Prof. Fang’s group and collaborators characterized differential white matter maturation from birth to 8 years of age

 

On December 23, 2019, a paper entitled “Differential White Matter Maturation from Birth to 8 Years of Age” was online published in Cerebral Cortex by Prof.Fang Fang’s group at the School of Psychological and Cognitive Sciences at Peking University, Peking-Tsinghua Center for Life Sciences and the PKU-IDG/McGovern Institute for Brain Research, and Prof.Hao Huang’s group at the University of Pennsylvania. This study acquired diffusion tensor imaging data from 118 typically developing children aged 0 to 8 years and 31 children with autism aged 2 to 7 years. Modelling with exponential function, the maturation of white matter was found spatially and temporally differential. The white matter maturation was further separated into fast, intermediate, and slow phase according to the developmental rate. This study provides a standard development reference, which can serve as a biomarker for early detection of neuropsychiatric disorders.

 

During infancy and childhood, the human brain white matter undergoes significant microstructural changes with dynamic axonal and myelin maturation. These white matter maturational processes underlie the structural basis of emerging brain circuits critical to memory, attention, intelligence, language, motor learning, musical proficiency, and cognitive control. Dysfunction of the white matter maturational processes is associated with a suite of neuropsychological disorders including schizophrenia, major depressive disorder, bipolar disorder, autism, and attention-deficit hyperactivity disorder (ADHD). Delineation of maturational white matter microstructural curves of all major white matter tracts and tract groups of typically developing (TD) brain could not only reveal the spatiotemporal differential circuit formation in normal brain development, but also set the stage for understanding aberrant brain development in neurodevelopmental disorders such as autistic spectrum disorder (ASD) and developmental brain disorders in general.

 

Diffusion MRI, especially diffusion tensor imaging (DTI), has been widely used to quantify white matter microstructure. Water molecules tend to diffuse more freely along the white matter tracts, rather than perpendicular to the tracts. These diffusion properties of water molecules around white matter tracts can be measured through DTI, which uses a tensor model to measure the water diffusion in vivo. DTI-derived metrics are sensitive to white matter microstructural changes during development. Fractional anisotropy (FA), ranging from 0 to 1, has been utilized to quantify the shape of the diffusion tensor. Radial diffusivity (RD) and axial diffusivity (AD), the diffusivity measurements perpendicular to and along the diffusion tensor, are associated with myelination and axonal growth, respectively. Mean diffusivity (MD) quantifying the size of the diffusion tensor usually decreases during brain development. In white matter, the microstructural maturation, including axonal growth, axonal packing, and fiber myelination, can be quantified by the changes of the four DTI-derived metrics (Fig. 1). 

 

Fig 1, Changes of FA, MD, RD, and AD caused by axonal growth, axonal packing and fiber myelination.

 

In previous studies, insufficient sample covering the critical development period from birth to early childhood brings enormous difficulty to quantify white matter development. To cover the deficiencies, we acquired DTI data from a larger sample size, 118 typically developing (TD) children, at the age with critical development, which is from 0 to 8 years. Fig. 2 illustrates the DTI metrics changes in the white matter from 2 to 95 months. At this period, the brain volume increased, white matter FA increase, MD and RD decreased.

 

Fig. 2, Changes of orientation-encoded colormap (OEC), FA, MD, RD, and AD from 2 to 95 months

 

For quantification white matter microstructure across subjects, the FA images from all subjects were registered to the white matter atlas JHU ICBM-DTI-81 (Mori et al. 2008). The white matter skeleton was then extracted using tract-based spatial statistics (TBSS) (Fig. 3A). white matter tracts are categorized into commissural (interhemispheric connection), brainstem (connectivity in brainstem and cerebellum), association (corticocortical connections), limbic (connectivity in limbic system), and projection (corticospinal connections) tract groups based on the tract functions (Wakana et al. 2004) (Fig. 3B). The labels of the white matter tracts were transferred from the atlas to the white matter skeleton (Fig. 3C).

 

Fig. 3, Registering FA images to the atlas, extracting white matter skeleton (A), and then separating white matter into different tracts and tract groups (B & C).

 

To find out the optimal white matter development model, the FA, MD, RD, and AD of the entire white matter of the 118 subjects were fitted with five candidate models, including linear, logarithmic, exponential, Poisson, and quadratic polynomial models, respectively (Fig. 4). The exponential model popped out with the highest goodness-of-fit (all higher than 76.8% for four DTI metrics) among five models. The Akaike information criterion result showed the exponential model had more than 99% probability to be the optimal model. Hence, the exponential model was applied to characterize white matter maturation.

 

Fig. 4, fitting FA, MD, RD, and AD of the entire white matter with five candidate models.

 

Next, we fitted the DTI metrics changes of the white matter tracts and tract groups with exponential model. Fig. 5 shows the maturation of each white matter tract from 0 to 8 years, companied with exponential fitted curve. We found that FA of all tracts increased nonlinearly, and RD, MD, and AD of most tracts decreased nonlinearly during development.