Decreased Parietal Beta Power as a Sign of Disease Progression in Patients with Mild Cognitive Impairment

Musaeus, Christian Sandøe; Nielsen, Malene Schjønning; Østerbye, Natascha Nellum; Høgh, Peter

doi:10.3233/jad-180384

Cited by 29 publications

(30 citation statements)

References 47 publications

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“…The data from the current study has been used in previous publications (Engedal et al, 2015) and some of the subjects have also participated in another study (Musaeus et al, 2018b). The results from the power analysis derived from the frequency spectrum are presented elsewhere (Musaeus et al, 2018a).…”

Section: Methods and Materials 21 Participantsmentioning

confidence: 99%

Oscillatory connectivity as a diagnostic marker of dementia due to Alzheimer’s disease

Musaeus

Engedal

Høgh

et al. 2019

Clinical Neurophysiology

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Section: Methods and Materials 21 Participantsmentioning

confidence: 99%

Oscillatory connectivity as a diagnostic marker of dementia due to Alzheimer’s disease

Musaeus

Engedal

Høgh

et al. 2019

Clinical Neurophysiology

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“…Patients with MCI have also shown increases in theta power and decreases in alpha power when compared with normal elderly subjects [21,27,28]. Additionally, increased theta power and decreased parietal beta power may predict disease progression to AD in patients with MCI [29,30]. Another study in non-demented and amyloid-positive subjects showed that higher delta and theta power were associated with clinical progression over time [31].…”

Section: Discussionmentioning

confidence: 99%

“…qEEG features were obtained at the sensor and at the source level. At the sensor level, relative power at eight frequency bands (delta [ [20][21][22][23][24][25][26][27][28][29][30], and gamma [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]) was calculated using a power spectrum analysis. In addition, power ratios (TBR2: theta/beta2) according to frequency bands were calculated.…”

Section: Qeeg Analysismentioning

confidence: 99%

Differences Between Encoding and Retrieval Failure in Mild Cognitive Impairment: Results from Quantitative Electroencephalography and Magnetic Resonance Volumetry

Han

Pyun

Yeo

et al. 2020

Preprint

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Abstract Background: Memory impairments in mild cognitive impairment can be classified into encoding (EF) and retrieval (RF) failure. We explored differences between the two using quantitative electroencephalography (qEEG) and magnetic resonance imaging volumetry.Methods: We compared qEEG power spectra and connectivity between 87 participants with EF and 78 with RF, and gray matter (GM) volume differences between participants with mild cognitive impairment and 71 controls. Demographic and cognitive assessment results between groups were compared with Student’s t-tests, and obtained qEEG features were analyzed according to statistically significant differences between the groups using iSyncBrain ™. GM volume changes underlying EF and RF in amnestic mild cognitive impairment were compared on processed magnetic resonance images of cognitively normal subjects using Student’s t-tests, with age and total intracranial volume classified as nuisance covariates. We used a voxel-based morphometry analysis to demonstrate significant atrophic GM areas in the two types of patients with amnestic mild cognitive impairment. Results were considered statistically significant at p < 0.05 and were corrected for family-wise errors to avoid multiple-comparison problems.Results: qEEG showed higher frontal theta and lower beta2 band power and higher theta and lower beta connectivity in patients with EF. GM volume reductions due to EF in the left thalamus and bilateral hippocampi and reductions due to RF in the left thalamus, right superior frontal lobe, right superior temporal lobe, and right middle cingulum were observed. Conclusions: Patients with mild cognitive impairment differ functionally and structurally according to their specific memory impairments. EF findings are more consistent with the prodromal Alzheimer's disease stage than RF findings. Since this study is a cross-sectional study, prospective follow-up studies are needed to investigate whether different types of memory impairments can predict the underlying pathology of amnestic MCI. Additionally, insufficient sample size may lead to ambiguous statistical finidings in direct comparisons, a larger patient cohort could more robustly identify differences in GM volume reductions between the EF and the RF group.

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“…These studies showed that the reduction in the functional connectivity of the brain regions is captured as a decrease in the coherence between EEG channels (31). Some reports have also assessed the relative value of the coherence of EEG channels for different frequency bands in the classification of AD patients and healthy participants (32) (33).…”

Section: Introductionmentioning

confidence: 99%

Olfactory Response as a Marker for Alzheimer's Disease: Evidence from Perceptual and Frontal Oscillation Coherence Deficit

Sedghizadeh

Hojjati

Ezzatdoost

et al. 2019

Preprint

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High-frequency oscillations of the frontal cortex are involved in functions of the brain that fuse processed data from different sensory modules or bind them with elements stored in the memory. These oscillations also provide inhibitory connections to neural circuits that perform lower-level processes. Deficit in the performance of these oscillations has been examined as a marker for Alzheimer's disease (AD). Additionally, the neurodegenerative processes associated with AD, such as the deposition of amyloid-beta plaques, do not occur in a spatially homogeneous fashion and progress more prominently in the medial temporal lobe in the early stages of the disease. This region of the brain contains neural circuitry involved in olfactory perception. Several studies have suggested that olfactory deficit can be used as a marker for early diagnosis of AD. A quantitative assessment of the performance of the olfactory system can hence serve as a potential biomarker for Alzheimer's disease, offering a relatively convenient and inexpensive diagnosis method. This study examines the decline in the perception of olfactory stimuli and the deficit in the performance of high-frequency frontal oscillations in response to olfactory stimulation as markers for AD. Two measurement modalities are employed for assessing the olfactory performance: 1) An interactive smell identification test is used to sample the response to a sizable variety of odorants, and 2) Electrophysiological data are collected in an olfactory perception task with a pair of selected odorants in order to assess the connectivity of frontal cortex regions. Statistical analysis methods are used to assess the significance of selected features extracted from the recorded modalities as Alzheimer's biomarkers. Olfactory decline regressed to age in both healthy and Mild AD groups are evaluated, and single- and multi-modal classifiers are also developed. The novel aspects of this study include: 1) Combining EEG response to olfactory stimulation with behavioral assessment of olfactory perception as a marker of AD, 2) Identification of odorants most significantly affected in Mild AD patients, 3) Identification of odorants which are still adequately perceived by Mild AD patients, 4) Analysis of the decline in the spatial coherence of different oscillatory bands in response to olfactory stimulation, and 5) Being the first study to quantitatively assess the performance of olfactory decline due to aging and AD in the Iranian population.

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Decreased Parietal Beta Power as a Sign of Disease Progression in Patients with Mild Cognitive Impairment

Cited by 29 publications

References 47 publications

Oscillatory connectivity as a diagnostic marker of dementia due to Alzheimer’s disease

Oscillatory connectivity as a diagnostic marker of dementia due to Alzheimer’s disease

Differences Between Encoding and Retrieval Failure in Mild Cognitive Impairment: Results from Quantitative Electroencephalography and Magnetic Resonance Volumetry

Olfactory Response as a Marker for Alzheimer's Disease: Evidence from Perceptual and Frontal Oscillation Coherence Deficit

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