Local Linear Discriminant Analysis (LLDA) for group and region of interest (ROI)-based fMRI analysis

TitleLocal Linear Discriminant Analysis (LLDA) for group and region of interest (ROI)-based fMRI analysis
Publication TypeJournal Article
Year of Publication2007
AuthorsMcKeown, M. J., J. Li, X. Huang, M. M. Lewis, S. Rhee, K. N. Y. Truong, and Z. J. Wang

A post-processing method for group discriminant analysis of fMR1 is proposed. It assumes that the fMRI data have been pre-processed and analyzed so that each voxel is given a statistic specifying task-related activation(s), and that individually specific regions of interest (ROIs) have been drawn for each subject. The method then utilizes Local Linear Discriminant Analysis (LLDA) to jointly optimize the individually-specific and group linear combinations of ROIs that maximally discriminates between groups (or between tasks, if using the same subjects). LLDA tries to linearly transform each subject's voxelbased activation statistics within ROIs to a common vector space of 1101 combinations, enabling the relative similarity of different subjects' activation to be assessed. We applied the method to data recorded from 10 normal subjects during a motor task expected to activate both cortical and subcortical structures. The proposed method detected activation in multiple cortical and subcortical structures that were not present when the data were analyzed by warping the data to a common space. We suggest that the method be applied to group fMRI data when warping to a common space may be ill-advised, such as examining activation in small subcortical structures susceptible to misregistration, or examining older or neurological patient populations. (c) 2007 Elsevier Inc. All rights reserved.


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