title: Assessing Microstructural Substrates of White Matter Abnormalities: A Comparative Study Using DTI and NODDI.
abstract: Neurite orientation dispersion and density imaging (NODDI) enables more specific characterization of tissue microstructure by estimating neurite density (NDI) and orientation dispersion (ODI), two key contributors to fractional anisotropy (FA). The present work compared NODDI- with diffusion tensor imaging (DTI)-derived indices for investigating white matter abnormalities in a clinical sample. We assessed the added value of NODDI parameters over FA, by contrasting group differences identified by both models. Diffusion-weighted images with multiple shells were acquired in a group of 8 healthy controls and 8 patients with an inherited metabolic disease. Both standard DTI and NODDI analyses were performed. Tract based spatial statistics (TBSS) was used for group inferences, after which overlap and unique contributions across different parameters were evaluated. Results showed that group differences in NDI and ODI were complementary, and together could explain much of the FA results. Further, compared to FA analysis, NDI and ODI gave a pattern of results that was more regionally specific and were able to capture additional discriminative voxels that FA failed to identify. Finally, ODI from single-shell NODDI analysis, but not NDI, was found to reproduce the group differences from the multi-shell analysis. To conclude, by using a clinically feasible acquisition and analysis protocol, we demonstrated that NODDI is of added value to standard DTI, by revealing specific microstructural substrates to white matter changes detected with FA. As the (simpler) DTI model was more sensitive in identifying group differences, NODDI is recommended to be used complementary to DTI, thereby adding greater specificity regarding microstructural underpinnings of the differences. The finding that ODI abnormalities can be identified reliably using single-shell data may allow the retrospective analysis of standard DTI with NODDI.
authors.affiliation: Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands.|Department of Rehabilitation Medicine, Maastricht University, Maastricht, the Netherlands.
authors.forename: Inge
authors.initials: I
authors.lastname: Timmers
authors.affiliation: Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands.|Maastricht Brain Imaging Center (M-BIC), Maastricht, the Netherlands.
authors.forename: Alard
authors.initials: A
authors.lastname: Roebroeck
authors.affiliation: Oxford Centre for Functional MRI of the Brain (FMRIB Centre), University of Oxford, Headington, Oxford, United Kingdom.
authors.forename: Matteo
authors.initials: M
authors.lastname: Bastiani
authors.affiliation: Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands.|Maastricht Brain Imaging Center (M-BIC), Maastricht, the Netherlands.
authors.forename: Bernadette
authors.initials: B
authors.lastname: Jansma
authors.affiliation: Department of Pediatrics and Laboratory Genetic Metabolic Diseases, Maastricht University Medical Center, Maastricht, the Netherlands.
authors.forename: Estela
authors.initials: E
authors.lastname: Rubio-Gozalbo
authors.affiliation: Department of Computer Science and Centre for Medical Image Computing, University College London, London, United Kingdom.
authors.forename: Hui
authors.initials: H
authors.lastname: Zhang
mesh_terms: D000293:Adolescent; D016022:Case-Control Studies; D038524:Diffusion Magnetic Resonance Imaging; D056324:Diffusion Tensor Imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008659:Metabolic Diseases / Q000378:metabolism / Q000473:pathology; D012189:Retrospective Studies; D066127:White Matter / Q000033:anatomy & histology / Q000737:chemistry / Q000000981:diagnostic imaging*; D055815:Young Adult