There has been considerable progress in understanding the biology of Parkinson's disease (PD). Reliable biomarkers are still lacking, however, for early stage detection of PD and for characterizing disease progression. Advances in biotechnology have led to the advent of mental health studies that collect large-scale, multi-dimensional data sets, including brain imaging data, genomic data, and biologic and clinical measures. Such studies provide an unprecedented opportunity for cross-cutting investigations that stand to gain a deeper understanding of PD. A major limiting factor to multidimensional biomarker development, however, is the lack of statistical tools available to accommodate diverse, large-scale data. Leveraging data from neuromelanin magnetic resonance imaging (NM-MRI) of the locus coeruleus and the substantia nigra, chemical shift imaging (CSI), diffusion tensor imaging (DTI), resting-state functional MRI, cerebrospinal fluid (CSF) analytes, genotype information, and numerous clinical variables, we plan to develop novel statistical techniques to identify multimodal PD biomarkers. Our data provide an unprecedented opportunity for cross-cutting methodological advances in multimodal PD biomarker discovery. Separately, we will consider a massive patient database with nearly 250,000 subscribers in Georgia. Building on our collective expertise in developing statistical and machine-learning methods for large-scale imaging data and in the pathophysiology of PD, we plan to advance methods for PD biomarker analyses and discovery through the following specific aims. First, we plan to develop new statistical techniques to reveal multimodal biomarkers for PD including imaging, clinical, and biologic variables. Secondly, we plan to utilize the massive clinical database to identify clinical risk factors for early stage PD. Thirdly, we will develop software equipped with a friendly graphical user interface (GUI) to implement the multimodal biomarker detection methods.

Public Health Relevance

There is a critical unmet need for the discovery of early-stage Parkinson's disease (PD) biomarkers to assist and accelerate the process for conducting clinical trials targeting neuroprotective treatments. Large studies with clinical, molecular, genetic, and neuroimaging measures produce complex multidimensional datasets, which may be useful to help establish such biomarkers. We plan to develop new statistical methods that integrate multiple high-dimensional data sets to identify accurate and robust multimodal biomarkers of PD.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Demonstration--Cooperative Agreements (U18)
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Special Emphasis Panel (ZNS1-SRB-J (02))
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Babcock, Debra J
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Emory University
Biostatistics & Other Math Sci
Schools of Public Health
United States
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Chen, Shuo; Grant, Edward; Wu, Tong Tong et al. (2014) Statistical Learning Methods for Longitudinal High-dimensional Data. Wiley Interdiscip Rev Comput Stat 6:10-18
Xue, Wenqiong; Kang, Jian; Bowman, F DuBois et al. (2014) Identifying functional co-activation patterns in neuroimaging studies via poisson graphical models. Biometrics 70:812-22
Bowman, F Dubois (2014) Brain Imaging Analysis. Annu Rev Stat Appl 1:61-85