Network Hyperexcitability in Early-Stage Alzheimer's Disease: Evaluation of Functional Connectivity Biomarkers in a Computational Disease Model.

Background: There is increasing evidence from animal and clinical studies that network hyperexcitability (NH) may be an important pathophysiological process and potential target for treatment in early Alzheimer's disease (AD). Measures of functional connectivity (FC) have been proposed as promising biomarkers for NH, but it is unknown which measure has the highest sensitivity for early-stage changes in the excitation/inhibition balance. Objective: We aim to test the performance of different FC measures in detecting NH at the earliest stage using a computational approach. Methods: We use a whole brain computational model of activity dependent degeneration to simulate progressive AD pathology and NH. We investigate if and at what stage four measures of FC (amplitude envelope correlation corrected [AECc], phase lag index [PLI], joint permutation entropy [JPE] and a new measure: phase lag time [PLT]) can detect early-stage AD pathophysiology. Results: The activity dependent degeneration model replicates spectral changes in line with clinical data and demonstrates increasing NH. Compared to relative theta power as a gold standard the AECc and PLI are shown to be less sensitive in detecting early-stage NH and AD-related neurophysiological abnormalities, while the JPE and the PLT show more sensitivity with excellent test characteristics. Conclusions: Novel FC measures, which are better in detecting rapid fluctuations in neural activity and connectivity, may be superior to well-known measures such as the AECc and PLI in detecting early phase neurophysiological abnormalities and in particular NH in AD. These markers could improve early diagnosis and treatment target identification.

Hub overload and failure as a final common pathway in neurological brain network disorders.

Understanding the concept of network hubs and their role in brain disease is now rapidly becoming important for clinical neurology. Hub nodes in brain networks are areas highly connected to the rest of the brain, which handle a large part of all the network traffic. They also show high levels of neural activity and metabolism, which makes them vulnerable to many different types of pathology. The present review examines recent evidence for the prevalence and nature of hub involvement in a variety of neurological disorders, emphasizing common themes across different types of pathology. In focal epilepsy, pathological hubs may play a role in spreading of seizure activity, and removal of such hub nodes is associated with improved outcome. In stroke, damage to hubs is associated with impaired cognitive recovery. Breakdown of optimal brain network organization in multiple sclerosis is accompanied by cognitive dysfunction. In Alzheimer's disease, hyperactive hub nodes are directly associated with amyloid-beta and tau pathology. Early and reliable detection of hub pathology and disturbed connectivity in Alzheimer's disease with imaging and neurophysiological techniques opens up opportunities to detect patients with a network hyperexcitability profile, who could benefit from treatment with anti-epileptic drugs.

Diagnostic and prognostic value of EEG in prodromal dementia with Lewy bodies.

OBJECTIVE: Early biomarkers for dementia with Lewy bodies (DLB) are lacking. To determine whether EEG differentiates the prodromal phase of DLB from other causes of mild cognitive impairment (MCI) and whether EEG is predictive for time to conversion from MCI to DLB, we compared EEGs and clinical follow-up of patients with MCI due to DLB with those of patients with MCI due to Alzheimer disease (MCI-AD). METHODS: We compared 37 patients with MCI who developed DLB during follow-up or had an abnormal 123I-PF-CIT SPECT scan (MCI-DLB) with 67 age-matched patients with MCI-AD. EEGs were assessed visually with a score of increasing abnormality (range 1-5). We performed fast Fourier transform to analyze the power spectrum. With survival analyses, EEG characteristics were related to time to progression to dementia. RESULTS: The visual EEG score was higher in MCI-DLB (score >2 in 60%) compared to MCI-AD (score >2 in 8%, p < 0.001). We found frontal intermittent delta activity in 22% of MCI-DLB, not in MCI-AD. Patients with MCI-DLB had a lower peak frequency (7.5 [6.0-9.9] Hz vs 8.8 [6.8-10.2] in MCI-AD, p < 0.001) and more slow-wave activity. Several individual EEG measures showed good performance to discriminate MCI-DLB from MCI-AD (areas under the curve up to 0.94). In MCI-DLB, high visual EEG score, diffuse abnormalities, and low α2 power were related to time to progression to dementia (hazard ratios 4.1, 9.9, 5.1, respectively). CONCLUSIONS: Profound EEG abnormalities are already present in the prodromal stage of DLB and have diagnostic and prognostic value. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that EEG abnormalities are more common in MCI-DLB than MCI-AD.

EEG Resting State Functional Connectivity in Adult Dyslexics Using Phase Lag Index and Graph Analysis.

Developmental dyslexia may involve deficits in functional connectivity across widespread brain networks that enable fluent reading. We investigated the large-scale organization of electroencephalography (EEG) functional networks at rest in 28 dyslexics and 36 typically reading adults. For each frequency band (delta, theta alpha and beta), we assessed functional connectivity strength with the phase lag index (PLI). Network topology was examined using minimum spanning tree (MST) graphs derived from the functional connectivity matrices. We found significant group differences in the alpha band (8-13 Hz). The graph analysis indicated more interconnected nodes, in dyslexics compared to typical readers. The graph metrics were significantly correlated with age in dyslexics but not in typical readers, which may indicate more heterogeneity in maturation of brain networks in dyslexics. The present findings support the involvement of alpha oscillations in higher cognition and the sensitivity of graph metrics to characterize functional networks in adult dyslexia. Finally, the current results extend our previous findings on children.

Strong Relation Between an EEG Functional Connectivity Measure and Postmenstrual Age: A New Potential Tool for Measuring Neonatal Brain Maturation.

Fetal and neonatal brain connectivity development is highly complex. Studies have shown that functional networks change dramatically during development. The purpose of the current study was to determine how the mean phase lag index (mPLI), a measure of functional connectivity (FC), assessed with electroencephalography (EEG), changes with postmenstrual age (PMA) during the early stages of brain development after birth. Neonates (N = 131) with PMA 27.6-45.3 weeks who underwent an EEG for a medical reason were retrospectively studied. For each recording, global FC was assessed by obtaining a whole-head average of all local PLI values (pairwise between sensor space EEG signals). Global FC results were consequently correlated with PMA values in seven frequency bands. Local results were obtained for the frequency band with the strongest global association. There was a strong negative correlation between mPLI and PMA in most frequency bands. The strongest association was found in the delta frequency band (R = -0.616, p < 0.001) which was therefore topographically explored; the strongest correlations were between pairs of electrodes with at least one electrode covering the central sulcus. Even in this heterogeneous group of neonates, global FC strongly reflects PMA. The decrease in PLI may reflect the process of segregation of specific brain regions with increasing PMA. This was mainly found in the central brain regions, in parallel with myelination of these areas during early development. In the future, there may be a role for PLI in detecting atypical FC maturation. Moreover, PLI could be used to develop biomarkers for brain maturation and expose segregation processes in the neonatal brain.

EEG Characteristics of Dementia With Lewy Bodies, Alzheimer's Disease and Mixed Pathology.

Introduction: Previous studies on electroencephalography (EEG) to discriminate between dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) have been promising. These studies did not consider the pathological overlap of the two diseases. DLB-patients with concomitant AD pathology (DLB/AD+) have a more severe disease manifestation. The EEG may also be influenced by a synergistic effect of the two pathologies. We aimed to compare EEG characteristics between DLB/AD+, "pure" DLB (DLB/AD-) and AD. Methods: We selected probable DLB patients who had an EEG and cerebrospinal fluid (CSF) available, from the Amsterdam Dementia Cohort (ADC). Concomitant AD-pathology was defined as a CSF tau/Aß-42 ratio > 0.52. Forty-one DLB/AD+ cases were matched for age (mean 70 (range 53-85)) and sex (85% male) 1:1 to DLB/AD- and AD-patients. EEGs were assessed visually, with Fast Fourier Transform (FFT), network- and connectivity measures. Results: EEG visual severity score (range 1-5) did not differ between DLB/AD- and DLB/AD+ (2.7 in both groups) and was higher compared to AD (1.9, p < 0.01). Both DLB groups had a lower peak frequency (7.0 Hz and 6.9 Hz in DLB vs. 8.2 in AD, p < 0.05), more slow-wave activity and more prominent disruptions of connectivity and networks, compared to AD. No significant differences were found between DLB/AD+ and DLB/AD-. Discussion: EEG abnormalities are more pronounced in DLB, regardless of AD co-pathology. This emphasizes the valuable role of EEG in discriminating between DLB and AD. It suggests that EEG slowing in DLB is influenced more by the α-synucleinopathy, or the associated cholinergic deficit, than by amyloid and tau pathology.

Meditation is associated with increased brain network integration.

INTRODUCTION: This study aims to identify novel quantitative EEG measures associated with mindfulness meditation. As there is some evidence that meditation is associated with higher integration of brain networks, we focused on EEG measures of network integration. METHODS: Sixteen novice meditators and sixteen experienced meditators participated in the study. Novice meditators performed a basic meditation practice that supported effortless awareness, which is an important quality of experience related to mindfulness practices, while their EEG was recorded. Experienced meditators performed a self-selected meditation practice that supported effortless awareness. Network integration was analyzed with maximum betweenness centrality and leaf fraction (which both correlate positively with network integration) as well as with diameter and average eccentricity (which both correlate negatively with network integration), based on a phase-lag index (PLI) and minimum spanning tree (MST) approach. Differences between groups were assessed using repeated-measures ANOVA for the theta (4-8 Hz), alpha (8-13 Hz) and lower beta (13-20 Hz) frequency bands. RESULTS: Maximum betweenness centrality was significantly higher in experienced meditators than in novices (P = 0.012) in the alpha band. In the same frequency band, leaf fraction showed a trend toward being significantly higher in experienced meditators than in novices (P = 0.056), while diameter and average eccentricity were significantly lower in experienced meditators than in novices (P = 0.016 and P = 0.028 respectively). No significant differences between groups were observed for the theta and beta frequency bands. CONCLUSION: These results show that alpha band functional network topology is better integrated in experienced meditators than in novice meditators during meditation. This novel finding provides the rationale to investigate the temporal relation between measures of functional connectivity network integration and meditation quality, for example using neurophenomenology experiments.

Large-scale network organization of EEG functional connectivity in newborn infants.

The organization of functional brain networks changes across human lifespan. The present study analyzed functional brain networks in healthy full-term infants (N = 139) within 1-6 days from birth by measuring neural synchrony in EEG recordings during quiet sleep. Large-scale phase synchronization was measured in six frequency bands with the Phase Lag Index. Macroscopic network organization characteristics were quantified by constructing unweighted minimum spanning tree graphs. The cortical networks in early infancy were found to be significantly more hierarchical and had a more cost-efficient organization compared with MST of random control networks, more so in the theta and alpha than in other frequency bands. Frontal and parietal sites acted as the main hubs of these networks, the topological characteristics of which were associated with gestation age (GA). This suggests that individual differences in network topology are related to cortical maturation during the prenatal period, when functional networks shift from strictly centralized toward segregated configurations. Hum Brain Mapp 38:4019-4033, 2017. © 2017 Wiley Periodicals, Inc.

Different functional connectivity and network topology in behavioral variant of frontotemporal dementia and Alzheimer's disease: an EEG study.

We investigated whether the functional connectivity and network topology in 69 Alzheimer's disease (AD), 48 behavioral variant of frontotemporal dementia (bvFTD) patients, and 64 individuals with subjective cognitive decline are different using resting-state electroencephalography recordings. Functional connectivity between all pairs of electroencephalography channels was assessed using the phase lag index (PLI). We subsequently calculated PLI-weighted networks, from which minimum spanning trees (MSTs) were constructed. Finally, we investigated the hierarchical clustering organization of the MSTs. Functional connectivity analysis showed frequency-dependent results: in the delta band, bvFTD showed highest whole-brain PLI; in the theta band, the whole-brain PLI in AD was higher than that in bvFTD; in the alpha band, AD showed lower whole-brain PLI compared with bvFTD and subjective cognitive decline. The MST results indicate that frontal networks appear to be selectively involved in bvFTD against the background of preserved global efficiency, whereas parietal and occipital loss of network organization in AD is accompanied by global efficiency loss. Our findings suggest different pathophysiological mechanisms in these 2 separate neurodegenerative disorders.

Hierarchical clustering in minimum spanning trees.

The identification of clusters or communities in complex networks is a reappearing problem. The minimum spanning tree (MST), the tree connecting all nodes with minimum total weight, is regarded as an important transport backbone of the original weighted graph. We hypothesize that the clustering of the MST reveals insight in the hierarchical structure of weighted graphs. However, existing theories and algorithms have difficulties to define and identify clusters in trees. Here, we first define clustering in trees and then propose a tree agglomerative hierarchical clustering (TAHC) method for the detection of clusters in MSTs. We then demonstrate that the TAHC method can detect clusters in artificial trees, and also in MSTs of weighted social networks, for which the clusters are in agreement with the previously reported clusters of the original weighted networks. Our results therefore not only indicate that clusters can be found in MSTs, but also that the MSTs contain information about the underlying clusters of the original weighted network.

Reproducibility of functional connectivity and graph measures based on the phase lag index (PLI) and weighted phase lag index (wPLI) derived from high resolution EEG.

Functional connectivity (FC) and graph measures provide powerful means to analyze complex networks. The current study determines the inter-subject-variability using the coefficient of variation (CoV) and long-term test-retest-reliability (TRT) using the intra-class correlation coefficient (ICC) in 44 healthy subjects with 35 having a follow-up at years 1 and 2. FC was estimated from 256-channel-EEG by the phase-lag-index (PLI) and weighted PLI (wPLI) during an eyes-closed resting state condition. PLI quantifies the asymmetry of the distribution of instantaneous phase differences of two time-series and signifies, whether a consistent non-zero phase lag exists. WPLI extends the PLI by additionally accounting for the magnitude of the phase difference. Signal-space global and regional PLI/wPLI and weighted first-order graph measures, i.e. normalized clustering coefficient (gamma), normalized average path length (lambda), and the small-world-index (SWI) were calculated for theta-, alpha1-, alpha2- and beta-frequency bands. Inter-subject variability of global PLI was low to moderate over frequency bands (0.12

Frontal midline theta connectivity is related to efficiency of WM maintenance and is affected by aging.

Representations in working memory (WM) are temporary, but can be refreshed for longer periods of time through maintenance mechanisms, thereby establishing their availability for subsequent memory tests. Frontal brain regions supporting WM maintenance operations undergo anatomical and functional changes with advancing age, leading to age related decline of memory functions. The present study focused on age-related functional connectivity changes of the frontal midline (FM) cortex in the theta band (4-8 Hz), related to WM maintenance. In the visual delayed-match-to-sample WM task young (18-26 years, N=20) and elderly (60-71 years N=16) adults had to memorize sample stimuli consisting of 3 or 5 items while 33 channel EEG recording was performed. The phase lag index was used to quantify connectivity strength between cortical regions. The low and high memory demanding WM maintenance periods were classified based on whether they were successfully maintained (remembered) or unsuccessfully maintained (unrecognized later). In the elderly reduced connectivity strength of FM brain region and decreased performance were observed. The connectivity strength between FM and posterior sensory cortices was shown to be sensitive to both increased memory demands and memory performance regardless of age. The coupling of frontal regions (midline and lateral) and FM-temporal cortices characterized successfully maintained trials and declined with advancing age. The findings provide evidence that a FM neural circuit of theta oscillations that serves a possible basis of active maintenance process is especially vulnerable to aging.

EEG network connectivity changes in mild cognitive impairment - Preliminary results.

Resting state EEGs were compared between patients with amnestic subtype of mild cognitive impairment (aMCI) and matched elderly controls at two times over a one year period. The study aimed at investigating the role of functional connectivity between and within different brain regions in relation to the progression of cognitive deficit in MCI. The EEG was recorded in two sessions during eyes closed and eyes open resting conditions. Functional brain connectivity was investigated based on the measurement of phase synchronization in different frequency bands. Delta and theta synchronization characteristics indicated decreased level of local and large-scale connectivity in the patients within the frontal, between the frontal and temporal, and frontal and parietal brain areas which was more pronounced 1year later. As a consequence of opening the eyes connectivity in the alpha1 band within the parietal lobe decreased compared to the eyes closed condition but only in the control group. The lack of alpha1 band reactivity following eye opening could reliably differentiate patients from controls. Our preliminary results support the notion that the functional disconnection between distant brain areas is a characteristic feature of MCI, and may prove to be predictive in terms of the progression of this condition.

EEG synchronization characteristics of functional connectivity and complex network properties of memory maintenance in the delta and theta frequency bands.

Task-dependent changes of nonlinear-linear synchronization features and graph theoretical properties of the delta and theta frequencies were analyzed in the present EEG study that were related to episodic memory maintenance processes. Synchronization was found to increase with respect to both the delta and theta bands within the frontal and parietal areas and also between these regions. Results of graph theoretical analysis indicated a task-related shift towards small-world network topology in the theta band.

Mild traumatic brain injury: graph-model characterization of brain networks for episodic memory.

Episodic memory is among the cognitive functions that can be affected in the acute phase following mild traumatic brain injury (MTBI). The present study used EEG recordings to evaluate global synchronization and network organization of rhythmic activity during the encoding and recognition phases of an episodic memory task varying in stimulus type (kaleidoscope images, pictures, words, and pseudowords). Synchronization of oscillatory activity was assessed using a linear and nonlinear connectivity estimator and network analyses were performed using algorithms derived from graph theory. Twenty five MTBI patients (tested within days post-injury) and healthy volunteers were closely matched on demographic variables, verbal ability, psychological status variables, as well as on overall task performance. Patients demonstrated sub-optimal network organization, as reflected by changes in graph parameters in the theta and alpha bands during both encoding and recognition. There were no group differences in spectral energy during task performance or on network parameters during a control condition (rest). Evidence of less optimally organized functional networks during memory tasks was more prominent for pictorial than for verbal stimuli.

The lesioned brain: still a small-world?

The intra-arterial amobarbital procedure (IAP or Wada test) is used to determine language lateralization and contralateral memory functioning in patients eligible for neurosurgery because of pharmaco-resistant epilepsy. During unilateral sedation, functioning of the contralateral hemisphere is assessed by means of neuropsychological tests. We use the IAP as a reversible model for the effect of lesions on brain network topology. Three artifact-free epochs (4096 samples) were selected from each electroencephalogram record before and after amobarbital injection. Functional connectivity was assessed by means of the synchronization likelihood. The resulting functional connectivity matrices were constructed for all six epochs per patient in four frequency bands, and weighted network analysis was performed. The clustering coefficient, average path length, small-world index, and edge weight correlation were calculated. Recordings of 33 patients were available. Network topology changed significantly after amobarbital injection: clustering decreased in all frequency bands, while path length decreased in the theta and lower alpha band, indicating a shift toward a more random network topology. Likewise, the edge weight correlation decreased after injection of amobarbital in the theta and beta bands. Network characteristics after injection of amobarbital were correlated with memory score: higher theta band small-world index and increased upper alpha path length were related to better memory score. The whole-brain network topology in patients eligible for epilepsy surgery becomes more random and less optimally organized after selective sedation of one hemisphere, as has been reported in studies with brain tumor patients. Furthermore, memory functioning after injection seems related to network topology, indicating that functional performance is related to topological network properties of the brain.

Small-world networks and disturbed functional connectivity in schizophrenia.

Disturbances in "functional connectivity" have been proposed as a major pathophysiological mechanism for schizophrenia, and in particular, for cognitive disorganization. Detection and estimation of these disturbances would be of clinical interest. Here we characterize the spatial pattern of functional connectivity by computing the "synchronization likelihood" (SL) of EEG at rest and during performance of a 2Back working memory task using letters of the alphabet presented on a PC screen in subjects with schizophrenia and healthy controls. The spatial patterns of functional connectivity were then characterized with graph theoretical measures to test whether a disruption of an optimal spatial pattern ("small-world") of the functional connectivity network underlies schizophrenia. Twenty stabilized patients with schizophrenia, who were able to work, and 20 healthy controls participated in the study. During the working memory (WM) task healthy subjects exhibited small-world properties (a combination of local clustering and high overall integration of the functional networks) in the alpha, beta and gamma bands. These properties were not present in the schizophrenia group. These findings are in accordance with a partially inadequate organization of neuronal networks in subjects with schizophrenia. This method could be helpful for diagnosis and evaluation of the severity of the disease, as well as understanding the pathophysiologic mechanisms underlying cognitive dysfunction in schizophrenia.

Scale-free dynamics of global functional connectivity in the human brain.

Higher brain functions depend upon the rapid creation and dissolution of ever changing synchronous cell assemblies. We examine the hypothesis that the dynamics of this process displays scale-free, self-similar properties. EEGs (19 channels, average reference, sample frequency 500 Hz) of 15 healthy subjects (10 men; mean age 22.5 years) were analyzed during eyes-closed and eyes-open no-task conditions. Mean level of synchronization as a function of time was estimated with the synchronization likelihood for five frequency bands (0.5-4, 4-8, 8-13, 13-30, and 30-48 Hz). Scaling in these time series was investigated with detrended fluctuation analysis (DFA). DFA analysis of global synchronization time series showed scale-free characteristics, suggesting neuronal dynamics do not necessarily have a characteristic time constant. The scaling exponent as determined with DFA differed significantly for different frequency bands and conditions. The exponent was close to 1.5 for low frequencies (delta, theta, and alpha) and close to 1 for beta and gamma bands. Eye opening decreased the exponent, in particular in alpha and beta bands. Fluctuations of EEG synchronization in delta, theta, alpha, beta, and gamma bands exhibit scale-free dynamics in eyes-closed as well as eyes-open no-task states. The decrease in the scaling exponent following eye opening reflects a relative preponderance of rapid fluctuations with respect to slow changes in the mean synchronization level. The existence of scaling suggests that the underlying dynamics may display self-organized criticality, possibly representing a near-optimal state for information processing.