The National Institute on Aging (NIA) awarded a $3,624,314 Project Grant (CFDA 93.866 - Aging Research) to Indiana University to develop computationally efficient Bayesian techniques for extracting precise functional brain features from fMRI data. The goal is to create novel fMRI-based biomarkers for Alzheimer's disease that can complement existing biomarkers. The project will address challenges with high noise levels in fMRI data and the use of naive statistical methods by developing models...
This Project Grant from the National Science Foundation Division of Social and Economic Science, under the Social, Behavioral, and Economic Sciences federal grant program (CFDA 47.075), provides $275,112 to the University of Pittsburgh for the project "Bayesian Inference of Whole-Brain Directed Networks using Neuroimaging Data." The project will develop new statistical models and computationally efficient algorithms to analyze functional magnetic resonance imaging (fMRI) data to better...
This $213,462 National Science Foundation award under the Mathematical and Physical Sciences program will support the development of new statistical methods for analyzing functional medical data with skewness and outliers. Specifically, the University of North Carolina at Charlotte will develop dimension reduction techniques for quantile regression to analyze functional magnetic resonance imaging and electroencephalogram data related to attention deficit hyperactivity disorder and alcoholism....
This National Science Foundation project grant of $239,962 will support the development of novel statistical methods for analyzing functional and imaging data supported on complex geometries through the Mathematical and Physical Sciences program (CFDA 47.049). Led by the University of Washington with a completion date of June 2025, key products include generalized linear models and regularized linear models to predict outcomes from functional predictors on multidimensional non-linear domains....
This $546,094 Project Grant award from the National Institute of Neurological Disorders and Stroke (NINDS), under the Extramural Research Programs in the Neurosciences and Neurological Disorders (CFDA 93.853) program, supports research to understand the mechanisms underlying brain folding in human infants. The primary award to the Trustees of Indiana University will use longitudinal MRI data from preterm infants and fetuses to assess whether patterns of cortical expansion align with gradients of...
This Project Grant award from the National Science Foundation (CFDA 47.049 - Mathematical and Physical Sciences) in the amount of $197,007 is supporting collaborative research to develop new statistical theories and methodologies for tackling issues related to false discovery rate control in regression analysis. The research aims to provide novel statistical tools for analyzing complex data from diverse scientific domains such as brain imaging, genome-wide association studies, and atmospheric...
The National Science Foundation (NSF) awarded a $237,438 Project Grant to Purdue University under the Social, Behavioral, and Economic Sciences grant program (CFDA 47.075) to advance statistical inference on dynamic systems. The 3-year project will leverage deep learning and statistical modeling to enhance the efficiency, accuracy, and interpretability of time-series analysis across various domains. The research will introduce a new neural inference framework for estimating and inferring dynamic...
The National Science Foundation (NSF) awarded a $550,299 Project Grant under the Social, Behavioral, and Economic Sciences (CFDA 47.075) program to Trustees of Indiana University, doing business as Indiana University, to conduct research on the transfer of statistical learning from speech perception to production. The project aims to understand the mechanisms by which the brain coordinates listening and speaking, even when changes are subtle and not consciously identifiable. Preliminary data...
This National Science Foundation Project Grant of $220,000 supports research into statistical modeling methods for large, complex datasets. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), the University of California, San Francisco will develop new Bayesian regression techniques using random data compression matrices. These approaches aim to enable efficient, scalable inference and prediction from high-dimensional biomedical data sources like brain imaging, genetics,...
The National Science Foundation Division of Mathematical Sciences awarded $299,997 under the Mathematical and Physical Sciences federal grant program (CFDA 47.049) to the University of California, Davis for a three-year project grant completing June 2025. The award will support research involving the development of new statistical testing methods and deep learning techniques for functional data analysis. Specifically, the university will conduct research projects to create a general framework...