Abstract Details
Activity Number:
|
583
|
Type:
|
Topic Contributed
|
Date/Time:
|
Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
|
Sponsor:
|
Section on Statistics in Imaging
|
Abstract - #308996 |
Title:
|
Matrix Decomposition Methods for Functional MRI Data
|
Author(s):
|
Ani Eloyan*+ and Brian Caffo and Ciprian M. Crainiceanu
|
Companies:
|
Johns Hopkins Bloomberg School of Public Health and Johns Hopkins University and The Johns Hopkins University
|
Keywords:
|
|
Abstract:
|
The field of functional neuroimaging is growing very rapidly resulting in a vast amount of data for analysis. Recently, several large resting state functional magnetic resonance images from different laboratories have been collected in freely available datasets for analysis including the 1000 Functional Connectomes Project Dataset, ADHD 200 among others. Statistical dimension reduction techniques such as singular value decomposition (SVD), independent component analysis (ICA), etc. are routinely used by the practitioners in the field of neuroimaging to analyze complex fMRI data. In this talk, the main dimension reduction approaches for fMRI data are discussed stressing the major issues in the applications and the advantages of the methods depending on the biological question at hand. Extensions of the methods to high dimensional data are presented where possible.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.