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Brain Development in Non-human Primates Assessed with Neurite Orientation Dispersion and Density Imaging
Mayu Iida1,2, Junichi Hata2,3,4, Marin Nishio1,2,3, Fumiko Seki2,3,4, Yawara Haga1,3,4, Erika Sasaki2,3, and Takako Shirakawa1

1Department of Radiological Sciences, Human Health Sciences, Tokyo Metropolitan University Graduate School, Tokyo, Japan, 2Live Imaging Center, Central Institute for Experimental Animals, Kanagawa, Japan, 3Department of Physiology, Keio University School of Medicine, Tokyo, Japan, 4Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Saitama, Japan

Synopsis

Neurite orientation dispersion and density imaging (NODDI) can specifically estimate the microstructure of neurites. Brain development characteristics were evaluated using NODDI in common marmosets. The study included six common marmosets (age, 5–30 months). In NODDI, the value of both dispersion and density increased. Additionally, the trend of increase varied from region to region. This pattern has previously been observed in human studies. Thus, NODDI can be a good approach to evaluate brain development in the common marmoset, and eventually a suitable parameter to study brain developmental disorders for diagnosis and treatment.

Introduction

Neurite orientation dispersion and density imaging (NODDI) has been proposed as a new diffusion imaging method, and it is being studied as an analytical method for quantitatively evaluating the neurodevelopment process of the brain. NODDI adopts a tissue model that distinguishes the following three types of microstructural environments: intra-cellular, extra-cellular, and CSF compartments. Images of the orientation dispersion index (ODI), neurite density index (NDI), and isotropic volume fraction (ISO) can be obtained.1,2 The benefits of using common marmosets, which are nonhuman primates, for research are as follows: 1) marmosets are small and easy to handle during experiments; 2) their brain structure and nerve paths are similar to those in humans; thus, neurological disorders similar to those in humans can be reproduced; and 3) their development tendency is easy to observe, as they become adults in about two years. In this study, brain development characteristics were evaluated using NODDI in common marmosets.

Methods

The study included six healthy common marmosets (four males and two females; age, 5–30 months). All data were acquired using a 7.0-T magnetic resonance imaging system (BioSpec 70/16; Bruker BioSpin, Ettlingen, Germany) with a conventional linear polarized birdcage resonator (Bruker BioSpin; inner diameter, 72 mm). The imaging parameters were as follows: multi-shot (four shots) echo-planar imaging sequence; repetition time/echo time, 5,000/20.75 ms; field of view, 51.2 × 51.2 mm; matrix size, 128 × 128; slice thickness, 0.8 mm; b-values, 0, 1,000, 2,000, and 3,000 (30, 30, and 60 axes, respectively). For NODDI fitting, NDI and ODI images were created using the NODDI MATLAB Toolbox (version 1.0.1, Developer: Gary Hui Zhang). Each image was normalized using a population-averaged standard brain template. Using ROIs based on an atlas, measurements were made in the visual cortex, frontal cortex, subcortex region, white matter (inferior front occipital fasciculus [IFOF]), and white matter (corpus callosum [CC]), and changes in the brain development process of each region were evaluated. This study was approved by the local Animal Experiment Committee and was conducted in accordance with the Guidelines for Conducting Animal Experiments of the Japanese Central Institute for Experimental Animals.

Results

The ODI showed a rapid increase in the cortex until puberty. The visual cortex and frontal cortex showed differing ranges of NDI values. In the cortex, the amount of dispersion converged until after puberty. The NDI gradually increased in the subcortex. However, in b = 1000 −30ax and 2000 −30ax, an increasing trend similar to that for the cortex was obtained. As the b value was high, the white matter signal was emphasized . Thus, we believe that the white matter signal was also added. The ODI showed little change because the white matter had an orientation structure.

Discussion

The NODDI analysis was able to capture microstructural changes associated with brain development involving the cortex and the white matter. In the ADC and T1 reported in a previous study, the volume of various brain regions steadily increased until puberty in the white matter and cortex. Thereafter, the cortex volume decreased considerably until adulthood.3,4 With regard to the NDI, the cortex and white matter had the same trend as that for the ADC, suggesting that the intercellular space in a voxel associated with myelination is decreased. With regard to NODDI, white matter showed no change in directionality. On the other hand, the developmental change of the cortex was larger than that of the ADC, and the volume emphasized a more directional dispersion. Thus, the ODI made it possible to assess the brain development sensitively. In studies on NODDI in humans, the NDI exhibited striking increases that followed a logarithmic growth pattern, while ODI increases followed an exponential growth pattern.5 The NDI increases rapidly in childhood (approximately 21 years of age5), while the ODI increases more slowly in childhood and the increase accelerates in adulthood. On the other hand, in the common marmoset, the micro-level neural structure derived from various brain regions continuously changes during childhood (approximately 24 months), the NDI increases more slowly, and the ODI increases rapidly in childhood.6

Conclusion

We successfully analyzed the brain development of the common marmoset using NODDI and evaluated changes in some regions quantitatively. NODDI can detect changes in the neural microstructure. Our findings suggest that NODDI can be used to evaluate brain development in the common marmoset.

Acknowledgements

This research is partially supported by the program for Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) from the Japan Agency for Medical Research and Development, AMED.

References

  1. Zhang H. NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage. 2012; 61:1000–1016.
  2. Nilsson M. Noninvasive mapping of water diffusional exchange in the human brain using filter-exchange imaging. Magn Reson Med. 2013; 69:1573-1581.
  3. Uematsu A. Mapping orbitofrontal-limbic maturation in non-human primates: A longitudinal magnetic resonance imaging study. NeuroImage. 2017; 163:55-67.
  4. Seki F. Developmental trajectories of microanatomical structures in common marmoset brain. Neuroscience. 2017; 364:143-156.
  5. Chang YS. White matter changes of neurite density and fiber orientation dispersion during human brain maturation. PLoS ONE. 2015; 10.
  6. David H. Aspects of common marmoset basic biology and life history important for biomedical research. AALAS. 2003; 53:339-350.

Figures

Fig. 1 Analysis Method

Using the NODDI MATLAB Toolbox for diffusion-weighted images, ODI and NDI images were acquired. We used ROIs divided into 52 areas according to an atlas. The ROIs were normalized to each of the obtained images, and statistical analyses of the cortex, subcortex, and white matter regions were performed.



Fig. 2 Images Obtained with NODDI Analysis

Imaging was performed at 5–30 months of age. The upper row presents ODI images, and the lower row presents NDI images. The normalized ROI was combined with each of the images shown and data were acquired. In the scale, a higher value indicates more directional scattering with regard to the ODI and higher density with regard to the NDI.


Fig. 3 Result Value of NDI and ODI

The vertical axis shows the respective measured values, and the horizontal axis shows age. The upper part of the figure presents NDI and the lower part presents ODI. The red dots represent plot data of b = 1000 − 30ax and 2000 − 30ax, while the green dots represent plot data of b = 1000 −30ax and 3000 −60ax. In the cortex and white matter, the value scale differs. From the left, the images are for the visual cortex, frontal cortex, subcortex region, white matter (IFOF), and white matter (CC).


Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
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