Intraindividual Cognitive Variability as an Early Marker of Alzheimer's Disease

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ABSTRACT Alzheimer’s disease (AD) is the most common form of dementia, with neuropathology such as beta- amyloid (Aβ) deposits and reduced hippocampal volume developing years before clinical cognitive impairment. Early identification of these preclinical AD stages is imperative for improving prevention and treatment efforts. Unfortunately, capturing the earliest subtle cognitive changes is difficult with traditional neuropsychological tests, which 1) were developed to detect overt clinical cognitive impairment, and 2) measure peak performance at a single, and potentially non-representative, time point in a well-controlled environment that is detached from daily life. These limitations underscore the need for valid assessment tools that capture not only the range of everyday cognition but also aspects of daily life, such as psychosocial factors, that signal resilience to AD clinical symptomatology. Mobile health (mHealth) assessments that utilize common smartphone technology may help overcome these limitations by sampling behavior multiple times per day in ‘real-time’, over the course of several days. Repeated sampling enables characterization of the time-course of a person’s behavior, both in terms of their cognition and their psychosocial context. The goal of this proposal is to examine the utility of a mHealth program to detect early markers of AD cognitive vulnerability. The specific aims are to examine whether variability over time on mHealth cognitive measures (i.e., intraindividual cognitive variability) is 1) a signature of AD neuropathologic risk, and 2) predicts longitudinal cognitive decline on traditional neuropsychological tests. An exploratory aim proposes to examine whether individuals with more AD neuropathology show stronger synchrony between their everyday psychosocial context (interpersonal interactions, mood, loneliness) and everyday cognitive performance. Participants will be cognitively normal Aβ positive and negative individuals recruited from ongoing longitudinal studies that gather neuropsychological and neuroimaging (PET, MRI) markers of AD neuropathology. Participants will undergo a 14-day mHealth protocol, twice over two years, that samples psychosocial factors and cognitive performance. The proposed training aims include 1) building foundational mHealth skills and specific expertise in using mHealth to measure cognition and psychosocial factors, 2) acquiring advanced expertise in psychosocial factors that contribute to resistance and resilience to AD symptoms, and 3) developing in-depth knowledge of PET and MRI imaging of AD neuropathologic risk factors. In summary, this proposed K23 award sets the foundation toward understanding whether individuals at high AD neuropathologic risk show early markers of cognitive vulnerability on mHealth assessments. The results of this study will establish the necessary groundwork for the PI’s development as an independent neuropsychologist-investigator using cutting-edge approaches for early identification of risk and resiliency factors that influence AD symptom development, and ultimately, AD treatment.