68 citations · 137 across the 10 of their papers we have counts for
10 papers
Bayesian inference for group-level cortical surface image-on-scalar-regression with Gaussian process priors
Andrew S. Whiteman, Timothy D. Johnson, Jian Kang
In regression-based analyses of group-level neuroimage data researchers typically fit a series of marginal general linear models to image outcomes at each spatially-referenced pixe…
Bayesian Inference for Brain Activity from Functional Magnetic Resonance Imaging Collected at Two Spatial Resolutions
Andrew S. Whiteman, Andreas J. Bartsch, Jian Kang +1
Neuroradiologists and neurosurgeons increasingly opt to use functional magnetic resonance imaging (fMRI) to map functionally relevant brain regions for noninvasive presurgical plan…
Bayesian Analysis of fMRI data with Spatially-Varying Autoregressive Orders
Ming Teng, Farouk S. Nathoo, Timothy D. Johnson
Statistical modeling of fMRI data is challenging as the data are both spatially and temporally correlated. Spatially, measurements are taken at thousands of contiguous regions, cal…
Bayesian Computation for Log-Gaussian Cox Processes--A Comparative Analysis of Methods
Ming Teng, Farouk S. Nathoo, Timothy D. Johnson
The Log-Gaussian Cox Process is a commonly used model for the analysis of spatial point patterns. Fitting this model is difficult because of its doubly-stochastic property, i.e., i…
The coordinate-based meta-analysis of neuroimaging data
Pantelis Samartsidis, Silvia Montagna, Thomas E. Nichols +1
Neuroimaging meta-analysis is an area of growing interest in statistics. The special characteristics of neuroimaging data render classical meta-analysis methods inapplicable and th…
Time Series Analysis of fMRI Data: Spatial Modelling and Bayesian Computation
Ming Teng, Timothy Johnson, Farouk Nathoo
Time series analysis of fMRI data is an important area of medical statistics for neuroimaging data. The neuroimaging community has embraced mean-field variational Bayes (VB) approx…