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Neuroimage. 2015 Aug 15;117:40-55. doi: 10.1016/j.neuroimage.2015.05.039. Epub 2015 May 22.

Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres.

Author information

1
Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia. Electronic address: david.raffelt@florey.edu.au.
2
Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia.
3
FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK.
4
Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.; Centre for the Developing Brain, King's College London, London, United Kingdom.
5
Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia; Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia.; Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia.
6
The Australian e-Health Research Centre, CSIRO-Digital Productivity Flagship, Royal Brisbane and Women's Hospital, Herston, Australia.
7
Department of Neurology, Royal Brisbane and Women's Hospital, Herston, Australia.

Abstract

In brain regions containing crossing fibre bundles, voxel-average diffusion MRI measures such as fractional anisotropy (FA) are difficult to interpret, and lack within-voxel single fibre population specificity. Recent work has focused on the development of more interpretable quantitative measures that can be associated with a specific fibre population within a voxel containing crossing fibres (herein we use fixel to refer to a specific fibre population within a single voxel). Unfortunately, traditional 3D methods for smoothing and cluster-based statistical inference cannot be used for voxel-based analysis of these measures, since the local neighbourhood for smoothing and cluster formation can be ambiguous when adjacent voxels may have different numbers of fixels, or ill-defined when they belong to different tracts. Here we introduce a novel statistical method to perform whole-brain fixel-based analysis called connectivity-based fixel enhancement (CFE). CFE uses probabilistic tractography to identify structurally connected fixels that are likely to share underlying anatomy and pathology. Probabilistic connectivity information is then used for tract-specific smoothing (prior to the statistical analysis) and enhancement of the statistical map (using a threshold-free cluster enhancement-like approach). To investigate the characteristics of the CFE method, we assessed sensitivity and specificity using a large number of combinations of CFE enhancement parameters and smoothing extents, using simulated pathology generated with a range of test-statistic signal-to-noise ratios in five different white matter regions (chosen to cover a broad range of fibre bundle features). The results suggest that CFE input parameters are relatively insensitive to the characteristics of the simulated pathology. We therefore recommend a single set of CFE parameters that should give near optimal results in future studies where the group effect is unknown. We then demonstrate the proposed method by comparing apparent fibre density between motor neurone disease (MND) patients with control subjects. The MND results illustrate the benefit of fixel-specific statistical inference in white matter regions that contain crossing fibres.

KEYWORDS:

Analysis; Connectivity; Diffusion; Fixel; MRI; Statistics

PMID:
26004503
PMCID:
PMC4528070
DOI:
10.1016/j.neuroimage.2015.05.039
[Indexed for MEDLINE]
Free PMC Article

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