Introduction

The monkey is a relatively active model employed in neuroscience study. The investigators are aiming to uncover the fundamental mechanisms underlying human brain through monkey-based experiments. In this way we need to gain more comprehension in the consistency and disparity across human and monkey brain regions to perform a better neuroscience research in degenerative disease, psychiatry disorders and neurophysiology field through monkey-based neuroimaging computation. Previous studies investigated the cross-species alignment based on diffusion imaging/anatomical imaging to uncover corresponding connectivity in the brain1,2. Considering Structural connectivity in the brain is the basis of functional connectivity. And complex network topology is the important characteristic of anatomical and functional brain network. To enhance the understanding of brain structural network across primate species, here we use structural MRI data from humans and macaque monkeys to investigate the alignment in structural network by a shared primate brain parcellation.

Methods

We used publicly avaliable healthy young adult human structural T1w scans acquired at 0.7-mm isotropic resolution as part of the Human Connectome Project (HCP). 100 unrelated subjects were selected for analysis from the S500 HCP data release3,4. For macaque structural T1w scans, we used previously acquired dataset, a group of 20 adult macaques scanned at 0.5-mm isotropic resolution5. For each human subject, T1w scans were initially processed using the minimal preprocessing pipelines developed for the HCP to minimize the distortions and blurring of the data. The HCP pipelines adapted for non-human primates was used to perform macaque T1w scans preprocessing. After completing preprocess, the cortical organizations of both macaques and humans were parcellated according to Regional Map template6,7. This generated a whole brain template with a total of 80 regions of interest for both monkeys and humans. The structural covariance (SCN) matrix of a group subjects is defined by estimating the inter-regional correlation of cortical thickness between all possible pairs of 80 regions defined above. The inter-regional correlations in cortical thickness were estimated to construct a (80´80) structural correlation matrix. A hard threshold was applied to cut off the false-positive correlation coefficient to retain the strongly positive correlations. Regional thickness as well as corresponding CT rank in the whole brain was also compared to illustrate the distribution pattern of CT in respective species.

Results

First, humans and macaques exhibit similar spatial distribution of cortical thickness (CT). Both visual area showed a relatively thinner CT while the whole frontal area as well as cingulate cortex exhibit a high and homogeneous CT value, which was verified by previous studies that primate brain structure followed a anterior-posterior axis. Second, several regions showed similar distribution level in SCN indexes. The dorsal and lateral frontal areas adjacent to anterior central gyrus were in the similar low rank of each average brain respectively. Meanwhile, the unimodal religions like visual area, temporal area were highly similar across species, which might indicate a functional concordance as homologous brain regions. Finally, there were difference in SCN distribution across species. The Broca’s area in the M1 region, which was concluded as mirror neuron system, suggest an inconsistency structure in the aligned parcellation.

Discussion

There are both correspondence and difference existing in the comparison results between humans and macaque monkeys. The unimodal area has low level cortical thickness while the multimodal regions exhibit thicker gray matter. The human and non-human primate also shared some macro-scale morphological similarity such as cortex-wise anterior-posterior gradient. This kind of similar distribution indicates parallel brain function in diverse brains, making a more solid evidence of brain alignment of corresponding regions. However, as for the higher-order brain regions, mainly mirror neuron network regions, which stays a mysterious part to explore in primate neuroscience, were not as consistent as other sensory/sensorimotor cortex, leading a deeper consideration of species difference beyond neuro-anatomical field. Besides, some frontal areas, namely PFCcl, PFCol and PFC pole were hollow in thickness distribution and structural covariance across species. The PFC delineation was reported dramatically variant in size and surface, suggesting a uniform PFC scaling. The disagreement in the detailed PFC regions still need to be investigated. There are studies relate transcriptome to connectome to elucidate the molecular mechanism of imaging phenotype8,9, which show us a new way to follow this issue.

Acknowledgements

Funding: This work was supported by the National Key R&D Program of China (2017YFE0103600), National Natural Science Foundation of China (81720108021), Zhongyuan Thousand Talents Plan Project(ZYQR201810117), Zhengzhou Collaborative Innovation Major Project (20XTZX05015).

References

1. Ting Xu, Karl-Heinz Nenning, Ernst Schwartz, et al. Cross-species functional alignment reveals evolutionary hierarchy within the connectome. Neuroimage. 2020;233.2. Ardesch, Dirk Jan, Longchuan Li, et al. Evolutionary Expansion of Connectivity between Multimodal Association Areas in the Human Brain Compared with Chimpanzees. Proceedings of the National Academy of Sciences of the United States of America. 2019;116(14):7101–7106.3. Matthew F. Glasser, Stamatios N. Sotiropoulos,J. Anthony Wilson, et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage. 2013;80:105-124.4. Chad J. Donahue,Matthew F. Glasser,Todd M. Preuss, et al. Quantitative assessment of prefrontal cortex in humans relative to nonhuman primates. Proceedings of the National Academy of Sciences of the United States of America. 2018;115(22).5. Qiming Lv, Mingchao Yan, Xiangyu Shen, et al. Normative Analysis of Individual Brain Differences Based on a Population MRI-Based Atlas of Cynomolgus Macaques. Cereb Cortex. 2020;31(1):341-355.6. R. Kotter, E. Wanke, Mapping brains without coordinates. Philos. Trans. R. Soc. Lond. B Biol Sci. 2005;360:751-766.7. G. Bezgin, V. A. Vakorin, A. J. van Opstal, A. R. McIntosh, R. Bakker, Hundreds of brain maps in one atlas: registering coordinate-independent primate neuro-anatomical data to a standard brain. Neuroimage. 2012;62:67-76.8. Romero-Garcia, R., Whitaker, K. J., Váša, F., et al. Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex. NeuroImage. 2017;171:256–267.9. Valk, S. L., Xu, T., Margulies, D. S., Masouleh, S. K., et al. Shaping brain structure: Genetic and phylogenetic axes of macroscale organization of cortical thickness. Science Advances. 2020;6(39).