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Epileptiform activity is common in critically ill patients, but movement-related artifacts-including electromyography (EMG) and myoclonus-can obscure EEG, limiting detection of epileptiform activity. We sought to determine the ability of pharmacologic paralysis and quantitative artifact reduction (AR) to improve epileptiform discharge detection.Retrospective analysis of patients who underwent continuous EEG monitoring with pharmacologic paralysis. Four reviewers read each patient's EEG pre- and post- both paralysis and AR, and indicated the presence of epileptiform discharges. We compared the interrater reliability (IRR) of identifying discharges at baseline, post-AR, and post-paralysis, and compared the performance of AR and paralysis according to artifact type.IRR of identifying epileptiform discharges at baseline was slight (N = 30; κ = 0.10) with a trend toward increase post-AR (κ = 0.26, p = 0.053) and a significant increase post-paralysis (κ = 0.51, p = 0.001). AR was as effective as paralysis at improving IRR of identifying discharges in those with high EMG artifact (N = 15; post-AR κ = 0.63, p = 0.009; post-paralysis κ = 0.62, p = 0.006) but not with primarily myoclonus artifact (N = 15).Paralysis improves detection of epileptiform activity in critically ill patients when movement-related artifact obscures EEG features. AR improves detection as much as paralysis when EMG artifact is high, but is ineffective when the primary source of artifact is myoclonus.In the appropriate setting, both AR and paralysis facilitate identification of epileptiform activity in critically ill patients.

To develop a natural language processing (NLP) algorithm to abstract seizure types and frequencies from electronic health records (EHR).Seizure frequency measurement is an epilepsy quality metric. Yet, abstraction of seizure frequency from the EHR is laborious. We present an NLP algorithm to extract seizure data from unstructured text of clinic notes. Algorithm performance was assessed at two epilepsy centers.We developed a rules-based NLP algorithm to recognize terms related to seizures and frequency within the text of an outpatient encounter. Algorithm output (e.g. number of seizures of a particular type within a time interval) was compared to seizure data manually annotated by two expert reviewers ("gold standard"). The algorithm was developed from 150 clinic notes from institution #1 (development set), then tested on a separate set of 219 notes from institution #1 (internal test set) with 248 unique seizure frequency elements. The algorithm was separately applied to 100 notes from institution #2 (external test set) with 124 unique seizure frequency elements. Algorithm performance was measured by recall (sensitivity), precision (positive predictive value), and F1 score (geometric mean of precision and recall).In the internal test set, the algorithm demonstrated 70% recall (173/248), 95% precision (173/182), and 0.82 F1 score compared to manual review. Algorithm performance in the external test set was lower with 22% recall (27/124), 73% precision (27/37), and 0.40 F1 score.These results suggest NLP extraction of seizure types and frequencies is feasible, though not without challenges in generalizability for large-scale implementation.

Introduction The authors tested the hypothesis that the EEG feature generalized polyspike train (GPT) is associated with drug-resistant idiopathic generalized epilepsy (IGE). Methods The authors conducted a single-center case-control study of patients with IGE who had outpatient EEGs performed between 2016 and 2020. The authors classified patients as drug-resistant or drug-responsive based on clinical review and in a masked manner reviewed EEG data for the presence and timing of GPT (a burst of generalized rhythmic spikes lasting less than 1 second) and other EEG features. A relationship between GPT and drug resistance was tested before and after controlling for EEG duration. The EEG duration needed to observe GPT was also calculated. Results One hundred three patients were included (70% drug-responsive and 30% drug-resistant patients). Generalized polyspike train was more prevalent in drug-resistant IGE (odds ratio, 3.8; 95% confidence interval, 1.3-11.4; P = 0.02). This finding persisted when controlling for EEG duration both with stratification and with survival analysis. A median of 6.5 hours (interquartile range, 0.5-12.7 hours) of EEG recording was required to capture the first occurrence of GPT. Conclusions The findings support the hypothesis that GPT is associated with drug-resistant IGE. Prolonged EEG recording is required to identify this feature. Thus, >24-hour EEG recording early in the evaluation of patients with IGE may facilitate prognostication.

Disease-causing variants in STXBP1 are among the most common genetic causes of neurodevelopmental disorders. However, the phenotypic spectrum in STXBP1-related disorders is wide and clear correlations between variant type and clinical features have not been observed so far. Here, we harmonized clinical data across 534 individuals with STXBP1-related disorders and analysed 19 973 derived phenotypic terms, including phenotypes of 253 individuals previously unreported in the scientific literature. The overall phenotypic landscape in STXBP1-related disorders is characterized by neurodevelopmental abnormalities in 95% and seizures in 89% of individuals, including focal-onset seizures as the most common seizure type (47%). More than 88% of individuals with STXBP1-related disorders have seizure onset in the first year of life, including neonatal seizure onset in 47%. Individuals with protein-truncating variants and deletions in STXBP1 (n = 261) were almost twice as likely to present with West syndrome and were more phenotypically similar than expected by chance. Five genetic hotspots with recurrent variants were identified in more than 10 individuals, including p.Arg406Cys/His (n = 40), p.Arg292Cys/His/Leu/Pro (n = 30), p.Arg551Cys/Gly/His/Leu (n = 24), p.Pro139Leu (n = 12), and p.Arg190Trp (n = 11). None of the recurrent variants were significantly associated with distinct electroclinical syndromes, single phenotypic features, or showed overall clinical similarity, indicating that the baseline variability in STXBP1-related disorders is too high for discrete phenotypic subgroups to emerge. We then reconstructed the seizure history in 62 individuals with STXBP1-related disorders in detail, retrospectively assigning seizure type and seizure frequency monthly across 4433 time intervals, and retrieved 251 anti-seizure medication prescriptions from the electronic medical records. We demonstrate a dynamic pattern of seizure control and complex interplay with response to specific medications particularly in the first year of life when seizures in STXBP1-related disorders are the most prominent. Adrenocorticotropic hormone and phenobarbital were more likely to initially reduce seizure frequency in infantile spasms and focal seizures compared to other treatment options, while the ketogenic diet was most effective in maintaining seizure freedom. In summary, we demonstrate how the multidimensional spectrum of phenotypic features in STXBP1-related disorders can be assessed using a computational phenotype framework to facilitate the development of future precision-medicine approaches.

Background and ObjectivesThe aim of this work was to test the accuracy of Persyst commercially available automated seizure detection in critical care EEG by comparing automated seizure detections to human review in a manually reviewed cohort and on a large scale.MethodsAutomated seizure detections (Persyst versions 12 and 13) were compared to human review in a pilot cohort of 229 seizures from 85 EEG records and then in an expanded cohort of 7,924 EEG records. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for individual seizures (pilot cohort) and for entire records (pilot and expanded cohorts). We assessed EEG features associated with the accuracy of automated seizure detections.ResultsIn the pilot cohort, accuracy of automated detection for individual seizures was modest (sensitivity 0.50, PPV 0.60). At the record level (did the recording contain seizures or not?), sensitivity was higher (pilot cohort 0.78, expanded cohort 0.91), PPV was low (pilot cohort 0.40, expanded cohort 0.08), and NPV was high (pilot cohort 0.88, expanded cohort 0.97). Different software versions (version 12 vs 13) performed similarly. Sensitivity was higher for records containing focal-onset seizures compared to generalized-onset seizures (0.93 vs 0.85, p = 0.012).DiscussionIn critical care continuous EEG recordings, automated detection of individual seizures had rates of both false negatives and false positives that bring into question its utility as a seizure alarm in clinical practice. At the level of entire EEG records, the absence of automated detections accurately predicted EEG records without true seizures. The true value of Persyst automated seizure detection appears to lie in triaging of low-risk EEGs.Classification of EvidenceThis study provides Class II evidence that an automated seizure detection program cannot accurately identify EEG records that contain seizures.

Subcellular membrane systems are highly enriched in dolichol, whose role in organelle homeostasis and endosomal-lysosomal pathway remains largely unclear besides being involved in protein glycosylation. DHDDS encodes for the catalytic subunit (DHDDS) of the enzyme cis-prenyltransferase (cis-PTase), involved in dolichol biosynthesis and dolichol-dependent protein glycosylation in the endoplasmic reticulum. An autosomal recessive form of retinitis pigmentosa (retinitis pigmentosa 59) has been associated with a recurrent DHDDS variant. Moreover, two recurring de novo substitutions were detected in a few cases presenting with neurodevelopmental disorder, epilepsy and movement disorder. We evaluated a large cohort of patients (n = 25) with de novo pathogenic variants in DHDDS and provided the first systematic description of the clinical features and long-term outcome of this new neurodevelopmental and neurodegenerative disorder. The functional impact of the identified variants was explored by yeast complementation system and enzymatic assay. Patients presented during infancy or childhood with a variable association of neurodevelopmental disorder, generalized epilepsy, action myoclonus/cortical tremor and ataxia. Later in the disease course, they experienced a slow neurological decline with the emergence of hyperkinetic and/or hypokinetic movement disorder, cognitive deterioration and psychiatric disturbances. Storage of lipidic material and altered lysosomes were detected in myelinated fibres and fibroblasts, suggesting a dysfunction of the lysosomal enzymatic scavenger machinery. Serum glycoprotein hypoglycosylation was not detected and, in contrast to retinitis pigmentosa and other congenital disorders of glycosylation involving dolichol metabolism, the urinary dolichol D18/D19 ratio was normal. Mapping the disease-causing variants into the protein structure revealed that most of them clustered around the active site of the DHDDS subunit. Functional studies using yeast complementation assay and in vitro activity measurements confirmed that these changes affected the catalytic activity of the cis-PTase and showed growth defect in yeast complementation system as compared with the wild-type enzyme and retinitis pigmentosa-associated protein. In conclusion, we characterized a distinctive neurodegenerative disorder due to de novo DHDDS variants, which clinically belongs to the spectrum of genetic progressive encephalopathies with myoclonus. Clinical and biochemical data from this cohort depicted a condition at the intersection of congenital disorders of glycosylation and inherited storage diseases with several features akin to of progressive myoclonus epilepsy such as neuronal ceroid lipofuscinosis and other lysosomal disorders.

An early genetic diagnosis can guide the time-sensitive treatment and care of individuals with genetic epilepsies. However, identification of a genetic cause often occurs long after onset of these disorders. Here, we aimed to identify early clinical features suggestive of genetic diagnoses in individuals with epilepsy by systematic large-scale analysis of clinical information from full-text patient notes in the electronic medical records (EMR).From the EMR of 32,112 individuals with childhood epilepsy, we retrieved 4,572,783 clinical notes spanning 203,369 total patient-years. A subcohort of 1,925 individuals had a known or presumed genetic epilepsy with 738 genetic diagnoses spanning 271 genes. We employed a customized natural language processing (NLP) pipeline to extract 89 million time-stamped standardized clinical annotations from free text of the retrieved clinical notes. Our analyses identified 47,641 clinical associations with a genetic cause at distinct ages prior to diagnosis. Notable among these associations were:SCN1Awith status epilepticus between 9 and 12 months of age (PSTXBP1with muscular hypotonia between 6 and 9 months (P=3.4×10−4, 95% CI=3.08-102);SCN2Awith autism between 1.5 and 1.75 years (PDEPDC5with focal-onset seizure between 5.75 and 6 years (PIQSEC2with myoclonic seizure between 2.75 and 3 years (P=2.5×10−4, 95% CI=11.3-1.15×104). We also identified associations between clinical terms and gene groups. Variants in ion channel gating mechanisms were associated with myoclonus between 3 and 6 months of age (PPKCNT1with migrating focal seizures from at 0 to 1.75 years (P15). A neurodevelopmental abnormality presenting between 6 and 9 months of age was strongly associated with an individual having any genetic diagnosis (PPP=5.5×10−3, 95% CI=1.23-3.35).In summary, we identified key clinical features that precede genetic diagnosis, leveraging EMR data at scale from a large cohort of individuals with genetic epilepsies. Our findings demonstrate that automated EMR analysis may assist clinical decision making, leading to earlier diagnosis and more precise prognostication and treatment of genetic epilepsies in the precision medicine era.

Purpose Pathogenic variants in SCN2A cause a wide range of neurodevelopmental phenotypes. Reports of genotype–phenotype correlations are often anecdotal, and the available phenotypic data have not been systematically analyzed. Methods We extracted phenotypic information from primary descriptions of SCN2A-related disorders in the literature between 2001 and 2019, which we coded in Human Phenotype Ontology (HPO) terms. With higher-level phenotype terms inferred by the HPO structure, we assessed the frequencies of clinical features and investigated the association of these features with variant classes and locations within the NaV1.2 protein. Results We identified 413 unrelated individuals and derived a total of 10,860 HPO terms with 562 unique terms. Protein-truncating variants were associated with autism and behavioral abnormalities. Missense variants were associated with neonatal onset, epileptic spasms, and seizures, regardless of type. Phenotypic similarity was identified in 8/62 recurrent SCN2A variants. Three independent principal components accounted for 33% of the phenotypic variance, allowing for separation of gain-of-function versus loss-of-function variants with good performance. Conclusion Our work shows that translating clinical features into a computable format using a standardized language allows for quantitative phenotype analysis, mapping the phenotypic landscape of SCN2A-related disorders in unprecedented detail and revealing genotype–phenotype correlations along a multidimensional spectrum.

Germline mutations in tubulin beta class I (TUBB), which encodes one of the β-tubulin isoforms, were previously associated with neurological and cutaneous abnormalities. Here, we describe the first case of inherited bone marrow (BM) failure, including marked thrombocytopenia, morphological abnormalities, and cortical dysplasia, associated with a de novo p.D249V variant in TUBB. Mutant TUBB had abnormal cellular localisation in transfected cells. Following interferon/ribavirin therapy administered for transfusion-acquired hepatitis C, severe pancytopenia and BM aplasia ensued, which was unresponsive to immunosuppression. Acquired chromosome arm 6p loss of heterozygosity was identified, leading to somatic loss of the mutant TUBB allele.

Neurology 99(3), e221-e233 (2022). doi:10.1212/WNL.0000000000200715, Published by Wolters Kluwer, Philadelphia, Pa.

While genetics evaluation is increasingly utilized in adult neurology patients, its usage and efficacy are not well characterized. Here, we report our experience with 1461 consecutive patients evaluated in an adult neurogenetics clinic at a large academic medical center between January 2015 and March 2020. Of the 1461 patients evaluated, 1215 patients were referred for the purposes of identifying a genetic diagnosis for an undiagnosed condition, 90.5% of whom underwent genetic testing. The modalities of genetic testing utilized varied across referral diagnostic categories, including a range of utilization of whole exome sequencing (WES) as an initial test in 13.9% of neuromuscular patients to 52.9% in white matter disorder patients. The usage of WES increased over time, from 7.7% of initial testing in 2015 to a peak of 27.3% in 2019. Overall, genetic testing yielded a causal genetic diagnosis in 30.7% of patients. This yield was higher in certain referring diagnosis categories, such as neuromuscular (39.0%) and epilepsy (29.8%). Our study demonstrates that evaluation at an adult neurogenetics referral center can yield diagnoses in a substantial fraction of patients. Additional research will be needed to determine optimal genetic testing strategies and cost effectiveness of adult neurogenetics evaluation.

OBJECTIVE: To assess whether the risk of epilepsy is higher in offspring of mothers with epilepsy than in offspring of fathers with epilepsy.METHODS: In a prospective population-based register study, we considered all singletons born in Denmark between 1981 and 2016 (N = 1,754,742). From the Danish National Patient Register since 1977, we identified epilepsy diagnoses in all study participants and their family members. Cox regression models were used to estimate hazard ratios (HRs) and corresponding 95% confidence intervals (CI), adjusted for relevant confounders.RESULTS: We included 1,754,742 individuals contributing > 30 million person-years of follow-up. The incidence rate of epilepsy in offspring of unaffected parents was 78.8 (95% CI: 77.8-79.8) per 100,000 person-years, while the corresponding rate in offspring with an affected father was 172 per 100,000 person-years (95% CI: 156-187) and in offspring with an affected mother was 260 per 100,000 person-years (95% CI: 243-277). Having an affected mother was associated with a 1.45-fold (95% CI: 1.30-1.63) higher risk of epilepsy in the offspring, compared to having an affected father. This maternal effect was found both in male (HR = 1.39, 95% CI: 1.19-1.62) and female offspring (HR = 1.53, 95% CI: 1.30-1.80), and across various ages at onset in the offspring. The maternal effect was also found in familial epilepsies (i.e. where the affected parent had an affected sibling; HR = 1.50, 95% CI: 1.04-2.16).INTERPRETATION: We found a clear maternal effect on offspring risk of epilepsy in this nationwide cohort study.

OBJECTIVE: To determine the roles of shared and distinct genetic influences on generalized and focal epilepsy operating in individuals who manifest features of both types (combined epilepsies), and in families manifesting both generalized and focal epilepsies in separate individuals (mixed families). METHODS: We analyzed the deeply phenotyped Epi4K cohort of multiplex families (≥3 affected individuals per family) using methods that quantify the aggregation of phenotypes within families and the relatedness of individuals with different phenotypes within family pedigrees. RESULTS: The cohort included 281 families containing 1021 individuals with generalized (n = 484), focal (304), combined (51), or unclassified (182) epilepsies. The odds of combined epilepsy was higher in relatives of participants with combined epilepsy than in relatives of those with other epilepsy types (odds ratio [OR] 5.2, 95% confidence interval [CI] 1.7-16.1, P = .004). Individuals with combined epilepsy co-occurred in families more often than expected by chance (P = .03). Within mixed families, individuals with each type of epilepsy were more closely related to relatives with the same type than to relatives with other types (P

Summary More than 100 genetic etiologies have been identified in developmental and epileptic encephalopathies (DEEs), but correlating genetic findings with clinical features at scale has remained a hurdle because of a lack of frameworks for analyzing heterogenous clinical data. Here, we analyzed 31,742 Human Phenotype Ontology (HPO) terms in 846 individuals with existing whole-exome trio data and assessed associated clinical features and phenotypic relatedness by using HPO-based semantic similarity analysis for individuals with de novo variants in the same gene. Gene-specific phenotypic signatures included associations of SCN1A with “complex febrile seizures” (HP: 0011172; p = 2.1 × 10−5) and “focal clonic seizures” (HP: 0002266; p = 8.9 × 10−6), STXBP1 with “absent speech” (HP: 0001344; p = 1.3 × 10−11), and SLC6A1 with “EEG with generalized slow activity” (HP: 0010845; p = 0.018). Of 41 genes with de novo variants in two or more individuals, 11 genes showed significant phenotypic similarity, including SCN1A (n = 16, p < 0.0001), STXBP1 (n = 14, p = 0.0021), and KCNB1 (n = 6, p = 0.011). Including genetic and phenotypic data of control subjects increased phenotypic similarity for all genetic etiologies, whereas the probability of observing de novo variants decreased, emphasizing the conceptual differences between semantic similarity analysis and approaches based on the expected number of de novo events. We demonstrate that HPO-based phenotype analysis captures unique profiles for distinct genetic etiologies, reflecting the breadth of the phenotypic spectrum in genetic epilepsies. Semantic similarity can be used to generate statistical evidence for disease causation analogous to the traditional approach of primarily defining disease entities through similar clinical features.

Purpose Autoantibodies against glutamic acid decarboxylase 65 (GAD-65) have been identified in patients with chronic epilepsy. In this study, we ask (1) what is the frequency of GAD-65 antibodies in chronic epilepsy? (2) what is the frequency and type of epilepsy in individuals with GAD-65 antibodies? Methods For cohort 1, serum samples of patients with epilepsy (without type I diabetes) were obtained from our biobank. Samples were tested for GAD-65 antibodies using a cell-based assay and confirmed by immunohistochemistry. For cohort 2, patients with positive GAD-65 antibodies were identified and their medical records were reviewed for the presence and characteristics of epilepsy. Results Cohort 1 included 270 patients, of which 53% were women; median age was 47 years; median duration of epilepsy was 16 years. Epilepsy was focal in 87% (temporal lobe in 20%), and drug-resistant in 45%. GAD-65 antibodies were present in two out of 270 cases (0.7%) and zero controls. Cohort 2 consisted of 23 patients with known GAD-65 antibodies, of which ten had epilepsy (43%). Of these, 80% were women with a median age of 40 years and a median duration of epilepsy of 18 years. All ten patients had focal epilepsy, nine had temporal lobe epilepsy, and seven were drug resistant. Conclusions In patients with chronic epilepsy, the frequency of GAD-65 antibodies detected with our cell-based assay was substantially lower than previously reported with use of other methods. When present, GAD-65 antibodies are associated with drug-resistant temporal lobe epilepsy. GAD-65 positive epilepsy patients merit further investigation.

Purpose Childhood epilepsies have a strong genetic contribution, but the disease trajectory for many genetic etiologies remains unknown. Electronic medical record (EMR) data potentially allow for the analysis of longitudinal clinical information but this has not yet been explored. Methods We analyzed provider-entered neurological diagnoses made at 62,104 patient encounters from 658 individuals with known or presumed genetic epilepsies. To harmonize clinical terminology, we mapped clinical descriptors to Human Phenotype Ontology (HPO) terms and inferred higher-level phenotypic concepts. We then binned the resulting 286,085 HPO terms to 100 3-month time intervals and assessed gene–phenotype associations at each interval. Results We analyzed a median follow-up of 6.9 years per patient and a cumulative 3251 patient years. Correcting for multiple testing, we identified significant associations between “Status epilepticus” with SCN1A at 1.0 years, “Severe intellectual disability” with PURA at 9.75 years, and “Infantile spasms” and “Epileptic spasms” with STXBP1 at 0.5 years. The identified associations reflect known clinical features of these conditions, and manual chart review excluded provider bias. Conclusion Some aspects of the longitudinal disease histories can be reconstructed through EMR data and reveal significant gene–phenotype associations, even within closely related conditions. Gene-specific EMR footprints may enable outcome studies and clinical decision support.

OBJECTIVE: Classification 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. METHODS: We 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. RESULTS: A 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. SIGNIFICANCE: Quantitative 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.

The developmental and epileptic encephalopathies (DEEs) are heterogeneous disorders with a strong genetic contribution, but the underlying genetic etiology remains unknown in a significant proportion of individuals. To explore whether statistical support for genetic etiologies can be generated on the basis of phenotypic features, we analyzed whole-exome sequencing data and phenotypic similarities by using Human Phenotype Ontology (HPO) in 314 individuals with DEEs. We identified a de novo c.508C>T (p.Arg170Trp) variant in AP2M1 in two individuals with a phenotypic similarity that was higher than expected by chance (p = 0.003) and a phenotype related to epilepsy with myoclonic-atonic seizures. We subsequently found the same de novo variant in two individuals with neurodevelopmental disorders and generalized epilepsy in a cohort of 2,310 individuals who underwent diagnostic whole-exome sequencing. AP2M1 encodes the mu-subunit of the adaptor protein complex 2 (AP-2), which is involved in clathrin-mediated endocytosis (CME) and synaptic vesicle recycling. Modeling of protein dynamics indicated that the p.Arg170Trp variant impairs the conformational activation and thermodynamic entropy of the AP-2 complex. Functional complementation of both the mu-subunit carrying the p.Arg170Trp variant in human cells and astrocytes derived from AP-2 mu conditional knockout mice revealed a significant impairment of CME of transferrin. In contrast, stability, expression levels, membrane recruitment, and localization were not impaired, suggesting a functional alteration of the AP-2 complex as the underlying disease mechanism. We establish a recurrent pathogenic variant in AP2M1 as a cause of DEEs with distinct phenotypic features, and we implicate dysfunction of the early steps of endocytosis as a disease mechanism in epilepsy.

Objective Improvement in epilepsy care requires standardized methods to assess disease severity. We report the results of implementing common data elements (CDEs) to document epilepsy history data in the electronic medical record (EMR) after 12 months of clinical use in outpatient encounters. Methods Data regarding seizure frequency were collected during routine clinical encounters using a CDE-based form within our EMR. We extracted CDE data from the EMR and developed measurements for seizure severity and seizure improvement scores. Seizure burden and improvement was evaluated by patient demographic and encounter variables for in-person and telemedicine encounters. Results We assessed a total of 1696 encounters in 1038 individuals with childhood epilepsies between September 6, 2019 and September 11, 2020 contributed by 32 distinct providers. Childhood absence epilepsy (n = 121), Lennox-Gastaut syndrome (n = 86), and Dravet syndrome (n = 42) were the most common epilepsy syndromes. Overall, 43% (737/1696) of individuals had at least monthly seizures, 17% (296/1696) had a least daily seizures, and 18% (311/1696) were seizure-free for >12 months. Quantification of absolute seizure burden and changes in seizure burden over time differed between epilepsy syndromes, including high and persistent seizure burden in patients with Lennox-Gastaut syndrome. Individuals seen via telemedicine or in-person encounters had comparable seizure frequencies. Individuals identifying as Hispanic/Latino, particularly from postal codes with lower median household incomes, were more likely to have ongoing seizures that worsened over time. Significance Standardized documentation of clinical data in childhood epilepsies through CDE can be implemented in routine clinical care at scale and enables assessment of disease burden, including characterization of seizure burden over time. Our data provide insights into heterogeneous patterns of seizure control in common pediatric epilepsy syndromes and will inform future initiatives focusing on patient-centered outcomes in childhood epilepsies, including the impact of telemedicine and health care disparities.

EBioMedicine 72, 103588 (2021). doi:10.1016/j.ebiom.2021.103588, Published by Elsevier, Amsterdam [u.a.]

Summary Both mild and severe epilepsies are influenced by variants in the same genes, yet an explanation for the resulting phenotypic variation is unknown. As part of the ongoing Epi25 Collaboration, we performed a whole-exome sequencing analysis of 13,487 epilepsy-affected individuals and 15,678 control individuals. While prior Epi25 studies focused on gene-based collapsing analyses, we asked how the pattern of variation within genes differs by epilepsy type. Specifically, we compared the genetic architectures of severe developmental and epileptic encephalopathies (DEEs) and two generally less severe epilepsies, genetic generalized epilepsy and non-acquired focal epilepsy (NAFE). Our gene-based rare variant collapsing analysis used geographic ancestry-based clustering that included broader ancestries than previously possible and revealed novel associations. Using the missense intolerance ratio (MTR), we found that variants in DEE-affected individuals are in significantly more intolerant genic sub-regions than those in NAFE-affected individuals. Only previously reported pathogenic variants absent in available genomic datasets showed a significant burden in epilepsy-affected individuals compared with control individuals, and the ultra-rare pathogenic variants associated with DEE were located in more intolerant genic sub-regions than variants associated with non-DEE epilepsies. MTR filtering improved the yield of ultra-rare pathogenic variants in affected individuals compared with control individuals. Finally, analysis of variants in genes without a disease association revealed a significant burden of loss-of-function variants in the genes most intolerant to such variation, indicating additional epilepsy-risk genes yet to be discovered. Taken together, our study suggests that genic and sub-genic intolerance are critical characteristics for interpreting the effects of variation in genes that influence epilepsy.

Collaborative studies exploit large data sets to discover new associations with diseases. However, analyses of clinical features struggle with big data. Beyond sheer volume, the challenge is in harmonisation of terminology and the level of detail of information provided for analysis by different sources; in epilepsy this is most pertinent to seizures.We applied two ontologies (representations of the relationship between different types of seizure) to the same sparse details from 82 individuals. We compared the amount of information imputed using the Human Phenotype Ontology (HPO, a reference of clinical phenotypes widely used by geneticists) and an ontology of seizures created by the Epilepsiome Task Force of the International League Against Epilepsy Genetics Commission (based upon ILAE classifications).We translated each individual’s seizure descriptions into 2 terms (median) from each ontology. Imputa- tion generated 2 additional terms per individual using the HPO and 4 using the Epilepsiome ontology. The Epilepsiome ontology was able to increase the amount of information available for analysis by two- thirds compared to the HPO.Well-designed ontologies can be used to impute descriptions of complex clinical features at multiple less specific levels of precision, addressing a major limiting step in harmonisation of data for collabora- tion or re-use.rhys.thomas@ncl.ac.uk

While genetic studies of epilepsies can be performed in thousands of individuals, phenotyping remains a manual, non-scalable task. A particular challenge is capturing the evolution of complex phenotypes with age. Here, we present a novel approach, applying phenotypic similarity analysis to a total of 3251 patient-years of longitudinal electronic medical record data from a previously reported cohort of 658 individuals with genetic epilepsies. After mapping clinical data to the Human Phenotype Ontology, we determined the phenotypic similarity of individuals sharing each genetic etiology within each 3-month age interval from birth up to a maximum age of 25 years. 140 of 600 (23%) of all 27 genes and 3-month age intervals with sufficient data for calculation of phenotypic similarity were significantly higher than expect by chance. 11 of 27 genetic etiologies had significant overall phenotypic similarity trajectories. These do not simply reflect strong statistical associations with single phenotypic features but appear to emerge from complex clinical constellations of features that may not be strongly associated individually. As an attempt to reconstruct the cognitive framework of syndrome recognition in clinical practice, longitudinal phenotypic similarity analysis extends the traditional phenotyping approach by utilizing data from electronic medical records at a scale that is far beyond the capabilities of manual phenotyping. Delineation of how the phenotypic homogeneity of genetic epilepsies varies with age could improve the phenotypic classification of these disorders, the accuracy of prognostic counseling, and by providing historical control data, the design and interpretation of precision clinical trials in rare diseases.

Status epilepticus is an emergency; however, prompt treatment of patients with status epilepticus is challenging. Clinical trials, such as the ESETT (Established Status Epilepticus Treatment Trial), compare effectiveness of antiepileptic medications, and rigorous examination of effectiveness of care delivery is similarly warranted. We reviewed the medical literature on observed deviations from guidelines, clinical significance, and initiatives to improve timely treatment. We found pervasive, substantial gaps between recommended and "real-world" practice with regard to timing, dosing, and sequence of antiepileptic therapy. Applying quality improvement methodology at the institutional level can increase adherence to guidelines and may improve patient outcomes. Ann Neurol 2017;82:155-165.

PURPOSE Childhood epilepsies have a strong genetic contribution, but the disease trajectory for many genetic etiologies remains unknown. Electronic medical record (EMR) data potentially allow for the analysis of longitudinal clinical information but this has not yet been explored. METHODS We analyzed provider-entered neurological diagnoses made at 62,104 patient encounters from 658 individuals with known or presumed genetic epilepsies. To harmonize clinical terminology, we mapped clinical descriptors to Human Phenotype Ontology (HPO) terms and inferred higher-level phenotypic concepts. We then binned the resulting 286,085 HPO terms to 100 3-month time intervals and assessed gene-phenotype associations at each interval. RESULTS We analyzed a median follow-up of 6.9 years per patient and a cumulative 3251 patient years. Correcting for multiple testing, we identified significant associations between "Status epilepticus" with SCN1A at 1.0 years, "Severe intellectual disability" with PURA at 9.75 years, and "Infantile spasms" and "Epileptic spasms" with STXBP1 at 0.5 years. The identified associations reflect known clinical features of these conditions, and manual chart review excluded provider bias. CONCLUSION Some aspects of the longitudinal disease histories can be reconstructed through EMR data and reveal significant gene-phenotype associations, even within closely related conditions. Gene-specific EMR footprints may enable outcome studies and clinical decision support.

Identifying the optimal treatment based on specific characteristics of each patient is the main promise of precision medicine. In the field of epilepsy, the identification of more than 100 causative genes provides the enticing possibility of treatments targeted to specific disease etiologies. These conditions include classical examples, such as the use of vitamin B6 in antiquitin deficiency or the ketogenic diet in GLUT1 deficiency, where the disease mechanism can be directly addressed by the selection of a specific therapeutic compound. For epilepsies caused by channelopathies there have been advances in understanding how the selection of existing medications can be targeted to the functional consequences of genetic alterations. We discuss the examples of the use of sodium channel blockers such as phenytoin and oxcarbazepine in the sodium channelopathies, quinidine in KCNT1-related epilepsies, and strategies in GRIN-related epilepsies as examples of epilepsy precision medicine. Assessing the clinical response to targeted treatments of these conditions has been complicated by genetic and phenotypic heterogeneity, as well as by various neurological and non-neurological comorbidities. Moving forward, the development of standardized outcome measures will be critical to successful precision medicine trials in complex and heterogeneous disorders like the epilepsies. Finally, we address new frontiers in epilepsy precision medicine, including the need to match the growing volume of genetic data with high-throughput functional assays to assess the functional consequences of genetic variants and the ability to extract clinical data at large scale from electronic medical records and apply quantitative methods based on standardized phenotyping language.

OBJECTIVE: We tested 2 hypotheses regarding age at onset within familial epilepsies: (1) family members with epilepsy tend to have similar ages at onset, independent of epilepsy syndrome; and (2) age at onset is younger in successive generations after controlling for sampling bias. METHODS: We analyzed clinical data collected by the Epi4K Consortium (303 multiplex families, 1,120 individuals). To test hypothesis 1, we used both linear mixed models commonly used for heritability analysis and Cox regression models with frailty terms to assess clustering of onset within families after controlling for other predictors. To test hypothesis 2, we used mixed effects models, pairwise analyses, and survival analysis to address sampling-related bias that may mimic anticipation. RESULTS: Regarding hypothesis 1, age at seizure onset was significantly heritable (intraclass correlation coefficient = 0.17, p < 0.001) after adjusting for epilepsy type, sex, site, history of febrile seizure, and age at last observation. This finding remained significant after adjusting for epilepsy syndromes, and was robust across statistical methods in all families and in generalized families. Regarding hypothesis 2, the mean age at onset decreased in successive generations (p < 0.001). After adjusting for age at last observation, this effect was not significant in mixed effects models (p = 0.14), but remained significant in pairwise (p = 0.0003) and survival analyses (p = 0.02). INTERPRETATION: Age at seizure onset is an independent familial trait, and may have genetic determinants distinct from the determinants of particular epilepsy syndromes. Younger onsets in successive generations can be explained in part by sampling bias, but the presence of genetic anticipation cannot be excluded. ANN NEUROL 2019.

Background and Purpose: Patients with posterior reversible encephalopathy syndrome (PRES) sometimes undergo analysis of cerebrospinal fluid (CSF) to exclude alternative diagnoses. This study’s objectives were to describe the CSF characteristics in patients with PRES and to identify clinical and radiologic findings associated with distinct CSF abnormalities. Methods: We identified a retrospective cohort of patients with PRES. We compared clinical and radiographic characteristics of those who did versus did not undergo lumbar puncture, described the observed range of CSF findings, and analyzed clinical and radiographic features associated with specific CSF abnormalities. Results: A total of 188 patients were included. Patients with (n = 77) and without (n = 111) CSF analysis had similar clinical and radiographic characteristics. Cerebrospinal fluid protein was elevated in 46 (60%) of 77, with median CSF protein 53 mg/dL (upper limit of normal 45 mg/dL). Protein elevation was significantly associated with radiographic severity ( P = .0058) but not with seizure, time from symptom onset, radiographic evidence of diffusion restriction, or contrast enhancement. Five (7%) patients had elevated CSF white blood cells, all of whom had infarction and/or hemorrhage on neuroimaging, and 4 of whom had eclampsia. Conclusion: The CSF of most patients with PRES shows a mild protein elevation commensurate with radiographic severity. Cerebrospinal fluid pleocytosis may mark a distinct subtype of PRES with predisposition toward infarction and/or hemorrhage. These findings help clinicians interpret CSF findings in these patients and generate new hypotheses about the pathophysiology of this syndrome.

Sequencing-based studies have identified novel risk genes for rare, severe epilepsies and revealed a role of rare deleterious variation in common epilepsies. To identify the shared and distinct ultra-rare genetic risk factors for rare and common epilepsies, we performed a whole-exome sequencing (WES) analysis of 9,170 epilepsy-affected individuals and 8,364 controls of European ancestry. We focused on three phenotypic groups; the rare but severe developmental and epileptic encephalopathies (DEE), and the commoner phenotypes of genetic generalized epilepsy (GGE) and non-acquired focal epilepsy (NAFE). We observed that compared to controls, individuals with any type of epilepsy carried an excess of ultra-rare, deleterious variants in constrained genes and in genes previously associated with epilepsy, with the strongest enrichment seen in DEE and the least in NAFE. Moreover, we found that inhibitory GABAA receptor genes were enriched for missense variants across all three classes of epilepsy, while no enrichment was seen in excitatory receptor genes. The larger gene groups for the GABAergic pathway or cation channels also showed a significant mutational burden in DEE and GGE. Although no single gene surpassed exome-wide significance among individuals with GGE or NAFE, highly constrained genes and genes encoding ion channels were among the top associations, including CACNA1G, EEF1A2, and GABRG2 for GGE and LGI1, TRIM3, and GABRG2 for NAFE. Our study confirms a convergence in the genetics of common and rare epilepsies associated with ultra-rare coding variation and highlights a ubiquitous role for GABAergic inhibition in epilepsy etiology in the largest epilepsy WES study to date.

Genomics now has an increasingly important role in neurology clinics. Regarding the epilepsies, innovations centred around technology, analytics, and collaboration have led to remarkable progress in gene discovery and have revealed the diverse array of genetic mechanisms and neurobiological pathways that contribute to these disorders. The new genomic era can present a challenge to clinicians, who now find themselves asked to interpret and apply genetic data to their daily management of patients with epilepsy. Navigation of this new era will require genetic literacy and familiarity with research advances in epilepsy genetics. Genetic epilepsy diagnoses now directly affect clinical care, and their importance will only increase as new targeted treatments continue to emerge. At the same time, new genetic insights challenge us to move from a deterministic view of genetic changes to a more nuanced appreciation of genetic risk within complex neurobiological systems that give rise to epilepsy.

STUDY OBJECTIVES: Dravet syndrome is a severe developmental and epileptic encephalopathy, in which 75% of patients have sleep disturbance. Melatonin is often used for sleep problems in childhood; however, there is no quality evidence supporting its use in Dravet syndrome. We hypothesized that melatonin would increase total sleep and quality of life for patients with Dravet syndrome. METHODS: A double-blind crossover randomized placebo-controlled trial was conducted, comparing 6 mg regular-release melatonin to placebo for patients with Dravet syndrome and sleep disturbance. The primary outcome measure was total sleep measured by actigraphy, with secondary outcomes including wakefulness after sleep onset (WASO), Sleep Disturbance Scale in Children and Quality of Life in Children with Epilepsy 55 questionnaires, caregiver reports of clinical change, seizure diary and serum antiepileptic drug levels. We also compared actigraphy data of patients with Dravet syndrome to an age-matched healthy control group. RESULTS: A total of 13 patients completed the study. There was no difference in total sleep or WASO between melatonin and placebo. However, of the 11 patients for whom caregivers reported a clear clinical difference between treatments (blinded), 8 reported improvement on melatonin (P < .05). Interestingly, when compared to patients in the control group, patients with Dravet syndrome had significantly increased total sleep (P = .002). CONCLUSIONS: Melatonin did not increase total sleep; however, blinded caregiver reports indicate treatment with melatonin provided considerable clinical benefit for some patients with Dravet syndrome and sleep disturbance. CLINICAL TRIAL REGISTRATION: Registry: Australian Government Department of Health, Therapeutic Goods Administration under the Clinical Trials Notification Scheme (protocol number 2241) CITATION: Myers KA, Davey MJ, Ching M, Ellis C, Grinton BE, Roten A, Lightfoot PA, Scheffer IE. Randomized controlled trial of melatonin for sleep disturbance in dravet syndrome: the DREAMS study. J Clin Sleep Med. 2018;14(10):1697–1704.

This article describes challenges encountered and lessons learned in an effort to explore the black box of rehabilitation. A multidisciplinary team created detailed, mutually exclusive operational definitions for the contents of learning-based treatments administered in a brain injury unit. The function and activity levels of the International Classification of Functioning, Disability and Health were used to organize content definitions, which included examples of therapy activities and therapist behaviors, such as cues. Pairs of trained coders independently identified defined learning episodes within each minute of 128 videotaped physical, occupational, or speech therapy sessions. Interrater agreement was generally acceptable and did not vary by discipline of session, discipline of coder, or whether coders were clinically trained. Disagreements typically involved the threshold for determining that a learning episode had occurred, or deciding between function and activity codes where the surface content of the sessions were similar. The focus on individual therapy sessions allowed for rich qualitative detail, but a less granular analysis will be necessary for comprehensive efforts to characterize the contents of therapy.

Mayer NH, Whyte J, Wannstedt G, Ellis CA. Comparative impact of 2 botulinum toxin injection techniques for elbow flexor hypertonia. Objective To compare 2 techniques of botulinum toxin injection for elbow flexor hypertonia. Design Parallel-group, randomized, controlled trial with blinded outcome assessment. Setting Laboratory, tertiary rehabilitation hospital. Participants Adults (N=31) with acquired brain injury (21 with traumatic brain injury, 8 with stroke, 2 with hypoxic encephalopathy) provided 36 sets of elbow flexors with Ashworth Scale scores equal to 3. Intervention Botulinum toxin type A (BTX-A) was injected with a motor point or a multisite injection technique after obtaining 2 baseline evaluations of the main outcome measures. Motor point technique involved decremental electric stimulation with delivery of 60U of BTX-A (Botox) in 2.4mL or 30U BTX-A in 1.2mL of preservative-free saline at single biceps and brachioradialis motor points, respectively. Distributed injection was performed using electromyographic feedback. Fifteen units in 0.6mL were delivered to each of 4 biceps sites and 2 brachioradialis sites. Total dose (90U) and total injection volume (3.6mL) were identical across groups. Only sites and injection techniques varied. The brachialis was not injected in either group. Main Outcome Measures Ashworth Scale, Tardieu catch angle, and root mean square surface electromyographic activity of the biceps, brachialis, and brachioradialis. Results Postintervention testing at 3 weeks showed no significant differences between groups ( P range, .31–.82 across 3 outcome measures). However, within each group, significant treatment effects were observed on all outcome measures (all P Conclusions In 31 adults with acquired brain injury, single motor point and multisite distributed injections of low-dose, high-volume BTX-A had similar impact. Findings suggest that low-dose, high-volume strategies may have a potential role in reducing drug cost and helping clinicians stay within accepted limits for total body dose in patients with upper motoneuron syndrome requiring many injections.

Attention deficits are nearly ubiquitous after traumatic brain injury (TBI). In the subacute phase of moderate to severe TBI, these deficits may be difficult to measure with the precision needed to predict outcomes, assess degree of recovery, and monitor treatment response. This article reports the findings of four studies, three observational and one a randomized, controlled treatment trial of methylphenidate (MP), designed to provide construct validation of the Moss Attention Rating Scale (MARS), an observational measure of attention dysfunction following TBI. One hundred seven participants with moderate to severe TBI were enrolled during treatment on an inpatient rehabilitation unit. MARS scores were provided independently by four rehabilitation disciplines (Physical, Occupational and Speech Therapies and Nursing). Results indicated that the MARS: (1) is more strongly related to concurrent measures of cognitive versus physical disability, supporting its validity as a measure of cognition, (2) is more strongly related to concurrent psychometric measures of attention versus measures thought to rely less on attention, supporting its validity as a measure of attention; and (3) predicts 1-year outcomes of TBI better than psychometric measures of attention. However, the MARS (4) was not differentially affected by MP versus placebo treatment. Results support the construct validity and utility of the MARS, with further research needed to clarify its role in treatment outcome assessment.

What is the functional relationship between face-selective and expertise-predicated object-selective regions in the human middle fusiform gyrus? In two separate fMRI experiments, superior behaviorally-measured bird expertise predicts both higher middle fusiform gyrus selectivity for birds and, concomitantly, lower selectivity for faces. This finding suggests a long-term dynamic reorganization of the neural mechanisms underlying the visual recognition of faces and non-face.

This literature review examines the issue of male rape and the possible counselling strategies that may assist in recovery. There are several misconceptions surrounding male rape which can result in the under-reporting and secondary or sanctuary victimisation of the survivor. Men who have been raped may believe that it attacks the very essence of what it is to be masculine and male. Many may not seek help unless they perceive a need for immediate attention, such as physical trauma requiring medical assistance. The literature also reveals that when men do seek help they may be treated poorly. Secondary victimisation or sanctuary trauma results when there is a lack of empathy and understanding of the effect that rape can have on the survivor, such as rape-trauma syndrome. Training in this area is needed for the police, emergency department staff, nurses, general practitioners, and community health services. Management of survivors starts with examining our own beliefs about male rape. Many of the reported counselling strategies are based on therapists' observations, trauma theories, and research related to child abuse or sexual assault of females which may not be transferable to men who have been raped. Education and counselling of survivors' support networks must be considered as part of a holistic approach to management. Secondary prevention strategies aimed at the broader community are required to assist survivors to come forward without fear of further victimisation because of stereotyping.