A general framework for analyzing the various contributions to reproducibility in brain morphometry

We propose a framework for differentiating the contributions to the reproducibility in brain morphometry from the true inter-individual differences, experimental procedures, and data-processing methods. As an application, we build a linear mixed-effect model to evaluate and compare two segmentation software tools, Freesurfer and vol2Brain, in their reproducibility in measuring volumes of 32 regions of interest. For both software and for most structures under study, our approach successfully reveals the dominance of inter-subject variability over noise. Vol2Brain introduces less noise than Freesurfer for all subcortical nuclei while Freesurfer shows better performance for gray matter, cortex, cerebral white matter, and cerebellum cortex.