Rajikha Raja1, Josef Ling1, Gary Rosenberg2, and Arvind Caprihan1
1The Mind Research Network, Albuquerque, NM, United States, 2Department of Neurology, University of New Mexico Health Sciences Center, Albuquerque, NM, United States
Synopsis
Binswanger's disease (BD) is a form of vascular dementia which is prevalent in older population. BD is characterized by the presence of white matter lesions due to injured small vessels in the brain. Magnetic resonance imaging findings related to BD reported large white matter lesions in FLAIR and high blood-brain barrier permeability in dynamic contrast enhanced MRI. Changes in water diffusion in white matter lesions and axonal damage were reported using diffusion tensor imaging metrics. While all the studies focused on exploring the voxel level measures as BD markers, we aimed to quantify the WM integrity through measures estimated for fibers within a voxel. In this study, we apply fixel-based analysis (FBA) to multi-shell diffusion data to evaluate the fiber specific measures such as fiber density (FD) and fiber cross-section (FC) in regions of white matter lesions in BD subjects and compared the measures with healthy controls. Reduced FD and FDC are revealed in areas of white matter lesions in BD subjects as compared to those in control group.
INTRODUCTION
Binswanger disease (BD) is a form of vascular
dementia which is accompanied by neuro-inflammation, breakdown of blood brain
barrier, and cerebral small vessel ischemia leading to extensive presence of
white matter lesions1. In this study we apply multi-tissue constrained
spherical deconvolution fixel-based analysis2 (FBA) method to
multi-shell diffusion data for characterizing white matter damage in BD
subjects. FBA decomposes crossing fibers
in a voxel into distinct fiber bundles in each voxel. The differences in fiber
density (FD), fiber cross-section (FC), and a combined measure FDC (=FDxFC) for
each fiber bundle are analyzed between health controls (HC) and BD subjects. We
compare each BD subject individually to group of controls because location of white matter damage can vary across BD subjects. METHODS
The diffusion data was acquired
on a Siemens 3T TRIO scanner with three non-zero b-values (800, 1600, and 2400
s/mm2) with 165 gradient directions equally distributed across three b-values and eight b=0 values. For each non-zero b-value there were equal
number of anterior-posterior and posterior-anterior phase-encoding directions
for distortion correction. We studied 10 healthy controls and 6 BD subjects. The University of New Mexico Human
Research Review committee approved the study. The BD subjects
were individually compared to group of controls.
The distortion and motion
correction was done based on TOPUP and the EDDY programs from FSL
software3. FBA analysis was performed using mrtrix3 software4.
Each data set was bias-field corrected and up-sampled to 1.25 mm isotropic
resolution to improve accuracy of spatial normalization.
A tissue response function was
found for each subject in voxel consisting of single direction pure white
matter in an unsupervised manner. The average response function across subjects
was used to calculate fiber orientation distribution function (fODF) for
each subject based on multi-tissue constrained spherical deconvolution method5.
A study specific fODF was found
for group of healthy controls by non-linear diffeomorphic registration. The
fODF of each subject was then non-linearly warped to the study template. The
fiber bundle measures (FD, FC, and FDC) were calculated for each subject based
on warped fODF.
The regions of WML were analysed by comparing the histograms
of FD, FC and FDC measures between control group and each of the BD subject.RESULTS
Figure 1 compares the difference in fODF for the
control template and a BD subject over the white matter lesion defined by the
FLAIR image. The flair image of a BD subject is shown as background image in Figures
1-3 to visually compare fibers inside WML regions. Figure 2 shows the fixel
plot for FD. The color scales indicates
fiber density with black being the lowest.
FD decreases in the WML as seen by the change in colors from red to
black. Figure 3 shows plots of the first fixel element for FD and FC. A similar
plot is not shown for FDC because of its visual similarity to the FD plot. The
scale of Figure 3 was chosen to visually emphasize the increase. FD decreases
in regions of WML as is to be expected but FC increases in and around white
matter lesions. The differences in the range of FD, FDC and FC values in the
WML regions are shown as histograms in Figure 4. The voxels for the histogram of
healthy control group were extracted from WML voxel positions of a representative
BD subject. The shift in the histogram peaks of BD as compared to that of HC is
shown. The decreasing patterns of FD and FDC measures are illustrated in Figure
5. Mean of FD and FDC values calculated over the voxels extracted from WML
regions are plotted for the control group and each of the BD subject. The
dotted line shows a clear distinction between the mean values of control group
and BD subjects.DISCUSSION AND CONCLUSION
The advent of multi-shell imaging allows us to
model crossing fibers in a voxel and estimate properties of different fiber
populations in a voxel. We have shown
that FD and FDC are sensitive parameters for characterizing white matter
damage. Changes observed in FC are inconsistent
and hard to explain. We have a limited number of BD subjects. This requires careful evaluation of FC to
find the source of variations in the FC pattern between BD subjects. The BD subjects exhibit large volumes of WML.
The decrease in values of FD and FDC for BD subjects makes these measures
potential markers for diagnosis of BD. There are other models to explain
multi-shell data (such as NODDI) and a comparison with standard parameters such
as FA and MD is warranted for white matter characterization.Acknowledgements
This research was supported by NIH grant UH2NS100598.References
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