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Alzheimers Dement
December 2024
Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, Irvine, CA, USA.
Background: Amyloid imaging biomarkers serve an increasingly important role in diagnosing Alzheimer's disease and determining eligibility for treatment with new disease-modifying therapies. Yet, psychological and behavioral reactions to receiving a biomarker informed diagnosis remain relatively unstudied, especially in diverse and underserved populations where the burden of disease is high and resources for support are often insufficient. We developed the Patient And family member Reactions to biomarker-informed ADRD DiagnosEs (PARADE) Study to address two key gaps in our understanding: 1) the range and trajectory of psychological and behavioral responses to a biomarker informed diagnosis and 2) the support needs of these individuals and their families.
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December 2024
Brain Institute of Rio Grande do Sul - Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.
Background: Although cognitive decline is a trait related to aging, some individuals are resilient to the aging process, defined as SuperAgers. Studying the neural underpinnings of SuperAgers may improve the understanding of AD pathology. In this study, our aim was to analyze amyloid and neurodegeneration imaging biomarkers in SuperAgers.
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December 2024
Alzheimer's Disease Neuroimaging Initiative, http://adni.loni.usc.edu/, CA, USA.
Background: Amyloid and tau pathologies are the hallmarks of Alzheimer's disease (AD). Previous research indicated notable connections between financial capacity and AD biomarkers. Here, we aimed to understand whether financial capacity is affected by the cerebral accumulation of tau and amyloid.
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December 2024
Laboratory of Neuro Imaging (LONI), University of Southern California, Los Angeles, CA, USA.
Background: Anti-amyloid therapy appears to have an increased effect on reducing cognitive decline in amyloid- and tau-positive individuals. However, clinical trials inclusion criteria require solely amyloid positivity. Herein, we developed a machine-learning prediction model to identify tau positivity in amyloid-positive individuals using clinical variables.
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December 2024
Laboratory of Neuro Imaging (LONI), University of Southern California, Los Angeles, CA, USA.
Background: Screen failure due to amyloid negativity is yet a problem in clinical trials for anti-amyloid drugs. In this context, clinical characteristics of patients presenting with cognitive decline may decrease the screen failure ratio by increasing the odds of selecting individuals with brain amyloid pathology. Herein, we aimed at estimating amyloid and tau positivity in individuals using clinical variables in a machine learning model of prediction.
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