Distinct Changes in Global Brain Synchronization in Early-Onset vs. Late-Onset Parkinson Disease

Abstract

Early- and late-onset Parkinson’s disease (EOPD and LOPD, respectively) have different risk factors, clinical features, and disease course; however, the functional outcome of these differences have not been well characterized. This study investigated differences in global brain synchronization changes and their clinical significance in EOPD and LOPD patients. Patients with idiopathic PD including 25 EOPD and 24 LOPD patients, and age- and sex-matched healthy control (HC) subjects including 27 younger and 26 older controls (YCs and OCs, respectively) were enrolled. Voxel-based degree centrality (DC) was calculated as a measure of global synchronization and compared between PD patients and HC groups matched in terms of disease onset and severity. DC was decreased in bilateral Rolandic operculum and left insula and increased in the left superior frontal gyrus (SFG) and precuneus of EOPD patients compared to YCs. DC was decreased in the right putamen, mid-cingulate cortex, bilateral Rolandic operculum, and left insula and increased in the right cerebellum-crus1 of LOPD patients compared to OCs. Correlation analyses showed that DC in the right cerebellum-crus1 was inversely associated with the Hamilton Depression Scale (HDS) score in LOPD patients. Thus, EOPD and LOPD patients show distinct alterations in global synchronization relative to HCs. Furthermore, our results suggest that the left SFG and right cerebellum-crus1 play important roles in the compensation for corticostriatal–thalamocortical loop injury in EOPD and LOPD patients, whereas the cerebellum is a key hub in the neural mechanisms underlying LOPD with depression. These findings provide new insight into the clinical heterogeneity of the two PD subtypes.

Alterations of Regional Homogeneity in the Mild and Moderate Stages of Parkinson’s Disease

“…In comparison with the HC group, subjects in the PD-moderate group exhibited reduced ReHo values in the cerebral cortex while subjects in the PD-mild group did not, including the Rolandic_Oper_R, Temporal_Sup_R, Postcentral_R, and Precentral_R regions. Some previous studies have reported structural or functional alterations in the Rolandic Operculum in PD patients (New et al, 2015 ; Xu et al, 2018 ; Liu et al, 2019 ; Wang T. et al, 2020 ). One previous study focused on the voice network of PD patients with vocalization impairment; this work identified alterations in the Rolandic Operculum (New et al, 2015 ).…”

Aberrant changes of dynamic global synchronization in patients with Parkinson's disease

“…As one of the robust fMRI metrics, degree centrality (DC) has been applied to map the integrated nodes in functional networks at the voxel level by calculating the functional connectivity of each voxel with the others and has revealed impaired DC in several cortices relevant to motor or cognitive dysfunction in PD. These findings have yielded evidence to support that disrupted global synchronization is an element in the neuropathology of PD (11)(12)(13). Nevertheless, the majority of these previous findings were adopted as the static pattern, which ignored the dynamic property of neuronal activities since intrinsic fluctuations are time-varying rather than constant during acquisition (14).…”

“…Referring to a previous setting (19)(20)(21), the parameters of the sliding time window were chosen with 50 TR (100 s) for the window length and five TR (10 s) for the step size, generating 39 consecutive windows within the entire time series. The DC map within each window was calculated separately as follows: (i) computation of the Pearson's correlation coefficient (r) between any pair of voxels within the prior probability whole-brain GM mask; (ii) generation of a whole-brain correlation matrix for an individual by thresholding the r at 0.25 as previously described (12,22); and (iii) creation of a graph of the DC values by calculating the weighted DC value of whole-brain network. The standard deviation (SD) of all DC maps across 39 moving windows was calculated and further standardized by subtracting the global mean value of the GM and then dividing that value by the SD, which was defined as the temporal variability of DC.…”

Ginkgo Biloba Extract Is Comparable With Donepezil in Improving Functional Recovery in Alzheimer’s Disease: Results From a Multilevel Characterized Study Based on Clinical Features and Resting-State Functional Magnetic Resonance Imaging

“…A matrix of Pearson correlation coefficients between a given voxel and all other voxels was generated to show the whole-brain functional connectivity matrix for each voxel. An undirected adjacency matrix was then generated by setting a threshold to each correlation at an r value more than 0.25 ( Buckner et al, 2009 ; Wang et al, 2018 ; Wang et al, 2020 ). DC is defined as the sum of weights (r-values) of significant functional connections ( r > 0.25) for each voxel.…”