Widespread increase of functional connectivity in Parkinson's disease with tremor: a resting-state FMRI study

Abstract

Parkinson's disease (PD) is a clinically heterogeneous disease in the symptomatology dominated by tremor, akinesia, or rigidity. Focusing on PD patients with tremor, this study investigated their discoordination patterns of spontaneous brain activity by combining voxel-wise centrality, seed-based functional connectivity, and network efficiency methods. Sixteen patients and 20 matched healthy controls (HCs) were recruited and underwent structural and resting-state functional MRI scan. Compared with the HCs, the patients exhibited increased centrality in the frontal, parietal, and occipital regions while decreased centrality in the cerebellum anterior lobe and thalamus. Seeded at these regions, a distributed network was further identified that encompassed cortical (default mode network, sensorimotor cortex, prefrontal and occipital areas) and subcortical (thalamus and basal ganglia) regions and the cerebellum and brainstem. Graph-based analyses of this network revealed increased information transformation efficiency in the patients. Moreover, the identified network correlated with clinical manifestations in the patients and could distinguish the patients from HCs. Morphometric analyses revealed decreased gray matter volume in multiple regions that largely accounted for the observed functional abnormalities. Together, these findings provide a comprehensive view of network disorganization in PD with tremor and have important implications for understanding neural substrates underlying this specific type of PD.

Keywords: Parkinson’s disease; centrality; connectome; resting functional connectivity; tremor.

Figure 1

Results of the within/between-group analysis on weighted degree centrality (WDC). (A), Mean WDC pattern for the HC group. (B), Mean WDC pattern for the PD group. (C), Between-group differences in the WDC. The results were mapped onto the brain surface using the BrainNet viewer (http://www.nitrc.org/projects/bnv/).

Figure 2

Between-group differences in seed-based functional connectivity. The seeds (top row) were defined as spherical ROIs (radius = 6 mm) centered at the peak voxels, with the strongest group effects in WDC for clusters in Figure 1C. Regions showing abnormal functional connectivity in the PD (bottom row) were mapped onto the brain surface using the BrainNet viewer (http://www.nitrc.org/projects/bnv/). See Table S1 in Supplementary Material for detailed information.

Figure 3

Between-group differences in network efficiency. (A) Brain surface representation of the 50 ROIs showing abnormal connectivity in PD. (B) mean connectivity patterns among the 50 ROIs for the PD and NC groups that were thresholded at a sparsity = 0.3. (C) Local and global efficiency the 50-ROI network in the PD and HC groups as a function of sparsity. *P < 0.05, FDR corrected.

Figure 4

Relationship between functional connectivity and clinical variables in PD patients. Significant correlations (P < 0.05, FDR corrected) of multiple functional connectivities were observed with the behavior performance of the patients. The figure shows the most significant correlations for the duration, tremor, and H–Y score. All the detected correlations are listed in Table S1 in Supplementary Material.

Figure 5

Relationship between network efficiency and clinical variables in PD patients. Significantly positive correlations were observed between the AUC of local and global efficiency and tremor score of the patients.

Figure 6

Receiver operating characteristic curve for distinguishing PD patients from healthy controls as a function of varying functional connectivity between the bilateral superior frontal gyrus (SFG). See Table S1 in Supplementary Material for detailed classification information for weighted degree centrality and other functional connectivities.

Figure 7

The effects of regional gray matter volume on the functional results of weighted degree centrality. (A), Spatial correlation between regional gray matter volume and weighted degree centrality. (B), Between- group differences in weighted degree centrality after controlling for regional gray matter volume. The results were obtained in one general linear model, with weighted degree centrality as the dependent variable, group status (0 or 1) and voxel-specific gray matter volume as independent variables, and gender, age, and head motion as unconcerned covariates. The results were mapped into the brain surface using the BrainNet viewer (http://www.nitrc.org/projects/bnv/).