|
Activity Number:
|
620
- Axles for Voxels: Recent Statistical Advances in Neuroimaging Data Analysis
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
ENAR
|
|
Abstract #328365
|
Presentation
|
|
Title:
|
Spatial Modeling of Diffusion Tensor Imaging Data from a Cocaine Addiction Study
|
|
Author(s):
|
Dipankar Bandyopadhyay* and Zhou Lan and Brian Reich and Joseph Guinness
|
|
Companies:
|
Virginia Commonwealth University and North Carolina State University and North Carolina State University and NC State University
|
|
Keywords:
|
Bayesian;
spatial process;
Nearest Neighbor Gaussian process;
Diffusion Tensor Imaging;
Cocaine addiction;
Markov chain Monte Carlo
|
|
Abstract:
|
In this talk, we propose a Bayesian spatial process model to capture spatial dependencies between proximal positive definite matrices (summary of DTI measures corresponding to each voxel), assuming the matrices to be marginally distributed as Wishart. An adaptation of the Nearest Neighbor Gaussian Process facilitates the corresponding Markov chain Monte Carlo computing framework. Simulation studies and applications to a real dataset on cocaine addiction illustrates the advantages of our method over available standard techniques in detecting brain hotspots.
|
Authors who are presenting talks have a * after their name.
