Abnormal functional connectivity in Alzheimer's disease: intrahemispheric EEG coherence during rest and photic stimulation

Systematic Review of EEG Coherence in Alzheimer's Disease

BACKGROUND

Magnitude-squared coherence (MSCOH) is an electroencephalography (EEG) measure of functional connectivity. MSCOH has been widely applied to investigate pathological changes in patients with Alzheimer's disease (AD). However, significant heterogeneity exists between the studies using MSOCH.

OBJECTIVE

We systematically reviewed the literature on MSCOH changes in AD as compared to healthy controls to investigate the clinical utility of MSCOH as a marker of AD.

METHODS

We searched PubMed, Embase, and Scopus to identify studies reporting EEG MSCOH used in patients with AD. The identified studies were independently screened by two researchers and the data was extracted, which included cognitive scores, preprocessing steps, and changes in MSCOH across frequency bands.

RESULTS

A total of 35 studies investigating changes in MSCOH in patients with AD were included in the review. Alpha coherence was significantly decreased in patients with AD in 24 out of 34 studies. Differences in other frequency bands were less consistent. Some studies showed that MSCOH may serve as a diagnostic marker of AD.

CONCLUSION

Reduced alpha MSCOH is present in patients with AD and MSCOH may serve as a diagnostic marker. However, studies validating MSCOH as a diagnostic marker are needed.

Functional (un-)Coupling: Impairment, Compensation, and Future Progression in Alzheimer's Disease

Background: Functional (un-)coupling (task-related change of functional connectivity) between different sites of the brain is a mechanism of general importance for cognitive processes. In Alzheimer's disease (AD), prior research identified diminished cortical connectivity as a hallmark of the disease. However, little is known about the relation between the amount of functional (un-)coupling and cognitive performance and decline in AD. Method: Cognitive performance (based on CERAD-Plus scores) and electroencephalogram (EEG)-based functional (un-)coupling measures (connectivity changes from rest to a Face-Name-Encoding task) were assessed in 135 AD patients (age: M = 73.8 years; SD = 9.0). Of these, 68 patients (M = 73.9 years; SD = 8.9) participated in a follow-up assessment of their cognitive performance 1.5 years later. Results: The amounts of functional (un-)coupling in left anterior-posterior and homotopic interhemispheric connections in beta1-band were related to cognitive performance at baseline (β = .340; p < .001; β = .274; P = .001, respectively). For both markers, a higher amount of functional coupling was associated with better cognitive performance. Both markers also were significant predictors for cognitive decline. However, while patients with greater functional coupling in left anterior-posterior connections declined less in cognitive performance (β = .329; P = .035) those with greater functional coupling in interhemispheric connections declined more (β = -.402; P = .010). Conclusion: These findings suggest an important role of functional coupling mechanisms in left anterior-posterior and interhemispheric connections in AD. Especially the complex relationship with cognitive decline in AD patients might be an interesting aspect for future studies.

Electroencephalographic functional connectivity is altered in persons with Down syndrome and Alzheimer's disease

BACKGROUND

Persons with Down syndrome (DS) are at increased risk of developing Alzheimer's dementia (DS-AD). Due to heterogeneity in the functioning in persons with DS, it is difficult to use cognitive testing to assess whether a person with DS has developed dementia due to AD. Electroencephalography (EEG) functional connectivity has shown promising results as a diagnostic tool for AD in persons without DS. In the current exploratory study, we investigated whether EEG functional connectivity could be used as a diagnostic marker of AD in persons with DS and the association with symptoms.

METHODS

Electroencephalography from 12 persons with DS and 16 persons with DS-AD were analysed, and both coherence and weighted phase lag index were calculated. In addition, we calculated the average coherence for fronto-parietal and temporo-parietal connections. Lastly, we investigated the correlation between the informant-based Dementia Screening Questionnaire in Intellectual Disability (DSQIID) and total alpha coherence.

RESULTS

Decreased alpha and increased delta coherence and weighted phase lag index were observed in DS-AD as compared with DS. The decrease in alpha coherence was more marked in the fronto-parietal connections as compared with the temporo-parietal connections. No significant correlation was found between DSQIID and total alpha coherence (P value = 0.095, rho = -0.335).

CONCLUSION

The decreased alpha coherence and weighted phase lag index have previously been found in AD. The increased delta coherence and weighted phase lag index may indicate a different initial neurophysiological presentation as compared with patients with AD or may be a sign of more advanced disease. Larger studies are needed to confirm the current findings.

Extracting Robust Biomarkers From Multichannel EEG Time Series Using Nonlinear Dimensionality Reduction Applied to Ordinal Pattern Statistics and Spectral Quantities

In this study, ordinal pattern analysis and classical frequency-based EEG analysis methods are used to differentiate between EEGs of different age groups as well as individuals. As characteristic features, functional connectivity as well as single-channel measures in both the time and frequency domain are considered. We compare the separation power of each feature set after nonlinear dimensionality reduction using t-distributed stochastic neighbor embedding and demonstrate that ordinal pattern-based measures yield results comparable to frequency-based measures applied to preprocessed data, and outperform them if applied to raw data. Our analysis yields no significant differences in performance between single-channel features and functional connectivity features regarding the question of age group separation.

Altered Low-Frequency EEG Connectivity in Mild Cognitive Impairment as a Sign of Clinical Progression

BACKGROUND

Mild cognitive impairment (MCI) is associated with clinical progression to Alzheimer's disease (AD) but not all patients with MCI convert to AD. However, it is important to have methods that can differentiate between patients with MCI who progress (pMCI) and those who remain stable (sMCI), i.e., for timely administration of disease-modifying drugs.

OBJECTIVE

In the current study, we wanted to investigate whether quantitative EEG coherence and imaginary part of coherency (iCoh) could be used to differentiate between pMCI and sMCI.

METHODS

17 patients with AD, 27 patients with MCI, and 38 older healthy controls were recruited and followed for three years and 2nd year was used to determine progression. EEGs were recorded at baseline and coherence and iCoh were calculated after thorough preprocessing.

RESULTS

Between pMCI and sMCI, the largest difference in total coherence was found in the theta and delta bands. Here, the significant differences for coherence and iCoh were found in the lower frequency bands involving the temporal-frontal connections for coherence and parietal-frontal connections for iCoh. Furthermore, we found a significant negative correlation between theta coherence and the Addenbrooke's Cognitive Examination (ACE) (p = 0.0378; rho = -0.2388).

CONCLUSION

These findings suggest that low frequency coherence and iCoh can be used to determine, which patients with MCI will progress to AD and is associated with the ACE score. Low-frequency coherence has previously been associated with increased hippocampal atrophy and degeneration of the cholinergic system and may be an early marker of AD pathology.

Classification Methods Based on Complexity and Synchronization of Electroencephalography Signals in Alzheimer's Disease

Electroencephalography (EEG) has long been studied as a potential diagnostic method for Alzheimer's disease (AD). The pathological progression of AD leads to cortical disconnection. These disconnections may manifest as functional connectivity alterations, measured by the degree of synchronization between different brain regions, and alterations in complex behaviors produced by the interaction among wide-spread brain regions. Recently, machine learning methods, such as clustering algorithms and classification methods, have been adopted to detect disease-related changes in functional connectivity and classify the features of these changes. Although complexity of EEG signals can also reflect AD-related changes, few machine learning studies have focused on the changes in complexity. Therefore, in this study, we compared the ability of EEG signals to detect characteristics of AD using different machine learning approaches one focused on functional connectivity and the other focused on signal complexity. We examined functional connectivity, estimated by phase lag index (PLI) in EEG signals in healthy older participants [healthy control (HC)] and patients with AD. We estimated signal complexity using multi-scale entropy. Utilizing a support vector machine, we compared the identification accuracy of AD based on functional connectivity at each frequency band and complexity component. Additionally, we evaluated the relationship between synchronization and complexity. The identification accuracy of functional connectivity of the alpha, beta, and gamma bands was significantly high (AUC 1.0), and the identification accuracy of complexity was sufficiently high (AUC 0.81). Moreover, the relationship between functional connectivity and complexity exhibited various temporal-scale-and-regional-specific dependency in both HC participants and patients with AD. In conclusion, the combination of functional connectivity and complexity might reflect complex pathological process of AD. Applying a combination of both machine learning methods to neurophysiological data may provide a novel understanding of the neural network processes in both healthy brains and pathological conditions.

A Deep Learning Approach for Mild Depression Recognition Based on Functional Connectivity Using Electroencephalography

Early detection remains a significant challenge for the treatment of depression. In our work, we proposed a novel approach to mild depression recognition using electroencephalography (EEG). First, we explored abnormal organization in the functional connectivity network of mild depression using graph theory. Second, we proposed a novel classification model for recognizing mild depression. Considering the powerful ability of CNN to process two-dimensional data, we applied CNN separately to the two-dimensional data form of the functional connectivity matrices from five EEG bands (delta, theta, alpha, beta, and gamma). In addition, inspired by recent breakthroughs in the ability of deep recurrent CNNs to classify mental load, we merged the functional connectivity matrices from the three EEG bands that performed the best into a three-channel image to classify mild depression-related and normal EEG signals using the CNN. The results of the graph theory analysis showed that the brain functional network of the mild depression group had a larger characteristic path length and a lower clustering coefficient than the healthy control group, showing deviation from the small-world network. The proposed classification model obtained a classification accuracy of 80.74% for recognizing mild depression. The current study suggests that the combination of a CNN and functional connectivity matrix may provide a promising objective approach for diagnosing mild depression. Deep learning approaches such as this might have the potential to inform clinical practice and aid in research on psychiatric disorders.

Global EEG coherence as a marker for cognition in older adults at risk for dementia

Quantitative electroencephalography (EEG) provides useful information about neurophysiological health of the aging brain. Current studies investigating EEG coherence and power for specific brain areas and frequency bands have yielded inconsistent results. This study assessed EEG coherence and power indices at rest measured over the whole skull and for a wide frequency range as global EEG markers for cognition in a sample at risk for dementia. Since global markers are more reliable and less error-prone than region- and frequency-specific indices they might help to overcome previous inconsistencies. Global EEG coherence (1-30 Hz) and an EEG slowing score were assessed. The EEG slowing score was calculated by low-frequency power (1-8 Hz) divided by high-frequency power (9-30 Hz). In addition, the prognostic value of the two EEG indices for cognition and cognitive decline was assessed in a 5-year follow-up pilot study. Baseline global coherence correlated positively with cognition at baseline, but not with cognitive decline or with cognition at the 5-year follow-up. The EEG slowing ratio showed no significant association, neither with cognition at baseline or follow-up, nor with cognitive decline over a period of 5 years. The results indicate that the resting state global EEG coherence might be a useful and easy to assess electrophysiological correlate for neurocognitive health in older adults at risk for dementia. Because of the small statistical power for the follow-up analyses, the prognostic value of global coherence could not be determined in the present study. Future studies should assess its prognostic value with larger sample sizes.

Short timescale abnormalities in the states of spontaneous synchrony in the functional neural networks in Alzheimer's disease

Alzheimer's disease (AD) is a prevalent neurodegenerative condition that can lead to severe cognitive and functional deterioration. Functional magnetic resonance imaging (fMRI) revealed abnormalities in AD in intrinsic synchronization between spatially separate regions in the so-called default mode network (DMN) of the brain. To understand the relationship between this disruption in large-scale synchrony and the cognitive impairment in AD, it is critical to determine whether and how the deficit in the low frequency hemodynamic fluctuations recorded by fMRI translates to much faster timescales of memory and other cognitive processes. The present study employed magnetoencephalography (MEG) and a Hidden Markov Model (HMM) approach to estimate spontaneous synchrony variations in the functional neural networks with high temporal resolution. In a group of cognitively healthy (CH) older adults, we found transient (mean duration of 150-250 ms) network activity states, which were visited in a rapid succession, and were characterized by spatially coordinated changes in the amplitude of source-localized electrophysiological oscillations. The inferred states were similar to those previously observed in younger participants using MEG, and the estimated cortical source distributions of the state-specific activity resembled the classic functional neural networks, such as the DMN. In patients with AD, inferred network states were different from those of the CH group in short-scale timing and oscillatory features. The state of increased oscillatory amplitudes in the regions overlapping the DMN was visited less often in AD and for shorter periods of time, suggesting that spontaneous synchronization in this network was less likely and less stable in the patients. During the visits to this state, in some DMN nodes, the amplitude change in the higher-frequency (8-30 Hz) oscillations was less robust in the AD than CH group. These findings highlight relevance of studying short-scale temporal evolution of spontaneous activity in functional neural networks to understanding the AD pathophysiology. Capacity of flexible intrinsic synchronization in the DMN may be crucial for memory and other higher cognitive functions. Our analysis yielded metrics that quantify distinct features of the neural synchrony disorder in AD and may offer sensitive indicators of the neural network health for future investigations.

The Contribution of Increased Gamma Band Connectivity to Visual Non-Verbal Reasoning in Autistic Children: A MEG Study

Some individuals with autism spectrum (AS) perform better on visual reasoning tasks than would be predicted by their general cognitive performance. In individuals with AS, mechanisms in the brain’s visual area that underlie visual processing play a more prominent role in visual reasoning tasks than they do in normal individuals. In addition, increased connectivity with the visual area is thought to be one of the neural bases of autistic visual cognitive abilities. However, the contribution of such brain connectivity to visual cognitive abilities is not well understood, particularly in children. In this study, we investigated how functional connectivity between the visual areas and higher-order regions, which is reflected by alpha, beta and gamma band oscillations, contributes to the performance of visual reasoning tasks in typically developing (TD) (n = 18) children and AS children (n = 18). Brain activity was measured using a custom child-sized magneto-encephalograph. Imaginary coherence analysis was used as a proxy to estimate the functional connectivity between the occipital and other areas of the brain. Stronger connectivity from the occipital area, as evidenced by higher imaginary coherence in the gamma band, was associated with higher performance in the AS children only. We observed no significant correlation between the alpha or beta bands imaginary coherence and performance in the both groups. Alpha and beta bands reflect top-down pathways, while gamma band oscillations reflect a bottom-up influence. Therefore, our results suggest that visual reasoning in AS children is at least partially based on an enhanced reliance on visual perception and increased bottom-up connectivity from the visual areas.

[The cholinergic deficiency syndrome in patients with depressed consciousness after severe brain injury]

AIM

To determine the clinical and electrophysiological (EEG) signs of cholinergic deficiency in the process of recovery of consciousness in patients with severe brain injury.

MATERIAL AND METHODS

Thirty-seven people (24 men and 13 women, mean age 32±14 years) were studied. A comprehensive study included assessment of neurological status, mental activity, and EEG.

RESULTS AND CONCLUSION

A set of neurological symptoms, including reduced muscle tone, autonomic disorders (dry mucous membranes and skin, tachycardia, hypotension, gastrointestinal tract), eye movement disorders, that were,in accordance with the literature, characteristicof the cholinergic deficiency syndrome was found. This syndrome was detected against the background of a comatose state, akinetic mutism and mutism with understanding of speech, disintegration of speech, disorientation and amnestic decline. EEG revealed stable over time (months) characteristic changes: slowing and asymmetric alpha activity, equivalent dipole sources of hippocampal and stem localization, persistent strengthening of intra-hemispheric coherent connections, especially on the left side. The regression of the cholinergic deficiency syndrome was accompanied by an increase of regularity, capacity and frequency of alpha-activity (from 7-8 to 9-10 Hz), prevalence of equivalent dipole sources in the hippocampus with their appearance in the occipital cortex, normalization of connections with right-brain coherence with the preservation of their pathologically high values on the left side.

Declining functional connectivity and changing hub locations in Alzheimer's disease: an EEG study

BACKGROUND

EEG studies have shown that patients with Alzheimer's disease (AD) have weaker functional connectivity than controls, especially in higher frequency bands. Furthermore, active regions seem more prone to AD pathology. How functional connectivity is affected in AD subgroups of disease severity and how network hubs (highly connected brain areas) change is not known. We compared AD patients with different disease severity and controls in terms of functional connections, hub strength and hub location.

METHODS

We studied routine 21-channel resting-state electroencephalography (EEG) of 318 AD patients (divided into tertiles based on disease severity: mild, moderate and severe AD) and 133 age-matched controls. Functional connectivity between EEG channels was estimated with the Phase Lag Index (PLI). From the PLI-based connectivity matrix, the minimum spanning tree (MST) was derived. For each node (EEG channel) in the MST, the betweenness centrality (BC) was computed, a measure to quantify the relative importance of a node within the network. Then we derived color-coded head plots based on BC values and calculated the center of mass (the exact middle had x and y values of 0). A shifting of the hub locations was defined as a shift of the center of mass on the y-axis across groups. Multivariate general linear models with PLI or BC values as dependent variables and the groups as continuous variables were used in the five conventional frequency bands.

RESULTS

We found that functional connectivity decreases with increasing disease severity in the alpha band. All, except for posterior, regions showed increasing BC values with increasing disease severity. The center of mass shifted from posterior to more anterior regions with increasing disease severity in the higher frequency bands, indicating a loss of relative functional importance of the posterior brain regions.

CONCLUSIONS

In conclusion, we observed decreasing functional connectivity in the posterior regions, together with a shifted hub location from posterior to central regions with increasing AD severity. Relative hub strength decreases in posterior regions while other regions show a relative rise with increasing AD severity, which is in accordance with the activity-dependent degeneration theory. Our results indicate that hubs are disproportionally affected in AD.

A review of EEG and MEG for brainnetome research

The majority of brain activities are performed by functionally integrating separate regions of the brain. Therefore, the synchronous operation of the brain's multiple regions or neuronal assemblies can be represented as a network with nodes that are interconnected by links. Because of the complexity of brain interactions and their varying effects at different levels of complexity, one of the corresponding authors of this paper recently proposed the brainnetome as a new -ome to explore and integrate the brain network at different scales. Because electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive and have outstanding temporal resolution and because they are the primary clinical techniques used to capture the dynamics of neuronal connections, they lend themselves to the analysis of the neural networks comprising the brainnetome. Because of EEG/MEG's applicability to brainnetome analyses, the aim of this review is to identify the procedures that can be used to form a network using EEG/MEG data in sensor or source space and to promote EEG/MEG network analysis for either neuroscience or clinical applications. To accomplish this aim, we show the relationship of the brainnetome to brain networks at the macroscale and provide a systematic review of network construction using EEG and MEG. Some potential applications of the EEG/MEG brainnetome are to use newly developed methods to associate the properties of a brainnetome with indices of cognition or disease conditions. Associations based on EEG/MEG brainnetome analysis may improve the comprehension of the functioning of the brain in neuroscience research or the recognition of abnormal patterns in neurological disease.

Hypercapnia affects the functional coupling of resting state electroencephalographic rhythms and cerebral haemodynamics in healthy elderly subjects and in patients with amnestic mild cognitive impairment

OBJECTIVE

Cerebral vasomotor reactivity (VMR) and coherence of resting state electroencephalographic (EEG) rhythms are impaired in Alzheimer's disease (AD) patients. Here we tested the hypothesis that these two variables could be related.

METHODS

We investigated VMR and coherence of resting state EEG rhythms in nine normal elderly (Nold) and in 10 amnesic mild cognitive impairment (MCI) subjects. Resting state eyes-closed EEG data were recorded at baseline pre-CO₂ (ambient air, 2 min), during 7% CO₂/air mixture inhalation (hypercapnia, 90 s) and post-CO₂ (ambient air, 2 min) conditions. Simultaneous frontal bilateral near-infrared spectroscopy (NIRS) was performed to assess VMR by cortical oxy- and deoxy-haemoglobin concentration changes. EEG coherence across all electrodes was computed at 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) bands.

RESULTS

In Nold subjects, 'total coherence' of EEG across all frequency bands and electrode pairs decreased during hypercapnia, with full recovery during post-CO₂. Total coherence resulted lower in pre-CO₂ and post-CO₂ and presented poor reactivity during CO₂ inhalation in MCI patients compared with Nold subjects. Hypercapnia increased oxy-haemoglobin and decreased deoxy-haemoglobin concentrations in both groups. Furthermore, the extent of changes in these variables during CO₂ challenge was correlated with the EEG coherence, as a reflection of neurovascular coupling.

CONCLUSIONS

Hypercapnia induced normal frontal VMR that was detected by NIRS in both Nold and amnesic MCI groups, while it produced a reactivity of global functional coupling of resting state EEG rhythms only in the Nold group.

SIGNIFICANCE

In amnesic MCI patients, global EEG functional coupling is basically low in amplitude and does not react to hypercapnia.

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

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

Brain integration as a driver for quality management success

Purpose

In this paper, research leading to quality management success is examined, elaborated, and highlighted in a new profound way by focusing on the most fundamental aspect of the human dimension, the brain. The purpose is to examine the relationship between brain functioning and quality management success. In this examination, the role of core values, profound organizational learning and values of quality management are explained.

Design/methodology/approach

The paper builds on a conceptual review of research in the areas of quality management success, values of quality management, core values and neurophysiology with focus on brain integration.

Findings

The relation of core values with brain functioning is described based on previous research. A framework with logical steps from brain integration, via core values, quality management values and quality management practices to quality management success is developed.

Research limitations/implications

The paper adds to the understanding of the role brain integration has for success in quality management efforts. A limitation is that it only builds on previous research.

Practical implications

The findings provide a deeper understanding of quality management success and should thus be valuable for quality managers and leaders striving for excellence for their organisations.

Originality/value

The importance and crucial role of brain integration for quality management success has not been elaborated in the literature of quality management before.

Electroencephalographic rhythms in Alzheimer's disease

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

Direction of Information Flow in Alzheimer's Disease and MCI Patients

Is directionality of electroencephalographic (EEG) synchronization abnormal in amnesic mild cognitive impairment (MCI) and Alzheimer's disease (AD)? And, do cerebrovascular and AD lesions represent additive factors in the development of MCI as a putative preclinical stage of AD? Here we reported two studies that tested these hypotheses. EEG data were recorded in normal elderly (Nold), amnesic MCI, and mild AD subjects at rest condition (closed eyes). Direction of information flow within EEG electrode pairs was performed by directed transfer function (DTF) at δ (2-4 Hz), θ (4-8 Hz), α1 (8-10 Hz), α2 (10-12 Hz), β1 (13-20 Hz), β2 (20-30 Hz), and γ (30-40 Hz). Parieto-to-frontal direction was stronger in Nold than in MCI and/or AD subjects for α and β rhythms. In contrast, the directional flow within interhemispheric EEG functional coupling did not discriminate among the groups. More interestingly, this coupling was higher at θ, α1, α2, and β1 in MCI with higher than in MCI with lower vascular load. These results suggest that directionality of parieto-to-frontal EEG synchronization is abnormal not only in AD but also in amnesic MCI, supporting the additive model according to which MCI state would result from the combination of cerebrovascular and neurodegenerative lesions.

k-Nearest neighbour local linear prediction of scalp EEG activity during intermittent photic stimulation

The characterization of the EEG response to photic stimulation (PS) is an important issue with significant clinical relevance. This study aims to quantify and map the complexity of the EEG during PS, where complexity is measured as the degree of unpredictability resulting from local linear prediction. EEG activity was recorded with eyes closed (EC) and eyes open (EO) during resting and PS at 5, 10, and 15 Hz in a group of 30 healthy subjects and in a case-report of a patient suffering from cerebral ischemia. The mean squared prediction error (MSPE) resulting from k-nearest neighbour local linear prediction was calculated in each condition as an index of EEG unpredictability. The linear or nonlinear nature of the system underlying EEG activity was evaluated quantifying MSPE as a function of the neighbourhood size during local linear prediction, and by surrogate data analysis as well. Unpredictability maps were obtained for each subject interpolating MSPE values over a schematic head representation. Results on healthy subjects evidenced: (i) the prevalence of linear mechanisms in the generation of EEG dynamics, (ii) the lower predictability of EO EEG, (iii) the desynchronization of oscillatory mechanisms during PS leading to increased EEG complexity, (iv) the entrainment of alpha rhythm during EC obtained by 10 Hz PS, and (v) differences of EEG predictability among different scalp regions. Ischemic patient showed different MSPE values in healthy and damaged regions. The EEG predictability decreased moving from the early acute stage to a stage of partial recovery. These results suggest that nonlinear prediction can be a useful tool to characterize EEG dynamics during PS protocols, and may consequently constitute a complement of quantitative EEG analysis in clinical applications.

Mapping the Alzheimer's brain with connectomics

Alzheimer's disease (AD) is the most common form of dementia. As an incurable, progressive, and neurodegenerative disease, it causes cognitive and memory deficits. However, the biological mechanisms underlying the disease are not thoroughly understood. In recent years, non-invasive neuroimaging and neurophysiological techniques [e.g., structural magnetic resonance imaging (MRI), diffusion MRI, functional MRI, and EEG/MEG] and graph theory based network analysis have provided a new perspective on structural and functional connectivity patterns of the human brain (i.e., the human connectome) in health and disease. Using these powerful approaches, several recent studies of patients with AD exhibited abnormal topological organization in both global and regional properties of neuronal networks, indicating that AD not only affects specific brain regions, but also alters the structural and functional associations between distinct brain regions. Specifically, disruptive organization in the whole-brain networks in AD is involved in the loss of small-world characters and the re-organization of hub distributions. These aberrant neuronal connectivity patterns were associated with cognitive deficits in patients with AD, even with genetic factors in healthy aging. These studies provide empirical evidence to support the existence of an aberrant connectome of AD. In this review we will summarize recent advances discovered in large-scale brain network studies of AD, mainly focusing on graph theoretical analysis of brain connectivity abnormalities. These studies provide novel insights into the pathophysiological mechanisms of AD and could be helpful in developing imaging biomarkers for disease diagnosis and monitoring.

Relating structural and functional anomalous connectivity in the aging brain via neural mass modeling

The structural changes that arise as the brain ages influence its functionality. In many cases, the anatomical degradation simply leads to normal aging. In others, the neurodegeneration is large enough to cause neurological disorders (e.g. Alzheimer's disease). Structure and function can be both currently measured using noninvasive techniques, such as magnetic resonance imaging (MRI) and electroencephalography (EEG) respectively. However, a full theoretical scheme linking structural and functional degradation is still lacking. Here we present a neural mass model that aims to bridge both levels of description and that reproduces experimentally observed multichannel EEG recordings of alpha rhythm in young subjects, healthy elderly subjects, and patients with mild cognitive impairment. We focus our attention in the dominant frequency of the signals at different electrodes and in the correlation between specific electrode pairs, measured via the phase-lag index. Our model allows us to study the influence of different structural connectivity pathways, independently of each other, on the normal and aberrantly aging brain. In particular, we study in detail the effect of the thalamic input on specific cortical regions, the long-range connectivity between cortical regions, and the short-range coupling within the same cortical area. Once the influence of each type of connectivity is determined, we characterize the regions of parameter space compatible with the EEG recordings of the populations under study. Our results show that the different types of connectivity must be fine-tuned to maintain the brain in a healthy functioning state independently of its age and brain condition.

Inter-hemispheric functional coupling of eyes-closed resting EEG rhythms in adolescents with Down syndrome

OBJECTIVE

We tested the hypothesis that inter-hemispheric directional functional coupling of eyes-closed resting EEG rhythms is abnormal in adolescents with Down syndrome (DS).

METHODS

Eyes-closed resting EEG data were recorded in 38 DS adolescents (18.7 years +/-0.67 SE, IQ=49+/-1.9 SE) and in 17 matched normal control subjects (NYoung=19.1 years +/-0.39 SE). The EEG data were recorded from 8 electrodes (Fp1, Fp2, C3, C4, T3, T4, O1, O2) referenced to vertex. 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). Power of EEG rhythms was evaluated by FFT for control purposes, whereas inter-hemispheric directional EEG functional coupling was computed by directed transfer function (DTF).

RESULTS

As expected, alpha, beta, and gamma power was widely higher in NYoung than DS subjects, whereas the opposite was true for delta power. As a novelty, DTF (directionality) values globally prevailed from right to left occipital areas in NYoung subjects and in the opposite direction in DS patients. A control experiment showed that this DTF difference could not be observed in the comparison between DS adults with mild cognitive impairment and normal age-matched adults.

CONCLUSIONS

These results indicate a peculiar abnormal directional inter-hemispheric interplay in visual occipital areas of DS adolescents.

SIGNIFICANCE

Direction of inter-hemispheric EEG functional coupling unveils a new abnormal brain network feature in DS adolescents.

CROSS-SECTIONAL AND LONGITUDINAL STUDY OF EFFECTS OF TRANSCENDENTAL MEDITATION PRACTICE ON INTERHEMISPHERIC FRONTAL ASYMMETRY AND FRONTAL COHERENCE

Two studies investigated frontal alpha lateral asymmetry and frontal interhemispheric coherence during eyes-closed rest, Transcendental Meditation (TM) practice, and computerized reaction-time tasks. In the first study, frontal coherence and lateralized asymmetry were higher in 13 TM subjects than in 12 controls. In the second study (N = 14), a one-year longitudinal study, lateral asymmetry did not change in any condition. In contrast, frontal coherence increased linearly during computer tasks and eyes-closed rest, and as a step-function during TM practice--rising to a high level after 2-months TM practice. Coherence was more sensitive than lateral asymmetry to effects of TM practice on brain functioning.

White matter vascular lesions are related to parietal-to-frontal coupling of EEG rhythms in mild cognitive impairment

Do cerebrovascular and Alzheimer's disease (AD) lesions represent additive factors in the development of mild cognitive impairment (MCI) as a putative preclinical stage of AD? Here we tested the hypothesis that directionality of fronto-parietal functional coupling of electroencephalographic (EEG) rhythms is relatively preserved in amnesic MCI subjects in whom the cognitive decline is mainly explained by white-matter vascular load. Resting EEG was recorded in 40 healthy elderly (Nold) and 78 amnesic MCI. In the MCI subjects, white-matter vascular load was quantified based on magnetic resonance images (0-30 visual rating scale). 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). Directionality of fronto-parietal functional coupling of EEG rhythms was estimated by directed transfer function software. As main results, (i) fronto-parietal functional coupling of EEG rhythms was higher in magnitude in the Nold than in the MCI subjects; (ii) more interestingly, that coupling was higher at theta, alpha1, alpha2, and beta1 in MCI V+ (high vascular load; N = 42; MMSE = 26) than in MCI V- group (low vascular load; N = 36; MMSE= 26.7). These results are interpreted as supporting the additive model according to which MCI state would result from the combination of cerebrovascular and neurodegenerative lesions.

Global Functional Coupling of Resting EEG Rhythms is Abnormal in Mild Cognitive Impairment and Alzheimer’s Disease

Alzheimer’s disease (AD) is typically associated with an impairment of brain networks and global cognitive function in aging. In this vein, the present study tested the hypothesis that the functional coupling of resting cortical electroencephalographic (EEG) rhythms is progressively abnormal in amnesic mild cognitive impairment (MCI) and AD subjects. Eyes-closed resting EEG data were recorded (10–20 system) in 33 mild AD, 52 amnesic MCI, and 47 normal elderly subjects (Nold). EEG rhythms of interest were delta (2–4 Hz), theta (4–8 Hz), alpha1 (8–10 Hz), alpha2 (10–13 Hz), beta1 (13–20 Hz), beta2 (20–30 Hz), and gamma (30–40 Hz). The global functional coupling of the EEG rhythms was indexed by means of spectral coherence for all combinations of electrode pairs (i.e., total coherence). The main results showed that the total coherence of delta rhythms was higher in the AD than the MCI group. It was also higher in the MCI than the Nold group. Furthermore, the delta total coherence was negatively correlated with global cognition (Mini Mental State Examination score) across the Nold, MCI, and AD subjects. Finally, the alpha1 total coherence was lower in the AD group than in the MCI and Nold groups. These results suggest that in the AD process an impairment of brain networks and global cognition is associated with a frequency-specific modulation of the global functional coupling of resting EEG rhythms.

Clinical neurophysiology of aging brain: from normal aging to neurodegeneration

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

Directionality of EEG synchronization in Alzheimer's disease subjects

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.

Is Alzheimer's disease a disconnection syndrome?

In Alzheimer's disease (AD), loss of connectivity in the patient's brain has been evidenced by a range of electrophysiological and neuroimaging studies. However, few neuropsychological research projects have sought to interpret the cognitive modifications following the appearance of AD in terms of a disconnection syndrome. In this paper, we sought to investigate brain connectivity in AD via the study of a crossmodal effect. More precisely, we examined the integration of auditory and visual speech information (the McGurk effect) in AD patients and matched control subjects. Our results revealed impaired crossmodal integration during speech perception in AD, which was not associated with disturbances in the separate processing of auditory and visual speech stimuli. In conclusion, our data suggest the occurrence of a specific, audio-visual integration deficit in AD, which might be the consequence of a connectivity breakdown and corroborate the observation from other studies of crossmodal deficits between the auditory and visual modalities in this population.

Conversion from mild cognitive impairment to Alzheimer's disease is predicted by sources and coherence of brain electroencephalography rhythms

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.

Fronto-parietal coupling of brain rhythms in mild cognitive impairment: a multicentric EEG study

Electroencephalographic (EEG) data were recorded in 69 normal elderly (Nold), 88 mild cognitive impairment (MCI), and 109 mild Alzheimer's disease (AD) subjects at rest condition, to test whether the fronto-parietal coupling of EEG rhythms is in line with the hypothesis that MCI can be considered as a pre-clinical stage of the disease at group level. Functional coupling was estimated by synchronization likelihood of Laplacian-transformed EEG data at electrode pairs, which accounts for linear and non-linear components of that coupling. Cortical rhythms of interest were delta (2-4Hz), theta (4-8Hz), alpha 1 (8-10.5Hz), alpha 2 (10.5-13Hz), beta 1 (13-20Hz), beta 2 (20-30Hz), and gamma (30-40Hz). Compared to the Nold subjects, the AD patients presented a marked reduction of the synchronization likelihood (delta to gamma) at both fronto-parietal and inter-hemispherical (delta to beta 2) electrodes. As a main result, alpha 1 synchronization likelihood progressively decreased across Nold, MCI, and mild AD subjects at midline (Fz-Pz) and right (F4-P4) fronto-parietal electrodes. The same was true for the delta synchronization likelihood at right fronto-parietal electrodes (F4-P4). For these EEG bands, the synchronization likelihood correlated with global cognitive status as measured by the Mini Mental State Evaluation. The present results suggest that at group level, fronto-parietal coupling of the delta and alpha rhythms progressively becomes abnormal though MCI and mild AD. Future longitudinal research should evaluate whether the present EEG approach is able to predict the cognitive decline in individual MCI subjects.

Estudo da coerência do eletrencefalograma na banda de frequência alfa em indivíduos adultos normais: resultados preliminares em 10 casos

A coerência espectral inter-hemisférica do eletrencefalograma da região occipital do escalpo (eletrodos O1 e O2) foi estimada usando a transformada rápida de Fourier. As médias de coerências na banda de freqüência alfa (alfa1 - 8,0 a 10,0 Hz e alfa2 -10,1 a 12,5 Hz) em indivíduos normais com mais de 50 anos foram comparadas com as obtidas em adultos jovens com idade inferior a 50 anos. Nossos resultados mostraram que não há diferença significativa dos níveis de coerência na banda alfa entre indivíduos em faixas etárias mais avançadas comparados aos adultos jovens.

Electrophysiological substrates of impulsiveness: potential effects on aggressive behavior

Previous investigations attempting to examine impulsiveness as a personality construct are likely confounded with a high incidence of aggressive and antisocial behavior. The present study assessed electroencephalographic activity at rest and during photic stimulation in two groups: (1) an impulsive group (n=10) scoring high on the Barratt Impulsiveness Scale (BIS-11) and reporting no indication of impulsive aggressive behavior; and (2) a non-aggressive control group (n=14) scoring within the normal range on the BIS-11. All subjects completed a brief battery of personality measures related to impulsivity and aggression. Resting EEG was recorded at 9 electrode sites. Photic stimulation was administered at three frequency levels. The primary findings were consistently lower frontal delta and theta activity in the impulsive group as well as a different topographical pattern of beta activity between the groups. These differences appeared to be independent of photic stimulation. Personality analyses indicated significantly greater hostility and lifetime history of aggression in the impulsive group. Taken together, the personality and EEG results suggest some similarity between the present impulsive group and research on groups regularly exhibiting premeditated aggression. These results provide unique insight into the construct of impulsivity and its role in the expression of specific subtypes of aggressive behavior.

EEG coherence analysis in never-medicated patients with panic disorder

To investigate the functional abnormalities in the central nervous system (CNS) of patients with panic disorder (PD), we compared the electroencephalography (EEG) coherence values in 18 never-medicated PD patients with those in age-matched normal control subjects, and examined the relationships between EEG coherence values and both the duration of disease and the severity of panic attacks. EEG data were recorded in the resting state. The PD patients had lower coherence values with significant differences in F3-F4, C3-C4, P3-P4, F7-T5, and F8-T6. There were positive correlations for the higher alpha band between coherence values and both the duration of disease and the severity of panic attacks. These findings provide further evidence that PD patients have a lower degree of inter-hemispheric functional connectivity in the frontal region and intra-hemispheric functional connectivity in the bilateral temporal region, and that chronic condition or frequent panic attacks in PD patients may be related to the pathophysiological CNS changes.

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.

Alzheimer's disease as a disconnection syndrome?

This paper reviews the growing amount of evidence supporting the hypothesis that Alzheimer's disease includes a disconnection syndrome. This evidence came mainly from neuropathological, electrophysiological, and neuroimaging studies. Moreover, a few recent neuropsychological studies have also explored the effects of a disconnection between cerebral areas on cognitive functioning. Finally, and more generally, the contribution of this interpretation to the understanding of Alzheimer's disease cognitive deficits is considered.

Patterns of EEG coherence, power, and contingent negative variation characterize the integration of transcendental and waking states

Long-term meditating subjects report that transcendental experiences (TE), which first occurred during their Transcendental Meditation (TM) practice, now subjectively co-exist with waking and sleeping states. To investigate neurophysiological correlates of this integrated state, we recorded EEG in these subjects and in two comparison groups during simple and choice contingent negative variation (CNV) tasks. In individuals reporting the integration of the transcendent with waking and sleeping, CNV was higher in simple but lower in choice trials, and 6-12 Hz EEG amplitude and broadband frontal EEG coherence were higher during choice trials. Increased EEG amplitude and coherence, characteristic of TM practice, appeared to become a stable EEG trait during CNV tasks in these subjects. These significant EEG differences may underlie the inverse patterns in CNV amplitude seen between groups. An 'Integration Scale,' constructed from these cortical measures, may characterize the transformation in brain dynamics corresponding to increasing integration of the transcendent with waking and sleeping.

Identification of diagnostic evoked response potential segments in Alzheimer's disease

Evoked response potentials (ERPs) to brief flashes of light were analyzed for constituent features that could be used to distinguish individuals with Alzheimer's disease (AD, n = 15) from matched control subjects (n = 17). Statistical k nearest-neighbor methods distinguished AD from control with a maximum sensitivity of 29% and false alarm rate of 12%. The comparable sensitivity/false-alarm values for a statistical projection pursuit method and an extended projection pursuit method, which selectively identify discriminative features for classification, were 75%/18% and 100%/6%, respectively. The results demonstrate that combinations of selected ERP time segments across different electrodes contain signal features that discriminate AD from control subjects with high sensitivity and specificity.

Correlation between cortical theta activity and hippocampal volumes in health, mild cognitive impairment, and mild dementia

Cognitive decline is known to be associated with both increased theta power over frontal regions and hippocampal atrophy. The aim of this study was to reveal the relation between these parameters in groups with mild dementia, mild cognitive impairment, and healthy control subjects. The authors examined a preliminary randomly selected sample of 39 right-handed subjects joining the Leipzig Longitudinal Study of the Aged, consisting of 17 normal elderly subjects, 12 patients with mild cognitive impairment, and 10 patients with mild dementia assessed by Clinical Dementia Rating. All subjects were between 75 and 85 years old (mean age, 78 years; standard deviation, 2.78 years) and underwent EEG and brain MRI. Mean spectral power densities were calculated, and hippocampal body volume was measured. Significant negative linear correlations between theta power over frontal regions and hippocampal volumes were found. The results support the assumption about a relationship between hippocampal atrophy and theta power, and may be helpful for a better understanding of the course of Alzheimer's disease.

[Alpha band coherence analysis of EEG in healthy adult's and Alzheimer's type dementia patients]

We studied the occipital inter-hemispheric coherence (IHCoh) of EEG (electrodes O1-O2) for alpha band (alpha1 - 8,0 to 10,0 Hz and alpha2 - 10,1 to 12,5 Hz) in healthy adults and Alzheimer's type dementia (ATD) subjects, to observe if there is any significant difference between these two groups that could help in the early diagnosis of ATD. We found a decrease of occipital IHCoh in ATD group for both alpha sub-bands. We believe that Coh analysis of EEG is a powerful auxiliary method in ATD diagnosis.

Autonomic and EEG patterns during eyes-closed rest and transcendental meditation (TM) practice: the basis for a neural model of TM practice

In this single-blind within-subject study, autonomic and EEG variables were compared during 10-min, order-balanced eyes-closed rest and Transcendental Meditation (TM) sessions. TM sessions were distinguished by (1) lower breath rates, (2) lower skin conductance levels, (3) higher respiratory sinus arrhythmia levels, and (4) higher alpha anterior-posterior and frontal EEG coherence. Alpha power was not significantly different between conditions. These results were seen in the first minute and were maintained throughout the 10-min sessions. TM practice appears to (1) lead to a state fundamentally different than eyes-closed rest; (2) result in a cascade of events in the central and autonomic nervous systems, leading to a rapid change in state (within a minute) that was maintained throughout the TM session; and (3) be best distinguished from other conditions through autonomic and EEG alpha coherence patterns rather than alpha power. Two neural networks that may mediate these effects are suggested. The rapid shift in physiological functioning within the first minute might be mediated by a "neural switch" in prefrontal areas inhibiting activity in specific and nonspecific thalamocortical circuits. The resulting "restfully alert" state might be sustained by a basal ganglia-corticothalamic threshold regulation mechanism automatically maintaining lower levels of cortical excitability.