Can NODDI provide a better characterisation of microstructural changes in ALS than DTI?
Matt Gabel1, Rebecca Broad2, Daniel C. Alexander3, Hui Zhang3, Nicholas G. Dowell1, Peter Nigel Leigh2, and Mara Cercignani1

1Clinical Imaging Sciences Centre, Brighton & Sussex Medical School, Falmer, United Kingdom, 2Trafford Centre for Medical Research, Brighton & Sussex Medical School, Falmer, United Kingdom, 3Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom


NODDI is a multi-compartment model of diffusion MRI that overcomes some of the limitations of DTI. Our aim was to assess whether voxelwise analysis of NODDI parameters could provide a more comprehensive picture than DTI in assessing the microstructural changes associated with ALS. We analysed NODDI and DTI parameters for 17 patients with ALS and 19 healthy controls using Advanced Normalization Tools (ANTs) 2.1.0 and SPM12, with age included as a covariate. Both NODDI and DTI indices are sensitive to pathological changes in ALS, but NODDI provides more specific tissue microstructure characterisation.


Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease, characterised by progressive degeneration of both the upper (UMN) and lower (LMN) motor neurones in the brain and spinal cord1. Diffusion Tensor imaging (DTI) has been applied to the study of ALS, and has consistently demonstrated microstructural damage in the corticospinal tract (CST), corpus callosum, and primary motor cortices, in the absence of macroscopic alterations2. This is primarily through the assessment of changes in fractional anisotropy (FA). FA, however, is sensitive to both orientation dispersion and fibre density, and is of little use in cortical grey matter. Neurite orientation dispersion and density imaging (NODDI3) is an alternative quantitative diffusion MRI technique that overcomes some of the limitations of DTI. NODDI has demonstrated specificity at localising abnormalities in various disease states4, as well as grey matter (GM) alterations associated with aging5. The aim of this work was to assess whether voxelwise analysis of NODDI parameters could provide a more comprehensive picture than DTI in assessing the microstructural changes associated with ALS.


Data from 17 patients with ALS (mean age=65.41 years, range=46-73 years) and 19 healthy controls (mean age=60.74 years, range=43-76 years) were acquired on a 1.5T MRI scanner, including multi-shell diffusion-weighted data (10 b=0 volumes, 9 directions with b=300 smm-2, 30 directions with b=800 smm-2 and 60 diffusion directions with b=2400 smm-2), optimised for NODDI. The diffusion data were analysed using the NODDI toolbox3, to yield maps of the orientation dispersion index (ODI), the neurite density index (NDI), and the volume of the isotropic component (FISO). The same data were also used to derive FA maps using single tensor fitting, performed using FSL 5.0.76. All the parametric maps were non-linearly co-registered using the Advanced Normalization Tools (ANTs) 2.1.07 to bring all maps into the same space. The resulting maps were then warped to MNI space. Smoothing at 6mm FWHM and VBA were performed using SPM128. Participant age was included as a covariate. Results are accepted as significant for p<0.05 after FWE correction at cluster level, clusters formed with p<0.001.


Consistent with previous studies, significantly reduced FA was found in patients compared to controls in portions of the CST, the genu of the corpus callosum and the thalamus. Mean diffusivity was increased near the ventricles and in the insular cortex (see Fig 1). When looking at the NODDI maps, significant reductions in neurite density (NDI) were found along the whole CST, bilaterally, and including the primary motor cortex (Fig 2). Both increased and decreased ODI were found in patients, respectively, in the thalamus and in the precentral gyrus (bilaterally), in the right CST, and in the frontal pole (Fig 2). No differences in the isotropic component were found between groups; however, FISO was significantly associated with increasing age, in all the areas known to shrink with aging (hippocampus/parahippocampus, precuneus, cingulate cortex, see Fig 3).


This study confirms that DTI indices are sensitive to pathological changes in ALS. However, MD and FA changes appear to be located not only in areas where microstructural changes are likely to occur, but also where age-related atrophy typically develops. NODDI indices indicate loss of neurites within the origin and in the course of the CST, with less specific changes in other areas, summarised by ODI. The striking correlations we found between FISO and age confirm the accurate modelling of this component, thanks to which NODDI results were not affected by atrophy as much as DTI measures were. We conclude that NODDI adds to our understanding of the nature and distribution of neuronal damage in ALS.


We would like to thank the MND Association for funding this research project.

We also acknowledge all of those who gave their time to participate in this study.


[1] Wijesekera LC, Leigh PN. Orphanet J Rare Dis. 2009, 3:4-3.

[2] Li J, et al. Neurobiol Aging. 2012, 33:1833-8.

[3] Zhang H, et al. Neuroimage. 2012, 61:1000-16.

[4] Winston GP. Quant Imaging Med Surg. 2015, 5:279-287.

[5] Nazeri A, et al. J Neurosci. 2015, 35:1753-1762.

[6] Analysis Group, FMRIB. 2015,

[7] Avants BB, et al. Neuroimage. 2011, 54:2033-44.

[8] Friston KJ, et al. NeuroImage. 1995, 40:223-235.


DTI results. Reduced FA (A) and increased MD (B) were found in areas previously implicated in ALS.

NODDI results. NDI was primarily reduced in the corticospinal tracts in their entirety, and also within the precentral gyrus (A). ODI was reduced in the thalamus (B) and increased in several regions (C). These less specific changes likely account for increased atrophy as well as selective degeneration of white matter fibres.

Correlation between FISO and age. No between-group differences were found in FISO, but the CSF density was significantly correlated with ages in the medial temporal lobe, the cingulate cortex and the insular cortex.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)