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Published in final edited form as: Exp Neurol. 2009 Apr;216(2):525–529. doi: 10.1016/j.expneurol.2008.12.026

Longitudinal Diffusion Tensor Imaging in Huntington’s Disease

Kurt E Weaver 1, Todd L Richards 1, Olivia Liang 1, Mercy Y Laurino 2, Ali Sami 3, Elizabeth H Aylward 1
PMCID: PMC10353299  NIHMSID: NIHMS1905476  PMID: 19320010

Abstract

Serial diffusion tensor imaging scans were collected at baseline and one year follow-up to investigate the neurodegenerative profile of white matter (WM) in seven individuals with the Huntington’s Disease (HD) gene mutation and seven control subjects matched on age and gender. In the HD subjects, but not controls, a significant reduction of fractional anisotropy (FA), a measure of WM integrity, between baseline and follow-up was evident throughout the brain. In addition, a DTI scalar associated with the stability of axons, axial diffusivity, showed significant longitudinal decreases from year 1 to year 2 in HD subjects, declines that overlapped to greater degree with FA discrepancies than longitudinal increases in radial diffusivity, a DTI variable sensitive to demylinization. These preliminary results provide the first longitudinal DTI evidence of WM degeneration in HD and support the notion that FA abnormalities in HD may be a result of axonal injury or withdrawal. These results suggest that longitudinal FA changes may serve as a neuropathological biomarker in HD.

Keywords: Huntington’s Disease, Diffusion tensor imaging, Fractional anisotropy, Axial diffusivity, Radial diffusivity, White matter

Modern neuroimaging technologies have supplanted the traditional conception that Huntington’s Disease (HD) is a neurodegenerative disorder primarily of the basal ganglia (see Weaver et al., 2008, for a review). In particular, a number of magnetic resonance imaging (MRI) based techniques have re-introduced the notion that HD degrades the integrity of white matter (WM – Ciarmiello et al., 2006; Reading et al., 2005; Rosas et al., 2006; Bartzokis et al., 2007). However, the precise nature of such impairments is still a matter of debate and it is not known to what extent WM degeneration is part of the HD neuropathological profile.

Much of the recent motivation to investigate WM pathology stems from a number of cross-sectional MRI-based volumetric studies revealing decreased WM volumes in HD individuals (Aylward et al., 1998; Beglinger et al., 2005; Ciarmiello et al., 2006). Reduced WM volume can develop as a consequence of a number of factors, including decreased number of axons within the affected region, increased demyelinization of those axons or both. Recently, diffusion tensor imaging (DTI) studies have observed decreased fractional anisotropy (FA) values, a measure of WM tissue integrity, in both presymptomatic and early stage HD individuals relative to matched gene-negative peers (Kloppel et al., 2008; Rosas et al., 2006; Reading et al., 2005). While decreased FA suggests compromised WM, it does not reveal the specific nature of WM impairments. Until recently it was not believed that DTI had the capacity to specifically distinguish between decreased number of axons or demyelinization. However, significant efforts combining immunohistochemical with DTI approaches in murine models of WM disease have revealed that two additional DTI scalars, axial (λ∥) and radial (λ⊥) diffusivity, are uniquely sensitive to axonal injury/degeneration and the degree of myelinization, respectively (Wu et al., 2007; Sun et al., 2006, 2007; Song et al., 2005, 2002; Budde et al., 2007; Mac Donald et al., 2007). This work has shown that a decrease in λ∥ is associated with axonal injury and/or retraction and an increase in λ⊥ reflects demyelization within a fiber tract (c.f. Budde et al., 2007). Quantitatively, λ∥ is equal to the magnitude of the primary eigenvalue or parallel diffusion, while λ⊥ is the mean of the two tertiary eignevalues or diffusion perpendicular to the primary axis.

In HD, a specific examination of these DTI scalars has not been reported. Some MRI evidence has provided support for a demyelinization hypothesis in HD (Mascalchi et al., 2004; Bartzokis et al., 2007), paralleling early postmortem, histopathological studies in HD patients (Bruyn, 1973). Conversely, a few imaging studies have observed cortical grey matter (GM) atrophy in HD, suggesting that WM volume loss may be a consequence of the withdrawal of the axons projecting from cortical neurons. For example, studies combining automatic or semi-automatic morphological analytical approaches with high resolution MRI have revealed significant cortical atrophy including a thinning of the cortical mantle cross-sectionally (Kassubek et al., 2004; Rosas et al., 2002, 2005; Douaud et al., 2006) and longitudinally (Ruocco et al., 2008). Together with histological studies indicating decreased numbers of layer 5 cortical projection cells (Sotrel et al., 1991; Cudkowicz & Kowall, 1990; Hedreen et al., 1991), this evidence supports the hypothesis that decreased axonal numbers in HD contribute to WM pathology possibly accounting for reduced FA values in previous DTI-based reports.

Despite the mounting evidence for WM impairments in HD, no study has observed progressive WM abnormalities with DTI using a longitudinal, within-subjects design. The purpose of the current investigation was to examine longitudinal DTI changes across a one-year period in a combined sample of presymptomatic and early stage HD individuals and control peers. In addition, we sought to identify whether abnormal FA values in HD are a result of compromised axons, demyelinization or both. To accomplish these aims we used tract-based spatial statistics (TBSS), part of the FSL’s DTI toolbox (http://www.fmrib.ox.ac.uk/fsl/tbss/index.html) to compare FA, λ⊥ and λ∥ values across time and between groups. We scanned 7 HD gene positive and 7 control gene negative individuals (see table 1 for biographical details) at a baseline (time 1) and at a year follow up (time 2). Groups did not differ significantly on age or gender composition. The DTI scan consisted of a single-shot echo-planar sequence with the following parameters: TR/TE/flip angle: 10.5secs/63ms/90°, a matrix size of 128x128, a FoV of 240x240 with a 2 mm slice thickness. Diffusion weighting consisted of 32 non-colinear gradient directions, a non-diffusion weighted b0 map and a b-factor set at 1000 s/mm2. Seventy-two contiguous, sagittally oriented slices covering the whole brain were collected per gradient vector. All experiments were approved by the University of Washington IRB and adhere to the declaration of Helsinki.

Table 1.

Subject characteristics

Diagnosis* Subject Gender Age Handedness CAG repeats LHDRS# motor UHDRS motor time 2 YTO/YSO%
HD
Pre HD003 M 50 R 41 11 21 −10.82
Pre HD007 F 19 R 48 10 5 −15.13
Pre HD013 M 38 L 42 7 3 −15.1
Pre HD020 F 30 R 45 11 8 −11.93
Early HD008 M 37 R 41 11 7 1
Early HD021 M 57 R 41 14 22 1
Early HD023 M 66 R 41 18 27 6
AVERAGE 4.2 +/− 6.13 11.71 13.29
Control
Ctrl HD011 M 29 R N/A 2 2 N/A
Ctrl HD014 F 32 R N/A 3 0 N/A
Ctrl HD016 M 52 R N/A 6 4 N/A
Ctrl HD017 M 40 L N/A 7 1 N/A
Ctrl HD019 F 23 R N/A 3 4 N/A
Ctrl HD024 M 60 L N/A 6 4 N/A
Ctrl HD026 F 27 R N/A 1 0 N/A
AVERACE 38 +/− 5.12 4.00 2.14
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All measurements taken at tine (scan) 1, unless otherwise specificed

*

Diagnosis was made by a trained neurologist (AS) and bared on the UHDRS' motor score, clinical ovaluation and diagnosis confidence level

#

UHDRS - Un fied Huntingont's Disease Rating Scale

%

YTO - years to onset. YSO - years since onset. Negative values reflect estimated years until onset of symptoms based upon Langbehn et al., 2004 and were calculated from http://www.hdni.org:8080/predicted/html/Mean%20Expected%20years%20Until%20HD%20Onset.com Positive values reflect the number of years since a diagnosis was made. Handedness was based on self report.

Analyses were done using FSL’s (FMRIB [The Oxford Centre for Functional Magnetic Resonance Imaging of the Brain] Software Library) FDT toolbox (http://www.fmrib.ox.ac.uk/fsl/fdt/index.html). Images were first motion and eddy current corrected to remove non-linear artifacts and distortions from the data sets (Jenkinson & Smith, 2001). Tensors were fit using the b-factor and diffusion direction matrix with the DTIfit toolbox. Eigenvalues (λ1,λ2 and λ3), the resulting eigenvectors and the FA indices were calculated for each voxel resulting in diffusion weighted brain maps, including whole brain FA maps. Group and time-point analyses were carried out using TBSS. In depth descriptions of TBSS methodology have been published elsewhere by the Oxford imaging group (Smith et al., 2006) and our group (Richards et al., 2008). Briefly, all FA maps were aligned into the MNI152 WM template brain with non-linear registration using the image registration toolkit (IRTK) (Rueckert et al., 1999). The mean FA image was next established and thinned to create a mean FA skeleton that was thresholded at an FA > 0.2 and represents the centers of all tracts common to all subjects and scans (the FA skeleton, see green voxels in Figure 1A). Each subject's aligned FA data was then projected onto this skeleton and the resulting values were extracted from just the voxels detailed in the skeleton (sampling from 203,907 1 × 1 × 1 mm3 voxels).

Figure 1. Longitudinal white matter degenerative effects in HD assessed with DTI.

Figure 1.

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A. Spatial FA changes between time points for HD and control groups are revealed using FSL's tract-based spatial statistics (TBSS) utilizing a WM skeletonization procedure. Non-parametric paired sample t-tests correcting for multiple comparisons using a modified cluster estimation procedure resulting in a corrected p value of 0.05 were used to calculate group FA changes at different time points. Results are displayed on the MNI152 template brain in radiologic convention. The first row details FA decreases in the HD group from time 1 to time 2 (i.e. the orange voxels – ballooned to better illuminate the localization of the time point differences). The second and third rows show a vastly smaller discrepancy in FA from time 2 to 1 in the HD group and from time 1 to time 2 in the control group, respectively. B. A region of interest (ROI) approach was used to illustrate the amount of FA change across time for HD individuals. The black arrow points to the ROI (orange voxels) located in the corpus callosum, a WM region previously shown to have decreased FA values cross-sectionally in HD (c.f. Reading et al., 2004; Rosas et al., 2006). FA values were extracted from the ROI for each individual and plotted (middle line graph). Note the dashed lines indicate early stage individuals and solid white lines denote presymptomatic individuals. The slope for the FA change from year 1 to year 2 was extracted from each HD individual, averaged across the presymptomatic and early stage groups and plotted (bar graph on left, error bars represent standard error of the means). C. Shows the mean longitudinal comparisons of axial (λ∥) and radial diffusivity (λ⊥) results for the HD group (displayed as black voxels) overlaid on top of the time 1 > time 2 FA differences. Again, non-parametric paired t-tests corrected to p = 0.05 revealed λ∥ differences that were calculated as time 1 > time 2 and λ⊥ differences that were calculated from the time 2 > time 1 analysis. D. This effect was quantified (# of voxels showing a mean difference) for time point comparisons for each of the three DTI scalars (FA, λ∥ & λ⊥). The inset illustrates the degree of spatial overlap between both λ∥ & λ⊥ results with the FA decreases across time (1>2) for the HD group.

Spatial-based voxelwise statistics were performed using a permutation-based inference tool for nonparametric statistical thresholding (FSL’s randomise Monte Carlo permutation toolkit). Group and time contrasts were tested using a non-parametric repeated-measures ANOVA applied to each DTI scalar map (FA, λ⊥ and λ∥) independently. The significance threshold for between-group and time differences was set at p < 0.05, corrected for multiple comparisons across voxels using the threshold-free cluster-enhancement (TFCE) option in the randomise permutation-testing tool in FSL (Smith and Nichols, 2007). In addition, a region of interest (ROI) analysis was conducted in order to investigate the rate of FA change in HD individuals over the one-year period.

The results from the TBSS analyses, shown in figure 1A, were ballooned, plotted on the FA skeleton and overlaid on the MNI152 image for more accurate anatomical identification. The non-parametric ANOVA revealed a main effect of group (for spatial display of results see online supplemental figure 1), paralleling previously reported cross sectional FA differences (Rosas et al., 2006; Reading et al., 2004). Time x group interactions were investigated using non-parametric paired sample t-tests correcting for multiple comparisons using TFCE. Significant changes (p < 0.05) in FA across time were evident, after correcting for multiple comparisons, in the HD group, but only when contrasting time 1 > time 2 (Fig. 1A, rows 1 & 2). The control group showed few FA differences at either time contrasts (row 3). Spatially, a majority of the voxels showing a time 1> 2 FA difference in HD were located within subcortical, callosal and frontostriatal tracts, including among others the ascending limb of the internal capsule and the superior corona radiata. Parietaloccipital and posterior WM tracts did not show much longitudinal change in the HD group. A significant laterality effect was evident with longitudinal change in the right hemisphere exceeding change in the left.

To characterize the rate of FA decline within a region of WM showing FA degeneration in the HD group, we conducted an ROI analysis (Fig 1B). The ROI was produced by selecting 114 voxels within a region of the skeleton overlaying the genu of the anterior corpus callosum from the TBSS generated HD time 1 > 2 FA map. This region was chosen based on previous cross-sectional studies showing smaller FA values in presymptomatic (Reading et al., 2005) and early stage (Rosas et al., 2006) HD individuals within this region relative to gene-negative peers. FA values were first extracted from each individual and plotted at each time point. Paralleling the TBSS results, this illustration reveals that all 7 HD individuals had decreased FA from time 1 to 2. This is in contrast to control subjects where only 4 individuals had decreased FA, 1 showed increased FA from time 1 to 2 and two individuals had nearly identical FA values at the two time points. The mean rates (slopes) of FA change within the ROI between time 1 and time 2 for the 4 presymptomatic and 3 early stage individuals were then calculated and plotted (right bar graph in Fig 1B). Although this mean slope decrease was larger within the presymptomatic HD group (−0.0398) relative to early stage individuals (−0.0283), this discrepancy was not statistically different (students t = −2.270, p = 0.72). The lack of statistical differences between presymptomatic and early stage HD within this region of WM parallels the rate volumetric decline of striatal structures in HD, where the rate of change remains relatively constant after its onset (around 10-12 years prior to estimated onset of symptoms - Aylward et al., 2004). However, a larger sample size and multiple scans across time will be needed to more precisely characterize the rates of FA change as a function of disease progression in HD.

Finally, figure 1C reveals the spatially localized results from the λ∥ and λ⊥ non-parametric t-tests (black voxels) from the HD group. There was a significantly greater differential, after correcting for multiple comparisons, in λ∥ when contrasting time 1 > 2, relative to the time 2 > 1 contrast or any of the λ⊥ contrasts. All of these effects were quantified by plotting the total number of voxels showing significant change between scans (either time 1 > 2 or time 2 >1) in FA, λ⊥ and λ∥ for both HD and control groups (Fig. 1D). The non-parametric paired sample t-tests revealed over 14,000 voxels showing greater FA values at time 1 than at time 2 for the HD group. When these voxels were coregistered with λ∥ and λ⊥ results, the overlap between voxels showing FA decrease and λ∥ decrease time 1 > time 2 (> 40% overlap, see inset Fig 1D) was greater than the overlap between voxels showing FA decrease and λ⊥ increase (time 2 > time 1).

These results provide the first DTI evidence of WM degeneration in HD using a within subjects design. We combined serial DTI scans with FSL’s TBSS analysis and non-parametric statistical analyses to reveal FA decreases within a number of WM tracts across a one-year period. This suggests that WM degeneration in HD is both pronounced and fairly rapid. The relatively low numbers of participants suggest that these results need to be interpreted with some degree of caution. Nonetheless, the localization of the FA discrepancies is in good agreement with previous cross-sectional studies of mean FA differences between HD individuals and control subjects (Reading et al., 2005; Rosas et al., 2006). Taken together, the WM that appears to be the most sensitive to anisotropic disruption in HD are tracts throughout corticostriatal and frontothalamic networks, WM that supports behaviors that constitute the primary phenotypic abnormalities in HD. Within these tracts, decreased FA in HD individuals relative to gene-negative peers provides evidence for a disruption of the WM micro-structure and longitudinal declines in FA within HD individuals suggest WM degeneration is likely part of the HD neurodegenerative profile.

We also observed greater spatial overlap of declining λ∥ with declining FA values in the HD sample, suggesting that a larger degree of WM micro-structural abnormalities in HD are a consequence of axonal injury rather then an active demyelination process (for a histological examination between these scalars see Budde et al., 2007). However, a few regions did show significantly increased λ⊥values at time 2 relative to time 1 indicating some degree of demyelinization. Given the previously reported evidence supporting demyelinization throughout prefrontal and subcortical WM tracts in HD (Bartzokis et al., 2007; Bryum, 1973) combined with several lines of evidence supporting a role for axonal injury within WM (Sortel et al., 1991; Cudkowicz & Kowall, 1990), it is likely that both scenarios contribute to the WM neuropathology in HD. Because our HD sample was fairly heterogeneous (i.e. 4 individuals relatively far from disease onset and 3 recently diagnosed individuals), a larger sample size of both presymptomatic and early stage individuals would allow a clearer classification between these two circumstances and allow for an examination of the relationships between estimated years to onset or duration, symptom severity, and declining FA values.

One possible application of these findings is the notion that decreased WM stability over time may have contributed to the lack of success of fetal cell transplants within the stiatum of HD patients (Hauser et al., 2002; Bachoud-Levi et al., 2006), a potential therapeutic strategy for the disease. For instance, the longitudinal decline of corticostriatal tracts would likely prevent the incorporation of proliferating neural precursors into the functional architecture of the striatum. With a lack of sufficient cortical afferent input, new neurons originating from implanted stem cells would not receive the normal levels of activity dependent support including neurotropic support that occurs under typical developmental conditions (Tepper et al., 1998; Zuccato & Cattaneo, 2007). The net effect would see a failure of communication between cortical structures and the striatum potentially resulting in the targeted death of the newly proliferated cells.

The precise nature and mechanisms underlying a greater right lateralized WM impairment is unclear. One possibility relates to the use-dependent plastic effects of handedness. Previous studies in gene-negative (control) individuals have reported WM asymmetries related to handedness. Using DTI, Buchel et al., (2004) showed asymmetric FA differences within the WM of the precentral gyrus between groups of left and right handed individuals (i.e. greater FA in the left hemisphere for right handed individuals and visa versa for left handed individuals). Further, Herve et al., (2006) found increased WM volumes in the contra-lateral hemisphere to the dominant hand. Our sample of HD individuals only included 1 left handed individual. Based on previous findings, it is possible that the handedness of our HD sample played a role in the asymmetry that appeared in declining FA values. That is, over reliance on the dominant hand for fine motor computations may preserve the contralateral WM from the degenerative effects of HD. This hypothesis draws comparisons with studies of increased exercise (van Dellen et al., 2008) and environmental enrichment (Hockly et al., 2002) in HD transgenic mice, interventions which have been shown to preserve motor declines and even reverse diseased-related cortical abnormalities (Spires et al., 2004). However, a larger sample of left-handed HD individuals is needed to test this speculation.

Declining FA values highlights the notion that DTI may be used as a biomarker of disease progression in HD for use in clinical trials (Bohanna et al., 2008, see also Aylward, 2007). We provide preliminary evidence that the rate of FA change (within the corpus callosum and over a one year period) is similar between different stages of the disease. This indicates a relatively stable rate of change independent of predicted disease onset, an essential characteristic underlying a reliable biomarker (Aylward, 2007). Moreover, FA maps can be obtained in a reliable fashion. The DTI analysis is a fairly streamlined process and the calculation of whole brain FA maps is not labor intensive. Combined with the fact that these scans are fairly short in duration suggests that DTI may be a useful means of tracking disease progression serving as a possible outcome marker.

Supplementary Material

1

Supplemental Figure 1

Voxel-based spatial maps showing a main effect of group stemming from the non-parametric repeated measures ANOVA at a p < 0.05 correcting for multiple comparisons using TFCE are shown (orange and yellow voxels reveal those where FA values are HC > HD collapsed across time). The results are ballooned, plotted on the FA skeleton and overlaid on the MNI152 image for more accurate anatomical identification. The localization of group differences are in good agreement with the year1 > year2 comparisons stemming from the HD group including (to a smaller degree) the lateralized discrepancy within the right hemisphere.

Acknowledgements:

This work was supported by a grant from the CHDI Foundation. The authors would like to thank Jenee O’Brien and Jeff Stevenson for their help with data collection and Dr. Natalia Kleinhans for assistance with data analysis and DRs Natalia Kleinhans and Julie Stout for their insight.

Footnotes

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Associated Data

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Supplementary Materials

1

Supplemental Figure 1

Voxel-based spatial maps showing a main effect of group stemming from the non-parametric repeated measures ANOVA at a p < 0.05 correcting for multiple comparisons using TFCE are shown (orange and yellow voxels reveal those where FA values are HC > HD collapsed across time). The results are ballooned, plotted on the FA skeleton and overlaid on the MNI152 image for more accurate anatomical identification. The localization of group differences are in good agreement with the year1 > year2 comparisons stemming from the HD group including (to a smaller degree) the lateralized discrepancy within the right hemisphere.

NIHMS1905476-supplement-1.tif (2.6MB, tif)

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