Faculty Bibliography

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

Rationale: Resective surgery is an important consideration for patients with treatment resistant epilepsy as it may offer the best chance for seizure freedom. Initial patient counseling and the decision to refer a patient to an epilepsy surgery center is usually based on the history and physical exam, routine electroencephalogram (EEG), and brain MRI. The aim of this review is to summarize the literature and identify factors typically available in the general office setting that are reliable predictors of epilepsy surgery outcome. Methods: A literature search was performed using Pubmed and Embase. Inclusion criteria included: English language, sample size e20 patients, MRI performed on e90% of patients, a median age e16 years, average of e1 year follow-up, and predictive value assessment of clinical factors, routine EEG and/or MRI brain for outcome in epilepsy surgical resections. Articles were independently reviewed by at least 2 authors and data on study design and predictive factors were abstracted. Results: Of the 2,248 articles related to predictors of epilepsy surgical outcome identified, 123 met all inclusion criteria. Only 12 articles had a prospective study design. Study populations varied in size and epilepsy characteristics with the majority focusing on mesial temporal lobe epilepsy or lesional epilepsies. The studies focused almost exclusively on patients that had undergone resection rather than all patients considered for surgery or undergoing invasive procedures (intention-to-treat analysis). Predictive factors were not uniformly assessed in most studies, nor were they uniformly defined across studies. For example, in various studies, unilateral routine EEG epileptiform activity was variably categorized as "only ipsilateral spikes", ">70% ipsilateral", ">80% ipsilateral", or ">90% ipsilateral". Although many studies used the Engel or modified Engel classification systems, many utilized non-standardized outcome determinations (e.g. "good"). Only 6 studies had a masked assessment of seizure outcome following surgery. Conclusions: The heterogeneous patient populations, methodologies and outcome determinations significantly limit the existing literature's ability to predict the likelihood of a patient achieving seizure freedom from resective surgery based upon pre-operative data, especially in patients with extra-temporal non-mesial epilepsy. Prospective multicenter studies based upon intention to treat (i.e. enrolling patients prior to invasive procedures to determine the number of patients that are considered for epilepsy surgery that do not progress to a resective procedure) are necessary to better facilitate and encourage early referral to comprehensive epilepsy centers, counsel patients and families and identify new strategies for improving outcomes. By establishing and utilizing accepted, standardized definitions (e.g. treatment resistant epilepsy), one can improve the generalizability of findings across patients and centers

Rationale: Magnetic resonance imaging has revolutionized the detection of small structural abnormalities in patients with epilepsy. However, many focal abnormalities remain undetected in routine visual inspection. Here we used morphometric MRI to quantify imaging features related to epileptogenic cortical malformations to detect abnormal cortical thickness and blurred gray-white matter boundaries that went undetected by routine clinical visual inspection. Methods: 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 and gray-white matter contrast (GWC). 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 establish the effectiveness of the technique, we compared the detected structural abnormalities to resection margins, seizure onset zones based on intracranial EEG and pathological features using post-resection histology. Results: We report optimal parameters by which cortical thickness and GWC features detected previously occult lesions. We present sensitivity and specificity measures for each threshold and smoothing level to allow for selection of parameters based on clinical need. Conclusions: This automated approach may be a valuable additional clinical tool to improve the detection of subtle or previously occult malformations and therefore may improve identification of patients with intractable focal epilepsy who may benefit from surgery