Alzheimer's Disease (AD) represents the most prevalent form of dementia and is considered a major health problem due to its high prevalence and its economic costs. An accurate characterization of the underlying neural dynamics in AD is crucial in order to adopt effective treatments. In this regard, mild cognitive impairment (MCI) is an important clinical entity, since it is a risk-state for developing dementia. In the present study, coupling patterns of 111 resting-state electroencephalography (EEG) recordings were analyzed. Specifically, we computed Cross-Approximate Entropy () and Cross-Sample Entropy () of 37 patients with dementia due to AD, 37 subjects with MCI, and 37 healthy control (HC) subjects. Our results showed that outperformed, revealing higher number of significant connections among the three groups (Kruskal-Wallis test, FDR-corrected -values < 0.05). AD patients exhibited statistically significant lower similarity values at θ and β frequency bands compared to HC. MCI is also characterized by a global decrease of similarity in all bands, being only significant at β. These differences shows that β band might play a significant role in the identification of early stages of AD. Our results suggest that could increase the insight into brain dynamics at different AD stages. Consequently, it may contribute to develop early AD biomarkers, potentially useful as diagnostic information.

Alzheimer's disease (AD) is the most common type of neurodegenerative disorder, typically causing dementia along aging. AD is mainly characterized by a pathological extracellular accumulation of amyloid-beta peptides that affects excitatory and inhibitory synaptic transmission, inducing aberrant patterns in neuronal circuits. Growing evidence shows that AD targets cortical neuronal networks related to cognitive functions including episodic memory and visuospatial attention. This is partially reflected by the abnormal mechanisms of cortical neural synchronization and coupling that generate resting state electroencephalographic (EEG) rhythms. The cortical neural synchronization is typically indexed by EEG power density. The EEG coupling between electrode pairs probes functional (inter-relatedness of EEG signals) and effective (casual effect from one over the other electrode) connectivity. The former is typically indexed by synchronization likelihood (linear and nonlinear) or spectral coherence (linear), the latter by granger causality or information theory indexes. Here we reviewed literature concerning EEG studies in condition of resting state in AD and mild cognitive impairment (MCI) subjects as a window on abnormalities of the cortical neural synchronization and functional and effective connectivity. Results showed abnormalities of the EEG power density at specific frequency bands (<12Hz) in the MCI and AD populations, associated with an altered functional and effective EEG connectivity among long range cortical networks (i.e. fronto-parietal and fronto-temporal). These results suggest that resting state EEG rhythms reflect the abnormal cortical neural synchronization and coupling in the brain of prodromal and overt AD subjects, possibly reflecting dysfunctional neuroplasticity of the neural transmission in long range cortical networks.Copyright © 2015. Published by Elsevier B.V.

Here we critically review studies that used electroencephalography (EEG) or event-related potential (ERP) indices as a biomarker of Alzheimer's disease. In the first part we overview studies that relied on visual inspection of EEG traces and spectral characteristics of EEG. Second, we survey analysis methods motivated by dynamical systems theory (DST) as well as more recent network connectivity approaches. In the third part we review studies of sleep. Next, we compare the utility of early and late ERP components in dementia research. In the section on mismatch negativity (MMN) studies we summarize their results and limitations and outline the emerging field of computational neurology. In the following we overview the use of EEG in the differential diagnosis of the most common neurocognitive disorders. Finally, we provide a summary of the state of the field and conclude that several promising EEG/ERP indices of synaptic neurotransmission are worth considering as potential biomarkers. Furthermore, we highlight some practical issues and discuss future challenges as well.

To identify quantitative EEG frequency and connectivity features (Phase Lag Index) characteristic of mild cognitive impairment (MCI) in Parkinson's disease (PD) patients and to investigate if these features correlate with cognitive measures of the patients.We recorded EEG data for a group of PD patients with MCI (n = 27) and PD patients without cognitive impairment (n = 43) using a high-resolution recording system. The EEG files were processed and 66 frequency along with 330 connectivity (phase lag index, PLI) measures were calculated. These measures were used to classify MCI vs. MCI-free patients. We also assessed correlations of these features with cognitive tests based on comprehensive scores (domains).PLI measures classified PD-MCI from non-MCI patients better than frequency measures. PLI in delta, theta band had highest importance for identifying patients with MCI. Amongst cognitive domains, we identified the most significant correlations between Memory and Theta PLI, Attention and Beta PLI.PLI is an effective quantitative EEG measure to identify PD patients with MCI.We identified quantitative EEG measures which are important for early identification of cognitive decline in PD.Copyright © 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Lewy body dementia includes dementia with Lewy bodies and Parkinson's disease dementia and is characterized by transient clinical symptoms such as fluctuating cognition, which might be driven by dysfunction of the intrinsic dynamic properties of the brain. In this context we investigated whole-brain dynamics on a subsecond timescale in 42 Lewy body dementia compared to 27 Alzheimer's disease patients and 18 healthy controls using an EEG microstate analysis in a cross-sectional design. Microstates are transiently stable brain topographies whose temporal characteristics provide insight into the brain's dynamic repertoire. Our additional aim was to explore what processes in the brain drive microstate dynamics. We therefore studied associations between microstate dynamics and temporal aspects of large-scale cortical-basal ganglia-thalamic interactions using dynamic functional MRI measures given the putative role of these subcortical areas in modulating widespread cortical function and their known vulnerability to Lewy body pathology. Microstate duration was increased in Lewy body dementia for all microstate classes compared to Alzheimer's disease (P < 0.001) and healthy controls (P < 0.001), while microstate dynamics in Alzheimer's disease were largely comparable to healthy control levels, albeit with altered microstate topographies. Correspondingly, the number of distinct microstates per second was reduced in Lewy body dementia compared to healthy controls (P < 0.001) and Alzheimer's disease (P < 0.001). In the dementia with Lewy bodies group, mean microstate duration was related to the severity of cognitive fluctuations (ρ = 0.56, PFDR = 0.038). Additionally, mean microstate duration was negatively correlated with dynamic functional connectivity between the basal ganglia (r = - 0.53, P = 0.003) and thalamic networks (r = - 0.38, P = 0.04) and large-scale cortical networks such as visual and motor networks in Lewy body dementia. The results indicate a slowing of microstate dynamics and disturbances to the precise timing of microstate sequences in Lewy body dementia, which might lead to a breakdown of the intricate dynamic properties of the brain, thereby causing loss of flexibility and adaptability that is crucial for healthy brain functioning. When contrasted with the largely intact microstate dynamics in Alzheimer's disease, the alterations in dynamic properties in Lewy body dementia indicate a brain state that is less responsive to environmental demands and might give rise to the apparent slowing in thinking and intermittent confusion which typify Lewy body dementia. By using Lewy body dementia as a probe pathology we demonstrate a potential link between dynamic functional MRI fluctuations and microstate dynamics, suggesting that dynamic interactions within the cortical-basal ganglia-thalamic loop might play a role in the modulation of EEG dynamics.© The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain.

Over the past 20 years, neuroimaging has become a predominant technique in systems neuroscience. One might envisage that over the next 20 years the neuroimaging of distributed processing and connectivity will play a major role in disclosing the brain's functional architecture and operational principles. The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. I accepted the invitation to write this review with great pleasure and hope to celebrate and critique the achievements to date, while addressing the challenges ahead.

In this study the best combination of quantitative electroencephalographic variables (qEEG) for the discrimination of groups with mild to moderate Alzheimer's disease (AD), mild cognitive impairment and healthy subjects was defined and related to neuropsychological performance. The study population included 18 patients with mild to moderate probable AD, 19 subjects with objective memory disturbance, 17 subjects with subjective memory complaints who did not have clinical evidence of memory disturbance, and 16 healthy controls. AD patients had significantly increased theta and decreased alpha relative power, mean frequency, and temporoparietal coherence. There was no significant difference in the mean frequency in the left temporal region between AD patients and subjects with objective memory disturbances. Temporoparietal coherence appeared as a discriminant variable together with alpha and theta relative power only between AD patients and controls giving 77.8% sensitivity and 100% specificity. Significant correlations between regional changes in qEEG variables and cognitive functions were found.

EEG coherence can be used to evaluate the functionality of cortical connections and to get information about the synchronization of the regional cortical activity. We studied EEG coherence in patients affected by clinically probable Alzheimer's disease (AD) in order to quantify the modifications in the cortico-cortical or cortico-subcortical connections. The EEGs were recorded in 10 AD patients (with mild or moderate degrees of dementia) and in 10 normal age-matched subjects, at rest and eye-closed, from 16 electrodes with linked-ears reference. Spectral parameters and coherence were calculated by a multichannel autoregressive model using 50 artifact-free epochs, 1 s duration each. Alpha coherence was significantly decreased in 6 patients, the decrease being more accentuated in the area near the electrode taken into account; a significant delta coherence increase was found in a few patients between frontal and posterior regions. The AD group showed a significant decrease of alpha band coherence, in particular in temporo-parieto-occipital areas, more evident in patients with a more severe cognitive impairment. These abnormalities could reflect two different pathophysiological changes: the alpha coherence decrease could be related to alterations in cortico-cortical connections, whereas the delta coherence increase could be related to the lack of influence of subcortical cholinergic structures on cortical electrical activity.

The National Institute on Aging and the Alzheimer's Association charged a workgroup with the task of revising the 1984 criteria for Alzheimer's disease (AD) dementia. The workgroup sought to ensure that the revised criteria would be flexible enough to be used by both general healthcare providers without access to neuropsychological testing, advanced imaging, and cerebrospinal fluid measures, and specialized investigators involved in research or in clinical trial studies who would have these tools available. We present criteria for all-cause dementia and for AD dementia. We retained the general framework of probable AD dementia from the 1984 criteria. On the basis of the past 27 years of experience, we made several changes in the clinical criteria for the diagnosis. We also retained the term possible AD dementia, but redefined it in a manner more focused than before. Biomarker evidence was also integrated into the diagnostic formulations for probable and possible AD dementia for use in research settings. The core clinical criteria for AD dementia will continue to be the cornerstone of the diagnosis in clinical practice, but biomarker evidence is expected to enhance the pathophysiological specificity of the diagnosis of AD dementia. Much work lies ahead for validating the biomarker diagnosis of AD dementia.Copyright © 2011. Published by Elsevier Inc.

The aim of this study was to determine the performance of several spectral indices of the EEG (ratios between fast and slow EEG activities) as descriptors of the EEG changes occurring at the onset and during the evolution of Alzheimer's disease (AD). These indices were calculated from quantitative analysis of EEGs recorded in AD patients and from a matched non-demented group of control subjects. One advantage of such indices is to be independent of the absolute value of power spectral densities, which may vary from subject to subject, another being to take into account fast EEG activities. Conventional statistic tests and Receiver Operating Curves (ROC) analysis were performed upon these data to determine the accuracy of the power ratios to discriminate a) between controls and patients (i.e., to detect dementia) and b) between subgroups of patients defined according to the Global Deterioration Scale of Reisberg (GDS). The defined ratios provided a good classification of AD patients for all cerebral regions except the frontal areas, because of eye movement artefacts; the results confirm the increase in slow activities and the concomitant decrease in fast activities early in AD patients. Moreover, our results demonstrate that these indices are adapted tools to perform a good discrimination between demented and non-demented patients in routine clinical practice. We therefore propose the use of these EEG power ratios to discriminate between different stages of Alzheimer's disease, and to perform long-term monitoring of AD patients.

The spatial aspects of brain electrical activity can be assessed by equivalent EEG frequency band generators. We aimed to describe alterations of these EEG generators in Alzheimer's disease (AD) and healthy aging and whether they could serve as predictive markers of AD in subjects at risk.The amplitude and 3-dimensional localization of equivalent EEG sources were evaluated using FFT dipole approximation in 38 mild AD patients, 31 subjects with mild cognitive impairment (MCI) and 24 healthy control subjects.AD patients showed an increase of delta and theta global field power (GFP), which corresponds to the generalized EEG amplitude, as well as a reduction of alpha GFP when compared to the controls. A decrease of alpha and beta GFP was found in AD patients, as compared to the MCI subjects. With respect to topography in the antero-posterior direction, sources of alpha and beta activity shifted more anteriorly in AD patients compared to both the controls and MCI subjects. No significant difference was found between MCI and controls. Combined alpha and theta GFP were the best discriminating variables between AD patients and controls (84% correct classification) and AD and MCI subjects (78% correctly classified). MCI subjects were followed longitudinally (25 months on average) in order to compare differences in baseline EEG variables between MCI subjects who progressed to AD (PMCI) and those who remained stable (SMCI). Compared to SMCI, PMCI had decreased alpha GFP and a more anterior localization of sources of theta, alpha and beta frequency. In a linear discriminant analysis applied on baseline values of the two MCI subgroups, the best predictor of future development of AD was found to be antero-posterior localization of alpha frequency.FFT dipole approximation and frequency analysis performed by conventional FFT showed comparable classification accuracy between the studied groups. We conclude that localization and amplitude of equivalent EEG sources could be promising markers of early AD.

Recordings were taken from single neurons in the hippocampus and dentate gyrus of rats during walking and urethane anesthesia. Firing histograms for these cells were constructed as a function of the phase of the concurrent extracellularly recorded hippocampal slow wave theta rhythm. Care was taken to be sure of the site of recording of the theta rhythm and its phase with respect to a reliable reference, so that comparisons of the phases of firing could be made across animals. The firing of most of these neurons is deeply modulated as a function of the phase of the theta rhythm. This is true whether the theta rhythm occurs during walking or during urethane anesthesia, but for some types of cells the mean phases of firing are different in the two types of theta rhythm. During walking, pyramidal cells and interneurons in all hippocampal subregions and dentate granule cells have a maximum probability of firing near the positive peak of the theta rhythm recorded in the outer molecular layer of the dentate (dentate theta). During urethane anesthesia, the maximum firing probability for interneurons in CA1 and for dentate granule cells occurs near the negative peak of the dentate theta, while the phases of maximum firing for pyramidal cells and interneurons in CA3 and CA4 become widely distributed. The phases of maximum firing of pyramidal cells in CA1 are, if anything, more narrowly distributed around the positive peak of the dentate theta during urethane anesthesia than during walking. These differences in the firing of hippocampal cells during walking and urethane anesthesia represent some of the differences in cellular mechanisms distinguishing two kinds of hippocampal theta rhythm.

To investigate the added value of hippocampal atrophy rates over whole brain volume measurements on MRI in patients with Alzheimer disease (AD), patients with mild cognitive impairment (MCI), and controls.We included 64 patients with AD (67 +/- 9 years; F/M 38/26), 44 patients with MCI (71 +/- 6 years; 21/23), and 34 controls (67 +/- 9 years; 16/18). Two MR scans were performed (scan interval: 1.8 +/- 0.7 years; 1.0 T), using a coronal three-dimensional T1-weighted gradient echo sequence. At follow-up, 3 controls and 23 patients with MCI had progressed to AD. Hippocampi were manually delineated at baseline. Hippocampal atrophy rates were calculated using regional, nonlinear fluid registration. Whole brain baseline volumes and atrophy rates were determined using automated segmentation and registration tools.All MRI measures differed between groups (p < 0.005). For the distinction of MCI from controls, larger effect sizes of hippocampal measures were found compared to whole brain measures. Between MCI and AD, only whole brain atrophy rate differed significantly. Cox proportional hazards models (variables dichotomized by median) showed that within all patients without dementia, hippocampal baseline volume (hazard ratio [HR]: 5.7 [95% confidence interval: 1.5-22.2]), hippocampal atrophy rate (5.2 [1.9-14.3]), and whole brain atrophy rate (2.8 [1.1-7.2]) independently predicted progression to AD; the combination of low hippocampal volume and high atrophy rate yielded a HR of 61.1 (6.1-606.8). Within patients with MCI, only hippocampal baseline volume and atrophy rate predicted progression.Hippocampal measures, especially hippocampal atrophy rate, best discriminate mild cognitive impairment (MCI) from controls. Whole brain atrophy rate discriminates Alzheimer disease (AD) from MCI. Regional measures of hippocampal atrophy are the strongest predictors of progression to AD.