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.
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.
|Langley, Jason; Huddleston, Daniel E; Merritt, Michael et al. (2016) Diffusion tensor imaging of the substantia nigra in Parkinson's disease revisited. Hum Brain Mapp 37:2547-56|
|Rosenthal, Liana S; Drake, Daniel; Alcalay, Roy N et al. (2016) The NINDS Parkinson's disease biomarkers program. Mov Disord 31:915-23|
|Chen, Shuo; Bowman, F DuBois; Mayberg, Helen S (2016) A Bayesian hierarchical framework for modeling brain connectivity for neuroimaging data. Biometrics 72:596-605|
|Bowman, F DuBois; Drake, Daniel F; Huddleston, Daniel E (2016) Multimodal Imaging Signatures of Parkinson's Disease. Front Neurosci 10:131|
|Kang, Jian; Bowman, F DuBois; Mayberg, Helen et al. (2016) A depression network of functionally connected regions discovered via multi-attribute canonical correlation graphs. Neuroimage 141:431-441|
|Langley, Jason; Huddleston, Daniel E; Chen, Xiangchuan et al. (2015) A multicontrast approach for comprehensive imaging of substantia nigra. Neuroimage 112:7-13|
|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|
|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|
|Simpson, Sean L; Bowman, F DuBois; Laurienti, Paul J (2013) Analyzing complex functional brain networks: Fusing statistics and network science to understand the brain(*ýýý) Stat Surv 7:1-36|
Showing the most recent 10 out of 11 publications