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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.
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