Mapping Levodopa Effects on Cortico-basal Ganglia Circuit Function in Parkinson's Disease

N/AActive Not RecruitingNCT06240624

Danish Research Centre for Magnetic Resonance75 enrolled

Overview

Levodopa-induced dyskinesia (LID) in Parkinson's disease (PD) are involuntary movements caused by long-term treatment with dopaminergic replacement therapy (levodopa). During the cause of PD, most patients develop LID. In this study, the investigators plan to investigate how the cortico-basal-ganglia networks are affected in LID. The investigators will examine PD patients with and without LID as well as healthy age-matched controls using fMRI and PET. During the fMRI experiment, participants will perform a novel go-no task engaging both motor, emotional and reward brain networks. Patients will be scanned before and after intake of levodopa to study the dynamic effects of dopaminergic therapy. Furthermore, a dopamine transporter PET will be acquired to study the dopaminergic degeneration of the patients with PD.

BACKGROUND Parkinson's disease (PD) is a neurodegenerative disease affecting the cortico-basal-ganglia network including dopaminergic degeneration. PD is treated with dopaminergic replacement therapy such as levodopa which is a natural precursor of dopamine. Unfortunately, most patients develop involuntary movements after many years of treatment which are called levodopa-induced dyskinesia (LID). Little is known about how brain networks are disturbed in LID. To investigate this, the investigators will conduct a multimodal characterization of PD patients with LID. Patients with and without LID will be studied using dynamic task-based fMRI to trace the levodopa-induced changes in functional brain activity and connectivity in the transition from the off-to-on medication state. For the task-based fMRI, the investigators will employ a novel experimental task that concurrently engages motor, emotional, and reward networks. Furthermore, the investigators will also map and quantify the dopaminergic presynaptic deficit using dopamine transporter (DAT) PET (\[18F\]PE2I-PET) to correlate the findings with MRI. This study is part of a larger study of brain phenotyping PD (ADAPT-PD) with MRI. AIM This sub-study aims to achieve a multimodal characterization of LID in PD using both fMRI and DAT PET. Furthermore, both motor, reward and emotional cortico-basal-ganglia networks will be investigated. The result of this study will lay the foundation of a future non-invasive brain stimulation study using transcranial magnetic stimulation (TMS) in PD. HYPOTHESES 1. Patients with LID will show increased responsiveness to an acute levodopa challenge which will lead to a stronger time-dependent increase in striato-cortical motor network activity (preSMA and putamen) during NoGo trials after levodopa intake compared to patients without LID (Herz et al., 2014). 2. Patients with LID will show increased activity in the SMA and pre-SMA and less activity of the rIFG during movement (go responses) compared to patients without LID in the off-medication state (Cerasa et al., 2012). 3. Patients with LID will show abnormal levodopa-induced, task-related connectivity between putamen and and M1 compared to patients without LID (Herz et al., 2015). 4. Patients with LID will show decreased dopamine transporter activity in putamen compared to patients without LID (Hong et al. 2014). The analysis regarding reward and emotional processing in patients with LID and the comparisons between fMRI and PET will be exploratory as no previous studies have been conducted. RESEARCH PLAN For the sub-study, the investigators aim to recruit 25 PD patients with LID, 25 PD patients without LID, and 25 age-matched healthy controls.

Outcomes

Primary Outcomes

Percent Mean Change in Blood Oxygen-level Dependent (BOLD) Scores During Modified Go/No-Go Task

For each participant and each run, we will create general linear models with 20 regressors of interest. The regressors are Motor: Right, Left, and No-Go trials Emotion: Happy, Neutral, Sad Reward: High/low reward, high/low loss All above regressors will be separately modeled for the high- and the low-reward context. We will add a linear time modulation to all regressors to model dynamic changes in activation over time.