Director: David Eidelberg, M.D.
Title: Functional Brain Networks: A Novel Approach to Address Clinical Challenges in PD
Budget End Date: 8/31/2016
The Udall Center at The Feinstein Institute for Medical Research focuses on a unique patient-oriented approach using validated functional brain networks to develop novel approaches/solutions to basic problems of diagnosis and management confronted each day by Parkinson’s disease (PD) patients, clinicians and caretakers. Findings from Center projects will shed light on the relationship between functional brain organization in the resting state (PET, rest state fMRI) and neural activation during cognitive processing (event-related fMRI), and provide unique information on how both interact to shape performance in PD patients and healthy people. Project 1 (David Eidelberg) addresses the serious clinical problem of levodopa-induced dyskinesias, which ultimately affect nearly all PD patients. Project 2 (David Eidelberg) 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 (Andrew Feigin) aims to establish the feasibility of a new network-based algorithm for providing earlier and more accurate differential diagnosis than is currently possible.
Six cores support the research activities of three Projects: the Administrative, Clinical, Cognitive and Behavioral, Statistics and Data Management, Imaging, and Training and Education cores. The activities of each core are independent. That said, these activities are highly coordinated across the Clinical, Cognitive and Behavioral, and Imaging Cores in that the data output of these cores are entered into a database created in the Statistics and Data Management core.
The specialized Udall Center mechanism allows Feinstein investigators to collaborate with scientific leaders in important translational research areas. For instance, the involvement of Dr. Angela Cenci-Nilsson at Lund University (Sweden) offers a true translational collaboration in the study of the role of the microvasculature in the pathogenesis of levodopa-induced dyskinesias (LID). Professor Cenci-Nilsson’s unique expertise provides the basis for a unique collaboration spanning the gap between experimental animal studies and human investigation in Parkinson’s disease. Similarly, investigators in Dr. Eidelberg’s laboratory have developed a productive working relationship with Dr. Mark Gluck and his team at Rutgers University (Newark, NJ) in the area of cognitive processing in Parkinson’s disease. This collaboration has developed further during the course of the fMRI/behavioral studies of probabilistic category learning and neuro-economic decision making. Moreover, studies conducted at the sleep disorders centers at North Shore – LIJ Health System, Stanford University, and the University of Pennsylvania offer insights into the network abnormalities that appear in individuals at risk for PD prior to the onset of motor symptoms.
Project 1 is directed at the innovative use of functional imaging tools to study levodopa-induced dyskinesias (LID), the most limiting physical side-effect of dopaminergic treatment in today’s PD patients. We are taking a body of observations regarding microvascular/hemodynamic changes in LID, rigorously delineating the phenomena in human patients and bringing the results for closer scrutiny in an existing, validated rodent model. The results will likely lead to the identification of new therapeutic targets for this major complication of PD treatment. Moreover, the flow of information from clinic to lab should inform future studies of vasculogenesis in the brain, a topic that is only recently beginning to garner attention in the neurodegeneration field.
Studies found that levodopa-induced dyskinesia (LID) in Parkinson’s disease (PD) is linked to dissociation of hemodynamic (vascular) and synaptic (metabolic) responses to drug in the putamen and in the motor cortex. In the putamen, levodopa-mediated flow-metabolism dissociation correlated with ongoing degeneration of nigrostriatal dopamine terminals. By contrast, the changes in the motor cortex, which were permissive for LID, were associated with degeneration of nigro-cortical dopamine projections occurring with advanced disease. Dissociation of cerebral blood flow and metabolism in the basal ganglia was also seen in the 6-OHDA rat model. These animals exhibited increased blood-brain-barrier (BBB) permeability in this region with levodopa treatment, a phenomenon that was linked to the appearance of dyskinesia in these animals.
Project 1 addresses Clinical Recommendation 10, “Identify risk factors and pathogenic mechanisms of motor fluctuations and dyskinesias to identify novel targets for prevention and symptomatic therapy,” including the development of imaging tools to understand neural networks responsible for the development of these symptoms.
Studies conducted under Project 2 of the Feinstein Udall Center bring a new set of intellectual tools to PD research: innovative behavioral assessments from cognitive neuroscience, as well as validated, widely available psychometric instruments. With these we are probing the neural basis for the cognitive changes associated with dopaminergic treatment. Levodopa can affect cognition in various ways, with a great deal of individual variability. Some effects are quite problematic, and it would benefit patients tremendously if we could predict their response to dopaminergic therapies.
Cognitive responses to levodopa varied substantially across subjects. However, cognitive performance generally improved with treatment in PD patients exhibiting abnormal baseline expression of a distinct disease-related cognitive pattern (PDCP). The PDCP network was found to be highly reproducible across populations and was unrelated to an analogous pattern seen in individuals with Alzheimer’s disease (AD).Recent data additionally suggest that the expression of the PD cognitive network in individual subjects is linked to the presence of specific PD mutations.
Project 2 addresses PD2014 Clinical Recommendation 2, “Develop effective treatmens and companion biomarkers for dopa-resistant featureos of PD,” including “non-motor symptoms, especially cognitive impairment.”
Project 3 tests our current algorithm prospectively in several distinct patient populations, including a group at high risk for developing PD. Accurate differential diagnosis of Parkinson’s disease vs. atypical parkinsonian variant conditions can be obtained in individual subjects using an automated network-based algorithm, even at early disease stages. This network algorithm was originally developed to facilitate differential diagnosis in patients scanned using PET. Recent technical advances from the Feinstein Udall Center have allowed the approach to be used with resting state fMRI, a less invasive, widely available imaging technique.
If successful, this approach would represent a reliable diagnostic biomarker for PD for research and clinical applications. Validation of such a novel disease biomarker cannot take place without extensive patient-based research, in addition to the functional imaging tools and unusual depth of expertise in bioinformatics that we have at the Feinstein Institute.
Project 3 addresses PD2014 Clinical Recommendation 1, “Define the features and natural history of prodromal PD including…biomarkers or other determinants of prodromal subtypes.”
Animal Models: Rat model of L-dopa-induced dyskinesia (LID) at Lund University.
Other Resources: Clinical ratings (off-state UPDRS), a standardized neuropsychological testing battery, off-state FDG PET data, off-state, routine anatomical MRI, DNA banking, and brain donation/banking.
The data collected to date, along with ongoing activities, will shed light on the relationship between functional brain organization in the resting state (PET, rest state fMRI) and neural activation during cognitive processing (event-related fMRI), and provide unique information on how both interact to shape performance in PD patients and healthy people. These studies will also systematically probe the role of the neurovascular unit in PD, particularly the unique alterations of structure and function that appear to occur with the development of treatment complications. This is an unchartered research area, with potential mechanistic relevance to other neurodegenerative disorders. In the same vein, the validation of an accurate and objective image-based diagnostic algorithm for the parkinsonian disorders would likely stimulate the development of analogous methods for other classes of brain disease.
Tripathi M, Tang CC, Feigin A, De Lucia I, Nazem A, Dhawan V, Eidelberg D. Automated Differential Diagnosis of Early Parkinsonism Using Metabolic Brain Networks: A Validation Study. J Nucl Med. 2016 Jan 57 (1): 60-6. PMID: 26449840
Meles SK, Tang CC, Teune LK, Dierckx RA, Dhawan V, Mattis PJ, Leenders KL, Eidelberg D. Abnormal metabolic pattern associated with cognitive impairment in Parkinson's disease: a validation study. J Cereb Blood Flow Metab. 2015 Sep;35(9):1478-84. PMID: 26058693
Spetsieris PG, Ko JH, Tang CC, Nazem A, Sako W, Peng S, Ma Y, Dhawan V, Eidelberg D. Metabolic resting-state networks in health and disease. Proc Natl Acad Sci USA 2015 Feb: 112 (8):2563-68. PMCID: PMC4345616
The Udall Center at The Feinstein Institute for Medical Research employs rigorously validated Parkinson’s disease (PD)-related networks to address vital issues that impact heavily on the care of today’s PD patients.
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 pathophysiology 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.
Last Modified March 8, 2016