Research Project
10283297
PROJECT SUMMARY / ABSTRACT Established biomarkers for Alzheimer's disease (AD), such as amyloid beta measured with PET, are expensive and invasive. Cost-efficient, quick, and easy-to-administer motor measures, such as grip strength and walking speed, have shown to precede the cognitive symptoms of AD by several years. Relative to single measures, combining ambulatory and strength measures boosts predictive value for AD. This suggests that a composite motor profile score that weighs functions spanning the breadth of motor domains will have optimal predictive power. Cognitive and motor brain regions that are known to degenerate early (e.g., the hippocampus and fornix) and later in the AD disease process (e.g., the cerebellum, (pre)motor cortex, and corticospinal tract) are candidates for the prediction of motor dysfunction in mild cognitive impairment (MCI) and AD. The objective of this K01 proposal is two-fold: 1) to further develop the research skills of the applicant with a series of mentored activities, and 2) to identify the behavioral and neural motor profiles of MCI and AD with the aim of developing a robust and valid risk scoring algorithm. Our central hypothesis is that a motor behavioral composite score and neural motor profile score distinguish individuals with MCI and AD from healthy control subjects, and are related to established AD biomarkers (amyloid burden, hippocampal volume, and APOE e4 status). The project has three specific aims: 1) Quantify behavioral and neural motor dysfunction in MCI and AD; 2) Identify behavioral and neural motor composite scores as novel AD biomarkers; and 3) Replicate a motor composite score as AD biomarker in a large independent sample from the Vietnam Era Twin Study of Aging. The training plan aims to establish expertise in areas that are crucial for the candidate to conduct the proposed research and to become an independent investigator in the field of AD. The following training goals have been identified in close collaboration with the mentoring team: a) establishing a clinical perspective and the conceptual framework required to implement effective research in MCI and AD; b) develop expertise in machine learning relevant to prediction modeling; c) gaining an in-depth understanding of neurological motor signs and function in MCI and AD; and d) develop expertise on AD biomarkers and their perceived role in AD etiology. The training will be closely supervised by clinical experts of AD (Dr. Duff), and leaders in the field of neural motor dysfunction in aging (Dr. Rosano), AD biomarkers (Dr. Foster), MCI risk factors (Dr. Kremen), and machine learning (Dr. Tasdizen). The candidate's optimal institutional environment further ensures the success of the training and research plan that will provide data for an R01 application on the prediction of MCI to AD transition from a motor profile composite score. The proposed research makes a significant contribution towards the development of novel cost-efficient AD biomarkers that can serve to enrich clinical trials and for diagnostic purposes, consistent with the mission of the NIA.
