The efficiency of fMRI region of interest analysis methods for detecting group differences

Abstract

Using a standard space brain template is an efficient way of determining region-of-interest (ROI) boundaries for functional magnetic resonance imaging (fMRI) data analyses. However, ROIs based on landmarks on subject-specific (i.e., native space) brain surfaces are anatomically accurate and probably best reflect the regional blood oxygen level dependent (BOLD) response for the individual. Unfortunately, accurate native space ROIs are often time-intensive to delineate even when using automated methods. We compared analyses of group differences when using standard versus native space ROIs using both volume and surface-based analyses. Collegiate and military-veteran participants completed a button press task and a digit-symbol verification task during fMRI acquisition. Data were analyzed within ROIs representing left and right motor and prefrontal cortices, in native and standard space. Volume and surface-based analysis results were also compared using both functional (i.e., percent signal change) and structural (i.e., voxel or node count) approaches. Results suggest that transformation into standard space can affect the outcome of structural and functional analyses (inflating/minimizing differences, based on cortical geography), and these transformations can affect conclusions regarding group differences with volumetric data. Caution is advised when applying standard space ROIs to volumetric fMRI data. However, volumetric analyses show group differences and are appropriate in circumstances when time is limited. Surface-based analyses using functional ROIs generated the greatest group differences and were less susceptible to differences between native and standard space. We conclude that surface-based analyses are preferable with adequate time and computing resources.