Leveraging multi-shell diffusion for studies of brain development in youth and young adulthood. (June 2020)

• Record Type: Journal Article
• Authors: Pines, Adam R.; Cieslak, Matthew; Larsen, Bart; Baum, Graham L.; Cook, Philip A.; Adebimpe, Azeez; Dávila, Diego G.; Elliott, Mark A.; Jirsaraie, Robert; Murtha, Kristin; Oathes, Desmond J.; Piiwaa, Kayla; Rosen, Adon F.G.; Rush, Sage; Shinohara, Russell T.; Bassett, Danielle S.; Roalf, David R.; Satterthwaite, Theodore D.
• Is Part Of: Developmental cognitive neuroscience. Volume 43(2020)
• Publication Date: 2020-06
• Subjects: Diffusion-weighted imaging -- Development -- Multi-shell diffusion -- Artifact -- Confound -- Structural connectivity -- Motion; Cognitive neuroscience -- Periodicals; Developmental neurobiology -- Periodicals; Neuropsychology -- Periodicals; Neuropsychiatry -- Periodicals; 612.8233
• DOI: 10.1016/j.dcn.2020.100788
• Languages: English
• ISSNs: 1878-9293

Abstract

Highlights: Multi-shell imaging sequences may improve sensitivity to developmental effects. Models that leverage multi-shell information are often less sensitive to the confounding effects of motion. Multi-shell sequences and models that leverage this data may be of particular utility for studying the developing brain. Abstract: 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 multi-shell imaging data and contemporary modeling techniques confer important advantages for studies of neurodevelopment.