Improvement of Non-motor Symptoms and Quality of Life After Deep Brain Stimulation for Refractory Dystonia: A 1-Year Follow-Up

Alpha Synuclein Toxicity and Non-Motor Parkinson's

Parkinson's disease (PD) is a common multisystem neurodegenerative disorder affecting 1% of the population over the age of 60 years. The main neuropathological features of PD are the loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the presence of alpha synuclein (αSyn)-rich Lewy bodies both manifesting with classical motor signs. αSyn has emerged as a key protein in PD pathology as it can spread through synaptic networks to reach several anatomical regions of the body contributing to the appearance of non-motor symptoms (NMS) considered prevalent among individuals prior to PD diagnosis and persisting throughout the patient's life. NMS mainly includes loss of taste and smell, constipation, psychiatric disorders, dementia, impaired rapid eye movement (REM) sleep, urogenital dysfunction, and cardiovascular impairment. This review summarizes the more recent findings on the impact of αSyn deposits on several prodromal NMS and emphasizes the importance of early detection of αSyn toxic species in biofluids and peripheral biopsies as prospective biomarkers in PD.

A decision support system based on recurrent neural networks to predict medication dosage for patients with Parkinson's disease

Using deep learning has demonstrated significant potential in making informed decisions based on clinical evidence. In this study, we deal with optimizing medication and quantitatively present the role of deep learning in predicting the medication dosage for patients with Parkinson's disease (PD). The proposed method is based on recurrent neural networks (RNNs) and tries to predict the dosage of five critical medication types for PD, including levodopa, dopamine agonists, monoamine oxidase-B inhibitors, catechol-O-methyltransferase inhibitors, and amantadine. Recurrent neural networks have memory blocks that retain crucial information from previous patient visits. This feature is helpful for patients with PD, as the neurologist can refer to the patient's previous state and the prescribed medication to make informed decisions. We employed data from the Parkinson's Progression Markers Initiative. The dataset included information on the Unified Parkinson's Disease Rating Scale, Activities of Daily Living, Hoehn and Yahr scale, demographic details, and medication use logs for each patient. We evaluated several models, such as multi-layer perceptron (MLP), Simple-RNN, long short-term memory (LSTM), and gated recurrent units (GRU). Our analysis found that recurrent neural networks (LSTM and GRU) performed the best. More specifically, when using LSTM, we were able to predict levodopa and dopamine agonist dosage with a mean squared error of 0.009 and 0.003, mean absolute error of 0.062 and 0.030, root mean square error of 0.099 and 0.053, and R-squared of 0.514 and 0.711, respectively.