Parkinson's disease (PD) is characterized by both motor and non-motor symptoms (cognitive impairment, affective disorder, and other clinical features). Data from experimental animal models and patients with PD indicate that the manifestations of this disease cannot be attributed to isolated dysfunction of the basal ganglia. Rather, the highly localized loss of nigral dopamine cells is associated with a broad, spatially distributed set of functional abnormalities involving cortico-striato-pallido-thalamocortical (CSPTC) loops and related pathways. By quantifying the activity of spatially distributed (large-scale) functional brain networks, comprising multiple interconnected brain regions, modern techniques of image-based analysis can provide valuable information concerning the widespread circuit abnormalities that underlie neurodegenerative disorders such as PD. The investigators at the Center for Neurosciences at The Feinstein Institute, led by Dr. Eidelberg, have pioneered the use of functional brain imaging and network analysis for the study of PD and related neurodegenerative diseases. Because ofthe noise inherent in """"""""small signals"""""""" analyses of this sort, we have emphasized rigorous validation of the disease-related functional patterns from both statistical and empiric standpoints. Indeed, high levels of measurement precision are needed before quantitative network measures can be considered as potential biomarkers of the disease process and its response to treatment. In this proposal, we seek to take this approach to a new level by employing rigorously validated PD-related networks to address a number of vital issues that impact heavily on the care of today's PD patients. Project 1 addresses the serious clinical problem of levodopa-induced dyskinesias, which ultimately affect nearly all PD patients. Project 2 examines the network basis for individual differences in the cognitive response to dopaminergic treatment with a view to predicting which patients will develop untoward cognitive side effects under different treatment conditions. Project 3 aims to establish the feasibility of a new network-based algorithm for providing earlier and more accurate differential diagnosis than is currently possible.

Public Health Relevance

Because dopaminergic treatment is generally so effective for the motor symptoms of PD, at least early on, it is easy to dismiss the very real problems that ultimately develop: levodopa-induced dyskinesias and cognitive and behavioral changes for some patients. Understanding these phenomena should not only help us improve the lives of patients, but will provide unique insight into the pathophysiolgy of PD and perhaps other neurodegenerative disorders. Likewise, the validation of an automated pattern-based method for early diagnosis will help streamline trials of new therapies for PD as well as for atypical parkinsonian syndromes.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Specialized Center (P50)
Project #
5P50NS071675-05
Application #
8741999
Study Section
Special Emphasis Panel (ZNS1-SRB-E)
Project Start
Project End
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
5
Fiscal Year
2014
Total Cost
$329,008
Indirect Cost
$109,896
Name
Feinstein Institute for Medical Research
Department
Type
DUNS #
110565913
City
Manhasset
State
NY
Country
United States
Zip Code
11030
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Vo, An; Sako, Wataru; Fujita, Koji et al. (2017) Parkinson's disease-related network topographies characterized with resting state functional MRI. Hum Brain Mapp 38:617-630
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Lerner, Renata P; Bimpisidis, Zisis; Agorastos, Stergiani et al. (2016) Dissociation of metabolic and hemodynamic levodopa responses in the 6-hydroxydopamine rat model. Neurobiol Dis 96:31-37
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Jourdain, Vincent A; Tang, Chris C; Holtbernd, Florian et al. (2016) Flow-metabolism dissociation in the pathogenesis of levodopa-induced dyskinesia. JCI Insight 1:e86615
Spetsieris, Phoebe G; Ko, Ji Hyun; Tang, Chris C et al. (2015) Metabolic resting-state brain networks in health and disease. Proc Natl Acad Sci U S A 112:2563-8

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