Pines, Adam R.; Matthew Cieslak; Bart Larsen; Graham L. Baum; Philip A. Cook; Azeez Adebimpe; Diego G. Davila; Mark A. Elliott; Robert Jirsaraie; Kristin Murtha; Desmond J. Oathes; Kayla Piiwaa; Adon F. G. Rosen; Sage Rush; Russell T. Shinohara; Danielle S. Bassett; David R. Roalf and Theodore D. Satterthwaite
Diffusion weighted imaging (DWI) has advanced our understanding of brain microstructure evolution over development. Recently, the use of multi-shell diffusion imaging sequences has coincided with advances in modeling the diffusion signal, such as Neurite Orientation Dispersion and Density Imaging (NODDI) and Laplacian-regularized Mean Apparent Propagator MRI (MAPL). However, the relative utility of recently-developed diffusion models for understanding brain maturation remains sparsely investigated. Additionally, despite evidence that motion artifact is a major confound for studies of development, the vulnerability of metrics derived from contemporary models to in-scanner motion has not been described. Accordingly, in a sample of 120 youth and young adults (ages 12-30) we evaluated metrics derived from diffusion tensor imaging (DTI), NODDI, and MAPL for associations with age and in-scanner head motion at multiple scales. Specifically, we examined mean white matter values, white matter tracts, white matter voxels, and connections in structural brain networks. Our results revealed that multi-shell diffusion imaging data can be leveraged to robustly characterize neurodevelopment, and demonstrate stronger age effects than equivalent single-shell data. Additionally, MAPL-derived metrics were less sensitive to the confounding effects of head motion. Our findings suggest that multishell imaging data and contemporary modeling techniques confer important advantages for studies of neurodevelopment.