AIMC Topic: Parkinson Disease

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Network abnormalities among non-manifesting Parkinson disease related LRRK2 mutation carriers.

Human brain mapping
Non-manifesting carriers (NMC) of the G2019S mutation in the LRRK2 gene represent an "at risk" group for future development of Parkinson's disease (PD) and have demonstrated task related fMRI changes. However, resting-state networks have received les...

A Multi-Layer Gaussian Process for Motor Symptom Estimation in People With Parkinson's Disease.

IEEE transactions on bio-medical engineering
The assessment of Parkinson's disease (PD) poses a significant challenge, as it is influenced by various factors that lead to a complex and fluctuating symptom manifestation. Thus, a frequent and objective PD assessment is highly valuable for effecti...

An Evaluation of the Effect of Vision on Standing Stability in the Early Stage of Parkinson's Disease.

European neurology
BACKGROUND: Postural instability is a common disorder in Parkinson's disease (PD). The aim of this study was to evaluate stance stability of the subjects at the early stage of PD with both linear and nonlinear approaches. Moreover, this study aimed t...

Effects of robot-assisted gait training in patients with Parkinson's disease: study protocol for a randomized controlled trial.

Trials
BACKGROUND: Robot-assisted gait training (RAGT) was developed to restore gait function by promoting neuroplasticity through repetitive locomotor training and has been utilized in gait training. However, contradictory outcomes of RAGT have been report...

Classification of gait patterns between patients with Parkinson's disease and healthy controls using phase space reconstruction (PSR), empirical mode decomposition (EMD) and neural networks.

Neural networks : the official journal of the International Neural Network Society
Parkinson's disease (PD) is a common neurodegenerative disorder that affects human's quality of life, especially leading to locomotor deficits such as postural instability and gait disturbances. Gait signal is one of the best features to characterize...

Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Parkinson's Disease (PD) is one of the most prevalent neurodegenerative diseases that affects tens of millions of Americans. PD is highly progressive and heterogeneous. Quite a few studies have been conducted in recent years on predictive or disease ...

Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-Wise Missing Data.

IEEE transactions on bio-medical engineering
As the collection of mobile health data becomes pervasive, missing data can make large portions of datasets inaccessible for analysis. Missing data has shown particularly problematic for remotely diagnosing and monitoring Parkinson's disease (PD) usi...

Automatic localization of the subthalamic nucleus on patient-specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation.

Human brain mapping
Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has shown clinical potential for relieving the motor symptoms of advanced Parkinson's disease. While accurate localization of the STN is critical for consistent across-patients effective D...

Self-selected speed gait training in Parkinson's disease: robot-assisted gait training with virtual reality versus gait training on the ground.

European journal of physical and rehabilitation medicine
BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disorder causing progressive gait disability. Although robot-assisted gait training (RAGT) using the Lokomat device has been demonstrated to improve gait in PD, it is not clear what the best...

Machine Learning for Predicting Cognitive Diseases: Methods, Data Sources and Risk Factors.

Journal of medical systems
Machine learning and data mining approaches are being successfully applied to different fields of life sciences for the past 20 years. Medicine is one of the most suitable application domains for these techniques since they help model diagnostic info...