Participants
A total of 108 participants were originally recruited as part of the IMAGE-HD longitudinal database, comprised of 36 healthy controls, 36 pre-HD and 36 symp-HD (Georgiou-Karistianis, Gray, et al. 2013). Controls were matched to pre-HD participants by age and gender. Clinical, cognitive and multimodal neuroimaging measures were acquired at three time-points: baseline, 18-month and 30-month. For this study, 11 controls, 11 pre-HD and 9 symp-HD were excluded from the sample due to incomplete data. Of these 31 participants excluded from the analysis, 28 were lost to follow-up (at 18-months or 30-months) and three were lost to image analysis fails. This left a total of 77 total participants remaining (25 controls, 25 pre-HD, 27 symp-HD). The average age of participants at baseline was 43.89 for controls, 41.13 for pre-HD and 53.20 for symp-HD.
Pre-HD and symp-HD participants underwent gene testing prior to enrolment for the study. CAG repeat length ranged from 39 to 49. A Unified Huntington’s Disease Rating Scale total motor score (UHDRS-TMS) was derived and as per Tabrizi et al. (2009), gene positive participants who scored ≤5 were included in the pre-HD group. Those who scored ≥5 were included in the symp-HD group. Lifetime exposure to the mutant huntingtin protein was measured by the Disease Burden Score (DBS) expressed as: Age x (CAG - 35.5). Demographic and clinical data are included in Table 1 (below).
Table 1. Demographic and Clinical Data for each group, at each time point.
|
|
Controls (n = 25)
Mean ± SD
|
Pre-HD (n = 25)
Mean ± SD
|
Symp-HD (n = 27)
Mean ± SD
|
Gender (M:F)
|
Baseline
|
|
|
|
Age (Years)
|
Baseline
|
43.88 ± 13.47
|
41.13 ± 9.71
|
53.20 ± 9.36
|
CAG
|
Baseline
|
-
|
42.36= ± 2.04
|
42.89 ± 2.26
|
UHDRS-TMS
|
Baseline
|
-
|
.92 ± 1.19
|
18.67 ± 9.81
|
|
18-month
|
-
|
2.84 ± 4.04a
|
22.52 ± 11.25a
|
|
30-month
|
-
|
2.80 ± 4.44
|
24.07 ± 12.96
|
DBS
|
Baseline
|
-
|
269.21 ± 59.44
|
376.84 ± 67.15
|
|
18-month
|
-
|
279.69 ± 61.39
|
388.18 ± 70.23
|
|
30-month
|
-
|
287.02 ± 63.01
|
396.01 ± 72.13
|
|
|
|
|
|
|
Note: SD = Standard Deviation; UHDRS-TMS = Unified Huntington’s Disease Rating Scale-Total Motor Score; DBS = Disease Burden Score; a = Significant from Baseline (p = 0.05); b = Significant from 18-month (p = 0.05).
MRI Data Acquisition
MRI scanning was conducted at the Murdoch Children’s Research Institute (Royal Children’s Hospital, Vic, Australia), using a Siemens 3 Tesla scanner. T1-weighted images were acquired for each participant (192 slices, 0.9mm slice thickness, 0.8mm x 0.8mm in-plane resolution, 320 x 320 field of view, TR = 1900ms, TE = 2.6ms, flip angle = 9°) (Refer to Dominguez et al., 2013 and Georgiou-Karistianis et al., 2014 for further details).
DTI whole brain images were acquired using double spin echo diffusion weighted EPI sequence (TR=5800msec, TE=82.3msec, acquisition matrix =128x128, FOV=24cm2, slice thickness 2.5mm, 50 contiguous axial slices). The diffusion-sensitizing gradient encoding (B1) was applied in 60 directions (b=1200s/mm2) and 5 images acquired without diffusion weighting (b =0s/mm2).
MRI pre-processing
Probabilistic tractography streamlines were generated using MRtrix3. MRtrix3 provides a suite of tools for image processing, analysis and visualisation of white matter using diffusion-weighted MRI (Tournier et al. 2019). Images were pre-processed using the DWI pre-processing pipeline outlined in [https://mrtrix.readthedocs.io/en/latest/dwi_preprocessing/denoising.html]. This included DWI distortion correction, image registration, atlas registration, DWI pre-processing and tissue segmentation.
Atlas registration was completed to provide cortical and subcortical (i.e. caudate) brain regions. This was completed using the well-validated Desikan-Killiany brain atlas, an automated labelling system (Desikan et al. 2006). Note, that this atlas automatically generates maps for each hemisphere separately. As such, all analyses were performed calculating mean values for both hemispheres of each brain region in order to minimise the number of multiple comparisons.
Once pre-processing was complete, images were processed to quantify measures of white matter integrity and generate a visual map of white matter tracts.
Tractography
Whole-brain tractography streamline reconstruction included brain mask generation, response function generation and streamline generation using “Second-order Integration Over Fiber Orientation Distributions” (iFOD2). “Spherical-deconvolution Informed Filtering of Tractograms” (SIFT) was used to improve the quality of tract reconstruction outlined in https://mrtrix.readthedocs.io/en/latest/quantitative_structural_connectivity/sift.html. White matter tractography measures were generated for the tracts of interest using subcortical and cortical parcellations from the Desikan-Killiany brain atlas as noted in the section above.
White matter tractography measures
The mean fractional anisotropy (FA), axial diffusivity (AD) and radial diffusivity (RD) were calculated as the mean value of all fixels (fibre bundles within each voxel), within each of the four regions of interest. The directionality of diffusivity (as measured by AD and RD) can provide more granular information into the nature of axonal damage or demyelination occurring in HD.
Fractional Anisotropy (FA)
FA is a metric that provides a simple and robust measure of the degree of anisotropic diffusion of water occurring within a region (Smith et al. 2012). Because FA reflects the degree of anisotropic diffusion, it will be high (ie, approaching unity) in regions of high organization (eg, corpus callosum), intermediate in regions with some degree of organization (eg, white matter regions that have no strong predominant axon fiber axis orientation), and low in tissues where the predominant cell shape, and therefore diffusion, is not specifically oriented (eg, grey matter) and approaching zero in free fluids (eg, CSF) (Pfefferbaum et al. 2000). Reductions in FA are associated with axonal damage.
Axial Diffusivity (AD)
AD is a metric which quantifies the diffusion of water parallel to white matter fibres. Whilst a decrease in AD is indicative of axonal damage (Song et al. 2003), an increase in AD may represent axonal degeneration and loss due to neuronal trimming (Beaulieu 2002).
Radial Diffusivity (RD) RD is a metric which quantifies the diffusion of water perpendicular to white matter fibres. An increasein RD is indicative of axonal damage (Song et al. 2005) and reflective of demyelination (Beaulieu 2002).
Statistical Analysis
Using IBM SPSS Version 24.0 for Windows, comparisons of tractography measures (FA, AD, RD), were conducted for within groups (baseline, 18-month, 30-month) and between groups (controls, pre-HD, symp-HD) using a general linear model (repeated measures ANCOVA) with Group and Time as Independent Variables (IVs) and each tractography measure (i.e., FA, AD, RD) as the dependent variables (DV). Age and gender were used as covariates for all analyses.
Summary statistics were calculated for the three groups on each of the white matter integrity measures and for each cortical region at each time point. Significant differences between groups and time points were calculated using t-tests. See Table 2.
Correlation Analysis
Pearson’s correlations were performed between clinical measures (i.e., UHDRS-TMS and DBS) and tractography measures for each region of interest. A correlation analysis between baseline tractography measures and UHDRS-TMS was conducted in the symp-HD group only. Correlations between change in mean cortical tractography measures and DBS was conducted for both HD groups.