Publications
Welcome to the publications page of the Framingham Heart Study Brain Aging Program (FHS-BAP), where we showcase the groundbreaking research driving advancements in Alzheimer’s Disease (AD) understanding. Our program is committed to revolutionizing AD research through a comprehensive approach that integrates surveillance, discovery, and collaboration. Here, you will find our latest publications, which reflect our work in enhancing cognitive decline assessments, brain imaging techniques, and the revitalization of our brain donation program. These publications highlight key findings in genetic factors, risk markers, and biomarkers related to AD, all aimed at fostering a global research community and accelerating the identification of therapeutic targets. Explore our research to learn more about our mission to uncover the biological foundations of AD and contribute to the preservation of cognitive health.
Digital neuropsychological measures by defense automated neurocognitive assessment: reference values and clinical correlates
Abstract Introduction: Although the growth of digital tools for cognitive health assessment, there's a lack of known reference values and clinical implications for these digital methods. This study aims to establish reference values for digital neuropsychological measures obtained through the smartphone-based cognitive assessment application, Defense Automated Neurocognitive Assessment (DANA), and to identify... More
APOE genotype-specific methylation patterns are linked to Alzheimer disease pathology and estrogen response
Abstract The joint effects of APOE genotype and DNA methylation on Alzheimer disease (AD) risk is relatively unknown. We conducted genome-wide methylation analyses using 2,021 samples in blood (91 AD cases, 329 mild cognitive impairment, 1,391 controls) and 697 samples in brain (417 AD cases, 280 controls). We identified differentially methylated... More
DREAMER: a computational framework to evaluate readiness of datasets for machine learning
Abstract Background: Machine learning (ML) has emerged as the predominant computational paradigm for analyzing large-scale datasets across diverse domains. The assessment of dataset quality stands as a pivotal precursor to the successful deployment of ML models. In this study, we introduce DREAMER (Data REAdiness for MachinE learning Research), an algorithmic framework leveraging... More
Asian Cohort for Alzheimer’s Disease (ACAD) pilot study on genetic and non-genetic risk factors for Alzheimer’s disease among Asian Americans and Canadian
Abstract Introduction: Clinical research in Alzheimer's disease (AD) lacks cohort diversity despite being a global health crisis. The Asian Cohort for Alzheimer's Disease (ACAD) was formed to address underrepresentation of Asians in research, and limited understanding of how genetics and non-genetic/lifestyle factors impact this multi-ethnic population. Methods: The ACAD started fully recruiting in October... More
Relative Contributions of Mixed Pathologies to Cognitive and Functional Symptoms in Brain Donors Exposed to Repetitive Head Impacts
Abstract Objective: Exposure to repetitive head impacts (RHI) is associated with later-life cognitive symptoms and neuropathologies, including chronic traumatic encephalopathy (CTE). Cognitive decline in community cohorts is often due to multiple pathologies; however, the frequency and contributions of these pathologies to cognitive impairment in people exposed to RHI are unknown. Here, we... More