Parkinson's Disease

Weighted gene co-expression network analysis reveals specific modules and biomarkers in Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) ranks as the second most frequently occurring neurodegenerative disease. The precise pathogenic mechanism of this disease remains unknown. The aim of the present study was to identify the biomarkers in PD and classify the primary differentially expressed genes (DEGs).

High-Resolution Motor State Detection in Parkinson's Disease Using Convolutional Neural Networks.

Patients with advanced Parkinson's disease regularly experience unstable motor states. Objective and reliable monitoring of these fluctuations is an unmet need. We used deep learning to classify motion data from a single wrist-worn IMU sensor recording in unscripted environments. For validation purposes, patients were accompanied by a movement disorder expert, and their motor state was passively e...

Brain morphological changes in hypokinetic dysarthria of Parkinson's disease and use of machine learning to predict severity.

BACKGROUND: Up to 90% of patients with Parkinson's disease (PD) eventually develop the speech and voice disorder referred to as hypokinetic dysarthria...

Machine Learning to Identify Flexibility Signatures of Class A GPCR Inhibition.

We show that machine learning can pinpoint features distinguishing inactive from active states in proteins, in particular identifying key ligand bindi...

Machine learning-based motor assessment of Parkinson's disease using postural sway, gait and lifestyle features on crowdsourced smartphone data.

OBJECTIVES: Remote assessment of gait in patients' homes has become a valuable tool for monitoring the progression of Parkinson's disease (PD). Howeve...

An Underwater Robotic Manipulator with Soft Bladders and Compact Depth-Independent Actuation.

An underwater manipulator is essential for underwater robotic sampling and other service operations. Conventional rigid body underwater manipulators g...

Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson's Patients.

In the last few years, the importance of measuring gait characteristics has increased tenfold due to their direct relationship with various neurologic...

Discriminating progressive supranuclear palsy from Parkinson's disease using wearable technology and machine learning.

BACKGROUND: Progressive supranuclear palsy (PSP), a neurodegenerative conditions may be difficult to discriminate clinically from idiopathic Parkinson...

Motion Biomarkers Showing Maximum Contrast Between Healthy Subjects and Parkinson's Disease Patients Treated With Deep Brain Stimulation of the Subthalamic Nucleus. A Pilot Study.

Classic motion abnormalities in Parkinson's disease (PD), such as tremor, bradykinesia, or rigidity, are well-covered by standard clinical assessment...

LFP-Net: A deep learning framework to recognize human behavioral activities using brain STN-LFP signals.

BACKGROUND: Recognition of human behavioral activities using local field potential (LFP) signals recorded from the Subthalamic Nuclei (STN) has applic...

Untargeted Metabolomics for Metabolic Diagnostic Screening with Automated Data Interpretation Using a Knowledge-Based Algorithm.

Untargeted metabolomics may become a standard approach to address diagnostic requests, but, at present, data interpretation is very labor-intensive. T...

A preliminary attempt to visualize nigrosome 1 in the substantia nigra for Parkinson's disease at 3T: An efficient susceptibility map-weighted imaging (SMWI) with quantitative susceptibility mapping using deep neural network (QSMnet).

PURPOSE: Visibility of nigrosome 1 in the substantia nigra (SN) is used as an MR imaging biomarker for Parkinson's disease. Because of lower susceptib...

A Super-Lightweight and Soft Manipulator Driven by Dielectric Elastomers.

Some applications are very sensitive to the weight of the robot, such as space manipulation or any untethered mobile manipulation. In this article, we...

Improvement of classification performance of Parkinson's disease using shape features for machine learning on dopamine transporter single photon emission computed tomography.

OBJECTIVE: To assess the classification performance between Parkinson's disease (PD) and normal control (NC) when semi-quantitative indicators and sha...

Prognostic factors of Rapid symptoms progression in patients with newly diagnosed parkinson's disease.

Tracking symptoms progression in the early stages of Parkinson's disease (PD) is a laborious endeavor as the disease can be expressed with vastly diff...

Plasma and Serum Alpha-Synuclein as a Biomarker of Diagnosis in Patients With Parkinson's Disease.

Parkinson's disease (PD) is the second most common neurodegenerative disease, and α-synuclein plays a critical role in the pathogenesis of PD. Studie...

Adaptive sparse learning using multi-template for neurodegenerative disease diagnosis.

Neurodegenerative diseases are excessively affecting millions of patients, especially elderly people. Early detection and management of these diseases...

Machine learning methods for optimal prediction of motor outcome in Parkinson's disease.

PURPOSE: It is vital to appropriately power clinical trials towards discovery of novel disease-modifying therapies for Parkinson's disease (PD). Thus,...

Real-time machine learning classification of pallidal borders during deep brain stimulation surgery.

OBJECTIVE: Deep brain stimulation (DBS) of the internal segment of the globus pallidus (GPi) in patients with Parkinson's disease and dystonia improve...

Prevalence and Diagnosis of Neurological Disorders Using Different Deep Learning Techniques: A Meta-Analysis.

This paper dispenses an exhaustive review on deep learning techniques used in the prognosis of eight different neuropsychiatric and neurological disor...