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

• Published in: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07 Vol. 1; pp. I-305 - I-308
• Main Authors: McKeown, M.J., Junning Li, Xuemei Huang, Wang, J.Z.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2007
• Subjects: Animal structures; Biological system modeling; Brain modeling; Data analysis; Discriminant Analysis; Diseases; FMRI; Group Analysis; Linear discriminant analysis; Magnetic analysis; Nervous system; Optimization methods; Regions of Interest; Smoothing methods
• ISBN: 9781424407279; 1424407273
• ISSN: 1520-6149
• DOI: 10.1109/ICASSP.2007.366677

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.