Towards HCP-Style macaque connectomes: 24-Channel 3T multi-array coil, MRI sequences and preprocessing

Joonas Autio ,Masahiro Ohno,Timothy S Coalson,Yuta Urushibata,Matteo Bastiani,Henry Kennedy,Takayuki Ose,Stamatios N Sotiropoulos,Chad J Donahue,Matthew F Glasser,Kantaro Nishigori,Yoshihiko Kawabata,Yuki Hori,Atsushi Yoshida,Katsutoshi Murata,David C Van Essen,Yasuhiro Go,Masataka Yamaguchi,Takuya Hayashi,Stephen M Smith ,Saâd Jbabdi

doi:10.1016/j.neuroimage.2020.116800

Abstract

Macaque monkeys are an important animal model where invasive investigations can lead to a better understanding of the cortical organization of primates including humans. However, the tools and methods for noninvasive image acquisition (e.g. MRI RF coils and pulse sequence protocols) and image data preprocessing have lagged behind those developed for humans. To resolve the structural and functional characteristics of the smaller macaque brain, high spatial, temporal, and angular resolutions combined with high signal-to-noise ratio are required to ensure good image quality. To address these challenges, we developed a macaque 24-channel receive coil for 3-T MRI with parallel imaging capabilities. This coil enables adaptation of the Human Connectome Project (HCP) image acquisition protocols to the in-vivo macaque brain. In addition, we adapted HCP preprocessing methods to the macaque brain, including spatial minimal preprocessing of structural, functional MRI (fMRI), and diffusion MRI (dMRI). The coil provides the necessary high signal-to-noise ratio and high efficiency in data acquisition, allowing four- and five-fold accelerations for dMRI and fMRI. Automated FreeSurfer segmentation of cortex, reconstruction of cortical surface, removal of artefacts and nuisance signals in fMRI, and distortion correction of dMRI all performed well, and the overall quality of basic neurobiological measures was comparable with those for the HCP. Analyses of functional connectivity in fMRI revealed high sensitivity as compared with those from publicly shared datasets. Tractography-based connectivity estimates correlated with tracer connectivity similarly to that achieved using ex-vivo dMRI. The resulting HCP-style in vivo macaque MRI data show considerable promise for analyzing cortical architecture and functional and structural connectivity using advanced methods that have previously only been available in studies of the human brain.

Highlights

Old World monkeys are an important neuroscientific model for understanding primate neuroanatomy (Brodmann K., 1905; Felleman and Van Essen, 1991; Van Essen et al, 2001)
At the level of cortical areas, high confidence homologies suggestive of a common evolutionary origin have only been firmly established for a modest number of early sensory and motor areas (Van Essen and Dierker, 2007) but are more challenging to delineate for higher cognitive regions such as prefrontal cortex (Mars et al, 2018a, 2018b)
Since the presented protocols used share similar strengths to the Human Connectome Project (HCP) image acquisition, and the data is stored in a common geometrical framework system (‘Connectivity Informatics Technology Initiative (CIFTI) greyordinates’), we anticipate that it will facilitate direct multi-modal comparisons with an unprecedented accuracy between macaque and human connectomes

Summary

Introduction

Old World monkeys are an important neuroscientific model for understanding primate neuroanatomy (Brodmann K., 1905; Felleman and Van Essen, 1991; Van Essen et al, 2001). Macaque monkeys have provided insights about neurovascular coupling (Goense and Logothetis, 2008), neural wiring (Markov et al, 2014) and the evolution of the human brain’s functional connectome (Passingham, 2009; Wang et al, 2012). Macaques are separated from humans by ~25 million years of evolution (Nei and Glazko, 2002), and exhibit major brain differences despite being members of the same primate order. Recent imaging studies have revealed substantial neuroanatomical differences between macaques and humans, for example in language-related connectivity and the proportion of cortex devoted to lightly myelinated association areas (Donahue et al, 2018; Glasser et al, 2014; Rilling et al, 2008). Other outstanding questions may include: what is the optimal interspecies registration between macaque and human cerebral cortices? What are the optimal methods for non-invasively estimating functional and structural connectivity as assessed by comparison with gold standard invasive tracers in macaques? Which brain networks are shared and which are different between macaques and humans? These issues may benefit from improvements to in vivo neuroimaging acquisition and preprocessing

Methods

Results

Discussion

Conclusion