Online Program Home
  My Program

Abstract Details

Activity Number: 585 - Statistical Methods for Studying Brain Connectivity and Networks
Type: Contributed
Date/Time: Wednesday, August 2, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Imaging
Abstract #324189 View Presentation
Title: Spatial Integration of Functional Connectivity Methods in the Default Mode Network of the Brain
Author(s): Priyangi Bulathsinhala* and Richard Gunst and Jeffrey Spence
Companies: and Southern Methodist University and Center for Brain Health, UT Dallas
Keywords:
Abstract:

Functional Magnetic Resonance Imaging (fMRI) has become a very powerful research component in the field of neuroscience. Early research works on fMRI data were more focused on detecting brain activation in response to performing a specific task. However, fMRI research goals often are focused on highlighting co-activation patterns across the brain, known as functional connectivity. In the past decade, there has been an increased interest in resting state fMRI. In the current literature, there are two com- monly used methods of assessing functional connectivity in the resting state fMRI data: seed-based correlation (SCA) analysis and independent component analysis (ICA). However, a common difficultly is the need for voxel-based statistical testing for several thousands of voxels. As a result, one might not see any statistical significance after adjusting for multiple comparisons. We propose to combine neighboring voxels into "blocks" using spatial semivariogram models to produce independent block averages for each time point. It is anticipated that such an approach will reduce the number of multiple comparisons while potentially enabling more powerful statistical tests.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2017 program

 
 
Copyright © American Statistical Association