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In recent decades, entropy measures have gained prominence in neuroscience due to the nonlinear behaviour exhibited by neural systems. This rationale justifies the application of methods from the theory of nonlinear dynamics to cerebral activity, aiming to detect and quantify its variability more effectively. In the context of electroencephalogram (EEG) signals, entropy analysis offers valuable insights into the complexity and irregularity of electromagnetic brain activity. By moving beyond linear analyses, entropy measures provide a deeper understanding of neural dynamics, particularly pertinent in elucidating the mechanisms underlying brain aging and various acute/chronic-progressive neurological disorders. Indeed, various pathologies can disrupt nonlinear structuring in neural activity, which may remain undetected by linear methods such as power spectral analysis. Consequently, the utilization of nonlinear tools, including entropy analysis, becomes crucial for capturing these alterations. To establish the relevance of entropy analysis and its potential to discern between physiological and pathological conditions, this review discusses its diverse applications in studying healthy brain aging and neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD). Various entropy parameters, such as approximate entropy (ApEn), sample entropy (SampEn), multiscale entropy (MSE), and permutation entropy (PermEn), are analysed within this context. By quantifying the complexity and irregularity of EEG signals, entropy analysis may serve as a valuable biomarker for early diagnosis, treatment monitoring, and disease management. Such insights offer clinicians crucial information for devising personalized treatment and rehabilitation plans tailored to individual patients., (© 2024. The Author(s), under exclusive licence to American Aging Association.)

Background: This article introduces a novel index aimed at uncovering specific brain connectivity patterns associated with Alzheimer's disease (AD), defined according to neuropsychological patterns., Methods: Electroencephalographic (EEG) recordings of 370 people, including 170 healthy subjects and 200 mild-AD patients, were acquired in different clinical centres using different acquisition equipment by harmonising acquisition settings. The study employed a new derived Small World (SW) index, SWcomb, that serves as a comprehensive metric designed to integrate the seven SW parameters, computed across the typical EEG frequency bands. The objective is to create a unified index that effectively distinguishes individuals with a neuropsychological pattern compatible with AD from healthy ones., Results: Results showed that the healthy group exhibited the lowest SWcomb values, while the AD group displayed the highest SWcomb ones., Conclusions: These findings suggest that SWcomb index represents an easy-to-perform, low-cost, widely available and non-invasive biomarker for distinguishing between healthy individuals and AD patients., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Geriatrics Society.)

Hyperventilation (HV) is a voluntary activity that causes changes in the neuronal firing characteristics noticeable in the electroencephalogram (EEG) signals. HV-related changes have been scribed to modulation of pO2/pCO2 blood contents. Therefore, an HV test is routinely used for highlighting brain abnormalities including those depending to neurobiological mechanisms at the basis of neurodegenerative disorders. The main aim of the present paper is to study the effectiveness of HV test in modifying the functional connectivity from the EEG signals that can be typical of a prodromal state of Alzheimer's disease (AD), the Mild Cognitive Impairment prodromal to Alzheimer condition. MCI subjects and a group of age-matched healthy elderly (Ctrl) were enrolled and subjected to EEG recording during HV, eyes-closed (EC), and eyes-open (EO) conditions. Since the cognitive decline in MCI seems to be a progressive disconnection syndrome, the approach we used in the present study is the graph theory, which allows to describe brain networks with a series of different parameters. Small world (SW), modularity (M), and global efficiency (GE) indexes were computed among the EC, EO, and HV conditions comparing the MCI group to the Ctrl one. All the three graph parameters, computed in the typical EEG frequency bands, showed significant changes among the three conditions, and more interestingly, a significant difference in the GE values between the MCI group and the Ctrl one was obtained, suggesting that the combination of HV test and graph theory parameters should be a powerful tool for the detection of possible cerebral dysfunction and alteration., (© 2023. The Author(s), under exclusive licence to American Aging Association.)

Introduction: Dementia in its various forms represents one of the most frightening emergencies for the aging population. Cognitive decline-including Alzheimer's disease (AD) dementia-does not develop in few days; disease mechanisms act progressively for several years before clinical evidence., Methods: A preclinical stage, characterized by measurable cognitive impairment, but not overt dementia, is represented by mild cognitive impairment (MCI), which progresses to-or, more accurately, is already in a prodromal form of-AD in about half cases; people with MCI are therefore considered the population at risk for AD deserving special attention for validating screening methods., Results: Graph analysis tools, combined with machine learning methods, represent an interesting probe to identify the distinctive features of physiological/pathological brain aging focusing on functional connectivity networks evaluated on electroencephalographic data and neuropsychological/imaging/genetic/metabolic/cerebrospinal fluid/blood biomarkers., Discussion: On clinical data, this innovative approach for early diagnosis might provide more insight into pathophysiological processes underlying degenerative changes, as well as toward a personalized risk evaluation for pharmacological, nonpharmacological, and rehabilitation treatments., (© 2022 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

In the human brain, physiological aging is characterized by progressive neuronal loss, leading to disruption of synapses and to a degree of failure in neurotransmission and information flow. However, there is increasing evidence to support the notion that the aged brain has a remarkable level of resilience (i.s. ability to reorganize itself), with the aim of preserving its physiological activity. It is therefore of paramount interest to develop objective markers able to characterize the biological processes underlying brain aging in the intact human, and to distinguish them from brain degeneration associated to age-related neurological progressive diseases like Alzheimer's disease. EEG, alone and combined with transcranial magnetic stimulation (TMS-EEG), is particularly suited to this aim, due to the functional nature of the information provided, and thanks to the ease with which it can be integrated in ecological scenarios including behavioral tasks. In this review, we aimed to provide the reader with updated information about the role of modern methods of EEG and TMS-EEG analysis in the investigation of physiological brain aging and Alzheimer's disease. In particular, we focused on data about cortical connectivity obtained by using readouts such graph theory network brain organization and architecture, and transcranial evoked potentials (TEPs) during TMS-EEG. Overall, findings in the literature support an important potential contribution of such neurophysiological techniques to the understanding of the mechanisms underlying normal brain aging and the early (prodromal/pre-symptomatic) stages of dementia., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Alzheimer's disease (AD) is the most common cause of dementia that involves a progressive and irrevocable decline in cognitive abilities and social behavior, thus annihilating the patient's autonomy. The theoretical assumption that disease-modifying drugs are most effective in the early stages hopefully in the prodromal stage called mild cognitive impairment (MCI) urgently pushes toward the identification of robust and individualized markers of cognitive decline to establish an early pharmacological intervention. This requires the combination of well-established neural mechanisms and the development of increasingly sensitive methodologies. Among the neurophysiological markers of attention and cognition, one of the sub-components of the 'cognitive brain wave' P300 recordable in an odd-ball paradigm -namely the P3b- is extensively regarded as a sensitive indicator of cognitive performance. Several studies have reliably shown that changes in the amplitude and latency of the P3b are strongly related to cognitive decline and aging both healthy and pathological. Here, we used a P3b spatial filter to enhance the electroencephalographic (EEG) characteristics underlying 175 subjects divided into 135 MCI subjects, 20 elderly controls (EC), and 20 young volunteers (Y). The Y group served to extract the P3b spatial filter from EEG data, which was later applied to the other groups during resting conditions with eyes open and without being asked to perform any task. The group of 135 MCI subjects could be divided into two subgroups at the end of a month follow-up: 75 with stable MCI (MCI-S, not converted to AD), 60 converted to AD (MCI-C). The P3b spatial filter was built by means of a signal processing method called Functional Source Separation (FSS), which increases signal-to-noise ratio by using a weighted sum of all EEG recording channels rather than relying on a single, or a small sub-set, of channels. A clear difference was observed for the P3b dynamics at rest between groups. Moreover, a machine learning approach showed that P3b at rest could correctly distinguish MCI from EC (80.6% accuracy) and MCI-S from MCI-C (74.1% accuracy), with an accuracy as high as 93.8% in discriminating between MCI-C and EC. Finally, a comparison of the Bayes factor revealed that the group differences among MCI-S and MCI-C were 138 times more likely to be detected using the P3b dynamics compared with the best performing single electrode (Pz) approach. In conclusion, we propose that P3b as measured through spatial filters can be safely regarded as a simple and sensitive marker to predict the conversion from an MCI to AD status eventually combined with other non-neurophysiological biomarkers for a more precise definition of dementia having neuropathological Alzheimer characteristics.

Alzheimer's disease (AD) is the most common neurodegenerative disorder in elderly subjects. Recent studies verified the effects of cognitive training combined with repetitive transcranial magnetic stimulation (rTMS-COG) in AD patients. Here, we analyzed neuropsychological and neurophysiological data, derived from electroencephalography (EEG), to evaluate the effects of a 6-week protocol of rTMS-COG in 72 AD. We designed a randomized, double-blind, sham-controlled trial to evaluate efficacy of rTMS on 6 brain regions obtained by an individual MRI combined with COG related to brain areas to stimulate (i.e., syntax and grammar tasks, comprehension of lexical meaning and categorization tasks, action naming, object naming, spatial memory, spatial attention). Patients underwent neuropsychological and EEG examination before (T 0 ), after treatment (T 1 ), and after 40 weeks (T 2 ), to evaluate the effects of rehabilitation therapy. "Small World" (SW) graph approach was introduced allowing us to model the architecture of brain connectivity in order to correlate it with cognitive improvements. We found that following 6 weeks of intensive daily treatment the immediate results showed an improvement in cognitive scales among AD patients. SW present no differences before and after the treatment, whereas a crucial SW modulation emerges at 40-week follow-up, emphasizing the importance of rTMS-COG rehabilitation treatment for AD. Additional results demonstrated that the delta and alpha1 SW seem to be diagnostic biomarkers of AD, whereas alpha2 SW might represent a prognostic biomarker of cognitive recovery. Derived EEG parameters can be awarded the role of diagnostic and predictive biomarkers of AD progression, and rTMS-COG can be regarded as a potentially useful treatment for AD., (© 2021. The Author(s), under exclusive licence to American Aging Association.)

Background: Physiological and pathological brain aging plays a central role in brain network modulation. The aim of the present article was to assess the stability of a proposed method for evaluation of small-world (SW) characteristics for the study of the human connectome. Subjects and Methods: Eighty subjects were recruited: 36 young healthy controls, 32 elderly healthy controls, and 12 patients affected by Alzheimer's disease (AD). Electroencephalograms (EEGs) were recorded during six separate sessions (480 recordings) at an average intersession interval of 3.8 ± 0.2 days. We applied graph theory functions to the weighted and undirected networks obtained by the lagged linear coherence estimated by exact low-resolution electromagnetic tomography (eLORETA). We explored the following frequency bands: 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), beta 2 (20-30 Hz), and gamma (30-40 Hz). Results: The proposed method for evaluation of SW characteristics exhibited good reproducibility and stability. Furthermore, results showed the pattern, Young>Elderly>AD, in low-frequency delta and theta bands and vice versa in the higher alpha band. Finally, a correlation with age was confirmed in healthy subjects, showing that the older the age, the higher the SW values for alpha 2. Discussion: Evidences from the present study confirm the stability of the SW index and suggest that the analysis of connectivity patterns evaluated from EEGs can be supported by the graph theory. The proposed method for evaluation of SW characteristics has shown good reproducibility and stability. This technique, applied to patient data, could provide more information on the pathophysiological processes underlying age-related brain disconnection, as well as on administration of rehabilitation treatments at the right time, which could allow patients to avoid unnecessary interventions. Impact statement The graph analysis tools described in this study represent an interesting approach to study the distinctive characteristics of physiological aging by focusing on functional connectivity networks. The proposed method for evaluation of small-world characteristics has shown good reproducibility and stability. This technique, applied to patient data, could provide more information on the pathophysiological processes underlying age-related brain disconnection, as well as on delivery of rehabilitation treatments at the right time, which could allow patients to avoid unnecessary interventions.

Background: Most common progressive brain diseases in the elderly are Alzheimer's disease (AD) and vascular dementia (VaD). They present with relatively similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms are different., Objective: The aim is to explore the brain connectivity differences between AD and VaD patients compared to mild cognitive impairment (MCI) and normal elderly (Nold) subjects applying graph theory, in particular the Small World (SW) analysis., Methods: 274 resting state EEGs were analyzed in 100 AD, 80 MCI, 40 VaD, and 54 Nold subjects. Graph theory analyses were applied to undirected and weighted networks obtained by lagged linear coherence evaluated by eLORETA tool., Results: VaD and AD patients presented more ordered low frequency structure (lower value of SW) than Nold and MCI subjects, and more random organization (higher value of SW) in low and high frequency alpha rhythms. Differences between patients have been found in high frequency alpha rhythms in VaD (higher value of SW) with respect to AD, and in theta band with a trend which is more similar to MCI and Nold than to AD. MCI subjects presented a network organization which is intermediate, in low frequency bands, between Nold and patients., Conclusion: Graph theory applied to EEG data has proved very useful in identifying differences in brain network patterns in subjects with dementia, proving to be a valid tool for differential diagnosis. Future studies will aim to validate this method to diagnose especially in the early stages of the disease and at single subject level.

Alzheimer's disease (AD) is the most common neurodegenerative disease among the elderly with a progressive decline in cognitive function significantly affecting quality of life. Both the prevalence and emotional and financial burdens of AD on patients, their families, and society are predicted to grow significantly in the near future, due to a prolongation of the lifespan. Several lines of evidence suggest that modifications of risk-enhancing life styles and initiation of pharmacological and non-pharmacological treatments in the early stage of disease, although not able to modify its course, helps to maintain personal autonomy in daily activities and significantly reduces the total costs of disease management. Moreover, many clinical trials with potentially disease-modifying drugs are devoted to prodromal stages of AD. Thus, the identification of markers of conversion from prodromal form to clinically AD may be crucial for developing strategies of early interventions. The current available markers, including volumetric magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebral spinal fluid (CSF) analysis are expensive, poorly available in community health facilities, and relatively invasive. Taking into account its low cost, widespread availability and non-invasiveness, electroencephalography (EEG) would represent a candidate for tracking the prodromal phases of cognitive decline in routine clinical settings eventually in combination with other markers. In this scenario, the present paper provides an overview of epidemiology, genetic risk factors, neuropsychological, fluid and neuroimaging biomarkers in AD and describes the potential role of EEG in AD investigation, trying in particular to point out whether advanced analysis of EEG rhythms exploring brain function has sufficient specificity/sensitivity/accuracy for the early diagnosis of AD., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Aim of this study was to explore the EEG functional connectivity in amnesic mild cognitive impairments (MCI) subjects with multidomain impairment in order to characterize the Default Mode Network (DMN) in converted MCI (cMCI), which converted to Alzheimer's disease (AD), compared to stable MCI (sMCI) subjects. A total of 59 MCI subjects were recruited and divided -after appropriate follow-up- into cMCI or sMCI. They were further divided in MCI with linguistic domain (LD) impairment and in MCI with executive domain (ED) impairment. Small World (SW) index was measured as index of balance between integration and segregation brain processes. SW, computed restricting to nodes of DMN regions for all frequency bands, evaluated how they differ between MCI subgroups assessed through clinical and neuropsychological four-years follow-up. In addition, SW evaluated how this pattern differs between MCI with LD and MCI with ED. Results showed that SW index significantly decreased in gamma band in cMCI compared to sMCI. In cMCI with LD impairment, the SW index significantly decreased in delta band, while in cMCI with ED impairment the SW index decreased in delta and gamma bands and increased in alpha1 band. We propose that the DMN functional alterations in cognitive impairment could reflect an abnormal flow of brain information processing during resting state possibly associated to a status of pre-dementia.

Background: Several studies investigated clinical and instrumental differences to make diagnosis of dementia in general and in Alzheimer's disease (AD) in particular with the aim to classify, at the individual level, AD patients and healthy controls cooperating with neuropsychological tests for an early diagnosis. Advanced network analysis of electroencephalographic (EEG) rhythms provides information on dynamic brain connectivity and could be used in classification processes. If successfully reached, this goal would add a low-cost, easily accessible, and non-invasive technique with neuropsychological tests., Objective: To investigate the possibility to automatically classify physiological versus pathological aging from cortical sources' connectivity based on a support vector machine (SVM) applied to EEG small-world parameter., Methods: A total of 295 subjects were recruited: 120 healthy volunteers and 175 AD. Graph theory functions were applied to undirected and weighted networks obtained by lagged linear coherence evaluated by eLORETA. A machine-learning classifier (SVM) was applied. EEG frequency bands were: delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz), and gamma (30-40 Hz)., Results: The receiver operating characteristic curve showed AUC of 0.97±0.03 (indicating very high classification accuracy). The classifier showed 95% ±5% sensitivity, 96% ±3% specificity, and 95% ±3% accuracy for the classification., Conclusion: EEG connectivity analysis via a combination of source/connectivity biomarkers, highly correlating with neuropsychological AD diagnosis, could represent a promising tool in identification of AD patients. This approach represents a low-cost and non-invasive method, one that utilizes widely available techniques which, when combined, reach high sensitivity/specificity and optimal classification accuracy on an individual basis (0.97 of AUC).

Objective: Mild cognitive impairment (MCI) is a condition intermediate between physiological brain aging and dementia. Amnesic-MCI (aMCI) subjects progress to dementia (typically to Alzheimer-Dementia = AD) at an annual rate which is 20 times higher than that of cognitively intact elderly. The present study aims to investigate whether EEG network Small World properties (SW) combined with Apo-E genotyping, could reliably discriminate aMCI subjects who will convert to AD after approximately a year., Methods: 145 aMCI subjects were divided into two sub-groups and, according to the clinical follow-up, were classified as Converted to AD (C-MCI, 71) or Stable (S-MCI, 74)., Results: Results showed significant differences in SW in delta, alpha1, alpha2, beta2, gamma bands, with C-MCI in the baseline similar to AD. Receiver Operating Characteristic(ROC) curve, based on a first-order polynomial regression of SW, showed 57% sensitivity, 66% specificity and 61% accuracy(area under the curve: AUC=0.64). In 97 out of 145 MCI, Apo-E allele testing was also available. Combining this genetic risk factor with Small Word EEG, results showed: 96.7% sensitivity, 86% specificity and 91.7% accuracy(AUC=0.97). Moreover, using only the Small World values in these 97 subjects, the ROC showed an AUC of 0.63; the resulting classifier presented 50% sensitivity, 69% specificity and 59.6% accuracy. When different types of EEG analysis (power density spectrum) were tested, the accuracy levels were lower (68.86%)., Interpretation: Concluding, this innovative EEG analysis, in combination with a genetic test (both low-cost and widely available), could evaluate on an individual basis with great precision the risk of MCI progression. This evaluation could then be used to screen large populations and quickly identify aMCI in a prodromal stage of dementia. Ann Neurol 2018 Ann Neurol 2018;84:302-314., (© 2018 American Neurological Association.)

Electroencephalographic (EEG) rhythms are linked to any kind of learning and cognitive performance including motor tasks. The brain is a complex network consisting of spatially distributed networks dedicated to different functions including cognitive domains where dynamic interactions of several brain areas play a pivotal role. Brain connectome could be a useful approach not only to mechanisms underlying brain cognitive functions, but also to those supporting different mental states. This goal was approached via a learning task providing the possibility to predict performance and learning along physiological and pathological brain aging. Eighty-six subjects (22 healthy, 47 amnesic mild cognitive impairment, 17 Alzheimer's disease) were recruited reflecting the whole spectrum of normal and abnormal brain connectivity scenarios. EEG recordings were performed at rest, with closed eyes, both before and after the task (Sensory Motor Learning task consisting of a visual rotation paradigm). Brain network properties were described by Small World index (SW), representing a combination of segregation and integration properties. Correlation analyses showed that alpha 2 SW in pre-task significantly predict learning (r  =  -0.2592, p < 0.0342): lower alpha 2 SW (higher possibility to increase during task and better the learning of this task), higher the learning as measured by the number of reached targets. These results suggest that, by means of an innovative analysis applied to a low-cost and widely available techniques (SW applied to EEG), the functional connectome approach as well as conventional biomarkers would be effective methods for monitoring learning progress during training both in normal and abnormal conditions.

Brain imaging plays an important role in the study of Alzheimer's disease (AD), where atrophy has been found to occur in the hippocampal formation during the very early disease stages and to progress in parallel with the disease's evolution. The aim of the present study was to evaluate a possible correlation between "Small World" characteristics of the brain connectivity architecture-as extracted from EEG recordings-and hippocampal volume in AD patients. A dataset of 144 subjects, including 110 AD (MMSE 21.3) and 34 healthy Nold (MMSE 29.8) individuals, was evaluated. Weighted and undirected networks were built by the eLORETA solutions of the cortical sources' activities moving from EEG recordings. The evaluation of the hippocampal volume was carried out on a subgroup of 60 AD patients who received a high-resolution T1-weighted sequence and underwent processing for surface-based cortex reconstruction and volumetric segmentation using the Freesurfer image analysis software. Results showed that, quantitatively, more correlation was observed in the right hemisphere, but the same trend was seen in both hemispheres. Alpha band connectivity was negatively correlated, while slow (delta) and fast-frequency (beta, gamma) bands positively correlated with hippocampal volume. Namely, the larger the hippocampal volume, the lower the alpha and the higher the delta, beta, and gamma Small World characteristics of connectivity. Accordingly, the Small World connectivity pattern could represent a functional counterpart of structural hippocampal atrophying and related-network disconnection.

In a recent study, analyzing the modulation of γ-band oscillations, Naro and colleagues demonstrated that transcranial alternating current stimulation could drive the gamma rhythms in the human EEG in cognitive healthy elderly subjects but not in mild cognitive impairment (MCI) prodromal to Alzheimer's disease (AD) and in early AD patients. Therefore, this method is proposed to intercept early in the disease course those MCI subjects who are in a pre-symptomatic stage of an already established AD. This prediction index may help the clinician to adopt a better prevention/follow-up strategy. In this direction, the novel advances in EEG analysis for the evaluation of brain reactivity and connectivity-namely via innovative mathematical approach, i.e., graph theory-represent a promising tool for a non-invasive and easy-to-perform neurophysiological marker that could be used for the pre-symptomatic diagnosis of AD and to predict MCI progression to dementia.

Several studies have shown that, in spite of the fact that motor symptoms manifest late in the course of Alzheimer's disease (AD), neuropathological progression in the motor cortex parallels that in other brain areas generally considered more specific targets of the neurodegenerative process. It has been suggested that motor cortex excitability is enhanced in AD from the early stages, and that this is related to disease's severity and progression. To investigate the neurophysiological hallmarks of motor cortex functionality in early AD we combined transcranial magnetic stimulation (TMS) with electroencephalography (EEG). We demonstrated that in mild AD the sensorimotor system is hyperexcitable, despite the lack of clinically evident motor manifestations. This phenomenon causes a stronger response to stimulation in a specific time window, possibly due to locally acting reinforcing circuits, while network activity and connectivity is reduced. These changes could be interpreted as a compensatory mechanism allowing for the preservation of sensorimotor programming and execution over a long period of time, regardless of the disease's progression. Hum Brain Mapp 37:2083-2096, 2016. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

Functional brain abnormalities including memory loss are found to be associated with pathological changes in connectivity and network neural structures. Alzheimer's disease (AD) interferes with memory formation from the molecular level, to synaptic functions and neural networks organization. Here, we determined whether brain connectivity of resting-state networks correlate with memory in patients affected by AD and in subjects with mild cognitive impairment (MCI). One hundred and forty-four subjects were recruited: 70 AD (MMSE Mini Mental State Evaluation 21.4), 50 MCI (MMSE 25.2) and 24 healthy subjects (MMSE 29.8). Undirected and weighted cortical brain network was built to evaluate graph core measures to obtain Small World parameters. eLORETA lagged linear connectivity as extracted by electroencephalogram (EEG) signals was used to weight the network. A high statistical correlation between Small World and memory performance was found. Namely, higher Small World characteristic in EEG gamma frequency band during the resting state, better performance in short-term memory as evaluated by the digit span tests. Such Small World pattern might represent a biomarker of working memory impairment in older people both in physiological and pathological conditions., (Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.)

Background: Meta-analyses show that percentages of non-Cp-Cu-copper that is not bound to ceruloplasmin (also known as 'free' copper)-in serum are higher in Alzheimer's disease (AD) patients. Genetic heterogeneity in AD patients stratified on the basis of non-Cp-Cu cut-off sustains the existence of a copper AD metabolic subtype. Non-Cp-Cu abnormalities correlated with alterations of electroencephalographic rhythms (EEG)., Objective: We aimed to determine whether an EEG-derived brain cortical rhythm's heterogeneity between two AD groups stratified on the basis of a copper marker., Method: We assessed levels of copper, ceruloplasmin, Non-Cp-Cu, and the APOE4 genotype in 67 AD patients and compared resting EEG-derived eLORETA cortical rhythms between AD groups stratified in terms of 'Normal' and 'High' non-Cp-Cu., Results: The High non-Cp-Cu group experienced a lower power in all bands (0.2-48 Hz) in the parietal cortices (p=0.019) and a more limited alpha band (8-13 Hz) power in the sensory lobes (temporal, occipital, and parietal p>0.05 consistently) than the Normal non-Cp-Cu AD group. When corrected for MMSE, the non-Cp-Cu levels correlated with a reduction of high-frequency brain activity (from high alpha to gamma, 10.5-48 Hz)., Conclusion: This neurophysiological heterogeneity in EEG-derived brain cortical rhythms between the two AD groups sustains a copper AD metabolic subtype; Non-Cp-Cu is a marker of this copper AD.

The aim of this study was to investigate the neuronal network characteristics in physiological and pathological brain aging. A database of 378 participants divided in three groups was analyzed: Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal elderly (Nold) subjects. Through EEG recordings, cortical sources were evaluated by sLORETA software, while graph theory parameters (Characteristic Path Length λ, Clustering coefficient γ, and small-world network σ) were computed to the undirected and weighted networks, obtained by the lagged linear coherence evaluated by eLORETA software. EEG cortical sources from spectral analysis showed significant differences in delta, theta, and alpha 1 bands. Furthermore, the analysis of eLORETA cortical connectivity suggested that for the normalized Characteristic Path Length (λ) the pattern differences between normal cognition and dementia were observed in the theta band (MCI subjects are find similar to healthy subjects), while for the normalized Clustering coefficient (γ) a significant increment was found for AD group in delta, theta, and alpha 1 bands; finally, the small world (σ) parameter presented a significant interaction between AD and MCI groups showing a theta increase in MCI. The fact that AD patients respect the MCI subjects were significantly impaired in theta but not in alpha bands connectivity are in line with the hypothesis of an intermediate status of MCI between normal condition and overt dementia.

Cortical gray matter volume and resting state cortical electroencephalographic rhythms are typically abnormal in subjects with amnesic mild cognitive impairment (MCI) and Alzheimer's disease (AD). Here we tested the hypothesis that in amnesic MCI and AD subjects, abnormalities of EEG rhythms are a functional reflection of cortical atrophy across the disease. Eyes-closed resting state EEG data were recorded in 57 healthy elderly (Nold), 102 amnesic MCI, and 108 AD patients. Cortical gray matter volume was indexed by magnetic resonance imaging recorded in the MCI and AD subjects according to Alzheimer's disease neuroimaging initiative project (http://www.adni-info.org/). EEG rhythms of interest were delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz), and gamma (30-40 Hz). These rhythms were indexed by LORETA. Compared with the Nold, the MCI showed a decrease in amplitude of alpha 1 sources. With respect to the Nold and MCI, the AD showed an amplitude increase of delta sources, along with a strong amplitude reduction of alpha 1 sources. In the MCI and AD subjects as a whole group, the lower the cortical gray matter volume, the higher the delta sources, the lower the alpha 1 sources. The better the score to cognitive tests the higher the gray matter volume, the lower the pathological delta sources, and the higher the alpha sources. These results suggest that in amnesic MCI and AD subjects, abnormalities of resting state cortical EEG rhythms are not epiphenomena but are strictly related to neurodegeneration (atrophy of cortical gray matter) and cognition., (Copyright © 2011 Wiley Periodicals, Inc.)

Patients suffering from prodromal (i.e., amnestic mild cognitive impairment, aMCI) and overt Alzheimer's disease (AD) show abnormal cortical sources of resting state electroencephalographic (EEG) rhythms. Here we tested the hypothesis that these sources show extensive abnormalities in liver cirrhosis (LC) patients with a cognitive impairment due to covert and diffuse hepatic encephalopathy (CHE). EEG activity was recorded in 64 LC (including 21 CHE), 21 aMCI, 21 AD, and 21 cognitively intact (Nold) subjects. 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 LORETA. Widespread sources of theta (all but frontal), alpha 1 (all but occipital), and alpha 2 (parietal, temporal) rhythms were higher in amplitude in all LC patients than in the Nold subjects. In these LC patients, the activity of central, parietal, and temporal theta sources correlated negatively, and parietal and temporal alpha 2 sources correlated positively with an index of global cognitive status. Finally, widespread theta (all but frontal) and alpha 1 (all but occipital) sources showed higher activity in the sub-group of LC patients with CHE than in the patients with aMCI or AD. These results unveiled the larger spatial-frequency abnormalities of the resting state EEG sources in the CHE compared to the AD condition.

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.

Introduction: The brain needs continuous oxygen supply even in resting-state. Hypoxia enhances resting-state electroencephalographic (EEG) rhythms in the delta range, and reduces those in the alpha range, with a pattern similar to that observed in Alzheimer's disease (AD). Here we tested whether resting-state cortical EEG rhythms in patients with congestive heart failure (CHF), as a model of acute hypoxia, present frequency similarities with AD patients, comparable by cognitive status revealed by the mini mental state examination (MMSE)., Methods: Eyes-closed EEG data were recorded in 10 CHF patients, 20 AD patients, and 20 healthy elderly subjects (Nold) as controls. LORETA software estimated cortical EEG generators., Results: Compared to Nold, both AD and CHF groups presented higher delta (2-4Hz) and lower alpha (8-13Hz) temporal sources. The highest delta and lowest alpha sources were observed in CHF subjects. In these subjects, the global amplitude of delta sources correlated with brain natriuretic peptide (BNP) level in the blood, as a marker of disease severity., Conclusions: Resting-state delta and alpha rhythms suggest analogies between the effects of acute hypoxia and AD neurodegeneration on the cortical neurons' synchronization., Significance: Acute ischemic hypoxia could affect the mechanisms of cortical neural synchronization generating resting state EEG rhythms, inducing the "slowing" of EEG rhythms typically observed in AD patients., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Objective: Here we test the hypothesis that cortical source mapping of resting state electroencephalographic (EEG) rhythms could characterize neurodegenerative disorders inducing cognitive impairment such as Parkinson's disease related dementia (PDD) and Alzheimer's disease (AD)., Methods: To address this issue, eyes-closed resting state EEG rhythms were recorded in 13 PDD, 20 AD, and 20 normal elderly (Nold) subjects. Age, gender, and education were carefully matched across the three groups. Mini Mental State Evaluation (MMSE) score probed subjects' global cognitive status, and was matched between the PDD and AD groups. EEG rhythms of interest were delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), and beta2 (20-30 Hz). EEG cortical sources were estimated by low resolution brain electromagnetic source tomography (LORETA)., Results: With respect to the Nold and AD groups, the PPD group was characterized by peculiar abnormalities of central delta sources and posterior cortical sources of theta and beta1 rhythms. With respect to the Nold group, the PDD and AD groups mainly pointed to lower posterior cortical sources of alpha1 rhythms, which were positively correlated to MMSE score across all PDD and AD subjects as a whole (the lower the alpha sources, the lower the MMSE score). This alpha decrease was greater in the AD than PPD patients., Conclusions: The results suggest that topography and frequency of eyes-closed resting state cortical EEG rhythms distinguished PDD and AD groups., Significance: We report the existence of different effects of neurodegeneration on the cortical neural synchronization mechanisms generating resting state EEG rhythms in PDD and AD patients., (Copyright © 2011. Published by Elsevier Ireland Ltd.)

Resting state electroencephalographic (EEG) rhythms do not deteriorate with the increase of white matter vascular lesion in amnesic mild cognitive impairment (MCI) subjects [1], although white matter is impaired along Alzheimer's disease (AD). Here we tested whether this is true even in AD subjects. Closed-eye resting state EEG data were recorded in 40 healthy elderly (Nold), 96 amnesic MCI, and 83 AD subjects. White matter vascular lesions were indexed by magnetic resonance imaging recorded in the MCI and AD subjects (about 42% of cases following ADNI standards). The MCI subjects were divided into two sub-groups based on the median of the white matter lesion, namely MCI+ (people with highest vascular load; n = 48) and MCI- (people with lowest vascular load; n = 48). The same was true for the AD subjects (AD+, n = 42; AD-, n = 41). EEG rhythms of interest were delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz), and gamma (30-40 Hz). LORETA software estimated cortical EEG sources. When compared to Nold group, MCI and AD groups showed well known abnormalities of delta and alpha sources. Furthermore, amplitude of occipital, temporal, and limbic alpha 1 sources were higher in MCI+ than MCI- group. As a novelty, amplitude of occipital delta sources was lower in AD+ than AD- group. Furthermore, central, parietal, occipital, temporal, and limbic alpha sources were higher in amplitude in AD+ than AD- group. Amplitude of these sources was correlated to global cognitive status (i.e., Mini Mental State Evaluation score). These results suggest that in amnesic MCI and AD subjects, resting state posterior delta and alpha EEG rhythms do not deteriorate with the increase of white-matter vascular lesion. These rhythms might be more sensitive to AD neurodegenerative processes and cognitive status rather than to concomitant lesions to white matter.

Older persons with Mild Cognitive Impairment (MCI) feature neurobiological Alzheimer's Disease (AD) in 50% to 70% of the cases and develop dementia within the next 5 to 7 years. Current evidence suggests that biochemical, neuroimaging, electrophysiological, and neuropsychological markers can track the disease over time since the MCI stage (also called prodromal AD). The amount of evidence supporting their validity is of variable strength. We have reviewed the current literature and categorized evidence of validity into three classes: Class A, availability of multiple serial studies; Class B a single serial study or multiple cross sectional studies of patients with increasing disease severity from MCI to probable AD; and class C, multiple cross sectional studies of patients in the dementia stage, not including the MCI stage. Several Class A studies suggest that episodic memory and semantic fluency are the most reliable neuropsychological markers of progression. Hippocampal atrophy, ventricular volume and whole brain atrophy are structural MRI markers with class A evidence. Resting-state fMRI and connectivity, and diffusion MR markers in the medial temporal white matter (parahippocampus and posterior cingulum) and hippocampus are promising but require further validation. Change in amyloid load in MCI patients warrant further investigations, e.g. over longer period of time, to assess its value as marker of disease progression. Several spectral markers of resting state EEG rhythms that might reflect neurodegenerative processes in the prodromal stage of AD (EEG power density, functional coupling, spectral coherence, and synchronization) suffer from lack of appropriately designed studies. Although serial studies on late event-related potentials (ERPs) in healthy elders or MCI patients are inconclusive, others tracking disease progression and effects of cholinesterase inhibiting drugs in AD, and cross-sectional including MCI or predicting development of AD offer preliminary evidence of validity as a marker of disease progression from the MCI stage. CSF Markers, such as Aβ 1-42, t-tau and p-tau are valuable markers which support the clinical diagnosis of Alzheimer's disease. However, these markers are not sensitive to disease progression and cannot be used to monitor the severity of Alzheimer's disease. For Isoprostane F2 some evidence exists that its increase correlates with the progression and the severity of AD.

Physiological brain aging is characterized by a combination of synaptic pruning, loss of cortico-cortical connections and neuronal apoptosis that provoke age-dependent decline of cognitive functions. Neural/synaptic redundancy and plastic remodeling 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. Unfortunately, in pathological situations, aging triggers neurodegenerative processes that impact on cognition, like Alzheimer's disease (AD). Oscillatory electromagnetic brain activity is a hallmark of neuronal network function in various brain regions. Modern neurophysiological techniques including digital electroencephalography (EEG) allow non-invasive analysis of cortico-cortical connectivity and neuronal synchronization of firing, and coherence of brain rhythmic oscillations at various frequencies. The present review of field EEG literature suggests that discrimination between physiological and pathological brain aging clearly emerges at the group level, with some promising result on the informative value of EEG markers at the individual level. Integrated approaches utilizing neurophysiological techniques together with biological markers and structural and functional imaging are promising for large-scale, low-cost, widely available on the territory and non-invasive screening of at-risk populations.

Cortical sources of resting eyes-closed alpha rhythms are typically abnormal in mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects. Here we tested the hypothesis of a progressive impairment of cortical alpha reactivity to eye-opening across amnesic MCI and mild AD subjects, reflecting another aspect of the impairment of cortical neural synchronization. Resting electroencephalography (EEG) data were recorded in 36 normal elderly subjects (Nold), 91 amnesic MCI, and 31 mild AD subjects during eyes-closed and -open conditions. EEG sources were estimated by LORETA software. In the eye-closed condition, posterior alpha 1 (8-10.5 Hz) sources were lower in MCI and AD than Nold subjects. The opposite was true for occipital delta sources (2-4 Hz). Reactivity to the eyes-open condition showed posterior alpha 1 and alpha 2 (10.5-13 Hz) sources was high in the Nold, intermediate in the MCI, and low in the AD subjects. Furthermore, occipital alpha 1 reactivity across MCI and AD subjects was correlated to the cognitive impairment as revealed by Mini-Mental State Examination score. In conclusion, at least at group level, the continuum across amnesic MCI and mild AD status is related to an impaired reactivity of cortical neuronal synchronization to eyes opening at alpha rhythms.

Atrophy of hippocampus and alteration of resting eyes-closed electroencephalographic (EEG) rhythms represent important features of mild cognitive impairment (MCI) and Alzheimer's disease (AD). Here we evaluated linear and non-linear aspects of the relationship between these features in the continuum along MCI and AD conditions, as a reflection of neurodegenerative processes. Eyes-closed resting EEG data were recorded in 60 healthy elderly (Nold), 88 MCI, and 35 Alzheimer's disease (AD) patients. Hippocampal volume was measured in magnetic resonance imaging of the MCI and AD subjects. Based on the normalized hippocampal volume, selected MCI subjects could be divided into two demographically paired sub-groups: those with larger hippocampal volume (MCI +h; N=40; mini mental state evaluation - MMSE - score=27.5+/-0.26 SE) and those with smaller hippocampal volume (MCI -h; N=40; h; MMSE=26.5+/-0.34 SE); the normalized hippocampal volume was statistically greater in the MCI +h than in the MCI -h and AD subjects (p<0.0001). 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). Cortical EEG generators were estimated by LORETA software. Results showed that the power of occipital, parietal, and temporal alpha 1 sources was maximum in MCI +h, intermediate in MCI -h, and low in AD patients. Furthermore, the power of these sources was linearly and non-linearly correlated with the normalized hippocampal volume. These 3 EEG sources were given as input for evaluating correlations (linear, exponential, logarithmic and power) with hippocampal volume. When subjects were considered as a unique group, there was a significant linear correlation of hippocampal volume with the magnitude of alpha 1 sources in the parietal, occipital and temporal areas. In general, the EEG sources showing significant linear correlation with hippocampal volume also supported a non-linear correlation with hippocampal volume strongly for the logarithmic one. The present results suggest that progressive atrophy of hippocampus correlates with decreased cortical alpha power, as estimated by using LORETA source modeling, in the continuum along MCI and AD conditions.

Is directionality of electroencephalographic (EEG) synchronization abnormal in amnesic mild cognitive impairment (MCI) and Alzheimer's disease (AD)? EEG data were recorded in 64 normal elderly (Nold), 69 amnesic MCI, and 73 mild AD subjects at rest condition (closed eyes). Direction of information flux within EEG functional coupling at electrode pairs was performed by directed transfer function (DTF) at delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10 Hz), alpha 2 (10-12 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-40 Hz). Parietal to frontal direction of the information flux within EEG functional coupling was stronger in Nold than in MCI and/or AD subjects, namely for alpha and beta rhythms. In contrast, the directional flow within inter-hemispheric EEG functional coupling did not discriminate among the three groups. These results suggest that directionality of parieto-to-frontal EEG synchronization is abnormal not only in AD but also in amnesic MCI.

Objective. Can quantitative electroencephalography (EEG) predict the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD)? Methods. Sixty-nine subjects fulfilling criteria for MCI were enrolled; cortical connectivity (spectral coherence) and (low resolution brain electromagnetic tomography) sources of EEG rhythms (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; beta 2=20-30 Hz; and gamma=30-40) were evaluated at baseline (time of MCI diagnosis) and follow up (about 14 months later). At follow-up, 45 subjects were still MCI (MCI Stable) and 24 subjects were converted to AD (MCI Converted). Results. At baseline, fronto-parietal midline coherence as well as delta (temporal), theta (parietal, occipital and temporal), and alpha 1 (central, parietal, occipital, temporal, limbic) sources were stronger in MCI Converted than stable subjects (P<0.05). Cox regression modeling showed low midline coherence and weak temporal source associated with 10% annual rate AD conversion, while this rate increased up to 40% and 60% when strong temporal delta source and high midline gamma coherence were observed respectively. Interpretation. Low-cost and diffuse computerized EEG techniques are able to statistically predict MCI to AD conversion.

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.

Alzheimer's disease is often characterized by an increase in plasma cortisol without clinical evidence of hypercorticism. Twenty-three consecutive patients with Alzheimer's disease and 23 age- and sex-matched healthy controls were studied by measuring plasma cortisol and dehydroepiandrosterone sulfate (DHEAS) (by enzyme immunoassay), the number of type I and type II corticosteroid receptors in mononuclear leukocytes (by radio-receptor-assay), and the lymphocyte subpopulations (by cytofluorimetry). Results are expressed in terms of median and range. In Alzheimer's disease, plasma cortisol was higher than in controls (median 0.74, range 0.47-1.21 vs 0.47, 0.36-0.77 mmol/L; p < 0.001). Plasma DHEAS, the DHEAS/cortisol ratio, and the number of type II corticosteroid receptors were significantly lower in AD than in controls (DHEAS: median 1.81, range 0.21-3.69 vs 3.51, 1.35-9.07 micromol/L; DHEAS/ cortisol: 2.04, range 0.3-5.8 vs 6.8, range 2.7-24 and type II receptors: 1219, 1000-2700 vs 1950, 1035- 2750 receptors per cell; p < 0.001). No correlation was found between the hormonal parameters, age, and mini-mental test score. These data support the hypothesis of a dysregulation of the adrenal pituitary axis in Alzheimer's disease, which is probably the consequence of damage to target tissues by corticosteroids.