Karthik R Sreenivasan1, Virendra Mishra1, Zhengshi Yang1, Christopher Bird1, Xiaowei Zhuang1, Dietmar Cordes1,2, and Ryan R Walsh3
1Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States, 2University of Colorado Boulder, Boulder, CO, United States, 3Muhammad Ali Parkinson Center at Barrow Neurological Institute, Phoenix, AZ, United States
Synopsis
Imaging biomarkers that reliably capture the impact of the spreading pathology of Parkinson’s disease (PD), including its impact on both white and graymatter, remain elusive. In this study, we applied graph-theoretical techniques to multi-site resting-state fMRI data from a cohort of unmedicated early PD-subjects in Parkinson’s Progressive Markers Initiative (PPMI) database. Altered functional connectivity and disrupted topological brain organization was seen in early PD-subjects. Our study opens new avenues to understanding disease progression and severity of PD from graph-theoretical approach.
Introduction
Parkinson's disease (PD) is a progressive movement disorder of the
central nervous system, which is characterized by altered motor and non-motor
aspects of function. Imaging biomarkers that capture the impact of this
spreading pathology in both white and gray matter remain elusive [1, 2]. Graph theoretical
approaches have the ability to characterize connectivity at both global and
local levels and can provide useful information about functional organization of
the brain [3]. Several studies have reported altered topology of brain function
in patients with PD [4, 5], but this has not been validated in unmedicated
patients nor in a multi-site cohort of early PD-subjects to the best of our
knowledge. Hence, we applied graph theoretical approaches to resting-state
functional MRI data from Parkinson’s Progressive Markers Initiative (PPMI)
database to determine how the functional network is altered in early stages of
PD prior to receiving medication.Methods
The data used in the preparation of this article were obtained from the
Parkinson’s Progression Markers Initiative (PPMI) database
(www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-info.org. Functional-MRI data
for 18 healthy controls and 22 unmedicated PD-subjects were obtained from the
PPMI database (see Table 1 for demographics). Imaging parameters are described
in detail at http://www.ppmi-info.org/. After standard
preprocessing, mean time series were obtained from 90 ROIs based on the AAL atlas
(excluding cerebellum and vermis). The connectivity between two ROIs was
estimated using Pearson’s correlation between their averaged time-series, and
subsequently a connectivity matrix (90 x 90) was obtained for each subject. Two-sample
t-tests were used to compare the functional connectivity between the two groups
regressing out age. Pathlength (Lp), clustering coefficient (Cp), Global (GE)
and local efficiency (LE), were computed for each subject at various sparsity
thresholds (S 0.05-0.4, ∆S=0.01). Graph theory measures were obtained using
GRETNA toolbox [6]. Nodal measures of GE, LE, Lp, and Cp were extracted at a
sparsity at which the network was fully connected for both groups [7]. Two-sample
t-tests with age as covariate were used to see if area under the curve for any
of the above metrics were significantly different between the two groups.Results
Fig. 1 shows the paths that were significantly greater in controls when
compared to the PD group. We found 11 connections that were significantly
(p<0.01) greater in controls compared to the unmedicated PD, primarily
comprising the pallidum, frontal superior orbital gyrus, superior and inferior
temporal gyrus and few other regions. Results were visualized with the BrainNet
Viewer (http://www.nitrc.org/projects/bnv/) [8]. There were no
paths with greater connectivity in the PD group compared to controls. Fig. 2
and Fig. 3 show the graph theory results. The evaluation of the AUC values revealed
significantly (p<0.05) lower Lp (Fig. 2a) and higher Cp (Fig. 2b) in the control
group when compared to PD. Nodal analysis revealed no nodes with a longer Lp in
controls and no nodes with higher Cp in PD (Fig.3a & Fig. 3b). Nodes with higher
Lp in PD were located at olfactory cortex, hippocampus, inferior parietal
lobule and superior temporal gyrus. (Fig.3a). Nodes with higher Cp in controls
were located at frontal mid orbital, olfactory, hippocampus, middle occipital
gyrus, fusiform gyrus, superior and inferior parietal cortex (Fig.3b). The area
under the curve for GE (Fig. 2c) and LE (Fig. 2d) were significantly
(p<0.05) greater in the controls when compared to the PD group. Nodal
comparisons of GE and LE revealed several nodes that showed significantly
higher global and local efficiency in controls when compared to the PD group
(Fig.3c and Fig.3d).Discussion
The current study revealed altered functional
connectivity and topological properties of networks in unmedicated early PD. Our
results show decreased functional connectivity in the PD group when compared to
controls between regions known to be implicated in PD, but importantly these
differences were detected using an unbiased whole-brain approach. Furthermore, PD
group showed reduced Cp, GE, and LE, and increased Lp, when compared to
controls. These observations point to a shift toward a disrupted network
topology with altered integration and segregation.Conclusion
Graph theoretical study of early PD subjects revealed
disrupted topological organization in PD subjects. These findings expand upon
prior such investigations in that 1) overall functional connectivity reduction
is recapitulated, 2) connectivity is impaired in both integration and
segregation in PD pathologic ROIs, and 3) these findings are evident in newly
diagnosed unmedicated patients with only mild disease severity and avoiding a
priori ROI assumptions by using a whole-brain unbiased approach, validating
this approach in PD.Acknowledgements
This study was partially supported by the NIH COBRE
grant 1P20GM109025-01A1 and the Elaine P Wynn and Family Foundation. The
Michael J. Fox Foundation supports the PPMI study for Parkinson’s Research. Other
funding partners include a consortium of industry players, non-profit organizations
and private individuals.References
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