Graph theory network function in Parkinson’s disease assessed with electroencephalography

Utianski, Rene L.; Caviness, John N.; Straaten, E.C.W. van; Beach, Thomas G.; Dugger, Brittany N.; Shill, Holly A.; Driver-Dunckley, Erika M; Sabbagh, Marwan N.; Mehta, Shyamal H.; Adler, Charles H.; Hentz, Joseph G.

doi:10.1016/j.clinph.2016.02.017

Cited by 81 publications

(95 citation statements)

References 41 publications

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“…Alterations in the alpha band were observed by many previous studies such as those reporting a loss in MEG functional connectivity in demented patients (Bosboom et al, 2009), a reduction in the global coherence (Andersson et al, 2008, Franciotti et al, 2006) and a loss in EEG network efficiency and hubs (van Dellen et al, 2015) in dementia with Lewy bodies and, very recently, a decrease in local integration at the alpha1 frequency bands between cognitively intact and demented Parkinson's disease patients (Utianski et al, 2016). Observing significant alteration between G1 and G2 only in the alpha band was not surprising.…”

Section: Discussionmentioning

confidence: 81%

Functional connectivity disruptions correlate with cognitive phenotypes in Parkinson's disease

Hassan

Chaton

Benquet

et al. 2017

NeuroImage: Clinical

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Cognitive deficits in Parkinson's disease are thought to be related to altered functional brain connectivity. To date, cognitive-related changes in Parkinson's disease have never been explored with dense-EEG with the aim of establishing a relationship between the degree of cognitive impairment, on the one hand, and alterations in the functional connectivity of brain networks, on the other hand.This study was aimed at identifying altered brain networks associated with cognitive phenotypes in Parkinson's disease using dense-EEG data recorded during rest with eyes closed. Three groups of Parkinson's disease patients (N = 124) with different cognitive phenotypes coming from a data-driven cluster analysis, were studied: G1) cognitively intact patients (63), G2) patients with mild cognitive deficits (46) and G3) patients with severe cognitive deficits (15). Functional brain networks were identified using a dense-EEG source connectivity method. Pairwise functional connectivity was computed for 68 brain regions in different EEG frequency bands. Network statistics were assessed at both global (network topology) and local (inter-regional connections) level.Results revealed progressive disruptions in functional connectivity between the three patient groups, typically in the alpha band. Differences between G1 and G2 (p < 0.001, corrected using permutation test) were mainly frontotemporal alterations. A statistically significant correlation (ρ = 0.49, p < 0.001) was also obtained between a proposed network-based index and the patients' cognitive score. Global properties of network topology in patients were relatively intact.These findings indicate that functional connectivity decreases with the worsening of cognitive performance and loss of frontotemporal connectivity may be a promising neuromarker of cognitive impairment in Parkinson's disease.

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Section: Discussionmentioning

confidence: 81%

Functional connectivity disruptions correlate with cognitive phenotypes in Parkinson's disease

Hassan

Chaton

Benquet

et al. 2017

NeuroImage: Clinical

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“…Here the brain was described as a complex network, and graph theory was used to characterize the structure and dynamics of relevant networks. With such methods, Stoffers et al (2008) observed that the increased resting-state functional connectivity in alpha range was a feature of PD from the earliest clinical stages, and the increases in theta or beta bands appeared later in the disease; Utianski et al (2016) recently found that the network alteration and breakdown was the robust attribute of the pathophysiology of PD cortical dysfunction.…”

Section: Introductionmentioning

confidence: 99%

Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease

Wang

Deng

et al. 2016

Cogn Neurodyn

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To investigate the abnormal brain activities in the early stage of Parkinson's disease (PD), the electroencephalogram (EEG) signals were recorded with 20 channels from non-dementia PD patients (18 patients, 8 females) and age matched healthy controls (18 subjects, 8 females) during the resting state. Two methods based on the ordinal patterns of the recorded series, i.e., permutation entropy (PE) and order index (OI), were introduced to characterize the complexity of the cortical activities for two groups. It was observed that the resting-state EEG of PD patients showed lower PE and higher OI than healthy controls, which indicated that the early-stage PD caused the reduced complexity of EEG. We further applied two methods to determine the complexity of EEG rhythms in five sub-bands. The results showed that the gamma, beta and alpha rhythms of PD patients were characterized by lower PE and higher OI, i.e., reduced complexity, than healthy subjects. No significant differences were observed in theta or delta rhythms between two groups. The findings suggested that PE and OI were promising methods to detect the abnormal changes in the dynamics of EEG signals associated with early-stage PD. Further, such changes in EEG complexity may be the early markers of the cortical or subcortical dysfunction caused by PD.

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“…Recently, the minimal spanning tree (MST) started to being applied in neuroscience (Tewarie et al, 2015; Utianski et al, 2016; van Dellen et al, 2015). This approach yields an unweighted backbone graph that is considered to reflect the functional core of the network (Stam et al, 2014).…”

mentioning

confidence: 99%

“…Specifically, maximum betweenness centrality, leaf fraction, diameter and average eccentricity all represent overall integration of the MST network. Characterization of network integration using this approach has been used for several neurological disorders, including dementia with Lewy bodies, Alzheimer’s disease and Parkinson’s disease (Utianski et al, 2016; van Dellen et al, 2015), but is yet to be applied to meditation training.…”

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confidence: 99%

Meditation is associated with increased brain network integration

et al. 2017

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Introduction This study aims to identify novel quantitative EEG measures associated with mindfulness meditation. As there is some evidence that meditation is associated with higher integration of brain networks, we focused on EEG measures of network integration. Methods Sixteen novice meditators and sixteen experienced meditators participated in the study. Novice meditators performed a basic meditation practice that supported effortless awareness, which is an important quality of experience related to mindfulness practices, while their EEG was recorded. Experienced meditators performed a self-selected meditation practice that supported effortless awareness. Network integration was analyzed with maximum betweenness centrality and leaf fraction (which both correlate positively with network integration) as well as with diameter and average eccentricity (which both correlate negatively with network integration), based on a phase-lag index (PLI) and minimum spanning tree (MST) approach. Differences between groups were assessed using repeated-measures ANOVA for the theta (4–8 Hz), alpha (8–13 Hz) and lower beta (13–20 Hz) frequency bands. Results Maximum betweenness centrality was significantly higher in experienced meditators than in novices (P=0.012) in the alpha band. In the same frequency band, leaf fraction showed a trend toward being significantly higher in experienced meditators than in novices (P=0.056), while diameter and average eccentricity were significantly lower in experienced meditators than in novices (P=0.016 and P=0.028 respectively). No significant differences between groups were observed for the theta and beta frequency bands. Conclusion These results show that alpha band functional network topology is better integrated in experienced meditators than in novice meditators during meditation. This novel finding provides the rationale to investigate the temporal relation between measures of functional connectivity network integration and meditation quality, for example using neurophenomenology experiments.

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Graph theory network function in Parkinson’s disease assessed with electroencephalography

Cited by 81 publications

References 41 publications

Functional connectivity disruptions correlate with cognitive phenotypes in Parkinson's disease

Functional connectivity disruptions correlate with cognitive phenotypes in Parkinson's disease

Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease

Meditation is associated with increased brain network integration

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