Brain Functional Connectivity in de novo Parkinson's Disease Patients Based on Clinical EEG

Abstract

In Parkinson's disease (PD), cortical–subcortical interplay plays a relevant role in affecting clinical performance. Functional MRI sequences described changes in functional connectivity at different stages of disease. Scarce are, instead, the investigations examining brain connectivity in patients with PD at early stages of disease. For this aim, here we analyzed the differences in functional connectivity between de novo, never treated, PD patients and healthy controls. The analyses were based upon custom-written scripts on the Matlab platform, combined with high-level functions of Fieldtrip, Brainstorm, and Brain Connectivity toolboxes. First, we proceeded to the spectral analysis of the EEG data in the five frequency bands (δ-θ-α-β-γ). Second, we calculated functional connectivity matrices based on both coherency (COH) and imaginary part of coherency (iCOH), in the δ-θ-α-β-γ frequency bands. Then, four network measures (density, transitivity, global efficiency, and assortativity) were computed in identified connectivity matrices. Finally, we compared the spectral density, functional connectivity matrices, and network measured between healthy controls and de novo PD patients through two-samples T-test. A total of 21 de novo PD patients and 20 healthy subjects were studied. No differences were observed in spectral analysis between the two groups, with the exception of the γ band where a significant increase in power density was found in PD patients. A reduced connectivity in the main EEG frequency bands (α-β frequency bands) was observed in PD patients compared to controls, while a hyperconnectivity was found in PD patients in γ band. Among the network measures, a reduced assortativity coefficient was found in de novo PD patients in α frequency band. Our results show the occurrence of early EEG functional connectivity alterations from the initial stages of PD. From this point of view, connectivity analysis may ease a better understanding of the complexity of PD physiopathology.

Capturing the Abnormal Brain Network Activity in Early Parkinsons Disease With Mild Cognitive Impairment Based on Dynamic Functional Connectivity

“…In comparison to HC, it was shown that ePD-nMCI patients had higher k P LI and lower RR in the alpha band. Conti et al [31] found that some cortical regions of early PD patients began to establish or increase connectivity with other regions in the alpha band to make up for the defects caused by PD, which accorded with our findings that the fluctuation of dFCs of ePD-nMCI patients was higher than that of HC in the alpha band. Evangelisti et al [32] found that the posterior cingulate was the primary driving region of the functional connectivity of early PD patients in the alpha band.…”

“…Li et al [33] discovered that the clustering coefficient and local efficiency of the EEGbased brain network in the alpha band in PD patients were significantly reduced, suggesting that their brain networks were more fragile and unstable than those of HC. On the other hand, it was reported that PD patients gamma band efficiency had increased overall [31]. It was consistent with the opposite findings, which showed that ePD-nMCI patients had higher RR and lower k P LI in the gamma band when compared to HC subjects.…”

“…Finally, the large noise interference which was difficult to be eliminated by the algorithm was removed by artificial eyes. The preprocessed EEG signals were divided into five frequency bands by zero-phase shift filtering, which were delta band (1-4Hz), theta band (4-8Hz), alpha band (8-13Hz), beta band (13-30Hz), and gamma band (30-45Hz) [21]. And we analyzed the characteristics of dynamic brain functional network based on the five sub-bands.…”

STN-DBS Induces Acute Changes in β-Band Cortical Functional Connectivity in Patients with Parkinson’s Disease

“…A cleaning algorithm for EEG data was used [ 15 ]. We applied detrending and re-referencing to each channel, and a notch filter at 50 Hz.…”

Parkinson’s disease detection based on multi-pattern analysis and multi-scale convolutional neural networks

“…Moreover, PD EEG abnormalities were also manifested in changes in functional brain connectivity. Compared with HCs, PD patients have reduced connectivity in the α-β band, increased connectivity in the γ band ( Conti et al, 2022 ), and exhibit loss of frontotemporal connectivity ( Hassan et al, 2017 ).…”