MonkeyCBP: A Toolbox for Connectivity-Based Parcellation of Monkey Brain

Abstract

Non-human primate models are widely used in studying the brain mechanism underlying brain development, cognitive functions, and psychiatric disorders. Neuroimaging techniques, such as magnetic resonance imaging, play an important role in the examinations of brain structure and functions. As an indispensable tool for brain imaging data analysis, brain atlases have been extensively investigated, and a variety of versions constructed. These atlases diverge in the criteria based on which they are plotted. The criteria range from cytoarchitectonic features, neurotransmitter receptor distributions, myelination fingerprints, and transcriptomic patterns to structural and functional connectomic profiles. Among them, brainnetome atlas is tightly related to brain connectome information and built by parcellating the brain on the basis of the anatomical connectivity profiles derived from structural neuroimaging data. The pipeline for building the brainnetome atlas has been published as a toolbox named ATPP (A Pipeline for Automatic Tractography-Based Brain Parcellation). In this paper, we present a variation of ATPP, which is dedicated to monkey brain parcellation, to address the significant differences in the process between the two species. The new toolbox, MonkeyCBP, has major alterations in three aspects: brain extraction, image registration, and validity indices. By parcellating two different brain regions (posterior cingulate cortex) and (frontal pole) of the rhesus monkey, we demonstrate the efficacy of these alterations. The toolbox has been made public (https://github.com/bheAI/MonkeyCBP_CLI, https: //github.com/bheAI/MonkeyCBP_GUI). It is expected that the toolbox can benefit the non-human primate neuroimaging community with high-throughput computation and low labor involvement.

Fine-Grained Topography and Modularity of the Macaque Frontal Pole Cortex Revealed by Anatomical Connectivity Profiles

“…2B). These results of validity indices suggested the consistency of the parcellations across subjects and the similarity of the topological organization distribution of the parcellation results between the bilateral hemispheres at the group level [42]. In addition, at the individual diffusion space level, the index results of PCA also suggested a segregation of the FPC into an average of 8 subdivisions for each of the hemispheres (left hemisphere, 8.31, see Fig.…”

“…However, in the current study, we mainly set two parameters (number of samples = 15000; step size = 0.2 mm), other parameters are based on the default values. All these parameters were based on previous studies [33,42] and the official instructions of FSL. Tournier et al have revealed that the dispersion in tractography is dependent on the step size; small step sizes reduce the spread of probabilistic tracking results [98].…”

“…After preprocessing, the macaque FPC was chosen as the seed and probabilistic tractography was carried out for the tractography-based parcellation. This process has been described in the toolbox for connectivity-based parcellation of the monkey brain [42] and is similar to that in another study [43]. Voxelwise estimates of the fiber orientation distribution were computed using Bedpostx.…”

“…After parcellation of the FPC using spectral clustering, the next step was to select the number of clusters. To avoid an arbitrary choice of this number, we used two prevailing validation methods, cross-validation indices to obtain a consistent segmentation for all eight subjects at the group level, and principal component analysis (PCA), to determine the optimal number of clusters across the subjects at the individual diffusion level [42].…”

A comprehensive macaque fMRI pipeline and hierarchical atlas

“…These data are freely available to researchers, enabling larger multi-site studies. The PRIME-DE has been instrumental in leading this push towards openness and reproducibility in the macaque community (Milham et al, 2020), and data from the PRIME-DE has already been used in several studies (Froudist-Walsh et al, 2018;He et al, 2020;Heuer et al, 2019;Oligschläger et al, 2019;Valk et al, 2020;Xu et al, 2019bXu et al, , 2019aXu et al, , 2018.…”