This Project Grant awarded by the National Institute on Aging (NIA), under the Aging Research federal grant program (CFDA 93.866), provides $366,648 to develop statistical methods for accurate estimation and prediction in Alzheimer's disease (AD) research. The key products and services to be delivered include: Methods for estimation and regression that enable the use of clinically relevant time origins in longitudinal cohort studies, while properly adjusting for complex truncation and censoring....
This $4,449,044 National Institute on Aging project grant will support the development of statistical models and biomarkers linking cognitive and physical decline to dementia and disability onset. Funded under the Aging Research program (CFDA 93.866), the University of Maryland, Baltimore will analyze data from over 11,000 older adults in eight cohorts. Researchers will test relationships between biomarkers of aging and longitudinal cognitive-physical decline as well as time to joint...
This Project Grant award from the National Institute on Aging (CFDA 93.866 - Aging Research) for $791,086 aims to develop computational models that can predict the presence of non-Alzheimer's disease neuropathological changes, such as Lewy body disease, TDP-43 proteinopathy, and cerebral amyloid angiopathy, in individuals with Alzheimer's disease neuropathological changes. The project will leverage data from autopsy-confirmed datasets, in vivo neuroimaging, and clinical assessments to build...
This Project Grant award from the National Institute on Aging (CFDA 93.866 - Aging Research) provides $678,029 to the University of Maryland, Baltimore to develop a Bayesian computational system for modeling longitudinal functional microcircuits and using it to examine microcircuit changes in a TDP-43 knockout mouse model. The project aims to advance the state-of-the-art in data analysis and modeling for longitudinal calcium imaging, leveraging Bayesian machine learning to address challenges...
This federal Project Grant award from the National Institute on Aging (NIA), under the Aging Research program (CFDA 93.866), provides $817,357 to the J. David Gladstone Institutes to develop advanced machine learning approaches for comprehensive behavioral and brain signal analysis in humanized Alzheimer's disease mouse models. The key products and services to be delivered include: Validating a machine learning platform called Variational Animal Motion Embedding (VAME) for high-resolution...
The National Institute on Aging (NIA) awarded a Project Grant (CFDA 93.866 - Aging Research) totaling $455,274 to The Johns Hopkins University to develop and apply statistical methods to improve scientific inferences in Alzheimer's disease (AD) research. The key objectives are to: Handle age-specific prevalent cases in case-control studies to address survivor bias, Develop regression methods for analyzing age at biomarker positivity and remaining time to onset of clinical symptoms, and Create...
This Project Grant award from the National Institute on Aging (CFDA 93.866 - Aging Research) provides $778,499 to the University of Texas Health Science Center at Houston to develop novel machine learning models to reveal and stratify heterogeneous subpopulations of Alzheimer's disease (AD) patients based on risk and progression patterns. The goal is to transform these subpopulations into potentially targetable groups to enable focused clinical trials, support future therapeutic development, and...
This $2,356,117 federal Project Grant award from the National Institute on Aging's Aging Research program (CFDA 93.866) will support research to improve the perioperative management of patients with Alzheimer's disease and related dementias. The primary objectives are to: (1) identify the extent to which surgical patients with these conditions are at increased risk for adverse postoperative outcomes, and (2) estimate the association between various perioperative interventions and the incidence...
This Project Grant award of $4,787,820.00 from the National Institute on Aging (CFDA 93.866 - Aging Research) aims to gain a better understanding of the individual- and system-level characteristics that influence diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease and related dementias (ADRD), and how diagnosis can moderate the effects of cognitive decline on employment, future care, and quality of life. The key objectives are to: 1) examine whether older adults experiencing...
This Project Grant award from the National Institute on Aging (CFDA 93.866 - Aging Research) will provide $802,034 to the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University to develop a Multimodal, Multiscale and Multistage Systems Biology (M3SB) infrastructure for precision medicine in Alzheimer's disease and longevity. The key products and services to be delivered include: Multi-modal analyses of genetics, multi-omics data, cross-species protein interactome...
This Project Grant award from the National Institute on Aging (NIA) under the Aging Research program (CFDA 93.866) is providing $2,331,210 to the University of Maryland, Baltimore to develop novel analytical methods to better understand and predict post-fracture recovery trajectories in older adults living with Alzheimer's disease and related dementias (ADRD). The key products and services to be delivered include:
Novel machine learning-assisted methods to identify unique patient characteristics associated with poor longitudinal recovery outcomes in geriatric settings with multi-level structured data.
A joint modeling approach to uncover shared mechanisms and enable individualized dynamic outcome prediction for multi-level and multi-variate post-fracture recovery outcomes.
A new ensemble machine learning method to identify causal factors that could be targeted for health system-level and pragmatic interventions to enhance recovery outcomes.
Leveraging Medicare data from over 20,000 patients treated at over 1,000 hospitals to understand the multi-level variability of post-fracture recovery outcomes for older adults living with ADRD.
The goal is to develop an analytical toolbox that can effectively handle high dimensional data, address multiple biases, and lead to unbiased analyses to disentangle the multi-level variability of post-fracture outcomes. The project will run from August 2024 through April 2029.