Sila Genc1, Charles B Malpas2, Scott K Holland3, Richard Beare4, Timothy J Silk5. 1. Murdoch Childrens Research Institute, Melbourne, Australia; Department of Paediatrics, University of Melbourne, Melbourne, Australia. Electronic address: sila.genc@mcri.edu.au. 2. Murdoch Childrens Research Institute, Melbourne, Australia; Department of Medical Education, Melbourne Medical School, The University of Melbourne, Melbourne, Australia. Electronic address: charles.malpas@mcri.edu.au. 3. Department of Radiology, Cincinnati Children's Hospital Research Foundation, University of Cincinnati, OH, USA. Electronic address: Scott.Holland@cchmc.org. 4. Murdoch Childrens Research Institute, Melbourne, Australia; Department of Medicine, Monash University, Melbourne, Australia. Electronic address: richard.beare@mcri.edu.au. 5. Murdoch Childrens Research Institute, Melbourne, Australia; Department of Paediatrics, University of Melbourne, Melbourne, Australia. Electronic address: tim.silk@mcri.edu.au.
Abstract
PURPOSE: White matter development during childhood and adolescence is characterised by increasing white matter coherence and organisation. Commonly used scalar metrics, such as fractional anisotropy (FA), are sensitive to multiple mechanisms of white matter change and therefore unable to distinguish between mechanisms that change during development. We investigate the relationship between age and neurite density index (NDI) from neurite orientation dispersion and density imaging (NODDI), and the age-classification accuracy of NDI compared with FA, in a developmental cohort. METHOD: Diffusion-weighted imaging data from 72 children and adolescents between the ages of 4-19 was collected (M=10.42, SD=3.99, 36 male). We compared NODDI metrics against conventional DTI metrics (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD] and radial diffusivity [RD]) in terms of their relationship to age. An ROC analysis was also performed to assess the ability of each metric to classify older and younger participants. RESULTS: NDI exhibited a stronger relationship with age (median R2=.60) compared with MD (median R2=.39), FA (median R2=.27), AD (median R2=.14), and RD (median R2=.35) in a high proportion of white matter tracts. When participants were divided into an older and younger group, NDI achieved the best classification (median area under the curve [AUC]=.89), followed by MD (median AUC=.81), FA (median AUC=.80), RD (median AUC=.81), and AD (median AUC=.64). CONCLUSION: Our results demonstrate the sensitivity of NDI to age-related differences in white matter microstructural organisation over development. Importantly, NDI is more sensitive to such developmental changes compared to commonly used DTI metrics. This knowledge provides justification for implementing NODDI metrics in developmental studies.
PURPOSE: White matter development during childhood and adolescence is characterised by increasing white matter coherence and organisation. Commonly used scalar metrics, such as fractional anisotropy (FA), are sensitive to multiple mechanisms of white matter change and therefore unable to distinguish between mechanisms that change during development. We investigate the relationship between age and neurite density index (NDI) from neurite orientation dispersion and density imaging (NODDI), and the age-classification accuracy of NDI compared with FA, in a developmental cohort. METHOD: Diffusion-weighted imaging data from 72 children and adolescents between the ages of 4-19 was collected (M=10.42, SD=3.99, 36 male). We compared NODDI metrics against conventional DTI metrics (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD] and radial diffusivity [RD]) in terms of their relationship to age. An ROC analysis was also performed to assess the ability of each metric to classify older and younger participants. RESULTS: NDI exhibited a stronger relationship with age (median R2=.60) compared with MD (median R2=.39), FA (median R2=.27), AD (median R2=.14), and RD (median R2=.35) in a high proportion of white matter tracts. When participants were divided into an older and younger group, NDI achieved the best classification (median area under the curve [AUC]=.89), followed by MD (median AUC=.81), FA (median AUC=.80), RD (median AUC=.81), and AD (median AUC=.64). CONCLUSION: Our results demonstrate the sensitivity of NDI to age-related differences in white matter microstructural organisation over development. Importantly, NDI is more sensitive to such developmental changes compared to commonly used DTI metrics. This knowledge provides justification for implementing NODDI metrics in developmental studies.
Authors: Julie Sato; Marlee M Vandewouw; Nicole Bando; Dawn V Y Ng; Helen M Branson; Deborah L O'Connor; Sharon L Unger; Margot J Taylor Journal: Brain Commun Date: 2021-04-01
Authors: J A Kimpton; D Batalle; M L Barnett; E J Hughes; A T M Chew; S Falconer; J D Tournier; D Alexander; H Zhang; A D Edwards; S J Counsell Journal: Neuroradiology Date: 2020-10-29 Impact factor: 2.804
Authors: Sheelakumari Raghavan; Robert I Reid; Scott A Przybelski; Timothy G Lesnick; Jonathan Graff-Radford; Christopher G Schwarz; David S Knopman; Michelle M Mielke; Mary M Machulda; Ronald C Petersen; Clifford R Jack; Prashanthi Vemuri Journal: Brain Commun Date: 2021-05-19