Project Grant RF1AG098697
- This federal Project Grant award for $366,648.00 was provided by the National Institute on Aging (NIA), under the Aging Research Federal Grant Program (CFDA 93.866), to New York University (NYU). The funding supports the development of statistical methods for improving estimation and prediction models related to Alzheimer's disease (AD) progression, leveraging data from longitudinal cohort studies. Specifically, the project aims to address challenges posed by complex truncation and censoring...
- This federal Project Grant award of $3,188,901.00 from the National Institute on Aging (CFDA 93.866 - Aging Research) aims to better understand the relationships between in-vivo biomarkers for amyloid, tau, and neurodegeneration (ATN), neuropsychiatric symptoms, vascular risk factors, and genetic risks in Alzheimer's disease. The study will leverage data from large research cohorts like the Alzheimer's Disease Neuroimaging Initiative to develop and optimize machine learning models that can...
- This Project Grant award of $762,636 from the National Institute on Aging (CFDA 93.866 - Aging Research) supports research by The Leland Stanford Junior University to develop interpretable machine learning (ML) methods for analyzing the genetics of Alzheimer's disease (AD). The objective is to discover causal genetic variants that could lead to new AD therapies. The work involves pairing rigorous feature selection in ML with causal inference to identify genetic variants that influence AD risk,...
- This $287,118 Project Grant was awarded on September 1, 2023 by the National Institute on Aging (NIA) under the Aging Research (CFDA 93.866) grant program. The grant will fund the development of techniques to use deep transfer learning and multimodal data, including brain MRIs, genetic information, and cognitive tests, to enable the early detection and forecasting of Alzheimer's disease progression. The University of Massachusetts (UMass), as the prime awardee, will create an end-to-end...
- This federal Project Grant award of $817,357 from the National Institute on Aging (CFDA 93.866 - Aging Research) aims to harness novel machine learning approaches for behavioral segmentation and brain signal integration in humanized models of Alzheimer's disease. The key products and services to be delivered under this grant include: Further validation of the grantee's "Variational Animal Motion Embedding (VAME)" machine learning platform for comprehensive behavioral phenotyping of...
- The National Institute on Aging, under the Aging Research Federal Grant Program (CFDA 93.866), awarded a $162,486 project grant to The Research Foundation for the State University of New York (Upstate Medical Center). The grant, titled "Advancing Alzheimer's Diagnosis: MRI-Based Predictive Modeling of Alzheimer's Disease Molecular Subtypes", aims to enhance understanding of the biological signatures that distinguish subgroups of Alzheimer's disease (AD) patients. The research will...
- This $6,925,230 Project Grant award from the National Institutes of Health (NIH) National Institute on Aging (CFDA 93.866 - Aging Research) will support a research project at The Trustees of Columbia University in the City of New York to develop a "Multi-Organ Chart of Personalized Susceptibility to Alzheimer's Disease and Aging." The overarching goal is to use large-scale multi-omics, multi-organ biomedical data, advanced artificial intelligence/machine learning, and computational...
- This federal Project Grant award of $495,731.00, awarded on September 20, 2025 by the National Institute on Aging (CFDA 93.866 Aging Research program), supports the evaluation of Viewmind's ability to detect beta-amyloid and tau burden consistent with Alzheimer's disease in persons with mild cognitive impairment (MCI) using machine learning, virtual reality, and eye-tracking technology. The project aims to develop a non-invasive, cost-effective, and rapid diagnostic tool that can accurately...
- This Project Grant award of $640,067 from the National Institute on Aging (CFDA 93.866 Aging Research) will fund the development of novel multimodal variational autoencoder (VAE) models to learn factors associated with cognitive resilience to Alzheimer's disease (AD) and related dementias. The University of Michigan, the awardee, will employ generative methods to discover new factors from existing multimodal data (molecular, anatomic, functional imaging, cognitive) and model dynamic...
- This $375,840 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of advanced machine learning models to enable accurate and early prediction of Alzheimer's disease and related dementias (ADRD). The project, led by the University of Virginia, will leverage novel natural language processing techniques to automatically extract personal risk factors for ADRD from electronic health...
This $1,012,879 Project Grant was awarded by the National Institute on Aging (CFDA 93.866 - Aging Research) to New York University (NYU) to develop three novel statistical machine learning methods for Alzheimer's disease (AD) research using multi-view imaging, genomic, and clinical data. The specific aims include: 1) developing a multi-view data decomposition method to construct brain and genomic networks across AD statuses, 2) developing an optimal false discovery rate control method to identify significant brain and genomic differences across AD statuses, and 3) developing a deep-learning-based diagnosis and survival framework for high-dimensional AD data. The research team plans to apply these methods to four large-scale AD datasets and disseminate the methods through an open software package. This project aims to enhance the understanding, diagnosis, and prediction of AD through innovative multi-view data analysis approaches.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $1.0m | 9/4/25 |