Genetic variant interpretation for the neurologist - A pragmatic approach in the next-generation sequencing era in childhood epilepsy.

Genetic advances over the past decade have enhanced our understanding of the genetic landscape of childhood epilepsy. However a major challenge for clinicians ha been understanding the rationale and systematic approach towards interpretation of the clinical significance of variant(s) detected in their patients. As the clinical paradigm evolves from gene panels to whole exome or whole genome testing including rapid genome sequencing, the number of patients tested and variants identified per patient will only increase. Each step in the process of variant interpretation has limitations and there is no single criterion which enables the clinician to draw reliable conclusions on a causal relationship between the variant and disease without robust clinical phenotyping. Although many automated online analysis software tools are available, these carry a risk of misinterpretation. This guideline provides a pragmatic, real-world approach to variant interpretation for the child neurologist. The focus will be on ascertaining aspects such as variant frequency, subtype, inheritance pattern, structural and functional consequence with regard to genotype-phenotype correlations, while refraining from mere interpretation of the classification provided in a genetic test report. It will not replace the expert advice of colleagues in clinical genetics, however as genomic investigations become a first-line test for epilepsy, it is vital that neurologists and epileptologists are equipped to navigate this landscape.

Metabolic causes of pediatric developmental & epileptic encephalopathies (DEE)- genetic variant analysis in a south Indian cohort.

PURPOSE: Drug-resistant epilepsy is seen in patients with inborn errors of metabolism and metabolic dysfunction in neurons is crucial to brain disorders associated with psychomotor impairment. Diagnostic rates of metabolic causes of developmental and epileptic encephalopathy (DEE) using next generation sequencing have been rarely studied in literature. METHODS: A prospective hospital study was carried out in 384 children with DEE, who underwent genetic testing. Metabolic disorders were evaluated with biochemical blood/urine assays and when required CSF estimations performed. RESULTS: A total of 154 pathogenic/likely pathogenic variants in 384 children were identified. Out of 384 children, 89 were clinically suspected to have probable or possible metabolic disorders. Pathogenic/likely pathogenic variants in metabolic genes were identified in 39 out of 89 (43.8 %) and promising VUS in 28 (31.4 %). These included variants for progressive myoclonus epilepsies (21; 53.8 %), DEE with focal/multifocal seizures (8; 20.5 %), generalized epilepsy (5;12.8 %), early myoclonic encephalopathy (2; 5.1 %), LGS (1; 2.6 %) and West syndrome (2; 5.1 %). CONCLUSION: Our cohort demonstrates for the first time from the Indian subcontinent that identification of metabolic variants can guide investigations and has therapeutic implications in patients with variable DEE phenotypes. A high utility is noted with regard to diagnosis and prognostication, given the low yield of available biochemical tests, indicating cost-effectiveness of this approach.

Impact of variant subtype on electro-clinical phenotype of Dravet syndrome- a South Indian cohort study.

OBJECTIVE: We aimed to compare the electroclinical correlates of truncating and missense variants of SCN1A variants in children with Dravet syndrome (DS) and to determine phenotypic features in relation to variants identified and seizure outcomes. METHODS: A single center prospective study was carried out on a South Indian cohort. Patients below 18 years of age who met the clinical criteria for DS who had undergone genetic testing and completed a minimum of one year follow up were included. We compared the differences in clinical profile, seizure outcome, developmental characteristics and anti-seizure medication (ASM) responsiveness profiles between patients with missense and truncating variants. RESULTS: Out of a total of 3967 children with drug-resistant epilepsy during the period 2015-2021, 49 patients who fulfilled the inclusion criteria were studied. Thirty-seven had positive genetic tests, out of which 29 were SCN1A variants and 9 were other novel variants. The proportion of missense (14; 48.3%) and truncating SCN1A variants (15; 51.7%) was similar. A significant trend for developing multiple seizure types was noted among children with truncating variants (p = 0.035) and seizure freedom was more likely among children with missense variants (p = 0.042). All patients with truncating variants had ASM resistant epilepsy (p = 0.020). Developmental outcomes did not differ between the variant subtypes. CONCLUSION: Our results show that children harbouring missense variants demonstrated a significantly lower propensity for multiple seizure subtypes and a higher proportion with seizure freedom. However developmental implications appear to be independent of variant subtype.

KCTD7-related progressive myoclonic epilepsy: Report of 42 cases and review of literature.

OBJECTIVE: KCTD7-related progressive myoclonic epilepsy (PME) is a rare autosomal-recessive disorder. This study aimed to describe the clinical details and genetic variants in a large international cohort. METHODS: Families with molecularly confirmed diagnoses of KCTD7-related PME were identified through international collaboration. Furthermore, a systematic review was done to identify previously reported cases. Salient demographic, epilepsy, treatment, genetic testing, electroencephalographic (EEG), and imaging-related variables were collected and summarized. RESULTS: Forty-two patients (36 families) were included. The median age at first seizure was 14 months (interquartile range = 11.75-22.5). Myoclonic seizures were frequently the first seizure type noted (n = 18, 43.9%). EEG and brain magnetic resonance imaging findings were variable. Many patients exhibited delayed development with subsequent progressive regression (n = 16, 38.1%). Twenty-one cases with genetic testing available (55%) had previously reported variants in KCTD7, and 17 cases (45%) had novel variants in KCTD7 gene. Six patients died in the cohort (age range = 1.5-21 years). The systematic review identified 23 eligible studies and further identified 59 previously reported cases of KCTD7-related disorders from the literature. The phenotype for the majority of the reported cases was consistent with a PME (n = 52, 88%). Other reported phenotypes in the literature included opsoclonus myoclonus ataxia syndrome (n = 2), myoclonus dystonia (n = 2), and neuronal ceroid lipofuscinosis (n = 3). Eight published cases died over time (14%, age range = 3-18 years). SIGNIFICANCE: This study cohort and systematic review consolidated the phenotypic spectrum and natural history of KCTD7-related disorders. Early onset drug-resistant epilepsy, relentless neuroregression, and severe neurological sequalae were common. Better understanding of the natural history may help future clinical trials.

Is intraoperative electrocorticography (ECoG) for long-term epilepsy-associated tumors (LEATs) more useful in children?-A Randomized Controlled Trial.

OBJECTIVES: The utility of intraoperative electrocorticography (ECoG)-guided resective surgery for pediatric long-term epilepsy-associated tumors (LEATs) with antiseizure medication (ASM) resistant epilepsy is not supported by robust evidence. As epilepsy networks and their ramifications are different in children from those in adults, the impact of intraoperative ECoG-based tailored resections in predicting prognosis and influencing outcomes may also differ. We evaluated this hypothesis by comparing the outcomes of resections with and without the use of ECoG in children and adults by a randomized study. METHODS: From June 2020 to January 2022, 42 patients (17 children and 25 adults) with LEATs and antiseizure medication (ASM)-resistant epilepsy were randomly assigned to one of the 2 groups (ECoG or no ECoG), prior to surgical resection. The 'no ECoG' arm underwent gross total lesion resection (GTR) without ECoG guidance and the ECoG arm underwent GTR with ECoG guidance and further additional tailored resections, as necessary. Factors evaluated were tumor location, size, lateralization, seizure duration, preoperative antiepileptic drug therapy, pre- and postresection ECoG patterns and tumor histology. Postoperative Engel score and adverse event rates were compared in the pediatric and adult groups of both arms. Eloquent cortex lesions and re-explorations were excluded to avoid confounders. RESULTS: Forty-two patients were included in the study of which 17 patients were in the pediatric cohort (age < 18 years) and 25 in the adult cohort. The mean age in the pediatric group was 11.11 years (SD 4.72) and in the adult group was 29.56 years (SD 9.29). The mean duration of epilepsy was 9.7 years (SD 4.8) in the pediatric group and 10.96 (SD 8.8) in the adult group. The ECoG arm of LEAT resections had 23 patients (9 children and 14 adults) and the non-ECoG arm had 19 patients (8 children and 11 adults). Three children and 3 adults from the ECoG group further underwent ECoG-guided tailored resections (average 1.33 additional tailored resections/per patient.).The histology of the tailored resection specimen was unremarkable in 3/6 (50%).Overall, the commonest histology in both groups was ganglioglioma and the temporal lobe, the commonest site of the lesion. 88.23% of pediatric cases (n = 15/17) had an excellent outcome (Engel Ia) following resection, compared to 84% of adult cases (n = 21/25) at a mean duration of follow-up of 25.76 months in children and 26.72 months in adults (p = 0.405).There was no significant difference in seizure outcomes between the ECoG and no ECoG groups both in children and adults, respectively (p > 0.05). Additional tailored resection did not offer any seizure outcome benefit when compared to the non-tailored resections. CONCLUSIONS: The use of intraoperative electrocorticography in LEATs did not contribute to postoperative seizure outcome benefit in children and adults. No additional advantage or utility was offered by ECoG in children when compared to its use in adults. ECoG-guided additional tailored resections did not offer any additional seizure outcome benefit both in children and adults.

Evaluation of 'Normal' Cognitive Functions and Correlation With MRI Volumetry: Towards a Definition of Vascular Cognitive Impairment.

Introduction It is important to establish criteria to define vascular cognitive impairment (VCI) in India as VCI is an image-based diagnosis and magnetic resonance imaging (MRI) changes resulting from age with prevalent vascular risk factors may confound MRI interpretation. The objective of this study was to establish normative community data for MRI volumetry including white matter hyperintensity volume (WMHV), correlated with age-stratified cognitive scores and vascular risk factors (VRFs), in adults aged 40 years and above.  Methods We screened 2651 individuals without known neurological morbidity, living in Mumbai and nearby rural areas, using validated Marathi translations of Kolkata Cognitive Battery (KCB) and geriatric depression score (GDS). We stratified 1961 persons with GDS ≤9 by age and cognitive score, and randomly selected 10% from each subgroup for MRI brain volumetry. Crude volumes were standardized to reflect percentage of intracranial volume.  Results MRI volumetry studies were done in 199 individuals (F/M = 90/109; 73 with body mass index (BMI) ≥25; 44 hypertensives; 29 diabetics; mean cognitive score 76.3). Both grey and white matter volumes decreased with increasing age. WMHV increased with age and hypertension. Grey matter volume (GMV) decreased with increasing WMHV. Positive predictors of cognition included standardized hippocampal volume (HCV), urban living, education, and BMI, while WMHV and age were negative predictors. Urban dwellers had higher cognitive scores than rural, and, paradoxically, smaller HCV.  Conclusion In this study of MRI volumetry correlated with age, cognitive scores and VRFs, increasing age and WMHV predicted lower cognitive scores, whereas urban living and hippocampal volume predicted higher scores. Age and WMHV also correlated with decreasing GMV. Further study is warranted into sociodemographic and biological factors that mutually influence cognition and brain volumes, including nutritional and endocrine factors, especially at lower cognitive score bands. In this study, at the lower KCB score bins, the lack of laboratory data pertaining to nutritional and endocrine deficiencies is a drawback that reflects the logistical limitations of screening large populations at the community level. Our volumetric data which is age and cognition stratified, and takes into account the vascular risk factors associated, nevertheless constitutes important baseline data for the Indian population. Our findings could possibly contribute to the formulation of baseline criteria for defining VCI in India and could help in early diagnosis and control of cognitive decline and its key risk factors.

Clinical Utility of Proband Only Clinical Exome Sequencing in Neurodevelopmental Disorders.

Chromosomal microarray is recommended as the first line of investigation in neurodevelopmental disorders (NDDs). However, advances in next-generation sequencing have unraveled more than 900 genes associated with NDDs, thus improving the genetic diagnosis. Therefore, this study was conducted to explore the utility of clinical exome sequencing (CES) in NDDs from a tertiary care centre in India. A retrospective observational analysis of 78 children with NDDs for whom CES was performed between 2017 and 2021 was conducted. The American College of Medical Genetics and Genomics (ACMG) criteria were used to classify the variants. The mean age was 5.8 ± 3.6 y, and 42 (53%) were male. Pathogenic, likely pathogenic, and variants of uncertain significance (VUS) were observed in 22 (28.2%), 10 (12.8%), and 26 (33.3%) patients, respectively, which included five copy number variants. The diagnostic yield for pathogenic and likely pathogenic variants in NDDs by CES was 41%, which was reasonably high.

A deep network-based model of hippocampal memory functions under normal and Alzheimer's disease conditions.

We present a deep network-based model of the associative memory functions of the hippocampus. The proposed network architecture has two key modules: (1) an autoencoder module which represents the forward and backward projections of the cortico-hippocampal projections and (2) a module that computes familiarity of the stimulus and implements hill-climbing over the familiarity which represents the dynamics of the loops within the hippocampus. The proposed network is used in two simulation studies. In the first part of the study, the network is used to simulate image pattern completion by autoassociation under normal conditions. In the second part of the study, the proposed network is extended to a heteroassociative memory and is used to simulate picture naming task in normal and Alzheimer's disease (AD) conditions. The network is trained on pictures and names of digits from 0 to 9. The encoder layer of the network is partly damaged to simulate AD conditions. As in case of AD patients, under moderate damage condition, the network recalls superordinate words ("odd" instead of "nine"). Under severe damage conditions, the network shows a null response ("I don't know"). Neurobiological plausibility of the model is extensively discussed.

Multi-Class Seizure Type Classification Using Features Extracted from the EEG.

In this study, we classify the seizure types using feature extraction and machine learning algorithms. Initially, we pre-processed the electroencephalogram (EEG) of focal non-specific seizure (FNSZ), generalized seizure (GNSZ), tonic-clonic seizure (TCSZ), complex partial seizure (CPSZ) and absence seizure (ABSZ). Further, 21 features from time (9) and frequency (12) domain were computed from the EEG signals of different seizure types. XGBoost classifier model was built for individual domain features and combination of time and frequency features and validated the results using 10-fold cross-validation. Our results revealed that the classifier model with combination of time and frequency features performed well followed by the time and frequency domain features. We obtained a highest multi-class accuracy of 79.72% for the classification of five types of seizure while using all the 21 features. The band power between 11-13 Hz was found to be the top feature in our study. The proposed study can be used for the seizure type classification in clinical applications.

Arginase deficiency-An unheralded cause of developmental epileptic encephalopathy.

Arginase deficiency, which leads to hyperargininaemia is a rare urea cycle disorder caused by a mutation in the ARG1 gene. It is an under-recognized cause of pediatric developmental epileptic encephalopathy, with the key coexistent clinical features being developmental delay or regression and spasticity. Detection of ARG1 gene mutation on genetic testing is the confirmatory diagnostic test. However, elevated levels of plasma arginine and low plasma arginase level can be considered as biochemical markers for diagnosis. We present two cases of arginase deficiency with genetically confirmed ARG1 mutation in one and biochemical confirmation in both. As the spectrum of epilepsy in arginase deficiency has been less explored, we attempted to elucidate the novel electroclinical features and syndromic presentations in these patients. Informed consent was obtained from families of patients. Electroclinical diagnosis was consistent with Lennox Gastaut syndrome (LGS) in the first patient while the second patient had refractory atonic seizures with electrophysiological features consistent with developmental and epileptic encephalopathy. Though primary hyperammonaemia is not a consistent feature, secondary hyperammonaemia in the setting of infectious triggers and drugs like valproate (valproate sensitivity) has been well described as also observed in our patient. In the absence of an overt antecedent in a child with spasticity and seizure disorder, with a progressive course consistent with a developmental epileptic encephalopathy, arginase deficiency merits consideration. Diagnosis often has important therapeutic implications with respect to dietary management and choice of the appropriate antiseizure medications.

Optimization of Pre-Ictal Interval Time Period for Epileptic Seizure Prediction Using Temporal and Frequency Features.

Epilepsy is a neurological disorder characterized by recurrent seizures. Automated prediction of epileptic seizures is essential in monitoring the health of an epileptic individual to avoid cognitive problems, accidental injuries, and even fatality. In this study, scalp electroencephalogram (EEG) recordings of epileptic individuals were used to predict seizures using a configurable Extreme Gradient Boosting (XGBoost) machine learning algorithm. Initially, the EEG data was preprocessed using a standard pipeline. We investigated 36 minutes before the onset of the seizure to classify between the pre-ictal and inter-ictal states. Further, temporal and frequency domain features were extracted from the different intervals of the pre-ictal and inter-ictal periods. Then, the XGBoost classification model was utilized to optimize the best interval for the pre-ictal state to predict the seizure by applying Leave one patient out cross-validation. Our results suggest that the proposed model could predict seizures 10.17 minutes before the onset. The highest classification accuracy achieved was 83.33 %. Thus, the suggested framework can be optimized further to select the best features and prediction interval for more accurate seizure forecasting.

Baseline Predictors of Longitudinal Cognitive Outcomes in Persons with Mild Cognitive Impairment.

INTRODUCTION: The study aimed to explore longitudinal cognitive outcomes and to ascertain predictors of conversion to dementia in a hospital-based mild cognitive impairment (MCI) cohort classified according to the neuropsychological phenotype at baseline. MATERIALS AND METHODS: Subjects aged >55 years who had a clinical diagnosis of MCI at initial visit between 2010 and 2018, with at least one formal neuropsychological assessment at baseline and follow-up of a minimum of 2 years were included. The prospective study was completed based on evaluation at last follow-up to gauge conversion to dementia, quantification of performance on activities of daily living and when available, longitudinal neuropsychological test scores. RESULTS: Ninety-five patients with MCI met the inclusion criteria with a mean age of 68.4 ± 6.4 years at baseline and a mean duration of follow-up for 6.4 ± 3.2 years. The cumulative conversion rate to dementia was 22.2% (21/95) and the annualized conversion rate was 3.3% per year of follow-up. The majority of subjects who had converted had multidomain MCI (66%). Only white matter changes on MRI brain revealed correlation with baseline neuropsychology tests. The multivariate logistic regression analysis revealed the utility of lower baseline list recognition (adjusted odds ratio: 0.735 [95% confidence interval: 0.589-0.916]; p 0.006), lower immediate logical memory (0.885 [0.790-0.990]; p 0.03), and high perseverative error scores on set shifting (3.116 [1.425-6.817]; p 0.004) as predictors of conversion. A model score of +2.615 could predict conversion with sensitivity of 72% and specificity of 98% over 6.4 years follow-up. CONCLUSION: There was a higher risk of conversion associated with multidomain MCI. Logistic regression-based estimations of dementia risk utilizing domain-based neuropsychology test scores in MCI have high specificity for diagnosis at baseline.

Satisfaction among persons with epilepsy towards physical consultation versus online video consultation for follow-up.

PURPOSE: Telemedicine gained popularity in the setting of the COVID-19 pandemic. We aimed to study the satisfaction levels of persons with epilepsy (PWE) with online video consultation (OVC) and physical consultation (PC). METHODS: This was a cross-sectional questionnaire-based study conducted in a tertiary referral care center for epilepsy in India. All PWE who had availed of both OVC and PC were included. Those who did not give consent to a questionnaire were excluded. A questionnaire was given to assess patients' satisfaction regarding OVC and PC. Scores for each question for both OVC and PC were compared. RESULTS: One hundred and forty-one patients who had PC earlier and later availed of OVC from December 2020 to July 2021 formed the cohort. Seventy one patients who responded to the questionnaire were included. 49% and 51% of the patients belonged to urban and rural regions respectively. 8.5% of the patients were off anti-seizure medications (ASM), while 5.6% and 85.9% were on single and multiple ASMs respectively. There were no differences between PC and OVC regarding ease of getting the appointment, privacy during a consultation, patients' perceived chances of missing consultations, and overall comfort and experience on either type of consultation. Physical consultation scored more than OVC in patients' satisfaction with the time doctor spent with them, an opportunity to communicate their queries well, clarifications received from the doctor, and the likelihood of patients recommending the particular type of consultation to others(p < 0.05). CONCLUSIONS: Online video consultation can be a satisfactory alternative to PC and can improve patient satisfaction if some of the issues in OVC are addressed properly.

Clinical Reasoning: A Teenager With Chronic Meningitis-Does Occam's Razor Apply?

A 14-year-old girl presented with subacute onset headache, fever, and vomiting and was managed initially with antibiotics for suspected bacterial meningitis. Her symptoms further evolved over the next few weeks with systemic signs and symptoms favoring chronic meningitis with raised intracranial pressure. After the etiologic workup was unrevealing, she was started on empirical antituberculous therapy. After a period of partial improvement, symptoms recurred with a new-onset focal seizure. Her imaging findings evolved from features suggestive of focal leptomeningitis to multifocal heterogeneous enhancing cortical and subcortical lesions with hemorrhagic foci, leading to brain biopsy that confirmed diagnosis. Our case highlights the utility of diagnostic biopsy in patients with "chronic meningitis" in uncertain cases rather than confining the approach to the law of parsimony. The decision to initiate empirical therapy in chronic meningitis should be considered on a case-by-case basis and take into account factors, such as clinical examination findings, immune status, recent exposures, and potential risks of treatment. Atypical MRI features should lower the threshold for meningocortical biopsy when indicated.

Resting-State Functional Connectivity in Relapsing-Remitting Multiple Sclerosis with Mild Disability: A Data-Driven, Whole-Brain Multivoxel Pattern Analysis Study.

Background: Multivoxel pattern analysis (MVPA) has emerged as a powerful unbiased approach for generating seed regions of interest (ROIs) in resting-state functional connectivity (RSFC) analysis in a data-driven manner. Studies exploring RSFC in multiple sclerosis have produced diverse and often incongruent results. Objectives: The aim of the present study was to investigate RSFC differences between people with relapsing-remitting multiple sclerosis (RRMS) and healthy controls (HC). Methods: We performed a whole-brain connectome-wide MVPA in 50 RRMS patients with expanded disability status scale ≤4 and 50 age and gender-matched HCs. Results: Significant group differences were noted in RSFC in three clusters distributed in the following regions: anterior cingulate gyrus, right middle frontal gyrus, and frontal medial cortex. Whole-brain seed-to-voxel RSFC characterization of these clusters as seed ROIs revealed network-specific abnormalities, specifically in the anterior cingulate cortex and the default mode network. Conclusions: The network-wide RSFC abnormalities we report agree with the previous findings in RRMS, the cognitive and clinical implications of which are discussed herein. Impact statement This study investigated resting-state functional connectivity (RSFC) in relapsing-remitting multiple sclerosis (RRMS) people with mild disability (expanded disability status scale ≤4). Whole-brain connectome-wide multivoxel pattern analysis was used for assessing RSFC. Compared with healthy controls, we were able to identify three regions of interest for significant differences in connectivity patterns, which were then extracted as a mask for whole-brain seed-to-voxel analysis. A reduced connectivity was noted in the RRMS group, particularly in the anterior cingulate cortex and the default mode network regions, providing insights into the RSFC abnormalities in RRMS.