Altered states of consciousness: Processed EEG in mental disease

qEEG as Biomarker for Alzheimer's Disease: Investigating Relative PSD Difference and Coherence Analysis

Purpose

Electroencephalography (EEG) is a non-intrusive technique that provides comprehensive insights into the electrical activities of the brain's cerebral cortex. The brain signals obtained from EEGs can be used as a neuropsychological biomarker to detect different stages of Alzheimer's disease (AD) through quantitative EEG (qEEG) analysis. This paper investigates the difference in the abnormalities of resting state EEG (rEEG) signals between eyes-open (EOR) and eyes-closed (ECR) in AD by analyzing 19-scalp electrode EEG signals and making a comparison with healthy controls (HC).

Participants and Methods

The rEEG data from 534 subjects (ages 40-90) consisting of 269 HC and 265 AD subjects in South Korea were used in this study. The qEEG for EOR and ECR states were performed separately for HC and AD subjects to measure the relative power spectrum density (PSD) and coherence with functional connectivity to evaluate abnormalities. The rEEG data were preprocessed and analyzed using EEGlab and Brainstorm toolboxes in MATLAB R2021a software, and statistical analyses were carried out using ANOVA.

Results

Based on the Welch method, the relative PSD of the EEG EOR and ECR states difference in the AD group showed a significant increase in the delta frequency band of 19 EEG channels, particularly in the frontal, parietal, and temporal, than the HC groups. The delta power band on the source level was increased for the AD group and decreased for the HC group. In contrast, the source activities of alpha, beta, and gamma frequency bands were significantly reduced in the AD group, with a high decrease in the beta frequency band in all brain areas. Furthermore, the coherence of rEEG among different EEG electrodes was analyzed in the beta frequency band. It showed that pair-wise coherence between different brain areas in the AD group is remarkably increased in the ECR state and decreased after subtracting out the EOR state.

Conclusion

The findings suggest that examining PSD and functional connectivity through coherence analysis could serve as a promising and comprehensive approach to differentiate individuals with AD from normal, which may benefit our understanding of the disease.

Inter-band Bispectral Analysis of EEG Background Activity to Characterize Alzheimer's Disease Continuum

The aim of this study was to characterize the EEG alterations in inter-band interactions along the Alzheimer's disease (AD) continuum. For this purpose, EEG background activity from 51 healthy control subjects, 51 mild cognitive impairment patients, 50 mild AD patients, 50 moderate AD patients, and 50 severe AD patients was analyzed by means of bispectrum. Three inter-band features were extracted from bispectrum matrices: bispectral relative power (BispRP), cubic bispectral entropy (BispEn), and bispectral median frequency (BispMF). BispRP results showed an increase of delta and theta interactions with other frequency bands and the opposite behavior for alpha, beta-1, and beta-2. Delta and theta interactions, along with the rest of the spectrum, also experimented a decrease of BispEn with disease progression, suggesting these bands interact with a reduced variety of components in advanced stages of dementia. Finally, BispMF showed a consistent reduction along the AD continuum in all bands, which is reflective of an interaction of the global spectrum with lower frequency bands as the disease develops. Our results indicate a progressive decrease in inter-band interactions with the severity of the disease, especially those involving high frequency components. Since inter-band coupling oscillations are related to complex and multi-scaled brain processes, these alterations likely reflect the neurodegeneration associated with the AD continuum.

Bispectral Index Monitoring With Density Spectral Array for Delirium Detection

BACKGROUND

Delirium in hospitalized patients often goes undetected. Cerebral state monitors, which measure limited-channel electroencephalography, have shown potential for improving delirium detection.

OBJECTIVE

The aim of this study was to compare an FDA-approved cerebral state monitor, bispectral index monitoring with density spectral array (DSA), for delirium identification with clinical screening methods.

METHODS

Hospitalized patients receiving psychiatric consultation were assessed for delirium using the 3-minute Diagnostic Interview for Confusion Assessment Method (3D-CAM) and underwent bispectral index monitor + DSA monitoring. Visual inspection of frequency band power of the DSA was performed by 2 trained independent raters. Average hue values were calculated for each frequency band using image analysis software as the device did not allow for extraction of raw electroencephalography data. Delirious versus nondelirious group averages, sensitivity, specificity, and area under the curve were calculated for significant DSA variables and the 3D-CAM.

RESULTS

In an initial cohort of 43 patients, visual ratings of the DSA were not associated with delirium (P > 0.1). In a larger cohort of 123 subjects, multiple band hue ratios were associated with delirium, although none survived correction for multiple comparisons. In a subgroup of 74 non-neurological patients, low theta/low delta ratio was significantly associated with delirium (P = 0.001) (sensitivity/specificity/area under the curve: 83%/70%/0.757; 3D-CAM: 67%/77%/0.717; paired-sample area under the curve difference: -0.040, P = 0.68). In 21 patients with dementia, low theta power demonstrated significantly greater sensitivity/specificity/area under the curve of 83%/78%/0.824, whereas 3D-CAM achieved 50%/78%/0.639 (P = 0.04).

CONCLUSION

Bispectral index monitor + DSA was similar to 3D-CAM for detecting delirium in hospitalized patients with and without neurological disorders, and was significantly more accurate in patients with dementia. More studies are needed to validate the use of cerebral state monitors for quantitative delirium detection.

Power spectral density and coherence analysis of Alzheimer's EEG

In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. The power spectral density (PSD) which represents the power distribution of EEG series in the frequency domain is used to evaluate the abnormalities of AD brain. Spectrum analysis based on autoregressive Burg method shows that the relative PSD of AD group is increased in the theta frequency band while significantly reduced in the alpha2 frequency bands, particularly in parietal, temporal, and occipital areas. Furthermore, the coherence of two EEG series among different electrodes is analyzed in the alpha2 frequency band. It is demonstrated that the pair-wise coherence between different brain areas in AD group are remarkably decreased. Interestingly, this decrease of pair-wise electrodes is much more significant in inter-hemispheric areas than that in intra-hemispheric areas. Moreover, the linear cortico-cortical functional connectivity can be extracted based on coherence matrix, from which it is shown that the functional connections are obviously decreased, the same variation trend as relative PSD. In addition, we combine both features of the relative PSD and the normalized degree of functional network to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha2 frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature. The obtained results show that analysis of PSD and coherence-based functional network can be taken as a potential comprehensive measure to distinguish AD patients from the normal, which may benefit our understanding of the disease.

Considerations during intravenous sedation in geriatric dental patients with dementia

OBJECTIVES

We retrospectively assessed the usability and precautions required during intravenous sedation (IVS) for dental treatment in geriatric outpatients with dementia.

MATERIALS AND METHODS

We investigated the intraoperative complications in 65 cases (25 geriatric dental patients with dementia) under IVS, from the standpoint of local anesthesia usage, water usage during treatment, and content of treatment.

RESULTS

Circulatory complications occurred in 46.2 % and respiratory complications in 52.3 % of all cases (n = 65). Bradycardia occurred in 13.8 % and hypotension in 12.3 % of cases in the former, while coughing spells occurred in 41.5 % and snoring in 16.9 % of cases in the latter. Many of the local anesthesia usage cases did not require water usage, such as during tooth extraction (p < 0.0001). Water usage cases, such as for caries treatment, needed longer sedation and treatment times, resulting in more propofol usage (p < 0.001, p < 0.0001, and p < 0.01, respectively). Many coughing spells developed in the water usage cases (p < 0.05). 81.8 % of snoring and 63.3 % of circulatory complications, such as hypotension and bradycardia, developed in the tooth extraction cases (p < 0.05).

CONCLUSIONS

All the scheduled dental treatments in dementia patients were smoothly performed under IVS. However, stringent attention should be paid to the prevention of aspiration of fluids retained in the pharynx, airway obstruction due to therapeutic maneuvers, respiratory inhibition by sedatives, and hemodynamic fluctuations caused by invasive procedures under local anesthesia.

CLINICAL RELEVANCE

In the future, with the growing need for dental procedures in dementia patients, dentists will require training in the general management of such patients.

Resting state cortical EEG rhythms in Alzheimer's disease: toward EEG markers for clinical applications: a review

The human brain contains an intricate network of about 100 billion neurons. Aging of the brain is characterized by a combination of synaptic pruning, loss of cortico-cortical connections, and neuronal apoptosis that provoke an age-dependent decline of cognitive functions. Neural/synaptic redundancy and plastic remodeling of brain networking, also secondary to mental and physical training, promote maintenance of brain activity and cognitive status in healthy elderly subjects for everyday life. However, age is the main risk factor for neurodegenerative disorders such as Alzheimer's disease (AD) that impact on cognition. Growing evidence supports the idea that AD targets specific and functionally connected neuronal networks and that oscillatory electromagnetic brain activity might be a hallmark of the disease. In this line, digital electroencephalography (EEG) allows noninvasive analysis of cortical neuronal synchronization, as revealed by resting state brain rhythms. This review provides an overview of the studies on resting state eyes-closed EEG rhythms recorded in amnesic mild cognitive impairment (MCI) and AD subjects. Several studies support the idea that spectral markers of these EEG rhythms, such as power density, spectral coherence, and other quantitative features, differ among normal elderly, MCI, and AD subjects, at least at group level. Regarding the classification of these subjects at individual level, the most previous studies showed a moderate accuracy (70-80%) in the classification of EEG markers relative to normal and AD subjects. In conclusion, resting state EEG makers are promising for large-scale, low-cost, fully noninvasive screening of elderly subjects at risk of AD.

Quantitative EEG as a predictive biomarker for Parkinson disease dementia

OBJECTIVE

We evaluated quantitative EEG (QEEG) measures as predictive biomarkers for the development of dementia in Parkinson disease (PD). Preliminary work shows that QEEG measures correlate with current PD cognitive state. A reliable predictive QEEG biomarker for PD dementia (PD-D) incidence would be valuable for studying PD-D, including treatment trials aimed at preventing cognitive decline in PD.

METHODS

A cohort of subjects with PD in our brain donation program utilizes annual premortem longitudinal movement and cognitive evaluation. These subjects also undergo biennial EEG recording. EEG from subjects with PD without dementia with follow-up cognitive evaluation was analyzed for QEEG measures of background rhythm frequency and relative power in δ, , α, and β bands. The relationship between the time to onset of dementia and QEEG and other possible predictors was assessed by using Cox regression.

RESULTS

The hazard of developing dementia was 13 times higher for those with low background rhythm frequency (lower than the grand median of 8.5 Hz) than for those with high background rhythm frequency (p < 0.001). Hazard ratios (HRs) were also significant for > median bandpower (HR = 3.0; p = 0.004) compared to below, and for certain neuropsychological measures. The HRs for δ, α, and β bandpower as well as baseline demographic and clinical characteristics were not significant.

CONCLUSION

The QEEG measures of background rhythm frequency and relative power in the band are potential predictive biomarkers for dementia incidence in PD. These QEEG biomarkers may be useful in complementing neuropsychological testing for studying PD-D incidence.

Electroencephalographic rhythms in Alzheimer's disease

Physiological brain aging is characterized by synapses loss and neurodegeneration that slowly lead to an age-related decline of cognition. Neural/synaptic redundancy and plastic remodelling of brain networking, also due to mental and physical training, promotes maintenance of brain activity in healthy elderly subjects for everyday life and good social behaviour and intellectual capabilities. However, age is the major risk factor for most common neurodegenerative disorders that impact on cognition, like Alzheimer's disease (AD). Brain electromagnetic activity is a feature of neuronal network function in various brain regions. Modern neurophysiological techniques, such as electroencephalography (EEG) and event-related potentials (ERPs), are useful tools in the investigation of brain cognitive function in normal and pathological aging with an excellent time resolution. These techniques can index normal and abnormal brain aging analysis of corticocortical connectivity and neuronal synchronization of rhythmic oscillations at various frequencies. The present review suggests that discrimination between physiological and pathological brain aging clearly emerges at the group level, with suggested applications also at the level of single individual. The possibility of combining the use of EEG together with biological/neuropsychological markers and structural/functional imaging is promising for a low-cost, non-invasive, and widely available assessment of groups of individuals at-risk.

EEG functional connectivity in term age extremely low birth weight infants

OBJECTIVE

The hypothesis is tested that electrocortical functional connectivity (quantified by coherence) of extremely low birth weight (ELBW) infants, measured at term post-menstrual age, has regional differences from that of full term infants.

METHODS

128 lead EEG data were collected during sleep from 8 ELBW infants with normal head ultrasound exams and 8 typically developing full term infants. Regional spectral power and coherence were calculated.

RESULTS

No significant regional differences in EEG power were found between infant groups. However, compared to term infants, ELBW infants had significantly reduced interhemispheric coherence (in frontal polar and parietal regions) and intrahemispheric coherence (between frontal polar and parieto-occipital regions) in the 1-12Hz band but increased interhemispheric coherence between occipital regions in the 24-50Hz band.

CONCLUSIONS

ELBW infants at term post-menstrual age manifest regional differences in EEG functional connectivity as compared to term infants.

SIGNIFICANCE

Distinctive spatial patterns of electrocortical synchrony are found in ELBW infants. These regional patterns may presage regional alterations in the structure of the cortex.

Clinical neurophysiology of aging brain: from normal aging to neurodegeneration

Physiological brain aging is characterized by a loss of synaptic contacts and neuronal apoptosis that provokes age-dependent decline of sensory processing, motor performance, and cognitive function. Neural redundancy and plastic remodelling of brain networking, also secondary to mental and physical training, promotes maintenance of brain activity in healthy elderly for everyday life and fully productive affective and intellectual capabilities. However, age is the main risk factor for neurodegenerative disorders such as Alzheimer's disease (AD) that impact on cognition. Oscillatory electromagnetic brain activity is a hallmark of neuronal network function in various brain regions. Modern neurophysiological techniques including electroencephalography (EEG), event-related potential (ERP), magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS) can accurately index normal and abnormal brain aging to facilitate non-invasive analysis of cortico-cortical connectivity and neuronal synchronization of firing and coherence of rhythmic oscillations at various frequencies. The present review provides a perspective of these issues by assaying different neurophysiological methods and integrating the results with functional brain imaging findings. It is concluded that discrimination between physiological and pathological brain aging clearly emerges at the group level, with applications at the individual level also suggested. Integrated approaches utilizing neurophysiological techniques together with biological markers and structural and functional imaging are promising for large-scale, low-cost and non-invasive evaluation of at-risk populations. Practical implications of the methods are emphasized.

Neurorehabilitation issues in states of disordered consciousness following traumatic brain injury

The assessment and care of persons with disorders of consciousness (DOC) following catastrophic traumatic brain injury is often difficult and filled with both challenges and potential controversies. Rates of misdiagnosis of low-level neurological state (LLNS) patients with signs of awareness as being vegetative have been noted to be unacceptably high and call for better education and training regarding the assessment methodologies of individuals with DOC. Clinician knowledge regarding prognostication and neural recovery from LLNSs following traumatic brain injury is often lacking, as is awareness of the neurorehabilitative interventions that can potentially facilitate recovery, as well as minimize morbidity and mortality in this unique population of neurological patients.