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On April 21, 2015, the first SCN8A Encephalopathy Research Group convened in Washington, DC, to assess current research into clinical and pathogenic features of the disorder and prepare an agenda for future research collaborations. The group comprised clinical and basic scientists and representatives of patient advocacy groups. SCN8A encephalopathy is a rare disorder caused by de novo missense mutations of the sodium channel gene SCN8A, which encodes the neuronal sodium channel Nav1.6. Since the initial description in 2012, approximately 140 affected individuals have been reported in publications or by SCN8A family groups. As a result, an understanding of the severe impact of SCN8A mutations is beginning to emerge. Defining a genetic epilepsy syndrome goes beyond identification of molecular etiology. Topics discussed at this meeting included (1) comparison between mutations of SCN8A and the SCN1A mutations in Dravet syndrome, (2) biophysical properties of the Nav1.6 channel, (3) electrophysiologic effects of patient mutations on channel properties, (4) cell and animal models of SCN8A encephalopathy, (5) drug screening strategies, (6) the phenotypic spectrum of SCN8A encephalopathy, and (7) efforts to develop a bioregistry. A panel discussion of gaps in bioregistry, biobanking, and clinical outcomes data was followed by a planning session for improved integration of clinical and basic science research. Although SCN8A encephalopathy was identified only recently, there has been rapid progress in functional analysis and phenotypic classification. The focus is now shifting from identification of the underlying molecular cause to the development of strategies for drug screening and prioritized patient care.

Summary The Common Data Element (CDE) Project was initiated in 2006 by the National Institute of Neurological Disorders and Stroke (NINDS) to develop standards for performing funded neuroscience-related clinical research. CDEs are intended to standardize aspects of data collection, decrease study start-up time, and provide more complete, comprehensive, and equivalent data across studies within a particular disease area. Therefore, CDEs will simplify data sharing and data aggregation across NINDS-funded clinical research, and where appropriate, facilitate the development of evidenced-based guidelines and recommendations. Epilepsy-specific CDEs were established in nine content areas: (1) Antiepileptic Drugs (AEDs) and Other Antepileptic Therapies (AETs), )2) Comorbidities, (3) Electrophysiology, (4) Imaging,(5) Neurological Exam, (6) Neuropsychology,(7) Quality of Life, (8) Seizures and Syndromes, and (9) Surgery and Pathology. CDEs were developed as a dynamic resource that will accommodate recommendations based on investigator use, new technologies, and research findings documenting emerging critical disease characteristics. The epilepsy-specific CDE initiative can be viewed as part of the larger international movement toward “harmonization” of clinical disease characterization and outcome assessment designed to promote communication and research efforts in epilepsy. It will also provide valuable guidance for CDE improvement during their further development, refinement, and implementation. This article describes the NINDS CDE Initiative, the process used in developing Epilepsy CDEs, and the benefits of CDEs for the clinical investigator and NINDS.

The Epilepsy Innovation Institute (Ei2) is a new research program of the Epilepsy Foundation designed to be an innovation incubator for epilepsy. Ei2 research areas are selected based on community surveys that ask people impacted by epilepsy what they would like researchers to focus on. In their 2016 survey, unpredictability was selected as a top issue regardless of seizure frequency or severity. In response to this need, Ei2 launched the My Seizure Gauge challenge, with the end goal of creating a personalized seizure advisory system device. Prior to moving forward, Ei2 convened a diverse group of stakeholders from people impacted by epilepsy and clinicians, to device developers and data scientists, to basic science researchers and regulators, for a state of the science assessment on seizure forecasting. From the discussions, it was clear that we are at an exciting crossroads. With the advances in bioengineering, we can utilize digital markers, wearables, and biosensors as parameters for a seizure-forecasting algorithm. There are also over a thousand individuals who have been implanted with ambulatory intracranial EEG recording devices. Pairing up peripheral measurements to brain states could identify new relationships and insights. Another key component is the heterogeneity of the relationships indicating that pooling findings across groups is suboptimal, and that data collection will need to be done on longer time scales to allow for individualization of potential seizure-forecasting algorithms.