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Electroencephalographic derived network differences in Lewy body dementia compared to Alzheimer's disease patients. Sci Rep 2018; 8:4637. [PMID: 29545639 PMCID: PMC5854590 DOI: 10.1038/s41598-018-22984-5] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 03/05/2018] [Indexed: 01/10/2023] Open
Abstract
Dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD) require differential management despite presenting with symptomatic overlap. Currently, there is a need of inexpensive DLB biomarkers which can be fulfilled by electroencephalography (EEG). In this regard, an established electrophysiological difference in DLB is a decrease of dominant frequency (DF)—the frequency with the highest signal power between 4 and 15 Hz. Here, we investigated network connectivity in EEG signals acquired from DLB patients, and whether these networks were able to differentiate DLB from healthy controls (HCs) and associated dementias. We analysed EEG recordings from old adults: HCs, AD, DLB and Parkinson’s disease dementia (PDD) patients. Brain networks were assessed with the minimum spanning tree (MST) within six EEG bands: delta, theta, high-theta, alpha, beta and DF. Patients showed lower alpha band connectivity and lower DF than HCs. DLB and PDD showed a randomised MST compared with HCs and AD in high-theta and alpha but not in DF. The MST randomisation in DLB and PDD reflects decreased brain efficiency as well as impaired neural synchronisation. However, the lack of network topology differences at the DF between all dementia groups and HCs may indicate a compensatory response of the brain to the neuropathology.
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van Lutterveld R, van Dellen E, Pal P, Yang H, Stam CJ, Brewer J. Meditation is associated with increased brain network integration. Neuroimage 2017; 158:18-25. [PMID: 28663069 DOI: 10.1016/j.neuroimage.2017.06.071] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Accepted: 06/26/2017] [Indexed: 11/26/2022] Open
Abstract
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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Affiliation(s)
- Remko van Lutterveld
- Center for Mindfulness, University of Massachusetts Medical School, Worcester, MA, USA.
| | - Edwin van Dellen
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Prasanta Pal
- Center for Mindfulness, University of Massachusetts Medical School, Worcester, MA, USA
| | - Hua Yang
- Center for Mindfulness, University of Massachusetts Medical School, Worcester, MA, USA
| | - Cornelis Jan Stam
- Department of Clinical Neurophysiology and MEG Centre, VU University Medical Center, Amsterdam, The Netherlands
| | - Judson Brewer
- Center for Mindfulness, University of Massachusetts Medical School, Worcester, MA, USA
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He X, Zhang Y, Chen J, Xie C, Gan R, Yang R, Wang L, Nie K, Wang L. The patterns of EEG changes in early-onset Parkinson's disease patients. Int J Neurosci 2017; 127:1028-1035. [PMID: 28281852 DOI: 10.1080/00207454.2017.1304393] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Xuetao He
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong General Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yuhu Zhang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong General Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Jieling Chen
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong General Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Chunge Xie
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong General Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Rong Gan
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong General Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Rong Yang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong General Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Limin Wang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong General Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Kun Nie
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong General Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Lijuan Wang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong General Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China
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Hassan M, Chaton L, Benquet P, Delval A, Leroy C, Plomhause L, Moonen AJH, Duits AA, Leentjens AFG, van Kranen-Mastenbroek V, Defebvre L, Derambure P, Wendling F, Dujardin K. Functional connectivity disruptions correlate with cognitive phenotypes in Parkinson's disease. NEUROIMAGE-CLINICAL 2017; 14:591-601. [PMID: 28367403 PMCID: PMC5361870 DOI: 10.1016/j.nicl.2017.03.002] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Revised: 02/28/2017] [Accepted: 03/04/2017] [Indexed: 01/21/2023]
Abstract
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. We test the use of dense-EEG to identify altered brain networks associated with cognitive phenotypes in Parkinson's disease. The functional connectivity decreases with the worsening of cognitive performance The loss of frontotemporal connectivity may be a promising neuromarker of cognitive impairment in Parkinson's disease.
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Affiliation(s)
- M Hassan
- INSERM, U1099, F-35000 Rennes, France; University of Rennes 1, LTSI, F-35000 Rennes, France
| | - L Chaton
- CHU Lille, Clinical Neurophysiology Department, F-59000 Lille, France
| | - P Benquet
- INSERM, U1099, F-35000 Rennes, France; University of Rennes 1, LTSI, F-35000 Rennes, France
| | - A Delval
- University of Lille, U1171 - Degenerative & Vascular Cognitive Disorders, F-59000 Lille, France; INSERM, U1171, F-59000 Lille, France; CHU Lille, Clinical Neurophysiology Department, F-59000 Lille, France
| | - C Leroy
- University of Lille, U1171 - Degenerative & Vascular Cognitive Disorders, F-59000 Lille, France; INSERM, U1171, F-59000 Lille, France; CHU Lille, Clinical Neurophysiology Department, F-59000 Lille, France
| | - L Plomhause
- University of Lille, U1171 - Degenerative & Vascular Cognitive Disorders, F-59000 Lille, France; INSERM, U1171, F-59000 Lille, France; CHU Lille, Clinical Neurophysiology Department, F-59000 Lille, France
| | - A J H Moonen
- Maastricht University Medical Center, Maastricht, The Netherlands
| | - A A Duits
- Maastricht University Medical Center, Maastricht, The Netherlands
| | - A F G Leentjens
- Maastricht University Medical Center, Maastricht, The Netherlands
| | | | - L Defebvre
- University of Lille, U1171 - Degenerative & Vascular Cognitive Disorders, F-59000 Lille, France; INSERM, U1171, F-59000 Lille, France; CHU Lille, Neurology and Movement Disorders Department, F-59000 Lille, France
| | - P Derambure
- University of Lille, U1171 - Degenerative & Vascular Cognitive Disorders, F-59000 Lille, France; INSERM, U1171, F-59000 Lille, France; CHU Lille, Clinical Neurophysiology Department, F-59000 Lille, France
| | - F Wendling
- INSERM, U1099, F-35000 Rennes, France; University of Rennes 1, LTSI, F-35000 Rennes, France
| | - K Dujardin
- University of Lille, U1171 - Degenerative & Vascular Cognitive Disorders, F-59000 Lille, France; INSERM, U1171, F-59000 Lille, France; CHU Lille, Neurology and Movement Disorders Department, F-59000 Lille, France
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Yi GS, Wang J, Deng B, Wei XL. Complexity of resting-state EEG activity in the patients with early-stage Parkinson's disease. Cogn Neurodyn 2016; 11:147-160. [PMID: 28348646 DOI: 10.1007/s11571-016-9415-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Revised: 09/21/2016] [Accepted: 10/14/2016] [Indexed: 01/21/2023] Open
Abstract
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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Affiliation(s)
- Guo-Sheng Yi
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, 30072 China
| | - Jiang Wang
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, 30072 China
| | - Bin Deng
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, 30072 China
| | - Xi-Le Wei
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, 30072 China
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