We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Multivariate statistical analysis in fMRI

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Rowe, D.B. ; Med. Coll. of Wisconsin, Milwaukee, WI, USA ; Hoffmann, R.G.

The paper briefly discussed different statistical analysis in functional magnetic resonance imaging (fMRI). Multivariate regression analysis with multiple comparisons corrections allows the determination of activated voxels that can then be grouped into regions of interest (ROIs). Principal component analysis (PCA) is useful in extracting common temporal response features of an ROI as well as differentiating the temporal response of groups of commonly responding ROI. It can also be used to examine differences in the temporal response of subgroups of subjects in the study. Structural equation modeling (SEM) is a technique that requires a priori knowledge of the connections and their direction between ROIs. It is particularly useful in identifying changes in connectivity that result from different interventions or different classes of patients.

Published in:

Engineering in Medicine and Biology Magazine, IEEE  (Volume:25 ,  Issue: 2 )