Leveraging multi-shell diffusion for studies of brain development in youth and young adulthood

Adam R. Pines, Matthew Cieslak, Bart Larsen, Graham L. Baum, Philip A. Cook, Azeez Adebimpe, Diego G. Dávila, 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, Theodore D. Satterthwaite

Research output: Contribution to journalArticlepeer-review

42 Scopus citations

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.

Original languageEnglish (US)
Article number100788
JournalDevelopmental Cognitive Neuroscience
Volume43
DOIs
StatePublished - Jun 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 The Author(s)

Keywords

  • Artifact
  • Confound
  • Development
  • Diffusion-weighted imaging
  • Motion
  • Multi-shell diffusion
  • Structural connectivity

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