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 disruptd 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 magneto encephalography (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 trans cranial 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.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Scientist Development Award - Research (K02)
Project #
Application #
Study Section
Neurological Sciences Training Initial Review Group (NST)
Program Officer
Sieber, Beth-Anne
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Colorado Denver
Schools of Medicine
United States
Zip Code
Buard, Isabelle; Berliner, Jean M; Kluger, Benzi M (2018) Low Frequency repetitive Transcranial Magnetic Stimulation: Potential role in treatment of patients with hemispheric cerebellar strokes. Brain Stimul 11:653-655
Braunlich, Kurt; Seger, Carol A; Jentink, Kade G et al. (2018) Rhythmic auditory cues shape neural network recruitment in Parkinson's disease during repetitive motor behavior. Eur J Neurosci :
Buard, Isabelle; Sciacca, David M; Martin, Christine S et al. (2018) Transcranial magnetic stimulation does not improve mild cognitive impairment in Parkinson's disease. Mov Disord 33:489-491
Kluger, Benzi M; Pedersen, Kenn Freddy; Tysnes, Ole-Bjorn et al. (2017) Is fatigue associated with cognitive dysfunction in early Parkinson's disease? Parkinsonism Relat Disord 37:87-91
Allen, Corey H; Kluger, Benzi M; Buard, Isabelle (2017) Safety of Transcranial Magnetic Stimulation in Children: A Systematic Review of the Literature. Pediatr Neurol 68:3-17
Litvan, Irene; Lees, Peter S J; Cunningham, Christopher R et al. (2016) Environmental and occupational risk factors for progressive supranuclear palsy: Case-control study. Mov Disord 31:644-52
Holden, Samantha K; Jones, Wallace E; Baker, Keith A et al. (2016) Outcome measures for Parkinson's disease dementia: a systematic review. Mov Disord Clin Pract 3:9-18
Brody, David M; Litvan, Irene; Warner, Steve et al. (2016) Relationship between uric acid levels and progressive supranuclear palsy. Mov Disord 31:663-7
Wang, Chao; Trongnetrpunya, Amy; Samuel, Immanuel Babu Henry et al. (2016) Compensatory Neural Activity in Response to Cognitive Fatigue. J Neurosci 36:3919-24
Kluger, Benzi M; Herlofson, Karen; Chou, Kelvin L et al. (2016) Parkinson's disease-related fatigue: A case definition and recommendations for clinical research. Mov Disord 31:625-31

Showing the most recent 10 out of 13 publications