The epidemic spreading model and the direction of information flow in brain networks.

The interplay between structural connections and emerging information flow in the human brain remains an open research problem. A recent study observed global patterns of directional information flow in empirical data using the measure of transfer entropy. For higher frequency bands, the overall direction of information flow was from posterior to anterior regions whereas an anterior-to-posterior pattern was observed in lower frequency bands. In this study, we applied a simple Susceptible-Infected-Susceptible (SIS) epidemic spreading model on the human connectome with the aim to reveal the topological properties of the structural network that give rise to these global patterns. We found that direct structural connections induced higher transfer entropy between two brain regions and that transfer entropy decreased with increasing distance between nodes (in terms of hops in the structural network). Applying the SIS model, we were able to confirm the empirically observed opposite information flow patterns and posterior hubs in the structural network seem to play a dominant role in the network dynamics. For small time scales, when these hubs acted as strong receivers of information, the global pattern of information flow was in the posterior-to-anterior direction and in the opposite direction when they were strong senders. Our analysis suggests that these global patterns of directional information flow are the result of an unequal spatial distribution of the structural degree between posterior and anterior regions and their directions seem to be linked to different time scales of the spreading process.

Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions.

Understanding the electrophysiological basis of resting state networks (RSNs) in the human brain is a critical step towards elucidating how inter-areal connectivity supports healthy brain function. In recent years, the relationship between RSNs (typically measured using haemodynamic signals) and electrophysiology has been explored using functional Magnetic Resonance Imaging (fMRI) and magnetoencephalography (MEG). Significant progress has been made, with similar spatial structure observable in both modalities. However, there is a pressing need to understand this relationship beyond simple visual similarity of RSN patterns. Here, we introduce a mathematical model to predict fMRI-based RSNs using MEG. Our unique model, based upon a multivariate Taylor series, incorporates both phase and amplitude based MEG connectivity metrics, as well as linear and non-linear interactions within and between neural oscillations measured in multiple frequency bands. We show that including non-linear interactions, multiple frequency bands and cross-frequency terms significantly improves fMRI network prediction. This shows that fMRI connectivity is not only the result of direct electrophysiological connections, but is also driven by the overlap of connectivity profiles between separate regions. Our results indicate that a complete understanding of the electrophysiological basis of RSNs goes beyond simple frequency-specific analysis, and further exploration of non-linear and cross-frequency interactions will shed new light on distributed network connectivity, and its perturbation in pathology.

The minimum spanning tree: an unbiased method for brain network analysis.

The brain is increasingly studied with graph theoretical approaches, which can be used to characterize network topology. However, studies on brain networks have reported contradictory findings, and do not easily converge to a clear concept of the structural and functional network organization of the brain. It has recently been suggested that the minimum spanning tree (MST) may help to increase comparability between studies. The MST is an acyclic sub-network that connects all nodes and may solve several methodological limitations of previous work, such as sensitivity to alterations in connection strength (for weighted networks) or link density (for unweighted networks), which may occur concomitantly with alterations in network topology under empirical conditions. If analysis of MSTs avoids these methodological limitations, understanding the relationship between MST characteristics and conventional network measures is crucial for interpreting MST brain network studies. Here, we firstly demonstrated that the MST is insensitive to alterations in connection strength or link density. We then explored the behavior of MST and conventional network-characteristics for simulated regular and scale-free networks that were gradually rewired to random networks. Surprisingly, although most connections are discarded during construction of the MST, MST characteristics were equally sensitive to alterations in network topology as the conventional graph theoretical measures. The MST characteristics diameter and leaf fraction were very strongly related to changes in the characteristic path length when the network changed from a regular to a random configuration. Similarly, MST degree, diameter, and leaf fraction were very strongly related to the degree of scale-free networks that were rewired to random networks. Analysis of the MST is especially suitable for the comparison of brain networks, as it avoids methodological biases. Even though the MST does not utilize all the connections in the network, it still provides a, mathematically defined and unbiased, sub-network with characteristics that can provide similar information about network topology as conventional graph measures.

Functional brain network analysis using minimum spanning trees in Multiple Sclerosis: an MEG source-space study.

Cognitive dysfunction in Multiple Sclerosis (MS) is closely related to altered functional brain network topology. Conventional network analyses to compare groups are hampered by differences in network size, density and suffer from normalization problems. We therefore computed the Minimum Spanning Tree (MST), a sub-graph of the original network, to counter these problems. We hypothesize that functional network changes analysed with MSTs are important for understanding cognitive changes in MS and that changes in MST topology also represent changes in the critical backbone of the original brain networks. Here, resting-state magnetoencephalography (MEG) recordings from 21 early MS patients and 17 age-, gender-, and education-matched controls were projected onto atlas-based regions-of-interest (ROIs) using beamforming. The phase lag index was applied to compute functional connectivity between regions, from which a graph and subsequently the MST was constructed. Results showed lower global integration in the alpha2 (10-13Hz) and beta (13-30Hz) bands in MS patients, whereas higher global integration was found in the theta band. Changes were most pronounced in the alpha2 band where a loss of hierarchical structure was observed, which was associated with poorer cognitive performance. Finally, the MST in MS patients as well as in healthy controls may represent the critical backbone of the original network. Together, these findings indicate that MST network analyses are able to detect network changes in MS patients, which may correspond to changes in the core of functional brain networks. Moreover, these changes, such as a loss of hierarchical structure, are related to cognitive performance in MS.

Structural degree predicts functional network connectivity: a multimodal resting-state fMRI and MEG study.

Communication between neuronal populations in the human brain is characterized by complex functional interactions across time and space. Recent studies have demonstrated that these functional interactions depend on the underlying structural connections at an aggregate level. Multiple imaging modalities can be used to investigate the relation between the structural connections between brain regions and their functional interactions at multiple timescales. We investigated if consistent modality-independent functional interactions take place between brain regions, and whether these can be accounted for by underlying structural properties. We used functional MRI (fMRI) and magnetoencephalography (MEG) recordings from a population of healthy adults together with a previously described structural network. A high overlap in resting-state functional networks was found in fMRI and especially alpha band MEG recordings. This overlap was characterized by a strongly interconnected functional core network in temporo-posterior brain regions. Anatomically realistically coupled neural mass models revealed that this strongly interconnected functional network emerges near the threshold for global synchronization. Most importantly, this functional core network could be explained by a trade-off between the product of the degrees of structurally-connected regions and the Euclidean distance between them. For both fMRI and MEG, the product of the degrees of connected regions was the most important predictor for functional network connectivity. Therefore, irrespective of the modality, these results indicate that a functional core network in the human brain is especially shaped by communication between high degree nodes of the structural network.

Local polymorphic delta activity in cortical lesions causes global decreases in functional connectivity.

Increasing evidence from neuroimaging and modeling studies suggests that local lesions can give rise to global network changes in the human brain. These changes are often attributed to the disconnection of the lesioned areas. However, damaged brain areas may still be active, although the activity is altered. Here, we hypothesize that empirically observed global decreases in functional connectivity in patients with brain lesions can be explained by specific alterations of local neural activity that are the result of damaged tissue. We simulated local polymorphic delta activity (PDA), which typically characterizes EEG/MEG recordings of patients with cerebral lesions, in a realistic model of human brain activity. 78 neural masses were coupled according to the human structural brain network. Lesions were created by altering the parameters of individual neural masses in order to create PDA (i.e. simulating acute focal brain damage); combining this PDA with weakening of structural connections (i.e. simulating brain tumors), and fully deleting structural connections (i.e. simulating a full resection). Not only structural disconnection but also PDA in itself caused a global decrease in functional connectivity, similar to the observed alterations in MEG recordings of patients with PDA due to brain lesions. Interestingly, connectivity between regions that were not lesioned directly also changed. The impact of PDA depended on the network characteristics of the lesioned region in the structural connectome. This study shows for the first time that locally disturbed neural activity, i.e. PDA, may explain altered functional connectivity between remote areas, even when structural connections are unaffected. We suggest that focal brain lesions and the corresponding altered neural activity should be considered in the framework of the full functionally interacting brain network, implying that the impact of lesions reaches far beyond focal damage.

Local MEG networks: the missing link between protein expression and epilepsy in glioma patients?

Connectivity and network analysis in neuroscience has been applied to multiple spatial scales, but the links between these different scales have rarely been investigated. In tumor-related epilepsy, altered network topology is related to behavior, but the molecular basis of these observations is unknown. We elucidate the associations between microscopic features of brain tumors, local network topology, and functional patient status. We hypothesize that expression of proteins related to tumor-related epilepsy is directly correlated with network characteristics of the tumor area. Glioma patients underwent magnetoencephalography, and functional network topology of the tumor area was used to predict tissue protein expression patterns of tumor tissue collected during neurosurgery. Protein expression and network topology were interdependent; in particular between-module connectivity was selectively associated with two epilepsy-related proteins. Total number of seizures was related to both the role of the tumor area in the functional network and to protein expression. Importantly, classification of protein expression was predicted by between-module connectivity with up to 100% accuracy. Thus, network topology may serve as an intermediate level between molecular features of tumor tissue and symptomatology in brain tumor patients, and can potentially be used as a non-invasive marker for microscopic tissue characteristics.

Comparing individual and group-level simulated neurophysiological brain connectivity using the Jansen and Rit neural mass model.

Computational models are often used to assess how functional connectivity (FC) patterns emerge from neuronal population dynamics and anatomical brain connections. It remains unclear whether the commonly used group-averaged data can predict individual FC patterns. The Jansen and Rit neural mass model was employed, where masses were coupled using individual structural connectivity (SC). Simulated FC was correlated to individual magnetoencephalography-derived empirical FC. FC was estimated using phase-based (phase lag index (PLI), phase locking value (PLV)), and amplitude-based (amplitude envelope correlation (AEC)) metrics to analyze their goodness of fit for individual predictions. Individual FC predictions were compared against group-averaged FC predictions, and we tested whether SC of a different participant could equally well predict participants' FC patterns. The AEC provided a better match between individually simulated and empirical FC than phase-based metrics. Correlations between simulated and empirical FC were higher using individual SC compared to group-averaged SC. Using SC from other participants resulted in similar correlations between simulated and empirical FC compared to using participants' own SC. This work underlines the added value of FC simulations using individual instead of group-averaged SC for this particular computational model and could aid in a better understanding of mechanisms underlying individual functional network trajectories.

Minimum spanning tree analysis of brain networks: A systematic review of network size effects, sensitivity for neuropsychiatric pathology, and disorder specificity.

Brain network characteristics' potential to serve as a neurological and psychiatric pathology biomarker has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. It is yet unknown whether this approach leads to more consistent findings across studies and converging outcomes of either disease-specific biomarkers or transdiagnostic effects. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies (N = 43) to study consistency of MST metrics between different network sizes and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. Analysis of data from control groups (12 studies) showed that MST leaf fraction but not diameter decreased with increasing network size. Studies showed a broad range in metric values, suggesting that specific processing pipelines affect MST topology. Contradicting findings remain in the inconclusive literature of MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders across pathologies, and is associated with symptom severity and disease progression; (2) neurophysiological studies in epilepsy show frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology in alpha band is found across disorders associated with attention impairments.

Impaired saccadic eye movements in multiple sclerosis are related to altered functional connectivity of the oculomotor brain network.

BACKGROUND: Impaired eye movements in multiple sclerosis (MS) are common and could represent a non-invasive and accurate measure of (dys)functioning of interconnected areas within the complex brain network. The aim of this study was to test whether altered saccadic eye movements are related to changes in functional connectivity (FC) in patients with MS. METHODS: Cross-sectional eye movement (pro-saccades and anti-saccades) and magnetoencephalography (MEG) data from the Amsterdam MS cohort were included from 176 MS patients and 33 healthy controls. FC was calculated between all regions of the Brainnetome atlas in six conventional frequency bands. Cognitive function and disability were evaluated by previously validated measures. The relationships between saccadic parameters and both FC and clinical scores in MS patients were analysed using multivariate linear regression models. RESULTS: In MS pro- and anti-saccades were abnormal compared to healthy controls A relationship of saccadic eye movements was found with FC of the oculomotor network, which was stronger for regional than global FC. In general, abnormal eye movements were related to higher delta and theta FC but lower beta FC. Strongest associations were found for pro-saccadic latency and FC of the precuneus (beta band ß = -0.23, p = .006), peak velocity and FC of the parietal eye field (theta band ß = -0.25, p = .005) and gain and FC of the inferior frontal eye field (theta band ß = -0.25, p = .003). Pro-saccadic latency was also strongly associated with disability scores and cognitive dysfunction. CONCLUSIONS: Impaired saccadic eye movements were related to functional connectivity of the oculomotor network and clinical performance in MS. This study also showed that, in addition to global network connectivity, studying regional changes in MEG studies could yield stronger correlations.

Non-invasively measured brain activity and radiological progression in diffuse glioma.

Non-invasively measured brain activity is related to progression-free survival in glioma patients, suggesting its potential as a marker of glioma progression. We therefore assessed the relationship between brain activity and increasing tumor volumes on routine clinical magnetic resonance imaging (MRI) in glioma patients. Postoperative magnetoencephalography (MEG) was recorded in 45 diffuse glioma patients. Brain activity was estimated using three measures (absolute broadband power, offset and slope) calculated at three spatial levels: global average, averaged across the peritumoral areas, and averaged across the homologues of these peritumoral areas in the contralateral hemisphere. Tumors were segmented on MRI. Changes in tumor volume between the two scans surrounding the MEG were calculated and correlated with brain activity. Brain activity was compared between patient groups classified into having increasing or stable tumor volume. Results show that brain activity was significantly increased in the tumor hemisphere in general, and in peritumoral regions specifically. However, none of the measures and spatial levels of brain activity correlated with changes in tumor volume, nor did they differ between patients with increasing versus stable tumor volumes. Longitudinal studies in more homogeneous subgroups of glioma patients are necessary to further explore the clinical potential of non-invasively measured brain activity.

The relationship between structural and functional connectivity: graph theoretical analysis of an EEG neural mass model.

We investigated the relationship between structural network properties and both synchronization strength and functional characteristics in a combined neural mass and graph theoretical model of the electroencephalogram (EEG). Thirty-two neural mass models (NMMs), each representing the lump activity of reasonably large groups of interacting excitatory and inhibitory neurons, were reciprocally and excitatory coupled using random rewiring as described by Watts and Strogatz. Numerical analysis of the network revealed an abrupt transition towards a synchronized state as a function of increasing coupling strength alpha. Synchronization increased with increasing degree and decreasing regularity of the network. Parameters of the functional network showed a diverse dependency on structural connectivity: normalized clustering coefficient gamma and path length lambda increased with increasing alpha. For sufficiently large alpha, however, gamma decreased with increasing rewiring probability p, while lambda increased. Hence, a structured functional network exists despite the randomness of the underlying structural network. That is, patterns of functional connectivity are influenced by patterns of the corresponding structural level but do not necessarily agree with those.

Scanning tunneling microscopy of insulators and biological specimens based on lateral conductivity of ultrathin water films.

Scanning tunneling microscopy is based on the flow of an electrical current and thus cannot be used to directly image insulating material. It has been found, however, that a very thin film of water (about one monolayer) adsorbed to a surface exhibits a surprisingly high conductivity that is sufficient to allow scanning tunneling microscope imaging at currents below 1 picoampere. Hydrophilic insulators, such as glass and mica, can thus be imaged in humid air. The same is true for biological specimens deposited on such surfaces, as demonstrated by the scanning tunneling microscope imaging of plasmid DNA on mica.

Effective electromagnetic noise cancellation with beamformers and synthetic gradiometry in shielded and partly shielded environments.

The major challenge of MEG, the inverse problem, is to estimate the very weak primary neuronal currents from the measurements of extracranial magnetic fields. The non-uniqueness of this inverse solution is compounded by the fact that MEG signals contain large environmental and physiological noise that further complicates the problem. In this paper, we evaluate the effectiveness of magnetic noise cancellation by synthetic gradiometers and the beamformer analysis method of synthetic aperture magnetometry (SAM) for source localisation in the presence of large stimulus-generated noise. We demonstrate that activation of primary somatosensory cortex can be accurately identified using SAM despite the presence of significant stimulus-related magnetic interference. This interference was generated by a contact heat evoked potential stimulator (CHEPS), recently developed for thermal pain research, but which to date has not been used in a MEG environment. We also show that in a reduced shielding environment the use of higher order synthetic gradiometry is sufficient to obtain signal-to-noise ratios (SNRs) that allow for accurate localisation of cortical sensory function.

Aberrant resting-state oscillatory brain activity in Parkinson's disease patients with visual hallucinations: An MEG source-space study.

To gain insight into possible underlying mechanism(s) of visual hallucinations (VH) in Parkinson's disease (PD), we explored changes in local oscillatory activity in different frequency bands with source-space magnetoencephalography (MEG). Eyes-closed resting-state MEG recordings were obtained from 20 PD patients with hallucinations (Hall+) and 20 PD patients without hallucinations (Hall-), matched for age, gender and disease severity. The Hall+ group was subdivided into 10 patients with VH only (unimodal Hall+) and 10 patients with multimodal hallucinations (multimodal Hall+). Subsequently, neuronal activity at source-level was reconstructed using an atlas-based beamforming approach resulting in source-space time series for 78 cortical and 12 subcortical regions of interest in the automated anatomical labeling (AAL) atlas. Peak frequency (PF) and relative power in six frequency bands (delta, theta, alpha1, alpha2, beta and gamma) were compared between Hall+ and Hall-, unimodal Hall+ and Hall-, multimodal Hall+ and Hall-, and unimodal Hall+ and multimodal Hall+ patients. PF and relative power per frequency band did not differ between Hall+ and Hall-, and multimodal Hall+ and Hall- patients. Compared to the Hall- group, unimodal Hall+ patients showed significantly higher relative power in the theta band (p = 0.005), and significantly lower relative power in the beta (p = 0.029) and gamma (p = 0.007) band, and lower PF (p = 0.011). Compared to the unimodal Hall+, multimodal Hall+ showed significantly higher PF (p = 0.007). In conclusion, a subset of PD patients with only VH showed slowing of MEG-based resting-state brain activity with an increase in theta activity, and a concomitant decrease in beta and gamma activity, which could indicate central cholinergic dysfunction as underlying mechanism of VH in PD. This signature was absent in PD patients with multimodal hallucinations.

MEG-guided analysis of 7T-MRI in patients with epilepsy.

PURPOSE: To study possible detection of structural abnormalities on 7T MRI that were not detected on 3T MRI and estimate the added value of MEG-guidance. For abnormalities found, analysis of convergence between clinical, MEG and 7T MRI localization of suspected epileptogenic foci. METHODS: In adult patients with well-documented localization-related epilepsy in whom a previous 3T MRI did not demonstrate an epileptogenic lesion but MEG indicated a plausible epileptogenic focus, 7T MRI was performed. Based on semiologic data, visual analysis of the 7T images was performed as well as based on prior MEG results. Correlation with other data from the patient charts, for as far as these were available, was analysed. To establish the level of concordance between the three observers the generalized or Fleiss kappa was calculated. RESULTS: In 3/19 patients abnormalities that, based on semiology, could plausibly represent an epileptogenic lesion were detected using 7T MRI. In an additional 3/19 an abnormality was detected after MEG-guidance. However, in these later cases there was no concordance among the three observers with regard to the presence of a structural abnormality. In one of these three cases intracranial recording was performed, proving the possible abnormality on 7T MRI to be the epileptogenic focus. CONCLUSIONS: In 32% of patients 7T MRI showed abnormalities that could indicate an epileptogenic lesion whereas previous 3T MRI did not, especially when visual inspection was guided by the presence of focal interictal MEG abnormalities.

Semantic and phonological task-set priming and stimulus processing investigated using magnetoencephalography (MEG).

In this study the neural substrates of semantic and phonological task priming and task performance were investigated using single word task-primes. Magnetoencephalography (MEG) data were analysed using Synthetic Aperture Magnetometry (SAM) to determine the spatiotemporal and spectral characteristics of cortical responses. Comparisons were made between the task-prime conditions for evidence of differential effects as a function of the nature of the task being primed, and between the task-prime and the task performance responses for evidence of parallels in activation associated with preparation for and completion of a specific task. Differential priming effects were found. Left middle temporal and inferior frontal voxels showed a statistically significant power decrease associated with the semantic task-prime, and a power increase associated with the phonological task-prime, within beta and gamma frequency bands respectively. Similarities between the task-related differential effects associated with task-prime presentation and those associated with target stimulus presentation were also found. For example, within the semantic task condition, left superior frontal and middle temporal regions showed a significant power decrease within both task-prime and target epochs; within the phonological task condition there were significant parietal and cerebellar power decreases within both types of epoch. In addition there was evidence within the priming epochs of dissociable patterns of activity which could be interpreted as indices of de-activation of task-irrelevant networks. Following a phonological task-prime, significant power increases were observed in those inferior frontal and middle temporal regions in which significant power decreases were associated with semantic task priming and performance.

Neural network topology in ADHD; evidence for maturational delay and default-mode network alterations.

OBJECTIVE: Attention-deficit/hyperactivity disorder (ADHD) has been associated with widespread brain abnormalities in white and grey matter, affecting not only local, but global functional networks as well. In this study, we explored these functional networks using source-reconstructed electroencephalography in ADHD and typically developing (TD) children. We expected evidence for maturational delay, with underlying abnormalities in the default mode network. METHODS: Electroencephalograms were recorded in ADHD (n=42) and TD (n=43) during rest, and functional connectivity (phase lag index) and graph (minimum spanning tree) parameters were derived. Dependent variables were global and local network metrics in theta, alpha and beta bands. RESULTS: We found evidence for a more centralized functional network in ADHD compared to TD children, with decreased diameter in the alpha band (ηp2=0.06) and increased leaf fraction (ηp2=0.11 and 0.08) in the alpha and beta bands, with underlying abnormalities in hub regions of the brain, including default mode network. CONCLUSIONS: The finding of a more centralized network is in line with maturational delay models of ADHD and should be replicated in longitudinal designs. SIGNIFICANCE: This study contributes to the literature by combining high temporal and spatial resolution to construct EEG network topology, and associates maturational-delay and default-mode interference hypotheses of ADHD.

Alzheimer's disease: The state of the art in resting-state magnetoencephalography.

Alzheimer's disease (AD) is accompanied by functional brain changes that can be detected in imaging studies, including electromagnetic activity recorded with magnetoencephalography (MEG). Here, we systematically review the studies that have examined resting-state MEG changes in AD and identify areas that lack scientific or clinical progress. Three levels of MEG analysis will be covered: (i) single-channel signal analysis, (ii) pairwise analyses over time series, which includes the study of interdependencies between two time series and (iii) global network analyses. We discuss the findings in the light of other functional modalities, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Overall, single-channel MEG results show consistent changes in AD that are in line with EEG studies, but the full potential of the high spatial resolution of MEG and advanced functional connectivity and network analysis has yet to be fully exploited. Adding these features to the current knowledge will potentially aid in uncovering organizational patterns of brain function in AD and thereby aid the understanding of neuronal mechanisms leading to cognitive deficits.

Directional information flow in patients with Alzheimer's disease. A source-space resting-state MEG study.

In a recent magnetoencephalography (MEG) study, we found posterior-to-anterior information flow over the cortex in higher frequency bands in healthy subjects, with a reversed pattern in the theta band. A disruption of information flow may underlie clinical symptoms in Alzheimer's disease (AD). In AD, highly connected regions (hubs) in posterior areas are mostly disrupted. We therefore hypothesized that in AD the information flow from these hub regions would be disturbed. We used resting-state MEG recordings from 27 early-onset AD patients and 26 healthy controls. Using beamformer-based virtual electrodes, we estimated neuronal oscillatory activity for 78 cortical regions of interest (ROIs) and 12 subcortical ROIs of the AAL atlas, and calculated the directed phase transfer entropy (dPTE) as a measure of information flow between these ROIs. Group differences were evaluated using permutation tests and, for the AD group, associations between dPTE and general cognition or CSF biomarkers were determined using Spearman correlation coefficients. We confirmed the previously reported posterior-to-anterior information flow in the higher frequency bands in the healthy controls, and found it to be disturbed in the beta band in AD. Most prominently, the information flow from the precuneus and the visual cortex, towards frontal and subcortical structures, was decreased in AD. These disruptions did not correlate with cognitive impairment or CSF biomarkers. We conclude that AD pathology may affect the flow of information between brain regions, particularly from posterior hub regions, and that changes in the information flow in the beta band indicate an aspect of the pathophysiological process in AD.