title: Enhancing reproducibility of fMRI statistical maps using generalized canonical correlation analysis in NPAIRS framework.
abstract: Common fMRI data processing techniques usually minimize a temporal cost function or fit a temporal model to extract an activity map. Here, we focus on extracting a highly, spatially reproducible statistical parametric map (SPM) from fMRI data using a cost function that does not depend on a model of the subjects' temporal response. Based on a generalized version of canonical correlation analysis (gCCA), we propose a method to extract a highly reproducible map by maximizing the sum of pair-wise correlations between some maps. In a group analysis, each map is calculated from a linear combination of fMRI scans of a subset of subjects under study. The proposed method is applied to BOLD fMRI datasets without any spatial smoothing from 10 subjects performing a simple reaction time (RT) task. Using the NPAIRS split-half resampling framework with a reproducibility measure based on SPM correlations, we compare the proposed approach with canonical variate analysis (CVA) and a simple general linear model (GLM). gCCA provides statistical parametric maps with higher reproducibility than CVA and GLM with correlation reproducibilities across independent split-half SPMs of 0.78, 0.46, and 0.41, respectively. Our results show that gCCA is an efficient approach for extracting the default mode network, assessing brain connectivity, and processing event-related and resting-state datasets in which the temporal BOLD signal varies from subject to subject.
authors.affiliation: Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
authors.forename: Babak
authors.initials: B
authors.lastname: Afshin-Pour
authors.forename: Gholam-Ali
authors.initials: GA
authors.lastname: Hossein-Zadeh
authors.forename: Stephen C
authors.initials: SC
authors.lastname: Strother
authors.forename: Hamid
authors.initials: H
authors.lastname: Soltanian-Zadeh
mesh_terms: D001921:Brain / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D015233:Models, Statistical; D015203:Reproducibility of Results