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.
Highlights
Non-human primates (NHPs) resemble high similarities in the neuroanatomical structures and cognitive functions to humans (Perretta, 2009)
Researchers have used connectivitybased parcellation (CBP) to form cartographic maps of specific brain regions or the entire cortex (Eickhoff et al, 2015), and the whole human brain—the human brainnetome atlas (Fan et al, 2016)—which is based on the anatomical connectivity profiles derived from structural neuroimaging data
We present a variation of Automatic Tractography-Based Brain Parcellation (ATPP), which is dedicated to monkey brain parcellation, to address the significant differences in the process between the two species
Summary
Non-human primates (NHPs) resemble high similarities in the neuroanatomical structures and cognitive functions to humans (Perretta, 2009). General-purpose MaS methods were reviewed in Iglesias and Sabuncu (2015) and was initially introduced to neuroimaging for segmenting brain into anatomical structures (Aljabar et al, 2009), and it was demonstrated that image similarity and age were both suitable for atlas selection. A hybrid method combining MaS for coarse extraction with surface deforming guided by local intensity and priors was developed and tested on both human and NHP brain images (Wang Y. et al, 2014). We developed a MaS-based protocol for automatically extracting brain tissues from structural MRI data of rhesus macaque (Macaca mulatta). The label fusion methods were applied to combine the mapped segmentations and extract the brain of the left-out subject. MaS, multi-atlas segmentation; MV, majority voting; JLF, joint label fusion
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