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546 active trials for Parkinson Disease

Vigor and the LDR in Parkinson Disease

Parkinson disease (PD) is a common disorder in which reduced speed of movement results from inadequate brain production of the chemical dopamine. The most effective treatment for PD is the drug levo-dopa, which partially replaces brain dopamine. Despite decades of successful use, how levo-dopa improves speed of movement in PD is not understood. This observational study recruits participants who have been prescribed levo-dopa by their treating physicians. Before their first dose, immediately after their first dose and later, when their dose has been stabilized, they will engage with the research team to participate in a few simple experiments to measure speed, grip strength, tremor, and stability (on and off of treatment). The purpose of these experiments is to understand how levo-dopa treatment in Parkinson disease enhances movement speed. An important but not understood component of levo-dopa action, the Long Duration Response (LDR), lasts for days to weeks. A basic function of dopamine signaling in the brain is modulation of motivation - the coupling between effort and action values. These experiments will determine if the LDR is associated with relative normalization of motivation function in the brain. The motivation behavior of recently diagnosed PD participants will be examined before and after treatment with levo-dopa to determine if the magnitude of the LDR is correlated with improvements in motivation behavior.

Start: February 2020
Predicting Outcomes From tDCS Intervention in Parkinson' Disease Using Electroencephalographic Biomarkers and Machine Learning Approach: the PREDICT Study Protocol

Parkinson's disease (PD) is a progressive and disabling neurodegenerative disease, clinically characterized by motor and non-motor symptoms. The potential of the "Transcranial direct current stimulation" (tDCS) for symptomatic improvement in these patients has been demonstrated, but the factors associated with the best therapeutic response are not known. The electroencephalogram (EEG) is considered as a diagnostic and prognostic biomarker of PD, and has been used in recent studies associated with machine-learning methods to identify predictors of responses in neurological and psychiatric conditions. Using connectivity-based prediction and machine-learning, the investigators intend to identify and compare characteristics related to baseline resting EEG between PD responders and non-responders to tDCS treatment. The recruited participants will be randomized to treatment with active tDCS associated with dual-task motor therapy or motor therapy with visual cues. A resting-state electroencephalography (EEG) will be recorded prior to the start of the treatment. The investigators will determine clinical improvement labels used for machine learning classification, in baseline and posttreatment assessments and will use three different methods to categorize the data into two classes (low or high improvement): Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Extreme Learning Machine (ELM). The functional label will be based on the Timed Up and Go Test recorded at baseline and posttreament of tDCS treatment.

Start: June 2021
Characterization of Complex Pulse Shapes in Deep Brain Stimulation for Movement Disorders Using EEG and Local Field Potential Recordings

Parkinson's disease and essential tremor are chronic movement disorders for which there is no cure. When medication is no longer effective, deep brain stimulation (DBS) is recommended. Standard DBS is a neuromodulation method that uses a simple monophasic pulse, delivered from an electrode to stimulate neurons in a target brain area. This monophasic pulse spreads out from the electrode creating a broad, electric field that stimulates a large neural population. This can often effectively reduce motor symptoms. However, many DBS patients experience side effects - caused by stimulation of non-target neurons - and suboptimal symptom control - caused by inadequate stimulation of the correct neural target. The ability to carefully manipulate the stimulating electric field to target specific neural subpopulations could solve these problems and improve patient outcomes. The use of complex pulse shapes, specifically biphasic pulses and asymmetric pre-pulses, can control the temporal properties of the stimulation field. Evidence suggests that temporal manipulations of the stimulation field can exploit biophysical differences in neurons to target specific subpopulations. Therefore, our aim is to evaluate the direct neurophysiological effects of complex pulse shapes in DBS movement disorder patients. This will be achieved using a two-stage investigation: stage one will study the neural response to different pulse shapes using electroencephalography (EEG) recordings. Stage two will study the neural responses to different pulse shapes using intra-operative local field potential (LFP) recordings. This study only relates only to the collection of EEG and LFP recordings in DBS patients. The protocol does not cover any surgical procedures, which already take place as part of the patient's normal clinical care.

Start: December 2020