ABSTRACT
Diffusion tensor imaging (DTI) has advanced our understanding of how brain microstructure evolves over development. However, the proliferation of multi-shell diffusion imaging sequences has coincided with notable advances in the modeling of neuronal diffusion patterns, such as Neurite Orientation Dispersion and Density Imaging (NODDI) and Laplacian-regularized Mean Apparent Propagator MRI (MAPL). The relative utility of these newer diffusion models for understanding brain maturation remains sparsely investigated. Additionally, despite evidence that motion artifact is a major confound for studies of development, the relative vulnerability of these models to in-scanner motion has not been described. Accordingly, in a sample of 123 youth (ages 12-30) we evaluated DTI, NODDI, and MAPL for associations with age and in-scanner head motion at multiple scales, including mean white matter values, voxelwise analyses, and tractography-based structural brain networks. Our results reveal that multi-shell diffusion imaging sequences can be leveraged to robustly characterize neurodevelopment, even within the framework of DTI. However, these metrics of diffusion are variably impacted by motion, highlighting the importance of modeling choices for studies of movement-prone populations. Our findings suggest that while traditional DTI is sensitive to neurodevelopmental trends, contemporary modeling techniques confer key advantages for neurodevelopmental inquiries.
Footnotes
DWI = diffusion-weighted imaging, DTI = diffusion tensor imaging, NODDI = neurite orientation dispersion and density imaging, MAPL = Laplacian-regularized MAP-MRI, FA = fractional anisotropy, MD = mean diffusivity, AD = axial diffusivity, RD = radial diffusivity, ICVF = intracellular volume fraction, ISOVF = isotropic volume fraction, ODI = orientation dispersion index, RTOP = return-to-origin probability, RTAP = return-to-axis probability, RTPP = return-to-plane probability.
We've expanded initial analyses from three metrics of diffusion to fourteen across DTI, NODDI, and MAPL. This includes DTI metrics calculated from the b=800 shell and the entire multi-shell dataset.