INTRODUCTION

The common marmoset is increasingly used as a non-human primate model to explore cortical organization underlying behaviors and disorders. Advances in genetic/viral techniques also attract researchers to use this species [1]. The human Connectome Project (HCP) has revealed that high-resolution multi-modal MRI provides powerful means to address cortical architecture and connectomics in living humans [2, 3]. Applying corresponding approaches will produce better results for comparative neuroscience, but this is challenging because of the cross-species diversity in brain size, shape and cortical convolutions. Past marmoset MRI studies have used dedicated high-field animal scanners, rather than a high performance clinical system like the HCP, nor have they generally used multi-channel head coils and HCP-Style sequences. Here, we show the result of our efforts to develop a 16-channel multi-array RF receive coil for marmoset brain imaging for a 3T-MRI scanner with powerful gradients. Basic quality assessments and adaption of HCP-style data acquisition and preprocessing pipelines are presented.

METHODS

The coil frame geometry was designed to closely fit the largest head dimension in our marmoset MRI database (Fig. 1A). The locations and overlaps of 16 coil elements were designed using a 3D design software (Fig. 1B) and drawn on the surface of the coil frame (Fig. 1C). Each element was composed of a circular thin wire of polyester coaxial copper cable, placed overlapping with each other to reduce coupling (Fig. 1D). The coil was designed for a 3T-MRI scanner (MAGNETOM Prisma, Siemens Healthcare, Erlangen, Germany), which has powerful gradients (80mT/m). Element coupling was assessed with noise correlation measurements. A custom phantom of a marmoset head was used to evaluate B₁ field inhomogeneity. Four anesthetized common marmosets (male, 381±5.3 g, 5.7±4.0 years old) were used for in-vivo MRI scanning with same pulse sequence types as in HCP (e.g. MPRAGE, SPACE, multi-band EPI) and parameters designed in line with principles of HCP [4]. The structural T1w and T2w images were obtained with isotropic spatial resolution of 0.18mm. Two resting state fMRI (rfMRI) scans, with opposing phase direction, were obtained with TR/TE=0.76sec/30msec, scan time of 50min, and spatial resolution of 1.1 mm, which was derived by comparing histograms of cortical thickness between human and marmosets to be roughly equivalent to 2mm in the human. High-resolution dMRI (0.8 mm) was scanned in three shells with the highest at b= 3000 s/mm² and 500 gradient directions in scan time of 30 min. All the data were analyzed and preprocessed with the HCP pipelines to correct for motion, distortion and intensity biasfield. Structural MRI was used for estimating a subcortical segmentation, cortical surface, and thickness and myelin maps. fMRI was analyzed with independent-component analysis to enable denoising and delineation of functional networks. dMRI was analyzed with a NODDI model for neurite imaging [5], bedpost fiber orientation modeling and probabilistic tractography [6].

RESULTS

The noise correlation coefficients (Fig. 2A) ranged from 0.01 to 0.67 with an average value of 0.29 ± 0.18. The B1 transmit field was quite homogeneous across brain regions (91.3 ± 1.5 [deg]), and the B1 receive map showed higher signal sensitivity in the periphery than in the center of the phantom (Fig. 2C, 2D) as expected. High-resolution structural T1w and T2w images in the marmosets provided high SNR and HCP-style biasfield correction achieved good homogeneity and contrast between grey and white matter (Fig. 3A, 3B), which further allowed us to obtain an accurate subcortical segmentation, surface estimation and myelin and thickness maps (Fig. 3C, 3D). The rfMRI data had higher temporal SNR than the HCP data and was corrected for motion and distortion and structured noise was removed using ICA+FIX [7]. Independent components representing several neural functional networks were found (Fig. 3E). Diffusion tractography successfully captured a known connection between the insula and anterior cingulate cortex (ACC), which is challenging to track because of crossing and kissing of longitudinal fascicules, commissural fibers and corona radiata (Fig. 3F).

DISCUSSION

Our custom marmoset-dedicated 16-channel coil, implemented for a high-gradient 3T MRI scanner, provided images with excellent SNR and homogeneity, and allowed us to adapt HCP-style high-resolution image acquisitions needed for mapping the marmoset connectome. HCP-style preprocessing generated maps of myelin, cortical thickness, and cortical neurite indices that agreed with those in the previous reports in macaques and humans. Functional and structural connectivity analyses also provided high quality results including mapping connections that have previously been seen with the HCP data in humans.

CONCLUSIONS

Our strategy of combining multi-array RF coil and high-gradient 3T scanner is potentially useful to establish marmoset connectomics in harmonized manner with human connectome.

Acknowledgements

We thank Prof. Van Essen DC in Washington University School of Medicine. This research is partially supported by the program for Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) from Japan Agency for Medical Research and development, AMED, by MEXT KAKENHI Grant (16H03300,16H03306, 16H01626, 15K12779), and by a grant from Sumitomo Dainippon Pharma Co., Ltd.

References

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