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

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.

Investigating white matter fibre density and morphology using fixel-based analysis

“…To obtain correspondence of fixels across subjects we applied the method outlined in Raffelt et al (2015). Briefly, this involves first identifying fixels of interest (i.e.…”

“…A major benefit of these so-called mixture models (Tournier et al, 2011) is that quantitative measures can be associated with a single fibre population within a voxel (Assaf and Basser, 2005, De Santis et al, 2016, Dell’Acqua et al, 2013, Raffelt et al, 2012b, Riffert et al, 2014, Scherrer et al, 2016, Scherrer and Warfield, 2012). We refer to such a single fi bre population within a vo xel as a fixel, 1 as introduced in Raffelt et al (2015). Unlike VBA, fixel-based analysis (FBA) can identify effects in specific fibre pathways even within regions containing crossing fibres (Raffelt et al, 2015),…”

“…We refer to such a single fi bre population within a vo xel as a fixel, 1 as introduced in Raffelt et al (2015). Unlike VBA, fixel-based analysis (FBA) can identify effects in specific fibre pathways even within regions containing crossing fibres (Raffelt et al, 2015),…”

“…While the simple schematic in Fig. 1 only depicts a single fibre bundle, we emphasise that the goal of a fixel-based analysis is to detect fibre density changes belonging to specific pathways, even in voxels containing multiple crossing fibres (Raffelt et al, 2015). …”

Motor, sensory and executive function brain networks in children with unilateral cerebral palsy

“…We attempted parcellation of 16 subjects recruited using the same protocol with cortical or deep grey matter lesions; none were able to be parcellated adequately; these children were therefore unsuitable for this study protocol. We also note that the presence of gross morphological malformations in CP prevented us from performing a whole-brain AFD fixel-based analysis 162 (due to imperfect image registration).…”