Topography of the maximum of the amplitude of EEG frequency bands in dementia of the Alzheimer type

Effects of Phytoncide Fragrance on Resting-State Brain Activity in Mild Cognitive Impairment: A Randomized Double-Blind Controlled Study

Introduction: The therapeutic potential of phytoncide fragrances may be optimal for patients with mild cognitive impairment (MCI) that display complex symptomatology. This study aimed to explore the clinical value of phytoncide by evaluating its electrophysiological effects in patients with MCI. Materials and Methods: This was a double-blind, randomized controlled trial. A total of 24 community-dwelling patients were randomly assigned to either a phytoncide or no-odor group. Participants wore a dental mask, for 30 min at rest that had either the fragrance stimulus or water added to it. The quantitative electroencephalography (EEG) during the resting state was recorded before and after a single intervention. Results: There were significant interaction effects in absolute EEG-power values in the occipital (F = 6.52, p = 0.018) and parietal (F = 5.41, p = 0.030) left hemisphere at β frequency. Phytoncide odor significantly decreased low and high β activity in the occipital (corrected p = 0.009) and parietal (corrected p = 0.047) left hemisphere, respectively. In source localization, phytoncide odor significantly decreased deep source activation in the left inferior and middle frontal gyri at β 2 frequency band compared with the no-odor group (threshold = 4.25, p < 0.05). Conclusions: Reductions in β, indicative of anxiety, depression, and stress, suggest relief from emotion-related symptoms that are common in patients with MCI. Trial Registration: Clinical Trials Registry Korea (registration: KCT0007317).

The effects of different acetylcholinesterase inhibitors on EEG patterns in patients with Alzheimer's disease: A systematic review

OBJECTIVE

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common type of dementia. The early diagnosis of AD is an important factor for the control of AD progression. Electroencephalography (EEG) can be used for early diagnosis of AD. Acetylcholinesterase inhibitors (AChEIs) are also used for the amelioration of AD symptoms. In this systematic review, we reviewed the effect of different AChEIs including donepezil, rivastigmine, tacrine, physostigmine, and galantamine on EEG patterns in patients with AD.

METHODS

PubMed electronic database was searched and 122 articles were found. After removal of unrelated articles, 24 articles were selected for the present study.

RESULTS

AChEIs can decrease beta, theta, and delta frequency bands in patients with AD. However, conflicting results were found for alpha band. Some studies have shown increased alpha frequency, while others have shown decreased alpha frequency following treatment with AChEIs. The only difference was the type of drug.

CONCLUSIONS

We found that studies reporting the decreased alpha frequency used donepezil and galantamine, while studies reporting the increased alpha frequency used rivastigmine and tacrine. It was suggested that future studies should focus on the effect of different AChEIs on EEG bands, especially alpha frequency in patients with AD, to compare their effects and find the reason for their different influence on EEG patterns. Also, differences between the effects of AChEIs on oligodendrocyte differentiation and myelination may be another important factor. This is the first article investigating the effect of different AChEIs on EEG patterns in patients with AD.

Home-based, Remotely Supervised, 6-Week tDCS in Patients With Both MCI and Depression: A Randomized Double-Blind Placebo-Controlled Trial

As depressive symptom is considered a prodrome, a risk factor for progression from mild cognitive impairment (MCI) to dementia, improving depressive symptoms should be considered a clinical priority in patients with MCI undergoing transcranial direct current stimulation (tDCS) intervention. We aimed to comprehensively evaluate the efficacy of the home-based and remotely monitored tDCS in patients with both MCI and depression, by integrating cognitive, psychological, and electrophysiological indicators. In a 6-week, randomized, double blind, and sham-controlled study, 37 community-dwelling patients were randomly assigned to either an active or a sham stimulation group, and received 30 home-based sessions of 2 mA tDCS for 30 min with the anode located over the left and cathode over the right dorsolateral prefrontal cortex. We measured depressive symptoms, neurocognitive function, and resting-state electroencephalography. In terms of effects of both depressive symptoms and cognitive functions, active tDCS was not significantly different from sham tDCS. However, compared to sham stimulation, active tDCS decreased and increased the activation of delta and beta frequencies, respectively. Moreover, the increase in beta activity was correlated with the cognitive enhancement only in the active group. It was not possible to reach a definitive conclusion regarding the efficacy of tDCS on depression and cognition in patients with both MCI and depression. Nevertheless, the relationship between the changes of electrophysiology and cognitive performance suggests potential neuroplasticity enhancement implicated in cognitive processes by tDCS.

Novelty detection-based approach for Alzheimer's disease and mild cognitive impairment diagnosis from EEG

Alzheimer’s disease is diagnosed via means of daily activity assessment. The EEG recording evaluation is a supporting tool that can assist the practitioner to recognize the illness, especially in the early stages. This paper presents a new approach for detecting Alzheimer’s disease and potentially mild cognitive impairment according to the measured EEG records. The proposed method evaluates the amount of novelty in the EEG signal as a feature for EEG record classification. The novelty is measured from the parameters of EEG signal adaptive filtration. A linear neuron with gradient descent adaptation was used as the filter in predictive settings. The extracted feature (novelty measure) is later classified to obtain Alzheimer’s disease diagnosis. The proposed approach was cross-validated on a dataset containing EEG records of 59 patients suffering from Alzheimer’s disease; seven patients with mild cognitive impairment (MCI) and 102 controls. The results of cross-validation yield 90.73% specificity and 89.51% sensitivity. The proposed method of feature extraction from EEG is completely new and can be used with any classifier for the diagnosis of Alzheimer’s disease from EEG records.

Analysis of Neuropsychiatric Symptoms in Patients with Alzheimer's Disease Using Quantitative EEG and sLORETA

OBJECTIVE

The electrocortical activities associated with the neuropsychiatric symptoms (NPSs) of Alzheimer's disease (AD) were investigated using frequency-domain electroencephalography (EEG) spectral source analysis, and the potential electrocortical indices identified.

MATERIALS AND METHODS

Scalp EEG data were obtained from 51 patients with AD to investigate the presence of four NPS subdomains, hyperactivity, psychosis, affective symptoms, and apathy. EEG power spectra and the standardized low-resolution brain electromagnetic tomography (sLORETA)-localized EEG cortical sources were compared between the groups with and without the four NPS subdomains in eight frequency bands: 1-4, 4-8, 8-10, 10-12, 12-18, 18-20, 20-30, and 30-45 Hz.

RESULTS

The power spectral analysis of EEG data showed that AD patients with psychosis had lower values at the α2-band in most areas. In patients with apathy, the θ-to-β power ratio showed a greater activity over the frontal and central regions. The cortical source analysis using sLORETA revealed that patients with psychosis showed decreased values in the α2-band and patients with apathy showed higher δ-values, especially in the right frontal and temporal regions.

CONCLUSION

The results of the present study showed that both classical EEG spectral and EEG source analysis could differentiate patients with and without NPSs, especially psychosis and apathy subdomains. Spectral and sLORETA analyses provided information helpful for a better characterization in patients with NPSs.

Dynamics analysis of the hippocampal neuronal model subjected to cholinergic action related with Alzheimer's disease

There are evidences that the region of hippocampus is affected in the early stage of Alzheimer's disease (AD). Moreover, the hippocampal pyramidal neurons receive cholinergic input from the medial septum. Thus, this study, based on the results of electrophysiological experiments, first constructs a modified hippocampal CA1 pyramidal neuronal model by introducing two new currents of M-current and calcium ion-activated potassium ion current to depict the cholinergic input receiving from the medial septum, and then explores how acetylcholine deficiency and beta-amyloid accumulation under the pathological condition of AD influence the neuronal dynamics in terms of theta band power and spiking frequency using computational approach. By simulating acetylcholine potentiated M-current and calcium ion-activated potassium ion current, numerical results reveal that the relative theta band power increases significantly and the firing rate decreases obviously when acetylcholine is deficient. Similarly, by simulating beta-amyloid enhanced delay rectification potassium ion current, we also detect that the relative theta band power increases as well as the firing rate decreases remarkably as beta-amyloid is accumulated. In addition, the mechanism underlying these dynamical changes in theta rhythm and firing behavior is investigated by nonlinear behavioral analysis, which demonstrates that both deficiency in acetylcholine and accumulation in beta-amyloid can promote the emergence of stable equilibrium state in this modified hippocampal neuronal model. Note that acetylcholine deficiency together with beta-amyloid deposition plays key role in the pathogenesis of AD. We expect these findings could have important implications on better understanding pathogenesis and expounding potential biomarkers for AD.

Usefulness of EEG Techniques in Distinguishing Frontotemporal Dementia from Alzheimer's Disease and Other Dementias

The clinical distinction of frontotemporal dementia (FTD) and Alzheimer's disease (AD) may be difficult. In this narrative review we summarize and discuss the most relevant electroencephalography (EEG) studies which have been applied to demented patients with the aim of distinguishing the various types of cognitive impairment. EEG studies revealed that patients at an early stage of FTD or AD displayed different patterns in the cortical localization of oscillatory activity across different frequency bands and in functional connectivity. Both classical EEG spectral analysis and EEG topography analysis are able to differentiate the different dementias at group level. The combination of standardized low-resolution brain electromagnetic tomography (sLORETA) and power parameters seems to improve the sensitivity, but spectral and connectivity biomarkers able to differentiate single patients have not yet been identified. The promising EEG findings should be replicated in larger studies, but could represent an additional useful, noninvasive, and reproducible diagnostic tool for clinical practice.

Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework

This study addresses the problem of Alzheimer’s disease (AD) diagnosis with Electroencephalography (EEG). The use of EEG as a tool for AD diagnosis has been widely studied by comparing EEG signals of AD patients only to those of healthy subjects. By contrast, we perform automated EEG diagnosis in a differential diagnosis context using a new database, acquired in clinical conditions, which contains EEG data of 169 patients: subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, possible Alzheimer’s disease (AD) patients, and patients with other pathologies. We show that two EEG features, namely epoch-based entropy (a measure of signal complexity) and bump modeling (a measure of synchrony) are sufficient for efficient discrimination between these groups. We studied the performance of our methodology for the automatic discrimination of possible AD patients from SCI patients and from patients with MCI or other pathologies. A classification accuracy of 91.6% (specificity = 100%, sensitivity = 87.8%) was obtained when discriminating SCI patients from possible AD patients and 81.8% to 88.8% accuracy was obtained for the 3-class classification of SCI, possible AD and other patients.

The Electrophysiological Phenomenon of Alzheimer's Disease: A Psychopathology Theory

The current understanding of Alzheimer's disease (AD) is based on the Aβ and tau pathology and the resulting neuropathological changes, which are associated with manifested clinical symptoms. However, electrophysiological brain changes may provide a more expansive understanding of AD. Hence, the objective of this systematic review is to propose a theory about the electrophysiological phenomenon of Alzheimer's disease (EPAD). The review of literature resulted from an extensive search of PubMed and MEDLINE databases. One-hundred articles were purposively selected. They provided an understanding of the concepts establishing the theory of EPAD (neuropathological changes, neurochemical changes, metabolic changes, and electrophysiological brain changes). Changes in the electrophysiology of the brain are foundational to the association or interaction of the concepts. Building on Berger's Psychophysical Model, it is evident that electrophysiological brain changes occur and affect cortical areas to generate or manifest symptoms from onset and across the stages of AD, which may be prior to pathological changes. Therefore, the interaction of the concepts demonstrates how the psychopathology results from affected electrophysiology of the brain. The theory of the EPAD provides a theoretical foundation for appropriate measurements of AD without dependence on neuropathological changes. Future research is warranted to further test this theory. Ultimately, this theory contributes to existing knowledge because it shows how electrophysiological changes are useful in understanding the risk and progression of AD across the stages.

Amyloid Beta-Protein and Neural Network Dysfunction

Understanding the neural mechanisms underlying brain dysfunction induced by amyloid beta-protein (Aβ) represents one of the major challenges for Alzheimer's disease (AD) research. The most evident symptom of AD is a severe decline in cognition. Cognitive processes, as any other brain function, arise from the activity of specific cell assemblies of interconnected neurons that generate neural network dynamics based on their intrinsic and synaptic properties. Thus, the origin of Aβ-induced cognitive dysfunction, and possibly AD-related cognitive decline, must be found in specific alterations in properties of these cells and their consequences in neural network dynamics. The well-known relationship between AD and alterations in the activity of several neural networks is reflected in the slowing of the electroencephalographic (EEG) activity. Some features of the EEG slowing observed in AD, such as the diminished generation of different network oscillations, can be induced in vivo and in vitro upon Aβ application or by Aβ overproduction in transgenic models. This experimental approach offers the possibility to study the mechanisms involved in cognitive dysfunction produced by Aβ. This type of research may yield not only basic knowledge of neural network dysfunction associated with AD, but also novel options to treat this modern epidemic.

Quantitative EEG and LORETA: valuable tools in discerning FTD from AD?

Drawing a clinical distinction between frontotemporal dementia (FTD) and Alzheimer's disease (AD) is tricky, particularly at the early stages of disease. This study evaluates the possibility in differentiating 39 FTD, 39 AD, and 39 controls (CTR) by means of power spectral analysis and standardized low resolution brain electromagnetic tomography (sLORETA) within delta, theta, alpha 1 and 2, beta 1, 2, and 3 frequency bands. Both analyses revealed in AD patients, relative to CTR, higher expression of diffuse delta/theta and lower central/posterior fast frequency (from alpha1 to beta2) bands. FTD patients showed diffuse increased theta power compared with CTR and lower delta relative to AD patients. Compared with FTD, AD patients showed diffuse higher theta power at spectral analysis and, at sLORETA, decreased alpha2 and beta1 values in central/temporal regions. Spectral analysis and sLORETA provided complementary information that might help characterizing different patterns of electroencephalogram (EEG) oscillatory activity in AD and FTD. Nevertheless, this differentiation was possible only at the group level because single patients could not be discerned with sufficient accuracy.

Computational study of hippocampal-septal theta rhythm changes due to β-amyloid-altered ionic channels

Electroencephagraphy (EEG) of many dementia patients has been characterized by an increase in low frequency field potential oscillations. One of the characteristics of early stage Alzheimer's disease (AD) is an increase in theta band power (4-7 Hz). However, the mechanism(s) underlying the changes in theta oscillations are still unclear. To address this issue, we investigate the theta band power changes associated with β-Amyloid (Aβ) peptide (one of the main markers of AD) using a computational model, and by mediating the toxicity of hippocampal pyramidal neurons. We use an established biophysical hippocampal CA1-medial septum network model to evaluate four ionic channels in pyramidal neurons, which were demonstrated to be affected by Aβ. They are the L-type Ca²⁺ channel, delayed rectifying K⁺ channel, A-type fast-inactivating K⁺ channel and large-conductance Ca²⁺-activated K⁺ channel. Our simulation results demonstrate that only the Aβ inhibited A-type fast-inactivating K⁺ channel can induce an increase in hippocampo-septal theta band power, while the other channels do not affect theta rhythm. We further deduce that this increased theta band power is due to enhanced synchrony of the pyramidal neurons. Our research may elucidate potential biomarkers and therapeutics for AD. Further investigation will be helpful for better understanding of AD-induced theta rhythm abnormalities and associated cognitive deficits.

Improving the specificity of EEG for diagnosing Alzheimer's disease

Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD). However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment) patients. Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest were θ (3.5–7.5 Hz), α₁ (7.5–9.5 Hz), α₂ (9.5–12.5 Hz), and β (12.5–25 Hz). The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models. Results. Enhanced EEG power in the θ range is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies. Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD.

Beta-amyloid protein (25-35) disrupts hippocampal network activity: role of Fyn-kinase

Early cognitive deficit characteristic of early Alzheimer's disease seems to be produced by the soluble forms of beta-amyloid protein. Such cognitive deficit correlates with neuronal network dysfunction that is reflected as alterations in the electroencephalogram of both Alzheimer patients and transgenic murine models of such disease. Correspondingly, recent studies have demonstrated that chronic exposure to betaAP affects hippocampal oscillatory properties. However, it is still unclear if such neuronal network dysfunction results from a direct action of betaAP on the hippocampal circuit or it is secondary to the chronic presence of the protein in the brain. Therefore, we aimed to explore the effect of acute exposure to betaAP(25-35) on hippocampal network activity both in vitro and in vivo, as well as on intrinsic and synaptic properties of hippocampal neurons. We found that betaAP(25-35), reversibly, affects spontaneous hippocampal population activity in vitro. Such effect is not produced by the inverse sequence betaAP(35-25) and is reproduced by the full-length peptide betaAP(1-42). Correspondingly betaAP(25-35), but not the inverse sequence betaAP(35-25), reduces theta-like activity recorded from the hippocampus in vivo. The betaAP(25-35)-induced disruption in hippocampal network activity correlates with a reduction in spontaneous neuronal activity and synaptic transmission, as well as with an inhibition in the subthreshold oscillations produced by pyramidal neurons in vitro. Finally, we studied the involvement of Fyn-kinase on the betaAP(25-35)-induced disruption in hippocampal network activity in vitro. Interestingly, we found that such phenomenon is not observed in slices obtained from Fyn-knockout mice. In conclusion, our data suggest that betaAP acutely affects proper hippocampal function through a Fyn-dependent mechanism. We propose that such alteration might be related to the cognitive impairment observed, at least, during the early phases of Alzheimer's disease.

Split-test Bonferroni correction for QEEG statistical maps

With statistical testing, corrections for multiple comparisons, such as Bonferroni adjustments, have given rise to controversies in the scientific community, because of their negative impact on statistical power. This impact is especially problematic for high-multidimensional data, such as multi-electrode brain recordings. With brain imaging data, a reliable method is needed to assess statistical significance of the data without losing statistical power. Conjunction analysis allows the combination of significance and consistency of an effect. Through a balanced combination of information from retest experiments (multiple trials split testing), we present an intuitively appealing, novel approach for brain imaging conjunction. The method is then tested and validated on synthetic data followed by a real-world test on QEEG data from patients with Alzheimer's disease. This latter application requires both reliable type-I error and type-II error rates, because of the poor signal-to-noise ratio inherent in EEG signals.

Extraction of spectral based measures from MEG background oscillations in Alzheimer's disease

In this study, we explored the ability of several spectral based measures to summarize the information of the power spectral density (PSD) function from spontaneous magnetoencephalographic (MEG) activity in Alzheimer's disease (AD). The MEGs of 20 AD patients and 21 elderly controls were recorded with eyes closed at rest during 5 min from 148 channels. Five spectral parameters were estimated from PSD: mean frequency (MF), individual alpha frequency (IAF), transition frequency (TF), 95% spectral edge frequency (SEF95) and spectral entropy (SE). To reduce the dimensionality of the problem, we applied a principal component analysis. According to our results, MF was the best discriminating index between both groups (85.00% sensitivity, 85.71% specificity) indicating a shift to the left of the power spectrum in AD. A significant MEG slowing was also observed using both IAF (p < 0.001) and TF (p < 0.01). The lowest classification statistics (65% sensitivity, 66.67% specificity) were obtained with SEF95. However, these results were also significant (p < 0.05). This fact points out that there is a variation in the spectral content at high frequencies of AD patients and controls. Finally, a significant decrease of irregularity in the AD group was observed with SE, with results close to those obtained with MF (90.00% sensitivity, 76.19% specificity). In conclusion, a complete description of PSD can help to increase our insight into brain dysfunction in AD and to extract spectral patterns specific to the disease.

Quantitative magnetoencephalography of spontaneous brain activity in Alzheimer disease: an exhaustive frequency analysis

Quantitative magnetoencephalography (qMEG) was used to investigate differences in the 2 to 60 Hz spectral power, between Alzheimer disease (AD) patients and control subjects. Twenty-two AD patients and 21 age-matched control subjects participated in this study. MEG signal analysis comprised the division of the entire 2 to 60 Hz spectrum in 2 Hz-width subbands. Both the relative power and the contribution of each subband to the correct classification of AD patients and controls were calculated. The relative power in 2 bands comprised between 2 to 4 Hz and 16 to 28 Hz was selected by a restrictive multiple-comparison test, among the entire 2 to 60 Hz spectrum. Using 2 to 4 Hz values it is possible to choose a classification rule with an estimate sensitivity and specificity given by 68% and 76% respectively. Alternatively, when 16 to 28 Hz values are used, it is possible to obtain a better classification rule with an estimate sensitivity and specificity given by 81% and 80%, respectively. To the best of our knowledge, this is the first electroencephalography or MEG study where a so exhaustive analysis of the magneto-electric spectrum has been performed. This study supports the notion that more attention should be devoted to the study of beta band in AD.

Altered states of consciousness: Processed EEG in mental disease

Due to increasing life expectancy and a rising elderly population in Europe, the incidence of mild cognitive impairment which may predict diseases like Alzheimer's Disease or Vascular Dementia, is rising. Neurophysiological techniques are simple and inexpensive tools for early diagnosis and provide useful and objective correlates of cognitive activity both in normal subjects and patients suffering from the above conditions. Cognitive impairment due to different mental disease is characterized by decreased power and coherence in the alpha/beta band, which suggests functional disconnection among cortical areas, whereas both power and coherence in the delta and theta bands increase as a sign of cortical deafferentation from subcortical structures. Quantification of power and phase relationship by bispectral analysis suggests the Bispectral Index could be a useful but simple tool for early diagnosis of mental disease.

Sources of cortical rhythms in adults during physiological aging: a multicentric EEG study

This electroencephalographic (EEG) study tested whether cortical EEG rhythms (especially delta and alpha) show a progressive increasing or decreasing trend across physiological aging. To this aim, we analyzed the type of correlation (linear and nonlinear) between cortical EEG rhythms and age. Resting eyes-closed EEG data were recorded in 108 young (Nyoung; age range: 18-50 years, mean age 27.3+/-7.3 SD) and 107 elderly (Nold; age range: 51-85 years, mean age 67.3+/-9.2 SD) subjects. The EEG rhythms of interest were delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), and beta 2 (20-30 Hz). EEG cortical sources were estimated by low-resolution brain electromagnetic tomography (LORETA). Statistical results showed that delta sources in the occipital area had significantly less magnitude in Nold compared to Nyoung subjects. Similarly, alpha 1 and alpha 2 sources in the parietal, occipital, temporal, and limbic areas had significantly less magnitude in Nold compared to Nyoung subjects. These nine EEG sources were given as input for evaluating the type (linear, exponential, logarithmic, and power) of correlation with age. When subjects were considered as a single group there was a significant linear correlation of age with the magnitude of delta sources in the occipital area and of alpha 1 sources in occipital and limbic areas. The same was true for alpha 2 sources in the parietal, occipital, temporal, and limbic areas. In general, the EEG sources showing significant linear correlation with age also supported a nonlinear correlation with age. These results suggest that the occipital delta and posterior cortical alpha rhythms decrease in magnitude during physiological aging with both linear and nonlinear trends. In conclusion, this new methodological approach holds promise for the prediction of dementia in mild cognitive impairment by regional source rather than surface EEG data and by both linear and nonlinear predictors.

Predictive Model for Assessing Cognitive Impairment by Quantitative Electroencephalography

OBJECTIVE

To assess the utility of quantitative electroencephalographic analysis as an indicator of cognitive impairment, we examined the correlation between Mini-Mental State Examination (MMSE) scores and quantitative electroencephalographic (QEEG) power values in elderly patients and constructed a regression model to predict MMSE scores.

BACKGROUND

Because of the growing number of elderly individuals with cognitive deficits, there is an increasing need for simple and objective methods with which to evaluate cognitive function. Although QEEG is reportedly a useful method for this purpose, few researchers have constructed a QEEG-based model for predicting the degree of cognitive impairment in clinical settings.

METHOD

We evaluated brain function using QEEG in 44 elderly patients with memory complaints and compared the results with their MMSE scores.

RESULTS

In the correlation analysis, no significant correlation was found between MMSE scores and QEEG power values. However, a regression model created using relative QEEG and gender for predicting MMSE scores had an adjusted R2 of 0.471.

CONCLUSIONS

This finding suggests that QEEG analysis may be a useful indicator of cognitive decline in patients with memory complaints.

Pain reactivity in Alzheimer patients with different degrees of cognitive impairment and brain electrical activity deterioration

Pain perception and autonomic responses to pain are known to be altered in dementia, although the mechanisms are poorly understood. We studied patients with Alzheimer's disease (AD) whose cognitive status was assessed through the Mini Mental State Examination test and whose brain electrical activity was measured by means of quantitative electroencephalography. After assessment of both cognitive impairment and brain electrical activity deterioration, these patients underwent sensory measurements in which the minimum stimulus intensity for both stimulus detection and pain sensation was determined. In addition, heart rate responses to pain threshold x 1.5 were recorded. We found that neither stimulus detection nor pain threshold was correlated to cognitive status and brain electrical activity decline. By contrast, we found a correlation between heart rate responses and deterioration of both cognitive functions and brain electrical activity. In particular, the heart rate increase after pain stimulation was correlated to the presence of slow brain electrical activity (delta and theta frequencies). This correlation was also found for the anticipatory heart rate increase just before pain stimulation. These results indicate that pain anticipation and reactivity depend on both the cognitive status and the frequency bands of the electroencephalogram, whereas both stimulus detection and pain threshold are not affected by the progression of AD. These findings indicate that, whereas the sensory-discriminative components of pain are preserved even in advanced stages of AD, the cognitive and affective functions, which are related to both anticipation and autonomic reactivity, are severely affected. This sensory-affective dissociation is well correlated with the neuropathological findings in AD.

Mapping distributed sources of cortical rhythms in mild Alzheimer's disease. A multicentric EEG study

The study aimed at mapping (i) the distributed electroencephalographic (EEG) sources specific for mild Alzheimer's disease (AD) compared to vascular dementia (VaD) or normal elderly people (Nold) and (ii) the distributed EEG sources sensitive to the mild AD at different stages of severity. Resting EEG (10-20 electrode montage) was recorded from 48 mild AD, 20 VaD, and 38 Nold subjects. Both AD and VaD patients had 24-17 of mini mental state examination (MMSE). EEG rhythms were delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), and beta 2 (20-30 Hz). Cortical EEG sources were modeled by low resolution brain electromagnetic tomography (LORETA). Regarding issue i, there was a decline of central, parietal, temporal, and limbic alpha 1 (low alpha) sources specific for mild AD group with respect to Nold and VaD groups. Furthermore, occipital alpha 1 sources showed a strong decline in mild AD compared to VaD group. Finally, distributed theta sources were largely abnormal in VaD but not in mild AD group. Regarding issue ii, there was a lower power of occipital alpha 1 sources in mild AD subgroup having more severe disease. Compared to previous field studies, this was the first investigation that illustrated the power spectrum profiles at the level of cortical (macroregions) EEG sources in mild AD patients having different severity of the disease with respect to VaD and normal subjects. Future studies should evaluate the clinical usefulness of this approach in early differential diagnosis, disease staging, and therapy monitoring.

Cortical Networks Generating Movement-Related EEG Rhythms in Alzheimer's Disease: An EEG Coherence Study

Patients with mild Alzheimer's disease (AD) present with abnormally strong values of frontal and ipsilateral central sensorimotor rhythms. The authors tested 2 working hypotheses of the related electroencephalographic (EEG) coherence: disconnection, defined as a sign of a reduced coordination within the frontoparietal and interhemispheric networks, and cooperation, defined as a reflection of the reorganization of the brain sensorimotor networks. Results showed that, compared with healthy controls, patients with mild AD had an unreactive and abnormally low interhemispheric EEG coherence and an unreactive and abnormally high frontoparietal EEG coherence. These findings support the hypothesis of an impaired mechanism of sensorimotor cortical coupling (disconnection) in mild AD.

Recurrent neural network-based approach for early recognition of Alzheimer's disease in EEG

OBJECTIVE

We explored the ability of specifically designed and trained recurrent neural networks (RNNs), combined with wavelet preprocessing, to discriminate between the electroencephalograms (EEGs) of patients with mild Alzheimer's disease (AD) and their age-matched control subjects.

METHODS

Twomin recordings of resting eyes-closed continuous EEGs (as well as their wavelet-filtered subbands) obtained from parieto-occipital channels of 10 early AD patients and 10 healthy controls were input into RNNs for training and testing purposes. The RNNs were chosen because they can implement extremely non-linear decision boundaries and possess memory of the state, which is crucial for the considered task.

RESULTS

The best training/testing results were achieved using a 3-layer RNN on left parietal channel level 4 high-pass wavelet subbands. When trained on 3 AD and 3 control recordings, the resulting RNN tested well on all remaining controls and 5 out of 7 AD patients. This represented a significantly better than chance performance of about 80% sensitivity at 100% specificity.

CONCLUSION

The suggested combined wavelet/RNN approach may be useful in analyzing long-term continuous EEGs for early recognition of AD. This approach should be extended on larger patient populations before its clinical diagnostic value can be established. Further lines of investigation might also require that EEGs be recorded from patients engaged in certain mental (cognitive) activities.

Movement-related electroencephalographic reactivity in Alzheimer disease

Event-related desynchronization/synchronization (ERD/ERS) of alpha and beta electroencephalographic (EEG) rhythms was investigated in normal subjects and mild Alzheimer Disease patients (AD), performing unilateral right finger movements (about 10 s intermovement interval). Electroencephalographic data were sampled based on 10-20 system electrode montage. Surface Laplacian estimate of the potential reduced the head-volume conductor effects and annulled electrode reference variations. Results showed that EEG reactivity (i.e., ERD/ERS) of modeled contralateral rolandic cortex and motor performance were preserved in mild to moderate AD. In contrast, modeled activity (i.e., ERD/ERS) of frontolateral, centromedial, and ipsilateral rolandic areas was abnormal. Furthermore, interrelatedness of cortical response and movement timing was abnormal in AD patients. These results would support the working hypothesis that mild to moderate AD is a global brain network disease, including processing of sensorimotor information (despite no overt movement disorder). Further investigations will ascertain the clinical relevance of these results.

The role of the electroencephalogram in the diagnosis of dementia of the Alzheimer type: an attempt at technology assessment

In a first attempt at technology assessment of the electroencephalogram (EEG) in the diagnosis of patients with Alzheimer's disease (AD), three conclusions were reached: notwithstanding the well defined clinical criteria, there remains a need for a laboratory technique to confirm the diagnosis since a 100% accuracy cannot be obtained by clinical methods only; although the EEG has a high sensitivity in separating AD patients from normal controls, the sensitivity of the EEG is at present not satisfactory when studying populations with a low prevalence of AD patients; the sensitivity of the EEG is higher or equal to the best other laboratory techniques (magnetic resonance imaging [MRI], computerized tomography [CT], SPECT, PET) available at the moment. Since the EEG and MRI can be helpful in different aspects of the differential diagnosis it is argued that all AD patients should be studied at least once by EEG as well as by MRI.