Brain Drivers, Cognition, and Parkinson's Disease: A Psychometric Approach
Brain Drivers, Cognition, and Parkinson's Disease: A Psychometric Approach
批准号:
10604827
负责人:
Lauren Kenney
金额:
$4.19万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-05-16 至 2025-05-15
关键词:
AccountingAddressAffectAgeAge-associated memory impairmentAgingAmyloid beta-ProteinAreaAstrocytesBiological MarkersBrainC-reactive proteinCategoriesCerebrovascular DisordersCluster AnalysisCognitionCognitiveCompetenceDataDementiaDisease ProgressionDoctor of PhilosophyEducationEnvironmentEquationEvaluationExecutive DysfunctionFunctional disorderFutureGenderGoalsGrantHomocysteineIdiopathic Parkinson DiseaseImpaired cognitionIndividualInterleukin-6Linear RegressionsLongitudinal StudiesMagnetic Resonance ImagingMeasuresMemoryMemory LossMentorsMethodologyMethodsModelingMotorMovement DisordersNeurobiologyNeurodegenerative DisordersParkinson DiseaseParticipantPerformancePersonsPhenotypePlasmaPredictive FactorPredictive ValuePriceProductivityProfessional CompetencePsychometricsResearchResearch PersonnelResourcesRiskRisk FactorsSamplingSubgroupSystemTNF geneTrainingUnited StatesUnited States National Institutes of HealthVariantVisitWhite Matter Hyperintensityalpha synucleincareercerebrovascularclinical prognosiscognitive changecognitive performancecohortcytokineexecutive functionexperiencefallsfollow-uphypoperfusionimprovedmotor symptomneuroinflammationneuropathologynon-motor symptompatient prognosisprecision medicineprogramsrisk predictionsexskill acquisitionstroke risktau Proteins
中文摘要
项目总结/文摘
英文摘要
PROJECT SUMMARY/ABSTRACT
Parkinson’s disease (PD) is one of the fastest-growing neurodegenerative disorders in the United States, with
cognitive decline being among its most debilitating non-motor symptoms. With disease progression, most
individuals eventually develop dementia. However, the trajectory of cognitive decline varies between
individuals—leading to a search for risk factors of impending decline. In 2019, Ryan and colleagues proposed a
precision aging model and suggested that typical age-related cognitive decline was influenced by three broad
categories of “brain drivers”: neuropathology (e.g., alpha-synuclein, tau), neuroinflammation (e.g., cytokines),
and cerebrovascular dysfunction (e.g., white matter hyperintensities). Past research has consistently measured
these brain driver factors in isolation, despite these factors all belonging to an interconnected, neurobiological
system. Thus, the goal of the proposed study is to determine whether cognitive variation in PD is better explained
by a combination of these neurobiological risk factors, relative to isolated factors. The central hypothesis is that
each category of brain drivers (i.e., neuropathology, neuroinflammation, cerebrovascular dysfunction) will
uniquely relate to cognitive performance (specifically executive function and memory), such that adding in each
category will better explain changes in each cognitive domain. The proposed study will examine data from an
existing, well-characterized cohort of individuals with idiopathic PD without dementia (N=112) to determine the
association between brain driver factors and cognitive performance cross-sectionally and longitudinally (at a 2-
year follow-up). To do so, brain driver relationships with cognition will be assessed in isolation (using correlations)
and in combination (using hierarchical linear regressions, adding in factors from each brain driver category
sequentially). Overall, this method shifts the focus towards a precision medicine approach—whereby examining
multiple brain drivers may allow for greater understanding of individualized risk of cognitive decline in individuals
with PD. Improving the assessment of cognitive risk could inform both clinical prognosis for patients with PD and
allow for a more targeted selection of participants into experimental trials aiming to slow impending cognitive
decline. The proposed training plan will provide the applicant with additional training experiences beyond that of
her Ph.D. program. Specific training goals include (1) gaining expertise in the methodologies measuring
neuroinflammatory and neuropathology biomarkers and their interpretation, (2) gaining proficiency with structural
magnetic resonance imaging (acquisition, processing, and interpretation) to measure white matter
hyperintensities (a metric of cerebrovascular dysfunction), (3) advancing statistical competencies and
experimental rigor, and (4) professional and career skills development. The proposed project and training goals
will be completed with the resources and support of a strong research environment, including a productive
mentoring team with specific expertise in the proposed area of study. Taken together, the proposed research
and other activities will help prepare the applicant as she transitions into a career as an independent investigator.
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