Movement disorders and nonmotor neuropsychological symptoms in children and adults with classical galactosemia

Although movement disorders (MDs) are known complications, the exact frequency and severity remains uncertain in patients with classical galactosemia, especially in children. We determined the frequency, classification and severity of MDs in a cohort of pediatric and adult galactosemia patients, and assessed the association with nonmotor neuropsychological symptoms and daily functioning. Patients from seven centers in the United Kingdom and the Netherlands with a confirmed galactosemia diagnosis were invited to participate. A videotaped neurological examination was performed and an expert panel scored the presence, classification and severity of MDs. Disease characteristics, nonmotor neuropsychological symptoms, and daily functioning were evaluated with structured interviews and validated questionnaires (Achenbach, Vineland, Health Assessment Questionnaire, SIP68). We recruited 37 patients; 19 adults (mean age 32.6 years) and 18 children (mean age 10.7 years). Subjective self-reports revealed motor symptoms in 19/37 (51.4%), similar to the objective (video) assessment, with MDs in 18/37 patients (48.6%). The objective severity scores were moderate to severe in one third (6/37). Dystonia was the overall major feature, with additional tremor in adults, and myoclonus in children. Behavioral or psychiatric problems were present in 47.2%, mostly internalizing problems, and associated with MDs. Daily functioning was significantly impaired in the majority of patients. Only one patient received symptomatic treatment for MDs. We show that MDs and nonmotor neuropsychological symptoms are frequent in both children and adults with classical galactosemia

Neural network hyperparameter tuning for online model parameter updating using inverse mapping models

To decrease the mismatch between a model and a physical system, physically interpretable model parameter values of nonlinear systems can be updated in real-time by using the Inverse Mapping Parameter Updating (IMPU) method. In this method, an Inverse Mapping Model (IMM), constituted by an Artificial Neural Network (ANN), is trained, offline, using simulated data that consists of features of output responses (ANN inputs) and corresponding parameter values (ANN outputs). In an online phase, the trained ANN can then be used to infer parameter values with high computational efficiency. The (non-trivial) choice of ANN-hyperparameters, e.g., ANN structure and training settings, may significantly influence the accuracy of the trained ANN. Therefore, this work discusses multiple ANN-hyperparameter tuning techniques to increase the accuracy of the IMPU method, of which the Bayesian search technique is the most promising considering accuracy and efficiency as it learns from previously evaluated hyperparameter values