Accumulated seizure burden predicts neurodevelopmental outcome at 36 months of age in patients with tuberous sclerosis complex

OBJECTIVE: Epilepsy and intellectual disability are common in tuberous sclerosis complex (TSC). Although early life seizures and intellectual disability are known to be correlated in TSC, the differential effects of age at seizure onset and accumulated seizure burden on development remain unclear. METHODS: Daily seizure diaries, serial neurodevelopmental testing, and brain magnetic resonance imaging were analyzed for 129 TSC patients followed from 0 to 36 months. We used machine learning to identify subgroups of patients based on neurodevelopmental test scores at 36 months of age and assessed the stability of those subgroups at 12 months. We tested the ability of candidate biomarkers to predict 36-month neurodevelopmental subgroup using univariable and multivariable logistic regression. Candidate biomarkers included age at seizure onset, accumulated seizure burden, tuber volume, sex, and earlier neurodevelopmental test scores. RESULTS: Patients clustered into two neurodevelopmental subgroups at 36 months of age, higher and lower scoring. Subgroup was mostly (75%) the same at 12 months. Significant univariable effects on subgroup were seen only for accumulated seizure burden (largest effect), earlier test scores, and tuber volume. Neither age at seizure onset nor sex significantly distinguished 36-month subgroups, although for girls but not boys there was a significant effect of age at seizure onset. In the multivariable model, accumulated seizure burden and earlier test scores together predicted 36-month neurodevelopmental group with 82% accuracy and an area under the curve of .86. SIGNIFICANCE: These results untangle the contributions of age at seizure onset and accumulated seizure burden to neurodevelopmental outcomes in young children with TSC. Accumulated seizure burden, rather than the age at seizure onset, most accurately predicts neurodevelopmental outcome at 36 months of age. These results emphasize the need to manage seizures aggressively during the first 3 years of life for patients with TSC, not only to promote seizure control but to optimize cognitive function

Epilepsy Risk Prediction Model for Patients With Tuberous Sclerosis Complex

BACKGROUND: Individuals with tuberous sclerosis complex are at increased risk of epilepsy. Early seizure control improves developmental outcomes, making identifying at-risk patients critically important. Despite several identified risk factors, it remains difficult to predict. The purpose of the study was to evaluate the combined risk prediction of previously identified risk factors for epilepsy in individuals with tuberous sclerosis complex. METHODS: The study group (n = 333) consisted of individuals with tuberous sclerosis complex who were enrolled in the Tuberous Sclerosis Complex Autism Center of Excellence Research Network and UT TSC Biobank. The outcome was defined as having an epilepsy diagnosis. Potential risk factors included sex, TSC genotype, and tuber presence. Logistic regression was used to calculate the odds ratio and P value for the association between each variable and epilepsy. A clinical risk prediction model incorporating all risk factors was built. Area under the curve was calculated to characterize the full model\u27s ability to discriminate individuals with tuberous sclerosis complex with and without epilepsy. RESULTS: The strongest risk for epilepsy was presence of tubers (95% confidence interval: 2.39 to 10.89). Individuals with pathogenic TSC2 variants were three times more likely (95% confidence interval: 1.55 to 6.36) to develop seizures compared with those with tuberous sclerosis complex from other causes. The combination of risk factors resulted in an area under the curve 0.73. CONCLUSIONS: Simple characteristics of patients with tuberous sclerosis complex can be combined to successfully predict epilepsy risk. A risk assessment model that incorporates sex, TSC genotype, protective TSC2 missense variant, and tuber presence correctly predicts epilepsy in 73% of patients with tuberous sclerosis complex

Profile of Autism Spectrum Disorder in Tuberous Sclerosis Complex: Results from a Longitudinal, Prospective, Multisite Study

OBJECTIVE: Tuberous sclerosis complex (TSC) is highly associated with autism spectrum disorder (ASD). Objectives of the study were to characterize autistic features in young children with TSC. METHODS: Participants included 138 children followed from ages 3 to 36 months with TSC from the Tuberous Sclerosis Complex Autism Center of Excellence Research Network (TACERN), a multicenter, prospective observational study aimed at understanding the underlying mechanisms of ASD in TSC. Developmental and autism-specific assessments were administered, and a clinical diagnosis of ASD was determined for all participants at 36 months. Further analyses were performed on 117 participants with valid autism assessments based on nonverbal mental age greater than 15 months. RESULTS: Prevalence of clinical diagnosis of ASD at 36 months was 25%. Nearly all autistic behaviors on the Autism Diagnostic Observation Schedule-2 (ADOS-2) and Autism Diagnostic Interview-Revised (ADI-R) were more prevalent in children diagnosed with ASD; however, autism-specific behaviors were also observed in children without ASD. Overall quality of social overtures, facial expressions, and abnormal repetitive interests and behaviors were characteristics most likely to distinguish children with ASD from those without an ASD diagnosis. Participants meeting ADOS-2 criteria but not a clinical ASD diagnosis exhibited intermediate developmental and ADOS-2 scores compared to individuals with and without ASD. INTERPRETATION: ASD is highly prevalent in TSC, and many additional individuals with TSC exhibit a broad range of subthreshold autistic behaviors. Our findings reveal a broader autism phenotype that can be identified in young children with TSC, which provides opportunity for early targeted treatments. ANN NEUROL 2021;90:874-886

Abnormality of Early White Matter Development in Tuberous Sclerosis Complex and Autism Spectrum Disorder: Longitudinal Analysis of Diffusion Tensor Imaging Measures

Background: Abnormalities in white matter development may influence development of autism spectrum disorder in tuberous sclerosis complex (TSC). Our goals for this study were as follows: (1) use data from a longitudinal neuroimaging study of tuberous sclerosis complex (TACERN) to develop optimized linear mixed effects models for analyzing longitudinal, repeated diffusion tensor imaging metrics (fractional anisotropy, mean diffusivity) pertaining to select white matter tracts, in relation to positive Autism Diagnostic Observation Schedule–Second Edition classification at 36 months, and (2) perform an exploratory analysis using optimized models applied to all white matter tracts from these data. Methods: Eligible participants (3-12 months) underwent brain magnetic resonance imaging (MRI) at repeated time points from ages 3 to 36 months. Positive Autism Diagnostic Observation Schedule–Second Edition classification at 36 months was used. Linear mixed effects models were fine-tuned separately for fractional anisotropy values (using fractional anisotropy corpus callosum as test outcome) and mean diffusivity values (using mean diffusivity right posterior limb internal capsule as test outcome). Fixed effects included participant age, within-participant longitudinal age, and autism spectrum disorder diagnosis. Results: Analysis included data from n = 78. After selecting separate optimal models for fractional anisotropy and mean diffusivity values, we applied these models to fractional anisotropy and mean diffusivity of all 27 white matter tracts. Fractional anisotropy corpus callosum was related to positive Autism Diagnostic Observation Schedule–Second Edition classification (coefficient = 0.0093, P = .0612), and mean diffusivity right inferior cerebellar peduncle was related to positive Autism Diagnostic Observation Schedule–Second Edition classification (coefficient = −0.00002071, P = .0445), though these findings were not statistically significant after multiple comparisons correction. Conclusion: These optimized linear mixed effects models possibly implicate corpus callosum and cerebellar pathology in development of autism spectrum disorder in tuberous sclerosis complex, but future studies are needed to replicate these findings and explore contributors of heterogeneity in these models

Limited Utility of Structural MRI to Identify the Epileptogenic Zone in Young Children With Tuberous Sclerosis

BACKGROUND AND PURPOSE: The success of epilepsy surgery in children with tuberous sclerosis complex (TSC) hinges on identification of the epileptogenic zone (EZ). We studied structural MRI markers of epileptogenic lesions in young children with TSC. METHODS: We included 26 children with TSC who underwent epilepsy surgery before the age of 3 years at five sites, with 12 months or more follow-up. Two neuroradiologists, blinded to surgical outcome data, reviewed 10 candidate lesions on preoperative MRI for characteristics of the tuber (large affected area, calcification, cyst-like properties) and of focal cortical dysplasia (FCD) features (cortical malformation, gray-white matter junction blurring, transmantle sign). They selected lesions suspect for the EZ based on structural MRI, and reselected after unblinding to seizure onset location on electroencephalography (EEG). RESULTS: None of the tuber characteristics and FCD features were distinctive for the EZ, indicated by resected lesions in seizure-free children. With structural MRI alone, the EZ was identified out of 10 lesions in 31%, and with addition of EEG data, this increased to 48%. However, rates of identification of resected lesions in non-seizure-free children were similar. Across 251 lesions, interrater agreement was moderate for large size (κ = .60), and fair (κ = .24) for all other features. CONCLUSIONS: In young children with TSC, the utility of structural MRI features is limited in the identification of the epileptogenic tuber, but improves when combined with EEG data

EEG Spectral Features in Sleep of Autism Spectrum Disorders in Children with Tuberous Sclerosis Complex.

Tuberous sclerosis complex (TSC) is a multisystem disorder with increased prevalence of autism spectrum disorders (ASDs). This project aimed to characterize the autism phenotype of TSC and identify biomarkers of risk for ASD. Because abnormalities of EEG during sleep are tied to neurodevelopment in children, we compared electroencephalographic (EEG) measures during Stage II sleep in TSC children who either did (ASD+) or did not (ASD-) exhibit symptoms of ASD over 36-month follow up. Relative alpha band power was significantly elevated in the ASD+ group at 24 months of age with smaller differences at younger ages, suggesting this may arise from differences in brain development. These findings suggest that EEG features could enhance the detection of risk for ASD

EEG Spectral Features in Sleep of Autism Spectrum Disorders in Children with Tuberous Sclerosis Complex.

Tuberous sclerosis complex (TSC) is a multisystem disorder with increased prevalence of autism spectrum disorders (ASDs). This project aimed to characterize the autism phenotype of TSC and identify biomarkers of risk for ASD. Because abnormalities of EEG during sleep are tied to neurodevelopment in children, we compared electroencephalographic (EEG) measures during Stage II sleep in TSC children who either did (ASD+) or did not (ASD-) exhibit symptoms of ASD over 36-month follow up. Relative alpha band power was significantly elevated in the ASD+ group at 24 months of age with smaller differences at younger ages, suggesting this may arise from differences in brain development. These findings suggest that EEG features could enhance the detection of risk for ASD

Deep learning in rare disease. Detection of tubers in tuberous sclerosis complex.

ObjectiveTo develop and test a deep learning algorithm to automatically detect cortical tubers in magnetic resonance imaging (MRI), to explore the utility of deep learning in rare disorders with limited data, and to generate an open-access deep learning standalone application.MethodsT2 and FLAIR axial images with and without tubers were extracted from MRIs of patients with tuberous sclerosis complex (TSC) and controls, respectively. We trained three different convolutional neural network (CNN) architectures on a training dataset and selected the one with the lowest binary cross-entropy loss in the validation dataset, which was evaluated on the testing dataset. We visualized image regions most relevant for classification with gradient-weighted class activation maps (Grad-CAM) and saliency maps.Results114 patients with TSC and 114 controls were divided into a training set, a validation set, and a testing set. The InceptionV3 CNN architecture performed best in the validation set and was evaluated in the testing set with the following results: sensitivity: 0.95, specificity: 0.95, positive predictive value: 0.94, negative predictive value: 0.95, F1-score: 0.95, accuracy: 0.95, and area under the curve: 0.99. Grad-CAM and saliency maps showed that tubers resided in regions most relevant for image classification within each image. A stand-alone trained deep learning App was able to classify images using local computers with various operating systems.ConclusionThis study shows that deep learning algorithms are able to detect tubers in selected MRI images, and deep learning can be prudently applied clinically to manually selected data in a rare neurological disorder