Parkinson's disease (PD) affects 1% of adults over age 65. While traditionally defined by motor symptoms, up to 75% of PD patients will eventually develop dementia making it the leading cause of nursing home placement in this population. Although there is currently no cure for PD, our ability to treat motor symptoms has advanced tremendously since the 1960's based on advances in our understanding of motor symptom neurophysiology. I propose that the treatment and prevention of dementia in PD may also prove possible through advances in our understanding of the neurophysiology of cognitive dysfunction. I will use modern network theory as a theoretical and mathematical framework for this endeavor. My long-term goal is to advance our fundamental understanding of the neurophysiology of cognitive dysfunction in PD to provide empirically testable models, clinically relevant biomarkers, and novel therapeutic targets. The central hypothesis of this proposal is that patterns of cortical functional connectivity critical to normal cognitive function are disrupted by subcortical pathology in PD and that interventions which normalize these patterns will improve cognition. This hypothesis has been formulated on the basis of preliminary data presented in this proposal and other previously published work. The research objectives of this proposal are to further our understanding of how cortical connectivity relates to cognitive dysfunction in PD, develop a novel biomarker for cognitive dysfunction in PD based on cortical physiology and to determine whether modulation of cortical connectivity may result in cognitive improvements in PD. We will accomplish the objectives of this proposal through three Specific Aims: 1) Determine whether graph theory measures of functional cortical activity measured with magnetoencephalography (MEG) are associated with cognitive dysfunction in PD subjects with and without mild cognitive impairment (MCI);2) Develop a novel state-defining biomarker for cognitive dysfunction in PD based on MEG features through a machine learning approach;and 3) Determine the effects of repetitive transcranial magnetic stimulation (rTMS) on MEG measures of cortical connectivity and cognitive outcomes in PD-MCI patients. The approach is innovative because it represents the first study to apply graph theory measures to understanding the relationship of cortical physiology and cognitive dysfunction in PD;the first study to apply machine learning approaches to cognitive PD biomarker development;and the first clinical trial or mechanistic study of rTMS in PD-MCI. The proposed research is significant because it is expected to advance our understanding of the pathophysiology of cognitive dysfunction in PD and will provide biomarkers and pilot data essential to planning future therapeutic interventions. The training objectives and related research activities of this proposal will provide new skills, manuscripts and pilot data related to advanced MEG analysis, graph theory, biomarker development and rTMS trials necessary to establish my independence in these areas and obtain R01 funding to advance this unique research program.

Public Health Relevance

Dementia is the leading cause of nursing home placement in Parkinson's disease (PD) yet little is known about the cause(s) of cognitive dysfunction in PD and there are no effective treatments. Our preliminary data and other published studies suggest that abnormalities in brain activity involving networks important for normal thinking and memory may contribute to cognitive dysfunction in PD and may represent a target for treatment. This proposal will identify abnormalities in cortical activity related to cognitive dysfunction in PD usng magneto encephalography and will perform a randomized control trial of frontal repetitive trans cranial magnetic stimulation to determine the therapeutic potential of modulating this brain activity.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Scientist Development Award - Research (K02)
Project #
1K02NS080885-01A1
Application #
8635587
Study Section
NST-2 Subcommittee (NST)
Program Officer
Sieber, Beth-Anne
Project Start
2013-09-23
Project End
2018-06-30
Budget Start
2013-09-23
Budget End
2014-06-30
Support Year
1
Fiscal Year
2013
Total Cost
$186,354
Indirect Cost
$13,804
Name
University of Colorado Denver
Department
Neurology
Type
Schools of Medicine
DUNS #
041096314
City
Aurora
State
CO
Country
United States
Zip Code
80045
Kluger, Benzi M; Brown, R Preston; Aerts, Shanae et al. (2014) Determinants of objectively measured physical functional performance in early to mid-stage Parkinson disease. PM R 6:992-8