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

A post-processing method for group discriminant analysis of fMRI 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 fo...

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Bibliographic Details
Published in:	NeuroImage (Orlando, Fla.) Vol. 37; no. 3; pp. 855 - 865
Main Authors:	McKeown, Martin J., Li, Junning, Huang, Xuemei, Lewis, Mechelle M., Rhee, Seungshin, Young Truong, K.N., Wang, Z. Jane
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01-09-2007 Elsevier Limited
Subjects:	Adult Brain Brain Mapping - methods Computer Simulation Discriminant Analysis Estimates Evoked Potentials, Motor - physiology Female fMRI Grants Group analysis Humans Hypotheses Image Interpretation, Computer-Assisted - methods Linear Models Linear programming Magnetic Resonance Imaging - methods Male Methods Middle Aged Models, Neurological Motor Cortex - physiology Regions of interest Registration Reproducibility of Results ROC Curve Sensitivity and Specificity fMRI Discriminant analysis Group analysis Regions of interest
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Summary:	A post-processing method for group discriminant analysis of fMRI 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 voxel-based activation statistics within ROIs to a common vector space of ROI 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 mis-registration, or examining older or neurological patient populations.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1053-8119 1095-9572
DOI:	10.1016/j.neuroimage.2007.04.072