3 research outputs found
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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
Recommended from our members
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