Tracking autobiographical thoughts: a smartphone-based approach to identifying cognitive correlates of Alzheimer's disease biomarkers and risk factors in clinically normal older adults

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While the earliest phase of Alzheimer’s disease (AD) pathology is often described as “clinically silent”, prior work raises the possibility that early AD is associated with detectable alterations in autobiographical thought – a class of cognition encompassing memories, plans, and other mental simulations related to our personal lives. Here we introduce two multi-modal studies that investigate whether cognitive markers of early AD neuropathology can be detected by deploying smartphone applications (apps) to track autobiographical thoughts in everyday life. Using two smartphone apps developed by our team to naturalistically assess cognition, the proposed studies will a) examine the sensitivity of real-world autobiographical thoughts to AD plasma and brain biomarkers in clinically normal older adults, b) reveal the predictive and scalable potential of measuring autobiographical thoughts in older adults for a host of longitudinal AD biomarker and associated health outcomes, and c) shed light on neurocognitive autobiographical thought characteristics that may separate normal from abnormal cognitive and brain aging. MPIs Dr. Grilli and Dr. Andrews-Hanna have formed a team of researchers with expertise in smartphone-based assessment of cognition, autobiographical thought, functional magnetic resonance imaging, healthy and pathological aging, and longitudinal analysis of large datasets. Leveraging our team’s interdisciplinary expertise, we will execute an innovative two-pronged project harnessing in-lab, at-home, and online assessment methods that will evaluate the relationships of AD biomarkers and aging to the autobiographical thoughts of 1,225+ adults, with a subset completing additional in-lab experimental cognitive tests, neuroimaging, plasma biomarker assays, and longitudinal follow-up. In Aim 1, we will test the hypothesis that among clinically normal older adults, smartphone measures of autobiographical thoughts are sensitive to plasma AD biomarkers, and resting state functional connectivity in the default network, a brain network targeted by early AD. Aim 2 tests the hypothesis that these smartphone measures predict future plasma biomarker accumulation among older adults who were clinically normal at enrollment, as well as future resting state functional connectivity of the default network, and daily psychosocial / instrumental decline. Aim 3 deploys one of our smartphone apps to a large remote, clinically normal, and genotyped cohort, providing an opportunity to evaluate questions about effects of age and genetic risk for AD on autobiographical thoughts at an unprecedented scale. Across the aims, we also examine how smartphone measures of autobiographical thoughts compare to in-lab cognitive tests, and if they improve sensitivity to aging and AD risk. To our knowledge, this project will be the first to use smartphones to track autobiographical thoughts as a means to identify cognitive correlates of AD biomarkers, despite theoretical tenets and evidence that doing so could tap into the primary brain pathway of AD. Ultimately, our mobile tools may lead to more accessible cognitive tests of early AD, including initial stages of amyloid and tau, with broad impact for scientists, clinicians and patients worldwide.