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Network Biomarkers for the Assessment of Parkinson's Disease: A Multi-modal Optimization Study

This study will compare positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) for measuring the activity of Parkinson's disease-specific brain networks.  In addition to assessing the sensitivity of each approach to the network changes that occur with disease progression, we will also compare network-level treatment responses measured using the two scanning techniques.  These studies will optimize the use of functional networks as imaging biomarkers of Parkinson's disease.

Project Description:
We propose to optimize the network biomarkers derived using FDG PET (brain metabolism), ASL MRI (cerebral perfusion), and resting state fMRI (resting neural activity) data. The study will be conducted in research subjects undergoing FDG PET as part of the Udall Center of Excellence for Parkinson’s Disease Research at The Feinstein Institute for Medical Research (P50 NS071675). In this ongoing research program, FDG PET scans are being acquired in over 100 patients and control subjects; 30 participants will undergo repeat imaging while on dpaminergic treatment. By additionally performing multi-sequence MRI scans in these subjects, we will be able to directly compare the "quality" of the network measurements obtained using different scanning approaches.

Relevance to Diagnosis/Treatment of Parkinson’s Disease:
We propose to implement a new iterative computational approach to define the analytical parameters needed to derive the most accurate PD network topographies for each imaging modality. Apart from optimizing the stability and sensitivity of the resulting PD-related network constructs, the study will provide essential information concerning statistical power and sample size. These data will help guide the choice of imaging tools in future clinical trials employing network biomarkers.

Anticipated Outcome:
We anticipate that PD-related network topographies analogous to those identified with FDG PET will be evident in the ASL MRI and rsfMRI data. That said, we anticipate substantial differences across modalities in the stability and accuracy of the resulting networks.



  • David Eidelberg, MD

    Manhasset, NY United States

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