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Medical students need to understand core neuroscience principles as a foundation for their required clinical experiences in neurology. In fact, they need a solid neuroscience foundation for their clinical experiences in all other medical disciplines also, because the nervous system plays such a critical role in the function of every organ system. Due to the rapid pace of neuroscience discoveries, it is unrealistic to expect students to master the entire field. It is also unnecessary, as students can expect to have ready access to electronic reference sources no matter where they practice. In the pre-clerkship phase of medical school, the focus should be on providing students with the foundational knowledge to use those resources effectively and interpret them correctly. This article describes an organizational framework for teaching the essential neuroscience background needed by all physicians. This is particularly germane at a time when many medical schools are re-assessing traditional practices and instituting curricular changes such as competency-based approaches, earlier clinical immersion, and increased emphasis on active learning. This article reviews factors that should be considered when developing the pre-clerkship neuroscience curriculum, including goals and objectives for the curriculum, the general topics to include, teaching and assessment methodology, who should direct the course, and the areas of expertise of faculty who might be enlisted as teachers or content experts. These guidelines were developed by a work group of experienced educators appointed by the Undergraduate Education Subcommittee (UES) of the American Academy of Neurology (AAN). They were then successively reviewed, edited, and approved by the entire UES, the AAN Education Committee, and the AAN Board of Directors.

ObjectiveClassification of epilepsy into types and subtypes is important for both clinical care and research into underlying disease mechanisms. A quantitative, data-driven approach may augment traditional electroclinical classification and shed new light on existing classification frameworks.MethodsWe used latent class analysis, a statistical method that assigns subjects into groups called latent classes based on phenotypic elements, to classify individuals with common familial epilepsies from the Epi4K Multiplex Families study. Phenotypic elements included seizure types, seizure symptoms, and other elements of the medical history. We compared class assignments to traditional electroclinical classifications and assessed familial aggregation of latent classes.ResultsA total of 1120 subjects with epilepsy were assigned to five latent classes. Classes 1 and 2 contained subjects with generalized epilepsy, largely reflecting the distinction between absence epilepsies and younger onset (class 1) versus myoclonic epilepsies and older onset (class 2). Classes 3 and 4 contained subjects with focal epilepsies, and in contrast to classes 1 and 2, these did not adhere as closely to clinically defined focal epilepsy subtypes. Class 5 contained nearly all subjects with febrile seizures plus or unknown epilepsy type, as well as a few subjects with generalized epilepsy and a few with focal epilepsy. Family concordance of latent classes was similar to or greater than concordance of clinically defined epilepsy types.SignificanceQuantitative classification of epilepsy has the potential to augment traditional electroclinical classification by (1) combining some syndromes into a single class, (2) splitting some syndromes into different classes, (3) helping to classify subjects who could not be classified clinically, and (4) defining the boundaries of clinically defined classifications. This approach can guide future research, including molecular genetic studies, by identifying homogeneous sets of individuals that may share underlying disease mechanisms.

BackgroundEffective communication between patients and their health care providers is recognized as critically important to improve the quality of health services for individuals with epilepsy. We aimed to describe in-office neurologist-patient conversations about epilepsy and focus on disease identification, shared decision-making, and care planning.MethodsTranscripts and audio recordings of conversations between patients and neurologists in the United States, Spain, and Germany were analyzed linguistically in the topic areas of epilepsy identification and diagnosis, disease education, treatments, and care planning. Analyses included word-level assessments, topic switching, strategies of information elicitation, identification of topics discussed, quantification of questions asked, and assessment of types of questions asked.ResultsConversations of 17 neurologists in the United States, 12 in Spain, and 6 in Germany, with 50, 20, and 16 patients, respectively, were analyzed. Neurologists tended to utilize an event-based, patient-friendly vocabulary to refer to seizures, and in the United States, they avoided using the term "epilepsy." Regardless of who initiated the treatment discussion, the neurologists in all 3 countries were unilaterally responsible for the treatment decision and choice of medication. When describing a new medication, neurologists most often discussed potential side effects but did not review potential benefits. Neurologists rarely defined seizure control and did not ask patients what seizure control meant to them.ConclusionsWe identified opportunities related to vocabulary, decision-making, and treatment goal setting that could be targeted to improve neurologist-patient communication about epilepsy, and ultimately, the overall treatment experience and outcomes for patients.

Gene identification in epilepsy has mainly been limited to large families segregating genes of major effect and de novo mutations in epileptic encephalopathies. Many families that present with common non-acquired focal epilepsies and genetic generalized epilepsies remain unexplained. We assembled a cohort of 'genetically enriched' common epilepsies by collecting and phenotyping families containing multiple individuals with unprovoked seizures. We aimed to determine if specific clinical epilepsy features aggregate within families, and whether this segregation of phenotypes may constitute distinct 'familial syndromes' that could inform genomic analyses. Families with three or more individuals with unprovoked seizures were studied across multiple international centres. Affected individuals were phenotyped and classified according to specific electroclinical syndromes. Families were categorized based on syndromic groupings of affected family members, examined for pedigree structure and phenotypic patterns and, where possible, assigned specific familial epilepsy syndromes. A total of 303 families were assembled and analysed, comprising 1120 affected phenotyped individuals. Of the 303 families, 117 exclusively segregated generalized epilepsy, 62 focal epilepsy, and 22 were classified as genetic epilepsy with febrile seizures plus. Over one-third (102 families) were observed to have mixed epilepsy phenotypes: 78 had both generalized and focal epilepsy features within the same individual (n = 39), or within first or second degree relatives (n = 39). Among the genetic generalized epilepsy families, absence epilepsies were found to cluster within families independently of juvenile myoclonic epilepsy, and significantly more females were affected than males. Of the 62 familial focal epilepsy families, two previously undescribed familial focal syndrome patterns were evident: 15 families had posterior quadrant epilepsies, including seven with occipito-temporal localization and seven with temporo-parietal foci, and four families displayed familial focal epilepsy of childhood with multiple affected siblings that was suggestive of recessive inheritance. The findings suggest (i) specific patterns of syndromic familial aggregation occur, including newly recognized forms of familial focal epilepsy; (ii) although syndrome-specificity usually occurs in multiplex families, the one-third of families with features of both focal and generalized epilepsy is suggestive of shared genetic determinants; and (iii) patterns of features observed across families including pedigree structure, sex, and age of onset may hold clues for future gene identification. Such detailed phenotypic information will be invaluable in the conditioning and interpretation of forthcoming sequencing data to understand the genetic architecture and inter-relationships of the common epilepsy syndromes.

Infantile spasms (IS) and Lennox-Gastaut syndrome (LGS) are epileptic encephalopathies characterized by early onset, intractable seizures, and poor developmental outcomes. De novo sequence mutations and copy number variants (CNVs) are causative in a subset of cases. We used exome sequence data in 349 trios with IS or LGS to identify putative de novo CNVs. We confirm 18 de novo CNVs in 17 patients (4.8%), 10 of which are likely pathogenic, giving a firm genetic diagnosis for 2.9% of patients. Confirmation of exome-predicted CNVs by array-based methods is still required due to false-positive rates of prediction algorithms. Our exome-based results are consistent with recent array-based studies in similar cohorts and highlight novel candidate genes for IS and LGS.

Emerging evidence indicates that epileptic encephalopathies are genetically highly heterogeneous, underscoring the need for large cohorts of well-characterized individuals to further define the genetic landscape. Through a collaboration between two consortia (EuroEPINOMICS and Epi4K/EPGP), we analyzed exome-sequencing data of 356 trios with the "classical" epileptic encephalopathies, infantile spasms and Lennox Gastaut syndrome, including 264 trios previously analyzed by the Epi4K/EPGP consortium. In this expanded cohort, we find 429 de novo mutations, including de novo mutations in DNM1 in five individuals and de novo mutations in GABBR2, FASN, and RYR3 in two individuals each. Unlike previous studies, this cohort is sufficiently large to show a significant excess of de novo mutations in epileptic encephalopathy probands compared to the general population using a likelihood analysis (p = 8.2 × 10(-4)), supporting a prominent role for de novo mutations in epileptic encephalopathies. We bring statistical evidence that mutations in DNM1 cause epileptic encephalopathy, find suggestive evidence for a role of three additional genes, and show that at least 12% of analyzed individuals have an identifiable causal de novo mutation. Strikingly, 75% of mutations in these probands are predicted to disrupt a protein involved in regulating synaptic transmission, and there is a significant enrichment of de novo mutations in genes in this pathway in the entire cohort as well. These findings emphasize an important role for synaptic dysregulation in epileptic encephalopathies, above and beyond that caused by ion channel dysfunction.

BackgroundEpilepsy is a common neurological disorder that affects approximately 50 million people worldwide. Both risk of epilepsy and response to treatment partly depend on genetic factors, and gene identification is a promising approach to target new prediction, treatment, and prevention strategies. However, despite significant progress in the identification of genes causing epilepsy in families with a Mendelian inheritance pattern, there is relatively little known about the genetic factors responsible for common forms of epilepsy and so-called epileptic encephalopathies. Study design The Epilepsy Phenome/Genome Project (EPGP) is a multi-institutional, retrospective phenotype-genotype study designed to gather and analyze detailed phenotypic information and DNA samples on 5250 participants, including probands with specific forms of epilepsy and, in a subset, parents of probands who do not have epilepsy.ResultsEPGP is being executed in four phases: study initiation, pilot, study expansion/establishment, and close-out. This article discusses a number of key challenges and solutions encountered during the first three phases of the project, including those related to (1) study initiation and management, (2) recruitment and phenotyping, and (3) data validation. The study has now enrolled 4223 participants.ConclusionsEPGP has demonstrated the value of organizing a large network into cores with specific roles, managed by a strong Administrative Core that utilizes frequent communication and a collaborative model with tools such as study timelines and performance-payment models. The study also highlights the critical importance of an effective informatics system, highly structured recruitment methods, and expert data review.