Dan Wu1, Laurent Younes2,3,4, Andreia V Faria1, Christopher A Ross5, Susumu Mori1,6, and Michael I Miller3,4,7
1Radiology, Johns Hopkins University School of Medicine, BALTIMORE, MD, United States, 2Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, United States, 3Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States, 4Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States, 5Departments of Psychiatry, Neurology, Neuroscience and Pharmacology, and Program in Cellular and Molecular Medicine, Johns Hopkins University School of Medicine, BALTIMORE, MD, United States, 6F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, 7Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
Synopsis
In order to understand the temporal and spatial
order of brain atrophy in Huntington’s disease (HD), we aim to characterize the
whole brain volumetric changes based on T1-weighted whole brain segmentation.
We adapted a novel multi-variant linear statistical model to capture the change
points of volumetric changing courses from 412 control and HD subjects. The
change point analysis revealed that the brain atrophy initiated in the deep
gray matter structures and progressed to the peripheral white matter and cortical
regions, and it also suggested the posterior brain atrophy proceeded the
anterior brain. Introduction
Huntington’s
disease (HD) is a neurodegenerative disorder that progressively affects motor
and cognitive functions. It is known that the disease is caused by CAG repeat
expansion in the gene
Hungtingtin (HTT), and is directly related to the CAG-age
product (CAP score)
1,2. Previous MRI studies have
shown structural degeneration in basal ganglia and a range of grey and white matter
structures
3,4. However, the spatial and temporal order of the
structural changes is not fully understood. In this study, we adapted a novel
statistic model, namely the change point analysis
5, to capture the critical turning points of brain volumes
against disease progression, based on whole brain parcellation of T1-weighted
images.
Methods
The
data used in this study are from the multicenter PREDICT-HD study4,6,7.
MPRAGE data from 412 subjects were used, including 121 controls and 291 premanifest
HD with CAP score ranged 140-586. The image were acquired around 1×1×1.5 mm3 resolution with heterogeneous
protocols4. The images were segmented using our online resource
MriCloud8,9 (www.mricloud.org),
which provides a fully automated T1 segmentation pipeline based on diffeomorphic
image registration and multi-atlas fusion10,11.
A two-step multivariant linear statistical model was established to
examine volumetric changes in relation to the CAP score, which is a reliable
indicator of the disease severity and reflects the temporal order. In the first
step, the group difference between the HD subjects and controls is estimated
based on the group label (g, 1 for HD and 0 for
control), CAP score (c), age (a), sex (s), and intracranial volume (i).
Model
1: yj=α+β⋅cj⋅gj+γ⋅gj+δ⋅aj+η⋅sj+λ⋅ij+ϵj
where y
denotes the volume, j is the subject
index, α is a constant, and ε is the noise. The model parameters [α, β, γ, δ, η, λ] are estimated using maximum
likelihood estimation. A group difference is detected when β ≠ 0 and γ ≠
0. In the second step, only the HD population is involved, and the change
point is modeled as an additional linear change that takes effect when CAP score
is greater than the change point Δ, in addition to the baseline change.
Model
2: yj=α+β⋅cj+β′⋅cj⋅H(cj−△)+η⋅sj+λ⋅ij+ϵj
where
H is a Heaviside function such that H(t)
= 1 if t>0 and H(t)
= 0 otherwise. The optimal change point is obtained at Δ* when the likelihood L(Δ*) is maximal among all Δs between 200-500. A
significant change point is found when β’
≠ 0 at Δ*. The significance is
evaluated by permutation test12, and the variance of the change
point is estimated via bootstrap5.
Results
Figure
1 showed the change points detected in several key structures that had
significant volumetric reduction as the CAP score increased. The volumes were
represented as the percentage differences with respect to the controls. Note
that, if a significant group difference is detected from model 1 but no
significant change point is detected from model 2, we assume a linear change with
CAP and assign the earliest change point (Δ*=150, in unit of CAP) for that
structure, such as the basal ganglia. We mapped the change points with familywise
significance of
p<0.05 onto a
T1-weighted brain template in Figure 2. The map revealed the earliest
volumetric changes in the basal ganglia and ventricle, followed by the white matter and then the occipital and frontal cortices, while the posterior white matter changed ahead of the anterior white matter. Figure 3 showed the rate of percentage volume
change after the change point, which suggested a rapid volume loss in the basal
ganglia and a drastic expansion in ventricle. In addition, the anterior brain structures showed
a relatively faster atrophy compared to the posterior brain.
Discussion and Conclusion
While
much is known about HD and its MRI markers at discrete time points and
individual structures, in this study, we mapped the spatiotemporal changing
pattern of whole brain atrophy using a novel statistical model. Our findings
agree with the general understanding of injury progression from the deep gray
matter to the white matter and then to the cortical regions. At the same time, the
data suggest a posterior-to-anterior gradient, where the posterior brain structures
change earlier but at a relatively slow rate and vice versa in the anterior
brain. Although the data we used in this study are not longitudinal, this large
population of cross-sectional data analysis provides important knowledge about
the temporal order of disease progression. In addition to the volumetric analysis,
measurements from diffusion MRI or other imaging contrasts could provide more
sensitive markers, and a multi-modality study is undergoing.
Acknowledgements
No acknowledgement found.References
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