By Topic

Local Linear Discriminant Analysis (LLDA) for Inference of Multisubject FMRI Data

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
$33 $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

4 Author(s)
Martin J. McKeown ; Pacific Parkinson's Research Centre, Brain Research Centre, University of British Columbia ; Junning Li ; Xuemei Huang ; Z. Jane Wang

Large intersubject variability is a well-described feature of fMRI studies, making inter-group inference, of critical importance for biological interpretation, difficult. Therefore, traditional approaches involve spatially transforming the data of each subject and heavily spatially smoothing the data. Here we propose an alternate method: after first defining individually-specific regions of interest (ROIs) of each subject, we utilize local linear discriminant analysis (LLDA) to jointly optimize the individually-specific and group linear combinations of ROIs that maximally discriminates between groups characterized by either disease status or task. The proposed method was applied to fMRI data recorded from eight normal subjects performing a motor task, and it was shown to successfully detect activation in multiple cortical and subcortical structures that were not present when the data were traditionally analyzed by warping the data to a common space. We suggest that the proposed method for group fMRI data analysis may be more suitable when examining co-activation in small subcortical regions susceptible to misregistration, or examining older or neurological patient populations.

Published in:

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07  (Volume:1 )

Date of Conference:

15-20 April 2007