Spatial characterization of fMRI activation maps using invariant 3-D moment descriptors

A novel approach is proposed for quantitatively characterizing the spatial patterns of activation statistics in functional magnetic resonance imaging (fMRI) activation maps. Specifically, we propose using 3-D invariant moment descriptors, as opposed to the traditionally-employed magnitude-based features such as mean voxel statistics or percentage of activated voxels, to characterize the task-specific spatial distribution of activation statistics within a given region of interest (ROI). The proposed method is applied to real fMRI data collected from 21 healthy subjects performing previously-learned right-handed finger tapping sequences that are either externally guided (EG) by a cue or internally guided (IG)-tasks expected to incur subtle differences in motor-related cortical and subcortical ROIs. Voxel-based activation statistics contrasting EG versus rest and IG versus rest are examined In multiple manually-drawn ROIs on unwarped brain images. Analyzing the activation statistics within each ROI using the proposed 3-D invariant moment descriptors detected significant group differences between the two tasks, thus quantitatively demonstrating that the spatial distribution of activation statistics within an ROI represent an important task-related attribute of brain activation. In contrast, conventional methods that solely rely on activation statistic magnitudes and disregard spatial information showed reduced discriminability. Normally, incorporating spatial information would merely increase inter-subject variability partly due to differences in brain size and subject's orientation in the scanner. Yet, our results suggest that the proposed spatial features, which are invariant to similarity transformations, can effectively account for such inter-subject variability, while enhancing the sensitivity in detecting task-specific activation. Thus, we argue that this novel quantitative description of the "3-D texture" of activation maps provides new directions to explore for ROI-based fMRI analysis.