Cognitive decline in Parkinson's disease is associated with slowing of resting-state brain activity: a longitudinal study

Dubbelink, K.T.E. Olde; Stoffers, Diederick; Deijen, J.B.; Twisk, Jos W. R.; Stam, Cornelis J.; Berendse, Henk W.

doi:10.1016/j.neurobiolaging.2012.02.029

Cited by 138 publications

(162 citation statements)

References 39 publications

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“…Decreased β and γ frequency energy and increased θ frequency energy was observed in the PD-D and AD patients, indicating these patients underwent a slowing of brain oscillatory activity, which is consistent with previous reports [8][9][10][11][12][13][14][15][16]. Slow EEG activity implies that neural activity in the information processing of the brain has become less complex and thus simpler dynamics exist in brains cognitively impaired by dementia.…”

Section: Discussionsupporting

confidence: 90%

“…Slow EEG activity implies that neural activity in the information processing of the brain has become less complex and thus simpler dynamics exist in brains cognitively impaired by dementia. In a longitudinal study that utilized magnetoencephalography (MEG), peak frequency decreased as time progressed, while θ power increased, and α1 and α2 powers decreased [10]. The referenced study indirectly revealed that slowed brain activity correlates with cognitive decline by longitudinally examining initially non-demented PD patients.…”

Section: Discussionmentioning

confidence: 97%

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Wavelet Energy and Wavelet Coherence as EEG Biomarkers for the Diagnosis of Parkinson’s Disease-Related Dementia and Alzheimer’s Disease

Jeong

Kim

Song

et al. 2015

Entropy

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Abstract:Parkinson's disease (PD) and Alzheimer's disease (AD) can coexist in severely affected; elderly patients. Since they have different pathological causes and lesions and consequently require different treatments; it is critical to distinguish PD-related dementia (PD-D) from AD. Conventional electroencephalograph (EEG) analysis has produced poor results. This study investigated the possibility of using relative wavelet energy (RWE) and wavelet coherence (WC) analysis to distinguish between PD-D patients; AD patients and healthy elderly subjects. In EEG signals; we found that low-frequency wavelet energy increased and high-frequency wavelet energy decreased in PD-D patients and AD patients relative to healthy subjects. This result suggests that cognitive decline in both diseases is potentially related to slow EEG activity; which is consistent with previous studies. More importantly; WC values were lower in AD patients and higher in PD-D patients compared with healthy subjects. In particular; AD patients exhibited decreased WC primarily in the γ band and in links related to frontal regions; while PD-D patients exhibited increased WC primarily in the α and β bands and in temporo-parietal links. Linear discriminant analysis (LDA) of RWE produced a maximum accuracy of 79.18% for diagnosing PD-D and 81.25% for diagnosing AD. The discriminant accuracy was 73.40% with 78.78% sensitivity and 69.47% specificity. In distinguishing between the two diseases; the maximum performance of LDA using WC was 80.19%. We suggest that using a wavelet approach to evaluate EEG results may facilitate discrimination between PD-D and AD. In particular; RWE is useful for differentiating individuals with and without dementia and WC is useful for differentiating between PD-D and AD.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 97%

Wavelet Energy and Wavelet Coherence as EEG Biomarkers for the Diagnosis of Parkinson’s Disease-Related Dementia and Alzheimer’s Disease

Jeong

Kim

Song

et al. 2015

Entropy

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“…However, several spectral studies with EEG/MEG (Olde Dubbelink et al 2013;Stoffers et al 2007;Neufeld et al 1994;Han et al 2013) have observed an increase in theta or delta powers for PD patients compared to controls. Our results of relative power in Fig.…”

Section: Discussionmentioning

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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“…neurology.org. For RCI calculation, a group of healthy controls who were also followed as part of this longitudinal project served as a reference group, 13 corresponding to an RCI of 8 points decrease in CAMCOG score over the 3 time points (p , 0.05). Unified Parkinson's Disease Rating Scale motor ratings 16 were obtained in the "ON" medication state by a trained physician.…”

Section: Participants From April 2003 To March 2006 Amentioning

confidence: 99%

“…Pathophysiologically oriented EEG and magnetoencephalographic (MEG) studies have associated (widespread) oscillatory slowing with cognitive impairment in both PDD and PD using crosssectional and longitudinal designs. [10][11][12][13] Cognitive testing is often used to assess cognitive decline and monitor the development of dementia in PD, but neurophysiologic markers derived from either EEG or MEG are generally not included in the routine clinical workup of patients. In the present study we assessed the predictive value of neurophysiologic markers combined with cognitive impairments for the future development of dementia in PD.…”

mentioning

confidence: 99%

Predicting dementia in Parkinson disease by combining neurophysiologic and cognitive markers

et al. 2014

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Cognitive decline in Parkinson's disease is associated with slowing of resting-state brain activity: a longitudinal study

Cited by 138 publications

References 39 publications

Wavelet Energy and Wavelet Coherence as EEG Biomarkers for the Diagnosis of Parkinson’s Disease-Related Dementia and Alzheimer’s Disease

Wavelet Energy and Wavelet Coherence as EEG Biomarkers for the Diagnosis of Parkinson’s Disease-Related Dementia and Alzheimer’s Disease

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

Predicting dementia in Parkinson disease by combining neurophysiologic and cognitive markers

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