4 citations · 11 across the 5 of their papers we have counts for
5 papers
Sources of residual autocorrelation in multiband task fMRI and strategies for effective mitigation
Fatma Parlak, Damon D. Pham, Daniel A. Spencer +2
In task fMRI analysis, OLS is typically used to estimate task-induced activation in the brain. Since task fMRI residuals often exhibit temporal autocorrelation, it is common practi…
Bayesian tensor regression using the Tucker decomposition for sparse spatial modeling
Daniel Spencer, Rajarshi Guhaniyogi, Russell Shinohara +1
Modeling with multidimensional arrays, or tensors, often presents a problem due to high dimensionality. In addition, these structures typically exhibit inherent sparsity, requiring…
Fast Bayesian estimation of brain activation with cortical surface and subcortical fMRI data using EM
Daniel Spencer, David Bolin, Mary Beth Nebel +1
Analysis of brain imaging scans is critical to understanding the way the human brain functions, which can be leveraged to treat injuries and conditions that affect the quality of l…
Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups
Daniel Spencer, Yu, Yue +3
The general linear model (GLM) is a widely popular and convenient tool for estimating the functional brain response and identifying areas of significant activation during a task or…
Bayesian Mixed Effect Sparse Tensor Response Regression Model with Joint Estimation of Activation and Connectivity
Daniel Spencer, Rajarshi Guhaniyogi, Raquel Prado
Brain activation and connectivity analyses in task-based functional magnetic resonance imaging (fMRI) experiments with multiple subjects are currently at the forefront of data-driv…