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BackgroundThere is a need for reliable and robust Parkinson's disease biomarkers that reflect severity and are sensitive to disease modifying investigational therapeutics.ObjectiveTo demonstrate the utility of EEG as a reliable, quantitative biomarker with potential as a pharmacodynamic endpoint for use in clinical assessments of neuroprotective therapeutics for Parkison's disease.MethodsA multi modal study was performed including aquisition of resting state EEG data and dopamine transporter PET imaging from Parkinson's disease patients off medication and compared against age-matched controls.ResultsQualitative and test/retest analysis of the EEG data demonstrated the reliability of the methods. Source localization using low resolution brain electromagnetic tomography identified significant differences in Parkinson's patients versus control subjects in the anterior cingulate and temporal lobe, areas with established association to Parkinson's disease pathology. Changes in cortico-cortical and cortico-thalamic coupling were observed as excessive EEG beta coherence in Parkinson's disease patients, and correlated with UPDRS scores and dopamine transporter activity, supporting the potential for cortical EEG coherence to serve as a reliable measure of disease severity. Using machine learning approaches, an EEG discriminant function analysis classifier was identified that parallels the loss of dopamine synapses as measured by dopamine transporter PET.ConclusionOur results support the utility of EEG in characterizing alterations in neurophysiological oscillatory activity associated with Parkinson's disease and highlight potential as a reliable method for monitoring disease progression and as a pharmacodynamic endpoint for Parkinson's disease modification therapy.

BACKGROUND AND PURPOSE:Neuroinflammation has been implicated in the pathophysiology of Parkinson's disease (PD), which might be influenced by successful neuroprotective drugs. The uptake of [11 C](R)-PK11195 (PK) is often considered to be a proxy for neuroinflammation, and can be quantified using the Logan graphical method with an image-derived blood input function, or the Logan reference tissue model using automated reference region extraction. The purposes of this study were (1) to assess whether these noninvasive image analysis methods can discriminate between patients with PD and healthy volunteers (HVs), and (2) to establish the effect size that would be required to distinguish true drug-induced changes from system variance in longitudinal trials. METHODS:The sample consisted of 20 participants with PD and 19 HVs. Two independent teams analyzed the data to compare the volume of distribution calculated using image-derived input functions (IDIFs), and binding potentials calculated using the Logan reference region model. RESULTS:With all methods, the higher signal-to-background in patients resulted in lower variability and better repeatability than in controls. We were able to use noninvasive techniques showing significantly increased uptake of PK in multiple brain regions of participants with PD compared to HVs. CONCLUSION:Although not necessarily reflecting absolute values, these noninvasive image analysis methods can discriminate between PD patients and HVs. We see a difference of 24% in the substantia nigra between PD and HV with a repeatability coefficient of 13%, showing that it will be possible to estimate responses in longitudinal, within subject trials of novel neuroprotective drugs.