Naturalistic Event Representation as a Novel Biomarker of Preclinical Alzheimer's Disease

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By 2060, the number of Americans 65 and older is projected to more than double from 46 million to 98 million, 24% of the total population. With this comes an increased prevalence of Alzheimer’s disease (AD), which will create significant burden on our society and government. At present, screening tools capable of differentiating healthy aging from AD are most effective a decade or more after the preclinical stage, when potential treatments would be most effective. Thus, discovery of novel and specific tools for assessing the aging brain are of utmost importance. Typical studies of cognitive ability involve recognition of learned objects or simple word associations. However, in real-world situations, the content of an event is segmented from a flow of multimodal information. Segmentation and representation of events is supported by a posterior-medial network (PMN) of brain areas. Critically, this very same brain network features the first regions affected by pathological accumulation of amyloid beta (Aβ), a key characteristic of AD. A recent report from an NIA working group defined asymptomatic Aβ accumulation as the earliest indicator of preclinical AD. Given the functional role of the PMN, we propose that this stage of disease may not be truly asymptomatic: subtle functional deficits may be evident if properly probed. To address this, we have developed a naturalistic paradigm to characterize event representation in the brain and subsequent memory. We aim to test the novel hypotheses that the brain’s ability to segment and represent complex events is compromised in preclinical AD, and that the extent of this disruption is predictive of deficient memory for the experienced events. We will acquire functional MRI (fMRI) scans while participants view a video narrative depicting naturalistic scenarios. We will additionally test memory performance related to details of the events depicted in the video both in and out of the scanner. Using representational similarity analysis (RSA) and machine learning analyses of functional MRI (fMRI) data, we will examine differences in the way the brain represents events into advanced aging in participants with and without ‘asymptomatic’ amyloid deposition (status obtained via existing PET scan data). This combination of approaches is highly innovative because current translational measures do not assess memory for rich, dynamic events that make up the majority of real-world experience. This project is expected to significantly improve our understanding of neural and cognitive disruptions that differentiate healthy aging from preclinical AD. By studying how the brain chunks and represents events, and how this relates to memory for those events, we can reveal significant insights into the way AD-related pathology affects day-to-day living. This can provide a mechanistic framework for understanding subtle, subjective memory complaints. The results of this work are anticipated to significantly advance our understanding of memory decline in the earliest possible stages of AD, providing a mechanistic basis for subtle issues that have to date been difficult to assess in the clinic.