Vervet MRI atlas and label map for fully automated morphometric analyses.

Currently available non-human primate templates typically require input of a skull-stripped brain for structural processing. This can be a manually intensive procedure, and considerably limits their utility. The purpose of this study was to create a vervet MRI population template, associated tissue probability maps (TPM), and a label atlas to facilitate true fully automated Magnetic Resonance Imaging (MRI) structural analyses for morphometric analyses. Structural MRI scans of ten vervet monkeys (Chlorocebus aethiops) scanned at three time points were used in this study. An unbiased population average template was created using a symmetric diffeomorphic registration (SyN) procedure. Skull stripping, segmentation, and label map generation were performed using the publically available rhesus INIA19 MRI template and NeuroMap label atlas. A six-class TPM and a six-layer two-class normalization template was created from the vervet segmentation for use within the Statistical Parametric Mapping (SPM) framework. Fully automated morphologic processing of all of the vervet MRI scans was then performed using the vervet TPM and vervet normalization template including skull-stripping, segmentation and normalization. The vervet template creation procedure resulted in excellent skull stripping, segmentation, and NeuroMap atlas labeling with 720 structures successfully registered. Fully automated processing was accomplished for all vervet scans, demonstrating excellent skull-stripping, segmentation, and normalization performance. We describe creation of an unbiased vervet structural MRI population template and atlas. The template includes an associated six-class TPM and DARTEL six-layer two-class normalization template for true fully automated skull-stripping, segmentation, and normalization of vervet structural T1-weighted MRI scans. We provide the most detailed vervet label atlas currently available based on the NeuroMaps atlas with 720 labels successfully registered. We additionally describe a novel method for atlas label generation that capitalizes on previous work in this area using high-dimensional highly accurate image matching procedures for inter-species morphologic normalization.