Brain Development From Newborn to Adolescence: Evaluation by Neurite Orientation Dispersion and Density Imaging.

Xueying Zhao,Xuchen Yu,Lei Wei,Boyu Zhang,Jingjing Shi,Chengyan Wang,He Wang,Wenzhen Zhu,Fei Dai

doi:10.3389/fnhum.2021.616132

Abstract

Neurite orientation dispersion and density imaging (NODDI) is a diffusion model specifically designed for brain magnetic resonance imaging. Despite recent studies suggesting that NODDI modeling might be more sensitive to brain development than diffusion tensor imaging (DTI), these studies were limited to a relatively small age range and mainly based on the manually operated region of interest analysis. Therefore, this study applied NODDI to investigate brain development in a large sample size of 214 subjects ranging in ages from 0 to 14. The whole brain was automatically segmented into 122 regions. The maturation trajectory of each region was characterized by the time course of diffusion metrics and further quantified using nonlinear regression. The NODDI-derived metrics, neurite density index (NDI) and orientation dispersion index (ODI), increased with age. And these two metrics were superior to the DTI-derived metrics in SVM regression models of age. The NDI in white matter exhibited a more rapid growth than that in gray matter (including the cortex and deep nucleus). These diffusion indicators experienced conspicuous increases during early childhood and the growth speed slowed down in adolescence. Region-specific maturation patterns were described throughout the brain, including white matter, cortical and deep gray matter. These development patterns were evaluated and discussed on the basis of NODDI’s model assumptions. To summarize, this study verified the high sensitivity of NODDI to age over a crucial developmental period from newborn to adolescence. Moreover, the existing knowledge of brain development has been complemented, suggesting that NODDI has a potential capability in the investigation of brain development.

Highlights

The human brain undergoes complex anatomical changes from infancy to adolescence, including axonal growth, myelination, dendritic arborization, synapse formation and neuronal pruning (Stiles and Jernigan, 2010)
To quantify the age sensitivity of these diffusion metrics, SVM models were trained with neurite density index (NDI), orientation dispersion index (ODI), fractional anisotropy (FA) and mean diffusivity (MD) measurements, separately
The R-Square of age regression using Neurite orientation dispersion and density imaging (NODDI)-derived NDI and ODI is 0.93, which is higher than the R-Square using Diffusion Tensor Imaging (DTI)-derived FA and MD measurements

Summary

Introduction

The human brain undergoes complex anatomical changes from infancy to adolescence, including axonal growth, myelination, dendritic arborization, synapse formation and neuronal pruning (Stiles and Jernigan, 2010). The majority of studies have demonstrated an exponential increase (e.g., A − Be−x/C, with A > 0, B > 0 and C > 0) in FA and an exponential decrease (e.g., A + Be−x/C, with A > 0, B > 0 and C > 0) in diffusivity with considerable regional variation (Mukherjee et al, 2001; Ben et al, 2005; Hermoye et al, 2006; Lebel et al, 2008; Faria et al, 2010) Both Paydar et al (2014) and Shi et al (2019) pointed out that except DTI, Diffusion Kurtosis Imaging (DKI) has been applied to normal brain development by capturing the diffusion kurtosis of water molecules. DKI-derived parameters have provided valuable insights into atypical brain development, such as preterm infants studies (Shi et al, 2016; Ouyang et al, 2019) and brain disorders like Huntington’s disease (Blockx et al, 2012)

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Results

Discussion

Conclusion