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

Patients with Alzheimer's disease dementia show partially preserved parietal 'hubs' modeled from resting-state alpha electroencephalographic rhythms

Introduction

Graph theory models a network by its nodes (the fundamental unit by which graphs are formed) and connections. 'Degree' hubs reflect node centrality (the connection rate), while 'connector' hubs are those linked to several clusters of nodes (mainly long-range connections).

Methods

Here, we compared hubs modeled from measures of interdependencies of between-electrode resting-state eyes-closed electroencephalography (rsEEG) rhythms in normal elderly (Nold) and Alzheimer's disease dementia (ADD) participants. At least 5 min of rsEEG was recorded and analyzed. As ADD is considered a 'network disease' and is typically associated with abnormal rsEEG delta (<4 Hz) and alpha rhythms (8-12 Hz) over associative posterior areas, we tested the hypothesis of abnormal posterior hubs from measures of interdependencies of rsEEG rhythms from delta to gamma bands (2-40 Hz) using eLORETA bivariate and multivariate-directional techniques in ADD participants versus Nold participants. Three different definitions of 'connector' hub were used.

Results

Convergent results showed that in both the Nold and ADD groups there were significant parietal 'degree' and 'connector' hubs derived from alpha rhythms. These hubs had a prominent outward 'directionality' in the two groups, but that 'directionality' was lower in ADD participants than in Nold participants.

Discussion

In conclusion, independent methodologies and hub definitions suggest that ADD patients may be characterized by low outward 'directionality' of partially preserved parietal 'degree' and 'connector' hubs derived from rsEEG alpha rhythms.

Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: Recommendations of an expert panel

The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and "interrelatedness" at posterior alpha (8-12 Hz) and widespread delta (< 4 Hz) and theta (4-8 Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact-free rsEEG periods are needed; (2) power density and "interrelatedness" rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes.

Levetiracetam Alters Oscillatory Connectivity in Alzheimer's Disease

Seizures occur at a higher frequency in people with Alzheimer's disease (AD) but overt, clinically obvious events are infrequent. Evidence from animal models and studies in mild cognitive impairment suggest that subclinical epileptic discharges may play a role in the clinical and pathophysiological manifestations of AD. In this feasibility study, the neurophysiological and cognitive effects of acute administration of levetiracetam (LEV) are measured in patients with mild AD to test whether it could have a therapeutic benefit. AD participants were administered low dose LEV (2.5 mg/kg), higher dose LEV (7.5 mg/kg), or placebo in a double-blind, within-subject repeated measures study with EEG recorded at rest before and after administration. After administration of higher dose of LEV, we found significant decreases in coherence in the delta band (1-3.99 Hz) and increases in the low beta (13-17.99 Hz) and the high beta band (24-29.99 Hz). Furthermore, we found trends toward increased power in the frontal and central regions in the high beta band (24-29.99 Hz). However, there were no significant changes in cognitive performance after this single dose administration. The pattern of decreased coherence in the lower frequency bands and increased coherence in the higher frequency bands suggests a beneficial effect of LEV for patients with AD. Larger longitudinal studies and studies with healthy age-matched controls are needed to determine whether this represents a relative normalization of EEG patterns, whether it is unique to AD as compared to normal aging, and whether longer term administration is associated with a beneficial clinical effect.

Passive Double-Sensory Evoked Coherence Correlates with Long-Term Memory Capacity

HIGHLIGHTS Memory correlates with the difference between single and double-sensory evoked steady-state coherence in the gamma range (ΔC).The correlation is most pronounced for the anterior brain region (ΔC_A ).The correlation is not driven by birth size, education, speed of processing, or intelligence.The sensitivity of ΔC_A for detecting low memory capacity is 90%. Cerebral rhythmic activity and oscillations are important pathways of communication between cortical cell assemblies and may be key factors in memory. We asked whether memory performance is related to gamma coherence in a non-task sensory steady-state stimulation. We investigated 40 healthy males born in 1953 who were part of a Danish birth cohort study. Coherence was measured in the gamma range in response to a single-sensory visual stimulation (36 Hz) and a double-sensory combined audiovisual stimulation (auditive: 40 Hz; visual: 36 Hz). The individual difference in coherence (ΔC) between the bimodal and monomodal stimulation was calculated for each subject and used as the main explanatory variable. ΔC in total brain were significantly negatively correlated with long-term verbal recall. This correlation was pronounced for the anterior region. In addition, the correlation between ΔC and long-term memory was robust when controlling for working memory, as well as a wide range of potentially confounding factors, including intelligence, length of education, speed of processing, visual attention and executive function. Moreover, we found that the difference in anterior coherence (ΔC_A ) is a better predictor of memory than power in multivariate models. The sensitivity of ΔC_A for detecting low memory capacity is 92%. Finally, ΔC_A was also associated with other types of memory: verbal learning, visual recognition, and spatial memory, and these additional correlations were also robust enough to control for a range of potentially confounding factors. Thus, the ΔC is a predictor of memory performance may be useful in cognitive neuropsychological testing.

Altered source-based EEG coherence of resting-state sensorimotor network in early-stage Alzheimer's disease compared to mild cognitive impairment

Although the altered coherence between cortical areas in Alzheimer's disease (AD) has been widely studied, it remains unclear whether the source-based coherence measures within sensorimotor network show significant difference between mild cognitive impairment (MCI) and AD. In the present study, resting-state electroencephalographic signals were recorded from 21 MCI and 21 mild AD patients. The spectral power and coherence in the sensorimotor areas were analyzed using the minimum norm estimate (MNE) combined with fast Fourier transform and coherence analysis in delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-25 Hz), and gamma (25-40 Hz) bands. Our results indicated that source-based coherence in AD showed increased delta coherences between the bilateral precentral, left supplementary motor area (SMA) and right precentral, and left SMA and right postcentral areas. However, no significant difference of spectral powers was observed between AD and MCI. To conclude, the phenotype conversion from MCI to AD may be associated with an altered connectivity of the sensorimotor cortical network. This is a promising finding; however, further large-scale studies are needed.

Modular reorganization of brain resting state networks and its independent validation in Alzheimer's disease patients

Previous studies have demonstrated disruption in structural and functional connectivity occurring in the Alzheimer's Disease (AD). However, it is not known how these disruptions alter brain network reorganization. With the modular analysis method of graph theory, and datasets acquired by the resting-state functional connectivity MRI (R-fMRI) method, we investigated and compared the brain organization patterns between the AD group and the cognitively normal control (CN) group. Our main finding is that the largest homotopic module (defined as the insula module) in the CN group was broken down to the pieces in the AD group. Specifically, it was discovered that the eight pairs of the bilateral regions (the opercular part of inferior frontal gyrus, area triangularis, insula, putamen, globus pallidus, transverse temporal gyri, superior temporal gyrus, and superior temporal pole) of the insula module had lost symmetric functional connection properties, and the corresponding gray matter concentration (GMC) was significant lower in AD group. We further quantified the functional connectivity changes with an index (index A) and structural changes with the GMC index in the insula module to demonstrate their great potential as AD biomarkers. We further validated these results with six additional independent datasets (271 subjects in six groups). Our results demonstrated specific underlying structural and functional reorganization from young to old, and for diseased subjects. Further, it is suggested that by combining the structural GMC analysis and functional modular analysis in the insula module, a new biomarker can be developed at the single-subject level.

How does a surgeon's brain buzz? An EEG coherence study on the interaction between humans and robot

Introduction

In humans, both primary and non-primary motor areas are involved in the control of voluntary movements. However, the dynamics of functional coupling among different motor areas have not been fully clarified yet. There is to date no research looking to the functional dynamics in the brain of surgeons working in laparoscopy compared with those trained and working in robotic surgery.

Experimental procedures

We enrolled 16 right-handed trained surgeons and assessed changes in intra- and inter-hemispheric EEG coherence with a 32-channels device during the same motor task with either a robotic or a laparoscopic approach. Estimates of auto and coherence spectra were calculated by a fast Fourier transform algorithm implemented on Matlab 5.3.

Results

We found increase of coherence in surgeons performing laparoscopy, especially in theta and lower alpha activity, in all experimental conditions (M1 vs. SMA, S1 vs. SMA, S1 vs. pre-SMA and M1 vs. S1; p < 0.001). Conversely, an increase in inter-hemispheric coherence in upper alpha and beta band was found in surgeons using the robotic procedure (right vs. left M1, right vs. left S1, right pre-SMA vs. left M1, left pre-SMA vs. right M1; p < 0.001).

Discussion

Our data provide a semi-quantitative evaluation of dynamics in functional coupling among different cortical areas in skilled surgeons performing laparoscopy or robotic surgery. These results suggest that motor and non-motor areas are differently activated and coordinated in surgeons performing the same task with different approaches. To the best of our knowledge, this is the first study that tried to assess semi-quantitative differences during the interaction between normal human brain and robotic devices.

Wavelet coherence model for diagnosis of Alzheimer disease

This article presents a wavelet coherence investigation of electroencephalograph (EEG) readings acquired from patients with Alzheimer disease (AD)  and healthy controls. Pairwise electrode wavelet coherence is calculated over each frequency band (delta, theta, alpha, and beta). For comparing the synchronization fraction of 2 EEG signals, a wavelet coherence fraction is proposed which is defined as the fraction of the signal time during which the wavelet coherence value is above a certain threshold. A one-way analysis of variance test shows a set of statistically significant differences in wavelet coherence between AD and controls. The wavelet coherence method is effective for studying cortical connectivity at a high temporal resolution. Compared with other conventional AD coherence studies, this study takes into account the time-frequency changes in coherence of EEG signals and thus provides more correlational details. A set of statistically significant differences was found in the wavelet coherence among AD and controls. In particular, temporocentral regions show a significant decrease in wavelet coherence in AD in the delta band, and the parietal and central regions show significant declines in cortical connectivity with most of their neighbors in the theta and alpha bands. This research shows that wavelet coherence can be used as a powerful tool to differentiate between healthy elderly individuals and probable AD patients.

Evidence of altered corticomotor system connectivity in early-stage Alzheimer's disease

BACKGROUND AND PURPOSE

There is increasing evidence for subtle motor dysfunction early in Alzheimer disease (AD), including common motor behaviors that were once considered unaffected early in the disease process. Our objective was to assess whether functional neural networks underlying motor behavior are altered by AD.

METHODS

We investigated AD-related differences in regional brain activation during motor performance. Nine older adults with early-stage AD and 10 without dementia underwent functional magnetic resonance imaging while performing a visually directed simple motor task (hand squeeze).

RESULTS

Despite some similarity in brain activation during motor performance, we found that individuals without dementia exhibited greater activation in accessory motor regions, supplementary motor area, and cerebellum compared with those with AD. We also assessed disease-related differences in regions where activity was functionally integrated with primary motor cortex. Using a psychophysiological interaction analysis, we found that those with AD displayed increased coactivation with primary motor cortex of bilateral motor and visual regions.

DISCUSSION AND CONCLUSIONS

These AD-related differences in regional coactivation during motor execution may represent inefficiency in the motor network as a consequence of the disease process. Alternatively, they may represent compensatory activation. These findings provide further evidence that in early stages of AD, neuromotor function is altered even during simple motor behaviors. The results may have implications for performance of more complex tasks and may be associated with the well-characterized decline in dual-task performance in those with AD.

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.

Physical activity and cognition in Alzheimer's disease: relationship to vascular risk factors, executive functions and gait

Epidemiological studies show a positive relationship between physical activity and cognition in patients with Alzheimer's disease (AD). A relatively small number of intervention studies have examined the effects of physical activity, such as walking, on cognition in AD patients. The results of these studies, reviewed here, include both positive and negative findings. The finding that physical activity does not improve cognition in all AD patients could be explained by two factors that have received little attention thus far: executive dysfunction and gait disturbances. These two factors are part of a cascade of events, initiated by cerebrovascular disease in AD. This cascade of events is addressed in detail. Finally, (non)pharmacological interventions to improve executive dysfunctions and gait disturbances in patients with AD are discussed.

Effects of surface EMG rectification on power and coherence analyses: An EEG and MEG study

Coherence between electromyography (EMG) and electroencephalography (EEG) or magnetoencephalography (MEG) is frequently examined to gain insights on neuromuscular binding. Commonly, EMG signals are rectified before coherence is computed. However, the appropriateness of EMG rectification in computing EMG-EEG/MEG coherence has never been validated. Since rectification is a non-linear operation and alters the EMG power spectrum, such a validation is important to ensure the accuracy of coherence calculation. In this study we experimentally investigated the effects of EMG rectification on EMG power spectra and its coherence with EEG/MEG signals. Subjects performed sustained isometric index finger abduction at approximately 5-10% maximal voluntary force (in both EEG-EMG and MEG-EMG experiments) and index finger tapping at approximately 2-4Hz (in EEG-EMG experiment only). Bipolar surface EMG data from the first dorsal interosseus (FDI) and EEG/MEG signals from the contralateral primary sensorimotor area (C3) were recorded simultaneously. Power spectra and coherence with the EEG/MEG were calculated before and after EMG rectification. The results show that rectification shifts EMG power to lower frequencies, possibly enhancing peaks of motor unit firing. Coherences with the EEG/MEG signals were not significantly changed by EMG rectification, indicating EMG rectification is overall an appropriate procedure in power and coherence analyses.

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.

Magnetoencephalographic evaluation of resting-state functional connectivity in Alzheimer's disease

Statistical interdependencies between magnetoencephalographic signals recorded over different brain regions may reflect the functional connectivity of the resting-state networks. We investigated topographic characteristics of disturbed resting-state networks in Alzheimer's disease patients in different frequency bands. Whole-head 151-channel MEG was recorded in 18 Alzheimer patients (mean age 72.1 years, SD 5.6; 11 males) and 18 healthy controls (mean age 69.1 years, SD 6.8; 7 males) during a no-task eyes-closed resting state. Pair-wise interdependencies of MEG signals were computed in six frequency bands (delta, theta, alpha1, alpha2, beta and gamma) with the synchronization likelihood (a nonlinear measure) and coherence and grouped into long distance (intra- and interhemispheric) and short distance interactions. In the alpha1 and beta band, Alzheimer patients showed a loss of long distance intrahemispheric interactions, with a focus on left fronto-temporal/parietal connections. Functional connectivity was increased in Alzheimer patients locally in the theta band (centro-parietal regions) and the beta and gamma band (occipito-parietal regions). In the Alzheimer group, positive correlations were found between alpha1, alpha2 and beta band synchronization likelihood and MMSE score. Resting-state functional connectivity in Alzheimer's disease is characterized by specific changes of long and short distance interactions in the theta, alpha1, beta and gamma bands. These changes may reflect loss of anatomical connections and/or reduced central cholinergic activity and could underlie part of the cognitive impairment.

Dynamic phenotypes: time series analysis techniques for characterizing neuronal and behavioral dynamics

We consider quantitative measures of behavioral and neuronal dynamics as a means of characterizing phenotypes. Such measures are important from a scientific perspective; because understanding brain function is contingent on understanding the link between the dynamics of the nervous system and behavioral dynamics. They are also important from a biomedical perspective because they provide a contrast to purely psychological characterizations of phenotype or characterizations via static brain images or maps, and are a potential means for differential diagnoses of neuropsychiatric illnesses. After a brief presentation of background work and some current advances, we suggest that more attention needs to be paid to dynamic characterizations of phenotypes. We will discuss some of the relevant time series analysis tools.