Event Abstract

Degree of Centrality of brain functional connectivity within the motor network for Parkinson’s Disease

  • 1 GIGA-Cyclotron Research Center In Vivo Imaging, University of Liege, Belgium
  • 2 Department of Neurology, University Hospital Liège, Belgium
  • 3 GIGA-In silico Medicine, University of Liège, Belgium

Introduction Patients with Parkinson’s disease (PD) present altered brain functional connectivity in the motor network [1]. However, little is known about the main clusters of connectivity at the voxel-level presented within this network, composed of the sensorimotor cortex, basal ganglia, cerebellum and thalamus. In this work, we looked at group differences in the Degree of Centrality (DC) at each node of the motor network between PD patients and healthy individuals. DC measures the number of connections that a voxel has with the whole brain or within a specific template [2,3]. DC is a measure of density of functional connectivity at the voxel-level. Additionally, DC can be used to define regions of interest (ROIs) which is based mainly on the degree of connectivity within the dataset of the studied population, rather than with predefined coordinates or by anatomical reference. Materials and Methods 40 PD patients “on” medication (25 males, age 66.5(8.6) years, mean disease duration 5.2(3.5) years, H&Y scale 1.5(0.6)) and 42 healthy controls (23 males, age 65.1(8.3) years) matched for age, gender and levels of education. Resting-state BOLD fMRI data were acquired using a short TR on a 3T MRI scanner (voxel size: 3.4x3.4x5.0 mm; matrix size: 64x64x20, TR: 1.3 s; 350 scans). fMRI data preprocessing with SPM12 included: inhomogeneity of field correction, head motion correction, coregistration into structural MRI, spatial normalization with DARTEL [4] and smoothing with 6mm FWHM. Data were linearly detrended and bandpass filtered (0.01 – 0.1Hz); time series of white matter, cerebrospinal fluid, six affine motion parameters and outliers found with ART were regressed out with CONN15. A template of the motor network was created by merging the map of the cortical sensorimotor network (from the group of 17 networks) [5] with the atlases of the thalamus [6], basal ganglia [7] and cerebellum [8], as shown in Fig. 1. This template was DARTEL-normalized, so that it could be adjusted to the MRI images of our population. We computed the DC per subject within this template. DC maps were created using the 3dDegreeCentrality function in AFNI with sparsity = 1.0 [3]. Group results were averaged and non-parametric statistical tests were performed using the toolbox SnPM13 of SPM12 with 1000 permutation tests. We considered the results that surpassed the threshold of p < 0.001. Each sub-atlas, i.e., thalamus, cerebellum, basal ganglia and motor cortex, was divided by its maximum DC with the aim of normalizing the dispersion of the density of connectivity of the final template, as presented in Fig. 2. Results The most densely connected regions among all the population are found in the primary motor cortex, supplementary motor area, the anterior putamen, the ventral and medial part of the thalamus and the motor cerebellum, as shown in Fig. 2. The maximum DC values for each sub-atlas are: sensorimotor cortex: 3843, thalamus: 1226, basal ganglia: 1277 and cerebellum: 1810. These values indicate the maximum number of connections that a voxel has with respect to the template of the motor network. The regions where statistical differences in DC were found are: R and L posterior putamen, R anterior putamen and supplementary motor area, for Control > PD; and the R and L motor cerebellum, for PD > Control. These results are shown in Fig. 3. Conclusions DC is a novel approach to look at the density of connectivity within certain broad regions of interest at the voxel-level. Normally, DC is calculated at the whole-brain level, but in this case, we have deliberately focused on the main regions of the motor network, which are known to be specifically affected in PD. With this framework, these group results are consistent with a decreased connectivity in basal ganglia-cortical circuits with increased compensatory cerebellar connectivity in PD as compared with healthy subjects. Future studies should test to what extent these changes in connectivity could be used at the individual level as a diagnostic biomarker and to monitor disease progression. From a more technical point of view, the mean of DC maps could be used to adjust the definition of the ROIs within the motor network. This mean map shows the regions that are most densely connected in the dataset, and therefore, the ROIs could be data-oriented updated. Figure labels Figure 1. Final template of the motor network. This template is composed of the map of the sensorimotor cortical network [5], merged with the atlases of the thalamus [6], basal ganglia [7] and cerebellum [8]. This template is used for the calculation of the DC. This figure was displayed using the BrainNet Viewer [9]. Figure 2. Mean of DC for the whole population (PD and control subjects). A. Shows the distribution mainly in the cortex and cerebellum. B. Shows the distribution in the putamen, thalamus, supplementary motor area and part of the cerebellum. Each structure was normalized according to its maximum value. Figure 3. Results of the non-parametric statistical maps with 1000 permutation tests of DC within the motor network (p<0.01 uncorrected for illustration purposes). Red indicates Control > PD, and blue PD > Control.

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[1] Wu, T. (2012), ‘Basal ganglia circuits changes in Parkinson's disease patients’, Neuroscience Letters, vol. 524, no. 1 pp. 55–59. [2] Zhang, J. (2015), ‘Abnormal functional connectivity density in Parkinson's disease’, Behavioural Brain Research, vol. 280, pp. 113-118. [3] Craddock, R. (2016), ‘Optimized implementations of voxel-wise degree centrality and local functional connectivity density mapping in AFNI’, GigaScience Database. [4] Ashburner, J. (2007), ‘A fast diffeomorphic image registration algorithm’, Neuroimage, Vol. 38, no. 1 pp. 95-113. [5] Yeo, B. (2011), ‘The organization of the human cerebral cortex estimated by intrinsic functional connectivity’, Journal of neurophysiology, Vol. 106, no. 3 pp. 1125-65. [6] Krauth, A. (2010), ‘A mean three-dimensional atlas of the human thalamus: Generation from multiple histological data’ Neuroimage, vol 49, no. 3 pp. 2053-2062. [7] Keuken, M. (2014), ‘Quantifying inter-individual anatomical variability in the subcortex using 7T structural MRI’, Neuroimage, vol. 94, pp. 40-46. [8] Diedrichsen, J. (2011) ‘Imaging the deep cerebellar nuclei: A probabilistic atlas and normalization procedure’, Neuroimage, vol. 54, no. 3, pp. 1786-94. [9] Xia, M. (2013) ‘BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics’, Plos One, vol. 8: e68910.

Keywords: Parkinsons disease (PD), Degree of centrality, Regions of Interest (ROIs), motor network connectivity, Resting state – fMRI

Conference: Belgian Brain Congress 2018 — Belgian Brain Council, LIEGE, Belgium, 19 Oct - 19 Oct, 2018.

Presentation Type: e-posters


Citation: Baquero K, Guldenmund P, Rouillard M, Depierreux FV, Balteau E, Phillips C, Bahri M and Garraux G (2019). Degree of Centrality of brain functional connectivity within the motor network for Parkinson’s Disease. Front. Neurosci. Conference Abstract: Belgian Brain Congress 2018 — Belgian Brain Council. doi: 10.3389/conf.fnins.2018.95.00031

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Received: 11 Aug 2018; Published Online: 17 Jan 2019.

* Correspondence: Miss. Katherine Baquero, GIGA-Cyclotron Research Center In Vivo Imaging, University of Liege, Liège, Liège, 4000, Belgium, kabaquero@uliege.be