Jae-Hyuk Shim1 and Hyeon-Man Baek1
1Gachon University, Incheon, Republic of Korea
Synopsis
Basal
ganglia structures, globus pallidus internal, globus pallidus external,
subthalamic nucleus, substantia nigra, red nucleus and striatum were
automatically segmented on 3T and 7T HCP preprocessed diffusion weighted
images. Connectivity between each basal ganglia structure was observed using
probabilistic tractography generated with FSL's diffusion tools such as
BEDPOSTX and PROBTRACKX. Tractography between basal ganglia was compared
between 3T and 7T to observe differences that arise from tradeoffs of each
acquisition.
Introduction
Certain
basal ganglia nuclei, such as the subthalamic nucleus (STN) and the globus
pallidus internal (GPi) have been common and effective targets in deep brain
stimulation (DBS) surgery for Parkinson's disease treatment1. As
such, accurate segmentation of basal ganglia structures is crucial in
documenting the correct locations of intended DBS target in relationship to
basal ganglia nuclei as well as to interconnected white matter pathways.
Additionally, detailing the structural connectivity between basal ganglia
nuclei can be beneficial in studying the effects of both Parkinson's disease
and Parkinson's disease treatments. For this study, Lead-DBS, a MATLAB based
toolbox, is used to segment globus pallidus external (GPe), GPi, STN,
substantia nigra (SN), red nucleus (RN) and the striatum2.
Subsequently, probabilistic tractography between the segmented structures were
generated through PROBTRACKX from the FMRIB software library (FSL) using 3T and
7T diffusion weighted images3. The results of tractography on 3T and
7T diffusion images were compared to describe the differences that arise due to
tradeoffs of each acquisition. Methods
3T MRI T1-weighted,
T2-weighted images, and 3T, 7T diffusion images from Human Connectome Project
(HCP), all of which were preprocessed using the HCP preprocessing pipeline were
used in this study4,5. Lead-DBS segmentation of left hemisphere
basal ganglia structures, GPe, GPi, STN, SN, RN and the striatum was done through
ANTs normalization of the DISTAL atlas on T1w and T2w images6. The results
of segmentations were overlaid on top of T2 images, where intensities of
segmentation targets are slightly visible, to check for accuracy as shown in Figure
1. Each segmented structure was registered through SPM12 to its respective
subject's 3T and 7T diffusion MRI data, which had already gone preprocessing
through eddy correction and BEDPOSTX7,8. Following the registration,
probabilistic tractography of each registered structure and between registered
structures were generated through PROBTRACKX9. The number of fiber
tracks generated from 3T and 7T diffusion images of each subject were averaged then
compared. Results
Figure 1
shows the 6 regions segmented by Lead-DBS. Figure 2 shows each segmented
structure that were overlaid on top of 3T and 7T T2w images for validation
matched the intensities of segmentation targets, confirming that segmentation
was mostly successful. Table 1 shows the normalized averages of fiber counts and
Table 2 shows the average deviations of normalized fiber counts between
segmentations generated from 3T and 7T diffusion weighted images. Highest
number of normalized averages of fiber counts (striatum, GPe; 5489.6) were
generated from 7T images and average deviation of fiber counts from 3T images
were generally lower than average deviation of fiber counts from 7T images. 3D
and 2D representations of fibers generated by PROBTRACKX are shown in Figure 3. Discussion
Parkinson's
disease pathology involves the death of dopamine producing neurons in the
substantia nigra, which disrupts the downstream pathways of the basal ganglia
structures regulated by dopamine. In addition, previous studies have shown that
deep brain stimulation of subthalamic nucleus have shown increased connectivity
strength of surrounding white matter tracts10. It is possible that
analyzing the interconnectivity of white matter tracts between basal ganglia
nuclei can help detect biomarkers that designate the progression of Parkinson's
disease as well as results of treatments for Parkinson's disease.
Tractography
between basal ganglia nuclei were compared between 3T and 7T HCP acquisitions.
The 7T acquisition had higher spatial resolution (1.05mm isotropic for 7T, 1.25
isotropic for 3T) at the cost of lower gradient strength (70 mT/m for 7T, 100
mT/m for 3T). The tradeoffs in acquisition parameters showed that
interconnectivity between basal ganglia nuclei in 3T images had lower average
deviation of normalized fiber counts than in 7T images. However, most nuclei
pairs had more fibers generated with 7T images than with 3T images. It is
possible that higher spatial resolution of 7T enhanced fiber generation between
basal ganglia nuclei but suffered from consistency due to lower angular
contrast than 3T images. Additionally, the fibers that were generated from 7T
images showed more detail and creases of fiber tracts than fibers generated
from 3T images. As more studies utilize higher field acquisitions, it would be
beneficial to account for such differences that arise due to differences in
acquisitions when comparing results of basal ganglia connectivity. Conclusion
This study
shows that segmenting basal ganglia nuclei and generating probabilistic
tractography between the segmentations are possible for quantifying structural
interconnectivity. Additionally, the comparison between 3T acquisitions with
higher angular contrast and 7T acquisitions with higher spatial resolution
showed that fibers generated from 7T acquisitions results in higher average
deviation of fiber counts but with increased fiber generation between basal
ganglia nuclei than from 3T acquisitions. Future studies can be done using a
similar pipeline to observe basal ganglia interconnectivity of Parkinson's
disease patients as well as deep brain stimulation treated subjects. Acknowledgements
Data were provided [in
part] by the Human Connectome Project, WU-Minn Consortium (Principal
Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the
16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience
Research; and by the McDonnell Center for Systems Neuroscience at Washington
University.
This research was
supported by the Brain Research Program through the National Research
Foundation of Korea (NRF) funded by the Ministry of Science and ICT
(NRF-2017M3C7A1044367).
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