AIMC Topic: Parkinson Disease

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Investigating the relationship between the SNCA gene and cognitive abilities in idiopathic Parkinson's disease using machine learning.

Scientific reports
Cognitive impairments are prevalent in Parkinson's disease (PD), but the underlying mechanisms of their development are unknown. In this study, we aimed to predict global cognition (GC) in PD with machine learning (ML) using structural neuroimaging, ...

Machine learning for automated EEG-based biomarkers of cognitive impairment during Deep Brain Stimulation screening in patients with Parkinson's Disease.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: A downside of Deep Brain Stimulation (DBS) for Parkinson's Disease (PD) is that cognitive function may deteriorate postoperatively. Electroencephalography (EEG) was explored as biomarker of cognition using a Machine Learning (ML) pipeline.

Olfactory Testing in Parkinson Disease and REM Behavior Disorder: A Machine Learning Approach.

Neurology
OBJECTIVE: We sought to identify an abbreviated test of impaired olfaction amenable for use in busy clinical environments in prodromal (isolated REM sleep behavior disorder [iRBD]) and manifest Parkinson disease (PD).

An artificial neural network approach to detect presence and severity of Parkinson's disease via gait parameters.

PloS one
INTRODUCTION: Gait deficits are debilitating in people with Parkinson's disease (PwPD), which inevitably deteriorate over time. Gait analysis is a valuable method to assess disease-specific gait patterns and their relationship with the clinical featu...

Effectiveness of robotic balance training on postural instability in patients with mild Parkinson's disease: A pilot, single blind, randomized controlled trial.

Journal of rehabilitation medicine
OBJECTIVE: To examine whether tailored robotic platform training could improve postural stability compared with conventional balance treatment in patients with mild Parkinson's disease.  Design: Randomized single-blind pilot study.

Machine Learning-Based Automatic Rating for Cardinal Symptoms of Parkinson Disease.

Neurology
OBJECTIVE: We developed and investigated the feasibility of a machine learning-based automated rating for the 2 cardinal symptoms of Parkinson disease (PD): resting tremor and bradykinesia.

Clinical outcome prediction from analysis of microelectrode recordings using deep learning in subthalamic deep brain stimulation for Parkinson`s disease.

PloS one
BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for improving the motor symptoms of advanced Parkinson's disease (PD). Accurate positioning of the stimulation electrodes is necessary for better clin...

Parkinson's disease: deep learning with a parameter-weighted structural connectome matrix for diagnosis and neural circuit disorder investigation.

Neuroradiology
PURPOSE: To investigate whether Parkinson's disease (PD) can be differentiated from healthy controls and to identify neural circuit disorders in PD by applying a deep learning technique to parameter-weighted and number of streamlines (NOS)-based stru...

Clinical value of machine learning-based interpretation of I-123 FP-CIT scans to detect Parkinson's disease: a two-center study.

Annals of nuclear medicine
PURPOSE: Our aim was to develop and validate a machine learning (ML)-based approach for interpretation of I-123 FP-CIT SPECT scans to discriminate Parkinson's disease (PD) from non-PD and to determine its generalizability and clinical value in two ce...

Schizotypy in Parkinson's disease predicts dopamine-associated psychosis.

Scientific reports
Psychosis is the most common neuropsychiatric side-effect of dopaminergic therapy in Parkinson's disease (PD). It is still unknown which factors determine individual proneness to psychotic symptoms. Schizotypy is a multifaceted personality trait rela...