Region and frequency specific changes of spectral power in Alzheimer’s disease and mild cognitive impairment

Roh, Jee Hoon; Park, Moon Ho; Ko, Deokwon; Park, Kun Woo; Lee, Dae Hie; Han, Changsu; Jo, Sangmee Anh; Yang, Kyung Sook; Jung, Ki Young

doi:10.1016/j.clinph.2011.03.023

Cited by 68 publications

(54 citation statements)

References 51 publications

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“…EEG has increasingly been used to describe cognitive impairment in neurodegenerative disorders (Fonseca et al, 2009, Roh et al, 2011). Resting-state recordings from Alzheimer's disease patients were characterized by a shift to lower frequencies (Bennys et al, 2001, Czigler et al, 2008, Penttila et al, 1985).…”

Section: Discussionmentioning

confidence: 99%

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: 99%

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

Hassan

Chaton

Benquet

et al. 2017

NeuroImage: Clinical

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“…One of the principal hallmarks of electroencephalography (EEG) abnormalities in AD patients is a shift of the power spectrum to lower frequencies (Jeong, 2004; Lizio et al, 2011; Roh et al, 2011) and this—among other alterations—correlates with the severity of the disease (Kowalski et al, 2001). Here we found that the synchronization peak in the gamma range during the Up states occurred at lower frequencies in SAMP8 than in control mice, and that the mean power spectrum between 0 Hz and 100 Hz presented an overall increase in its lower range together with a decrease in the upper range.…”

Section: Discussionmentioning

confidence: 99%

Slow and Fast Neocortical Oscillations in the Senescence-Accelerated Mouse Model SAMP8

Castano-Prat

Pérez-Zabalza

Perez-Mendez

et al. 2017

Front. Aging Neurosci.

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The senescence-accelerated mouse prone 8 (SAMP8) model is characterized by accelerated, progressive cognitive decline as well as Alzheimer’s disease (AD)-like neurodegenerative changes, and resembles the etiology of multicausal, sporadic late-onset/age-related AD in humans. Our aim was to find whether these AD-like pathological features, together with the cognitive deficits present in the SAMP8 strain, are accompanied by disturbances in cortical network activity with respect to control mice (SAM resistance 1, SAMR1) and, if so, how the alterations in cortical activity progress with age. For this purpose, we characterized the extracellular spontaneous oscillatory activity in different regions of the cerebral cortex of SAMP8 and SAMR1 mice under ketamine anesthesia at 5 and 7 months of age. Under these conditions, slow oscillations and fast rhythms generated in the cortical network were recorded and different parameters of these oscillations were quantified and compared between SAMP8 and their control, SAMR1 mice. The average frequency of slow oscillations in SAMP8 mice was decreased with respect to the control mice at both studied ages. An elongation of the silent periods or Down states was behind the decreased slow oscillatory frequency while the duration of active or Up states remained stable. SAMP8 mice also presented increased cycle variability and reduced high frequency components during Down states. During Up states, the power peak in the gamma range was displaced towards lower frequencies in all the cortical areas of SAMP8 with respect to control mice suggesting that the spectral profile of SAMP8 animals is shifted towards lower frequencies. This shift is reminiscent to one of the principal hallmarks of electroencephalography (EEG) abnormalities in patients with Alzheimer’s disease, and adds evidence in support of the suitability of the SAMP8 mouse as a model of this disease. Although some of the differences between SAMP8 and control mice were emphasized with age, the evolution of the studied parameters as SAMR1 mice got older indicates that the SAMR1 phenotype tends to converge with that of SAMP8 animals. To our knowledge, this is the first systematic characterization of the cortical slow and fast rhythms in the SAMP8 strain and it provides useful insights about the cellular and synaptic mechanisms underlying the reported alterations.

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“…Spectral power of EEG series and their correlations with neuropsychological tests can provide valuable information in distinguishing normal and diseased brain function (Roh et al, 2011). The theta and delta spectral power tended to increase in the selected brain regions according to cognitive impairment from normal through aMCI to AD, whereas alpha and beta2 power showed a decreasing tendency (Roh et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…The theta and delta spectral power tended to increase in the selected brain regions according to cognitive impairment from normal through aMCI to AD, whereas alpha and beta2 power showed a decreasing tendency (Roh et al, 2011). In particular, relative power has been used as a feature to classify the MCI and mild AD patients from age-matched controls (Jelic et al, 1996; Dauwels et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Relative power and coherence of EEG series are related to amnestic mild cognitive impairment in diabetes

Bian

Wang

et al. 2014

Front. Aging Neurosci.

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Objective: Diabetes is a risk factor for dementia and mild cognitive impairment. The aim of this study was to investigate whether some features of resting-state EEG (rsEEG) could be applied as a biomarker to distinguish the subjects with amnestic mild cognitive impairment (aMCI) from normal cognitive function in type 2 diabetes.Materials and Methods: In this study, 28 patients with type 2 diabetes (16 aMCI patients and 12 controls) were investigated. Recording of the rsEEG series and neuropsychological assessments were performed. The rsEEG signal was first decomposed into delta, theta, alpha, beta, gamma frequency bands. The relative power of each given band/sum of power and the coherence of waves from different brain areas were calculated. The extracted features from rsEEG and neuropsychological assessments were analyzed as well.Results: The main findings of this study were that: (1) compared with the control group, the ratios of power in theta band [P(theta)] vs. power in alpha band [P(alpha)] [P(theta)/P(alpha)] in the frontal region and left temporal region were significantly higher for aMCI, and (2) for aMCI, the alpha coherences in posterior, fronto-right temporal, fronto-posterior, right temporo-posterior were decreased; the theta coherences in left central-right central (LC-RC) and left posterior-right posterior (LP-RP) regions were also decreased; but the delta coherences in left temporal-right temporal (LT-RT) region were increased.Conclusion: The proposed indexes from rsEEG recordings could be employed to track cognitive function of diabetic patients and also to help in the diagnosis of those who develop aMCI.

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Region and frequency specific changes of spectral power in Alzheimer’s disease and mild cognitive impairment

Cited by 68 publications

References 51 publications

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

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

Slow and Fast Neocortical Oscillations in the Senescence-Accelerated Mouse Model SAMP8

Relative power and coherence of EEG series are related to amnestic mild cognitive impairment in diabetes

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