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

Raffelt, David A., Smith, Robert E., Ridgway, Gerard R., Tournier, J-Donald, Vaughan, David N., Rose, Stephen, Henderson, Robert and Connelly, Alan (2015) Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres. NeuroImage, 117 40-55. doi:10.1016/j.neuroimage.2015.05.039


Author Raffelt, David A.
Smith, Robert E.
Ridgway, Gerard R.
Tournier, J-Donald
Vaughan, David N.
Rose, Stephen
Henderson, Robert
Connelly, Alan
Title Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres
Journal name NeuroImage   Check publisher's open access policy
ISSN 1095-9572
1053-8119
Publication date 2015-08-15
Year available 2015
Sub-type Article (original research)
DOI 10.1016/j.neuroimage.2015.05.039
Open Access Status Not Open Access
Volume 117
Start page 40
End page 55
Total pages 16
Place of publication Amsterdam, Netherlands
Publisher Elsevier BV
Language eng
Formatted 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.
Keyword Analysis
Connectivity
Diffusion
Fixel
MRI
Statistics
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: UQ Centre for Clinical Research Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 11 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 10 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Sat, 02 Jul 2016, 04:16:09 EST by System User on behalf of Learning and Research Services (UQ Library)