilepsy. Future work will increase the size of the current cohorts and replicate these studies in independent cohorts

Summary: Clinical treatment trials for seizures disorders are usually consist of trying to enroll a patient population that has a high frequency of a specific seizure type, then blindly and randomly treating them with an antiseizure drug versus placebo and comparing seizure frequency before and after treatment. This study design is adequate to evaluate effectiveness to a limited extent for short-term oral medication treatment trials. However, epilepsy clinical investigators are often inspired by their patients to study other important outcomes including cognitive, behavioral and endocrine outcomes. Further, novel treatment approaches currently under development such as gamma-knife treatment for temporal lobe epilepsy and anti-inflammatory molecular interventions for intractable epilepsy require using different timelines and outcomes measures than standard antiseizure drug trials. Finally the influence of the basic science epilepsy researchers on clinical trials in humans is enormous yet the strategies of testing hypotheses derived in the lab to human clinical trials remains unsystematic and unsatisfying for investigators on both sides of the aisle. We will provide a forum for presenting several real and several proposed clinical trial designs that incorporate novel outcome measures and are informed by basic research. There will also be discussion and critique of the trials. Clinical investigators and especially basic science investigators are encourage to attend and to participate in the discussion The clinical trials under discussion will include a trial of cognitive preservation in temporal lobe epilepsy, testosterone levels and behavior alterations with antiseizure drugs, the trial design of using gamma-knife to treat temporal lobe epilepsy and a proposed approach to evaluating anti-inflammatory antiseizure treatments, incorporating such considerations as an appropriate study population and biomarkers for efficacy or toxicity. The presenters will be Drs. Quigg, Perucca and Harden. The discussion leaders will be Drs. Herman and French

Rationale: Resective surgical treatment can be curative in a large subset of patients with treatment resistant epilepsy. There is a need for a simple surgical grading tool which can be employed by the referring neurologist, ideally utilizing information obtained prior to diagnostic hospitalization. Our hypothesis was that a model using interictal EEG, brain MRI, seizure semiology and IQ could stratify patients with treatment resistant epilepsy with respect to their likelihood of achieving seizure freedom following assessment for resective epilepsy surgery. Methods: A prospectively identified cohort of 211 patients from the University of Alabama was combined with a retrospectively identified cohort of 193 consecutive patients at New York University presented in surgical multidisciplinary conference and either proceeded to surgery or were excluded as surgical candidates. All met inclusion criteria: age e18, focal epilepsy diagnosis e2 years, failed e1 medication, e1 seizure 3 months prior to admission, follow-up>6 months. Patients were classified as seizure free following resective surgery or not seizure free following resective surgery/no surgery. Pre-operative EEG, MRI, seizure semiology and IQ data were reviewed, systematically categorized (Table 1) and were utilized in a decision tree algorithm to predict seizure freedom. When p<0.05 was utilized, a simplistic dichotomous algorithm resulted. Therefore, to further explore, a relaxed, exploratory statistical significance level of p<0.25 was used. Nodes resulting in >50% of patients becoming seizure free were considered predictive of seizure freedom. Results: The overall seizure freedom rate was 46.8%. The exploratory decision tree analysis (Figure 1) resulted in two EEG groups: F, G, H (bilateral temporal, bilateral extra-temporal, bisynchronous; node 2; N=97) versus all others (node 3; N=307). For node 3, MRI was employed for further stratification: b (unilateral mesial temporal sclerosis (MTS); node 4; N=75) versus all others (node 7; N=232). Node 4 was further stratified based upon IQ: below 70 (node 5; N=11), above 70 (node 6; N=64). Semiology had no statistically significant impact. The model correctly predicts outcome in 62% of patients, yielding a positive predictive value of 55%, and a negative predictive value of 71%, with sensitivity of 74% and specificity of 51%. Conclusions: The relatively low overall seizure freedom rate is due to the fact that patients considered for, but ultimately not undergoing, surgery were included in the analysis, which better reflects the actual decision process for patients and neurologists. Of interest, the model fails to identify the commonly considered "best surgical" group of unilateral MTS with concordant interictal activity as a unique node. Notably, the main predictive factor was interictal EEG; other factors were only statistically viable with a more exploratory statistical approach. The positive and negative predictive values in this model are probably not sufficient for clinical use