Frequency-dependent functional connectivity within resting-state networks: an atlas-based MEG beamformer solution

Threat of shock increases excitability and connectivity of the intraparietal sulcus

Anxiety disorders affect approximately 1 in 5 (18%) Americans within a given 1 year period, placing a substantial burden on the national health care system. Therefore, there is a critical need to understand the neural mechanisms mediating anxiety symptoms. We used unbiased, multimodal, data-driven, whole-brain measures of neural activity (magnetoencephalography) and connectivity (fMRI) to identify the regions of the brain that contribute most prominently to sustained anxiety. We report that a single brain region, the intraparietal sulcus (IPS), shows both elevated neural activity and global brain connectivity during threat. The IPS plays a key role in attention orienting and may contribute to the hypervigilance that is a common symptom of pathological anxiety. Hyperactivation of this region during elevated state anxiety may account for the paradoxical facilitation of performance on tasks that require an external focus of attention, and impairment of performance on tasks that require an internal focus of attention.

DOI:http://dx.doi.org/10.7554/eLife.23608.001

Anxiety disorders affect around one in five Americans, and in many cases people experience anxiety so intensely that they have difficulties performing day-to-day activities. To help these people, it is important to understand how anxiety works. Current research suggests that anxiety disorders are caused when the connections in the brain that control our response to threat are either excessively or inappropriately activated. However, it was not clear what causes the anxiety to last for long periods. To better understand this phenomenon, Balderston et al. studied the brains of over 30 volunteers using two types of measurements called magnetoencephalography and fMRI.

In the each experiment, participants experienced periods of threat, where they could receive unpredictable electric shocks. In the first experiment, Balderston et al. measured the brain activity by recording the magnetic fields generated in the brain. In the second experiment, they used fMRI to record changes in the blood flow throughout the brain to measure how the different regions in the brain communicate.

The recordings identified a single part of the brain that increased its activity and changed its communication pattern with the other regions in the brain, when people are anxious. This region in a part of the brain called parietal lobe, is also important for processing attention, which suggests that anxiety might make people also more aware of their surroundings. However, this extra awareness might also make it more difficult for people to concentrate.

Future studies may be able to stimulate this area of the brain through the scalp to potentially reduce anxiety, as the affected area is close to the skull.

DOI:http://dx.doi.org/10.7554/eLife.23608.002

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.

Horizontal visibility graph transfer entropy (HVG-TE): A novel metric to characterize directed connectivity in large-scale brain networks

We propose a new measure, horizontal visibility graph transfer entropy (HVG-TE), to estimate the direction of information flow between pairs of time series. HVG-TE quantifies the transfer entropy between the degree sequences of horizontal visibility graphs derived from original time series. Twenty-one Rössler attractors unidirectionally coupled in the posterior-to-anterior direction were used to simulate 21-channel Electroencephalography (EEG) brain networks and validate the performance of the HVG-TE. We showed that the HVG-TE is robust to different levels of coupling strengths between the coupled Rössler attractors, a wide range of time delays, different sample sizes, the effects of noise and linear mixing, and the choice of reference for EEG data. We also applied HVG-TE to EEG data in 20 healthy controls and compared its performance to a recently introduces phase-based TE measure (PTE). We found that compared with PTE, HVG-TE consistently detected stronger posterior-to-anterior information flow patterns in the alpha-band (8-13Hz) EEG brain networks for three different references. Moreover, in contrast to PTE, HVG-TE does not require an assumption on the periodicity of input signals, therefore it can be more widely applicable, even for non-periodic signals. This study shows that the HVG-TE is a directed connectivity measure to characterise the direction of information flow in large-scale brain networks.

Changes in resting-state directed connectivity in cortico-subcortical networks correlate with cognitive function in Parkinson's disease

OBJECTIVE

The pathophysiological mechanisms underlying Parkinson's disease (PD)-related cognitive decline and conversion to PD dementia are poorly understood. In the healthy human brain, stable patterns of posterior-to-anterior cortical information flow have recently been demonstrated in the higher frequency bands using magnetoencephalography (MEG). In this study we estimated PD-related changes in information flow patterns, as well as the contribution of subcortical regions.

METHODS

Resting-state MEG recordings were acquired in moderately advanced PD patients (n=34; mean Hoehn and Yahr-stage 2.5) and healthy controls (n=12). MEG signals were projected to both cortical and subcortical brain regions, following which we estimated the balance between incoming and outgoing information flow per region.

RESULTS

In PD patients, compared to controls, preferential beta band information outflow was significantly higher for the basal ganglia and frontotemporal cortical regions, and significantly lower for parieto-occipital regions. In addition, in patients, low preferential information outflow from occipital regions correlated with poor global cognitive performance.

CONCLUSION

In the PD brain, a shift in balance towards more anterior-to-posterior beta band information flow takes place and is associated with poorer cognitive performance.

SIGNIFICANCE

Our results indicate that a reversal of the physiological posterior-to-anterior information flow may be an important mechanism in PD-related cognitive decline.

MEG Beamformer-Based Reconstructions of Functional Networks in Mild Cognitive Impairment

Subjects with mild cognitive impairment (MCI) have an increased risk of developing Alzheimer’s disease (AD), and their functional brain networks are presumably already altered. To test this hypothesis, we compared magnetoencephalography (MEG) eyes-closed resting-state recordings from 29 MCI subjects and 29 healthy elderly subjects in the present exploratory study. Functional connectivity in different frequency bands was assessed with the phase lag index (PLI) in source space. Normalized weighted clustering coefficient (normalized Cw) and path length (normalized Lw), as well as network measures derived from the minimum spanning tree [MST; i.e., betweenness centrality (BC) and node degree], were calculated. First, we found altered PLI values in the lower and upper alpha bands in MCI patients compared to controls. Thereafter, we explored network differences in these frequency bands. Normalized Cw and Lw did not differ between the groups, whereas BC and node degree of the MST differed, although these differences did not survive correction for multiple testing using the False Discovery Rate (FDR). As an exploratory study, we may conclude that: (1) the increases and decreases observed in PLI values in lower and upper alpha bands in MCI patients may be interpreted as a dual pattern of disconnection and aberrant functioning; (2) network measures are in line with connectivity findings, indicating a lower efficiency of the brain networks in MCI patients; (3) the MST centrality measures are more sensitive to detect subtle differences in the functional brain networks in MCI than traditional graph theoretical metrics.

Increased spontaneous MEG signal diversity for psychoactive doses of ketamine, LSD and psilocybin

What is the level of consciousness of the psychedelic state? Empirically, measures of neural signal diversity such as entropy and Lempel-Ziv (LZ) complexity score higher for wakeful rest than for states with lower conscious level like propofol-induced anesthesia. Here we compute these measures for spontaneous magnetoencephalographic (MEG) signals from humans during altered states of consciousness induced by three psychedelic substances: psilocybin, ketamine and LSD. For all three, we find reliably higher spontaneous signal diversity, even when controlling for spectral changes. This increase is most pronounced for the single-channel LZ complexity measure, and hence for temporal, as opposed to spatial, signal diversity. We also uncover selective correlations between changes in signal diversity and phenomenological reports of the intensity of psychedelic experience. This is the first time that these measures have been applied to the psychedelic state and, crucially, that they have yielded values exceeding those of normal waking consciousness. These findings suggest that the sustained occurrence of psychedelic phenomenology constitutes an elevated level of consciousness - as measured by neural signal diversity.

Dynamic hub load predicts cognitive decline after resective neurosurgery

Resective neurosurgery carries the risk of postoperative cognitive deterioration. The concept of ‘hub (over)load’, caused by (over)use of the most important brain regions, has been theoretically postulated in relation to symptomatology and neurological disease course, but lacks experimental confirmation. We investigated functional hub load and postsurgical cognitive deterioration in patients undergoing lesion resection. Patients (n = 28) underwent resting-state magnetoencephalography and neuropsychological assessments preoperatively and 1-year after lesion resection. We calculated stationary hub load score (SHub) indicating to what extent brain regions linked different subsystems; high SHub indicates larger processing pressure on hub regions. Dynamic hub load score (DHub) assessed its variability over time; low values, particularly in combination with high SHub values, indicate increased load, because of consistently high usage of hub regions. Hypothetically, increased SHub and decreased DHub relate to hub overload and thus poorer/deteriorating cognition. Between time points, deteriorating verbal memory performance correlated with decreasing upper alpha DHub. Moreover, preoperatively low DHub values accurately predicted declining verbal memory performance. In summary, dynamic hub load relates to cognitive functioning in patients undergoing lesion resection: postoperative cognitive decline can be tracked and even predicted using dynamic hub load, suggesting it may be used as a prognostic marker for tailored treatment planning.

Theta and high-beta networks for feedback processing: a simultaneous EEG-fMRI study in healthy male subjects

The reward system is important in assessing outcomes to guide behavior. To achieve these purposes, its core components interact with several brain areas involved in cognitive and emotional processing. A key mechanism suggested to subserve these interactions is oscillatory activity, with a prominent role of theta and high-beta oscillations. The present study used single-trial coupling of simultaneously recorded electroencephalography and functional magnetic resonance imaging data to investigate networks associated with oscillatory responses to feedback during a two-choice gambling task in healthy male participants (n=19). Differential associations of theta and high-beta oscillations with non-overlapping brain networks were observed: Increase of high-beta power in response to positive feedback was associated with activations in a largely subcortical network encompassing core areas of the reward network. In contrast, theta-band power increase upon loss was associated with activations in a frontoparietal network that included the anterior cingulate cortex. Trait impulsivity correlated significantly with activations in areas of the theta-associated network. Our results suggest that positive and negative feedback is processed by separate brain networks associated with different cognitive functions. Communication within these networks is mediated by oscillations of different frequency, possibly reflecting different modes of dopaminergic signaling.

Contrasting Effects of Posttraumatic Stress Disorder and Mild Traumatic Brain Injury on the Whole-Brain Resting-State Network: A Magnetoencephalography Study

The aim of this study was to evaluate alterations in whole-brain resting-state networks associated with posttraumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI). Networks were constructed from locations of peak statistical power on an individual basis from magnetoencephalography (MEG) source series data by applying the weighted phase lag index and surrogate data thresholding procedures. Networks representing activity in the alpha bandwidth as well as wideband activity (DC-80 Hz) were created. Statistical comparisons were adjusted for age and education level. Alpha network results demonstrate reductions in network structure associated with PTSD, but no differences associated with mTBI. Wideband network results demonstrate a shift in connectivity from the alpha to theta bandwidth in both PTSD and mTBI. Also, contrasting alterations in network structure are noted, with increased randomness associated with PTSD and increased structure associated with mTBI. These results demonstrate the potential of the analysis of MEG resting-state networks to differentiate two highly comorbid conditions. The importance of the alpha bandwidth to resting-state connectivity is also highlighted, while demonstrating the necessity of considering activity in other bandwidths during network construction.

Alterations of Intrinsic Brain Connectivity Patterns in Depression and Bipolar Disorders: A Critical Assessment of Magnetoencephalography-Based Evidence

Despite being the object of a thriving field of clinical research, the investigation of intrinsic brain network alterations in psychiatric illnesses is still in its early days. Because the pathological alterations are predominantly probed using functional magnetic resonance imaging (fMRI), many questions about the electrophysiological bases of resting-state alterations in psychiatric disorders, particularly among mood disorder patients, remain unanswered. Alongside important research using electroencephalography (EEG), the specific recent contributions and future promise of magnetoencephalography (MEG) in this field are not fully recognized and valued. Here, we provide a critical review of recent findings from MEG resting-state connectivity within major depressive disorder (MDD) and bipolar disorder (BD). The clinical MEG resting-state results are compared with those previously reported with fMRI and EEG. Taken together, MEG appears to be a promising but still critically underexploited technique to unravel the neurophysiological mechanisms that mediate abnormal (both hyper- and hypo-) connectivity patterns involved in MDD and BD. In particular, a major strength of MEG is its ability to provide source-space estimations of neuromagnetic long-range rhythmic synchronization at various frequencies (i.e., oscillatory coupling). The reviewed literature highlights the relevance of probing local and interregional rhythmic synchronization to explore the pathophysiological underpinnings of each disorder. However, before we can fully take advantage of MEG connectivity analyses in psychiatry, several limitations inherent to MEG connectivity analyses need to be understood and taken into account. Thus, we also discuss current methodological challenges and outline paths for future research. MEG resting-state studies provide an important window onto perturbed spontaneous oscillatory brain networks and hence supply an important complement to fMRI-based resting-state measurements in psychiatric populations.

Relation between carotid stiffness, cognitive performance and brain connectivity in a healthy middle-aged population: an observational neurophysiological cohort study with magnetoencephalography

OBJECTIVE

Impaired blood flow of the carotid artery can result in cognitive impairment, but how these vascular impairments lead to global cognitive disturbances is largely unknown. Problems in functional connectivity between brain areas may be responsible for these widespread effects. Therefore, the aim of this study was to examine the association between carotid stiffness, functional connectivity and cognitive performance in relatively young and healthy adults before clinical vascular pathology occurs.

DESIGN

The Amsterdam Growth and Health Longitudinal Study: an observational study.

SETTING

Participants were included by attending 1 of the 2 selected secondary schools in The Netherlands.

PARTICIPANTS

Men (n=110) and women (n=120) aged 41-44 years (42±0.7).

PRIMARY AND SECONDARY OUTCOME MEASURES

Data were obtained with regard to local carotid stiffness captured measured with the Young's elastic modulus (YEM). All participants underwent a commonly used Dutch intelligence test and resting-state eyes-closed magnetoencephalography (MEG). Five artefact-free epochs were analysed. The phase lag index (PLI) was used as a measure of functional connectivity between all sensors and was assessed in six frequency bands (δ-γ).

RESULTS

Carotid stiffness was significantly associated with increased functional connectivity in the α2 band in men (β: 0.287; p=0.008). The same results were found for women in the β band (β: 0.216; p=0.040). Furthermore, carotid stiffness was associated with superior cognitive function in men (β: 0.238; p=0.007). In addition, there was neither a significant association nor a consistent pattern between cognitive function and functional connectivity.

CONCLUSIONS

The increased connectivity might be a maladaptive phenomenon caused by disinhibition of neurons which may explain the direction of the results. This study suggests that detection of increased (local) carotid stiffness may be promising to identify a disturbance in the organisation of the functional brain network, even before clinical vascular pathology occurs.

Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach

OBJECTIVE

Combined source-imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a noninvasive fashion. Source-imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source-imaging algorithms to both find the network nodes [regions of interest (ROI)] and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses, and apply Granger analysis on the extracted series to study brain networks under realistic conditions.

METHODS

Source-imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from interictal and ictal signals recorded by EEG and/or Magnetoencephalography (MEG).

RESULTS

Localization errors of network nodes are less than 5 mm and normalized connectivity errors of ∼20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection.

CONCLUSION

Our study indicates that combined source-imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity).

SIGNIFICANCE

The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions.

Identifying the epileptogenic zone in interictal resting-state MEG source-space networks

OBJECTIVE

In one third of patients, seizures remain after epilepsy surgery, meaning that improved preoperative evaluation methods are needed to identify the epileptogenic zone. A potential framework for such a method is network theory, as it can be applied to noninvasive recordings, even in the absence of epileptiform activity. Our aim was to identify the epileptogenic zone on the basis of hub status of local brain areas in interictal magnetoencephalography (MEG) networks.

METHODS

Preoperative eyes-closed resting-state MEG recordings were retrospectively analyzed in 22 patients with refractory epilepsy, of whom 14 were seizure-free 1 year after surgery. Beamformer-based time series were reconstructed for 90 cortical and subcortical automated anatomic labeling (AAL) regions of interest (ROIs). Broadband functional connectivity was estimated using the phase lag index in artifact-free epochs without interictal epileptiform abnormalities. A minimum spanning tree was generated to represent the network, and the hub status of each ROI was calculated using betweenness centrality, which indicates the centrality of a node in a network. The correspondence of resection cavity to hub values was evaluated on four levels: resection cavity, lobar, hemisphere, and temporal versus extratemporal areas.

RESULTS

Hubs were localized within the resection cavity in 8 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (57% sensitivity, 100% specificity, 73% accuracy). Hubs were localized in the lobe of resection in 9 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (64% sensitivity, 100% specificity, 77% accuracy). For the other two levels, the true negatives are unknown; hence, only sensitivity could be determined: hubs coincided with both the resection hemisphere and the resection location (temporal versus extratemporal) in 11 of 14 seizure-free patients (79% sensitivity).

SIGNIFICANCE

Identifying hubs noninvasively before surgery is a valuable approach with the potential of indicating the epileptogenic zone in patients without interictal abnormalities.

Seizure Onset Zone Localization from Ictal High-Density EEG in Refractory Focal Epilepsy

Epilepsy surgery is the most efficient treatment option for patients with refractory epilepsy. Before surgery, it is of utmost importance to accurately delineate the seizure onset zone (SOZ). Non-invasive EEG is the most used neuroimaging technique to diagnose epilepsy, but it is hard to localize the SOZ from EEG due to its low spatial resolution and because epilepsy is a network disease, with several brain regions becoming active during a seizure. In this work, we propose and validate an approach based on EEG source imaging (ESI) combined with functional connectivity analysis to overcome these problems. We considered both simulations and real data of patients. Ictal epochs of 204-channel EEG and subsets down to 32 channels were analyzed. ESI was done using realistic head models and LORETA was used as inverse technique. The connectivity pattern between the reconstructed sources was calculated, and the source with the highest number of outgoing connections was selected as SOZ. We compared this algorithm with a more straightforward approach, i.e. selecting the source with the highest power after ESI as the SOZ. We found that functional connectivity analysis estimated the SOZ consistently closer to the simulated EZ/RZ than localization based on maximal power. Performance, however, decreased when 128 electrodes or less were used, especially in the realistic data. The results show the added value of functional connectivity analysis for SOZ localization, when the EEG is obtained with a high-density setup. Next to this, the method can potentially be used as objective tool in clinical settings.

Integrating cross-frequency and within band functional networks in resting-state MEG: A multi-layer network approach

Neuronal oscillations exist across a broad frequency spectrum, and are thought to provide a mechanism of interaction between spatially separated brain regions. Since ongoing mental activity necessitates the simultaneous formation of multiple networks, it seems likely that the brain employs interactions within multiple frequency bands, as well as cross-frequency coupling, to support such networks. Here, we propose a multi-layer network framework that elucidates this pan-spectral picture of network interactions. Our network consists of multiple layers (frequency-band specific networks) that influence each other via inter-layer (cross-frequency) coupling. Applying this model to MEG resting-state data and using envelope correlations as connectivity metric, we demonstrate strong dependency between within layer structure and inter-layer coupling, indicating that networks obtained in different frequency bands do not act as independent entities. More specifically, our results suggest that frequency band specific networks are characterised by a common structure seen across all layers, superimposed by layer specific connectivity, and inter-layer coupling is most strongly associated with this common mode. Finally, using a biophysical model, we demonstrate that there are two regimes of multi-layer network behaviour; one in which different layers are independent and a second in which they operate highly dependent. Results suggest that the healthy human brain operates at the transition point between these regimes, allowing for integration and segregation between layers. Overall, our observations show that a complete picture of global brain network connectivity requires integration of connectivity patterns across the full frequency spectrum.

Relationships between cortical myeloarchitecture and electrophysiological networks

The human brain relies upon the dynamic formation and dissolution of a hierarchy of functional networks to support ongoing cognition. However, how functional connectivities underlying such networks are supported by cortical microstructure remains poorly understood. Recent animal work has demonstrated that electrical activity promotes myelination. Inspired by this, we test a hypothesis that gray-matter myelin is related to electrophysiological connectivity. Using ultra-high field MRI and the principle of structural covariance, we derive a structural network showing how myelin density differs across cortical regions and how separate regions can exhibit similar myeloarchitecture. Building upon recent evidence that neural oscillations mediate connectivity, we use magnetoencephalography to elucidate networks that represent the major electrophysiological pathways of communication in the brain. Finally, we show that a significant relationship exists between our functional and structural networks; this relationship differs as a function of neural oscillatory frequency and becomes stronger when integrating oscillations over frequency bands. Our study sheds light on the way in which cortical microstructure supports functional networks. Further, it paves the way for future investigations of the gray-matter structure/function relationship and its breakdown in pathology.

Magnetoencephalography for the Detection of Intervention Effects of a Specific Nutrient Combination in Patients with Mild Alzheimer's Disease: Results from an Exploratory Double-Blind, Randomized, Controlled Study

Synaptic loss is an early pathological finding in Alzheimer’s disease (AD) and correlates with memory impairment. Changes in macroscopic brain activity measured with electro- and magnetoencephalography (EEG and MEG) in AD indicate synaptic changes and may therefore serve as markers of intervention effects in clinical trials. EEG peak frequency and functional networks have shown, in addition to improved memory performance, to be sensitive to detect an intervention effect in mild AD patients of the medical food Souvenaid containing the specific nutrient combination Fortasyn^® Connect, which is designed to enhance synapse formation and function. Here, we explore the value of MEG, with higher spatial resolution than EEG, in identifying intervention effects of the nutrient combination by comparing MEG spectral measures, functional connectivity, and networks between an intervention and a control group. Quantitative markers describing spectral properties, functional connectivity, and graph theoretical aspects of MEG from the exploratory 24-week, double-blind, randomized, controlled Souvenir II MEG sub-study (NTR1975, http://www.trialregister.nl) in drug naïve patients with mild AD were compared between a test group (n = 27), receiving Souvenaid, and a control group (n = 28), receiving an isocaloric control product. The groups were unbalanced at screening with respect to Mini-Mental State Examination. Peak frequencies of MEG were compared with EEG peak frequencies, recorded in the same patients at similar time points, were compared with respect to sensitivity to intervention effects. No consistent statistically significant intervention effects were detected. In addition, we found no difference in sensitivity between MEG and EEG peak frequency. This exploratory study could not unequivocally establish the value of MEG in detecting interventional effects on brain activity, possibly due to small sample size and unbalanced study groups. We found no indication that the difference could be attributed to a lack of sensitivity of MEG compared with EEG. MEG in randomized controlled trials is feasible but its value to disclose intervention effects of Souvenaid in mild AD patients needs to be studied further.

Beta-band activity and connectivity in sensorimotor and parietal cortex are important for accurate motor performance

Accurate motor performance may depend on the scaling of distinct oscillatory activity within the motor cortex and effective neural communication between the motor cortex and other brain areas. Oscillatory activity within the beta-band (13-30Hz) has been suggested to provide distinct functional roles for attention and sensorimotor control, yet it remains unclear how beta-band and other oscillatory activity within and between cortical regions is coordinated to enhance motor performance. We explore this open issue by simultaneously measuring high-density cortical activity and elbow flexor and extensor neuromuscular activity during ballistic movements, and manipulating error using high and low visual gain across three target distances. Compared with low visual gain, high visual gain decreased movement errors at each distance. Group analyses in 3D source-space revealed increased theta-, alpha-, and beta-band desynchronization of the contralateral motor cortex and medial parietal cortex in high visual gain conditions and this corresponded to reduced movement error. Dynamic causal modeling was used to compute connectivity between motor cortex and parietal cortex. Analyses revealed that gain affected the directionally-specific connectivity across broadband frequencies from parietal to sensorimotor cortex but not from sensorimotor cortex to parietal cortex. These new findings provide support for the interpretation that broad-band oscillations in theta, alpha, and beta frequency bands within sensorimotor and parietal cortex coordinate to facilitate accurate upper limb movement.

SUMMARY STATEMENT

Our findings establish a link between sensorimotor oscillations in the context of online motor performance in common source space across subjects. Specifically, the extent and distinct role of medial parietal cortex to sensorimotor beta connectivity and local domain broadband activity combine in a time and frequency manner to assist ballistic movements. These findings can serve as a model to examine whether similar source space EEG dynamics exhibit different time-frequency changes in individuals with neurological disorders that cause movement errors.

Measurement of dynamic task related functional networks using MEG

The characterisation of dynamic electrophysiological brain networks, which form and dissolve in order to support ongoing cognitive function, is one of the most important goals in neuroscience. Here, we introduce a method for measuring such networks in the human brain using magnetoencephalography (MEG). Previous network analyses look for brain regions that share a common temporal profile of activity. Here distinctly, we exploit the high spatio-temporal resolution of MEG to measure the temporal evolution of connectivity between pairs of parcellated brain regions. We then use an ICA based procedure to identify networks of connections whose temporal dynamics covary. We validate our method using MEG data recorded during a finger movement task, identifying a transient network of connections linking somatosensory and primary motor regions, which modulates during the task. Next, we use our method to image the networks which support cognition during a Sternberg working memory task. We generate a novel neuroscientific picture of cognitive processing, showing the formation and dissolution of multiple networks which relate to semantic processing, pattern recognition and language as well as vision and movement. Our method tracks the dynamics of functional connectivity in the brain on a timescale commensurate to the task they are undertaking.

•

A method is developed to track dynamic electrophysiological networks using MEG.

•

Method based on ICA applied to timecourses measuring evolution of connectivity.

•

Method allows a unique picture of transient networks that support cognition.

•

Method validated in MEG data recorded during a Sternberg working memory task.

•

Sensory networks observed include visual and sensorimotor.

•

Cognitive networks relate to semantic processing, pattern recognition and language.

On the Potential of a New Generation of Magnetometers for MEG: A Beamformer Simulation Study

Magnetoencephalography (MEG) is a sophisticated tool which yields rich information on the spatial, spectral and temporal signatures of human brain function. Despite unique potential, MEG is limited by a low signal-to-noise ratio (SNR) which is caused by both the inherently small magnetic fields generated by the brain, and the scalp-to-sensor distance. The latter is limited in current systems due to a requirement for pickup coils to be cryogenically cooled. Recent work suggests that optically-pumped magnetometers (OPMs) might be a viable alternative to superconducting detectors for MEG measurement. They have the advantage that sensors can be brought to within ~4 mm of the scalp, thus offering increased sensitivity. Here, using simulations, we quantify the advantages of hypothetical OPM systems in terms of sensitivity, reconstruction accuracy and spatial resolution. Our results show that a multi-channel whole-head OPM system offers (on average) a fivefold improvement in sensitivity for an adult brain, as well as clear improvements in reconstruction accuracy and spatial resolution. However, we also show that such improvements depend critically on accurate forward models; indeed, the reconstruction accuracy of our simulated OPM system only outperformed that of a simulated superconducting system in cases where forward field error was less than 5%. Overall, our results imply that the realisation of a viable whole-head multi-channel OPM system could generate a step change in the utility of MEG as a means to assess brain electrophysiological activity in health and disease. However in practice, this will require both improved hardware and modelling algorithms.

Longitudinal Changes in Depressive Circuitry in Response to Neuromodulation Therapy

Background: Major depressive disorder (MDD) is a public health problem worldwide. There is increasing interest in using non-invasive therapies such as repetitive transcranial magnetic stimulation (rTMS) to treat MDD. However, the changes induced by rTMS on neural circuits remain poorly characterized. The present study aims to test whether the brain regions previously targeted by deep brain stimulation (DBS) in the treatment of MDD respond to rTMS, and whether functional connectivity (FC) measures can predict clinical response.

Methods: rTMS (20 sessions) was administered to five MDD patients at the left-dorsolateral prefrontal cortex (L-DLPFC) over 4 weeks. Magnetoencephalography (MEG) recordings and Montgomery-Asberg depression rating scale (MADRS) assessments were acquired before, during and after treatment. Our primary measures, obtained with MEG source imaging, were changes in power spectral density (PSD) and changes in FC as measured using coherence.

Results: Of the five patients, four met the clinical response criterion (40% or greater decrease in MADRS) after 4 weeks of treatment. An increase in gamma power at the L-DLPFC was correlated with improvement in symptoms. We also found that increases in delta band connectivity between L-DLPFC/amygdala and L-DLPFC/pregenual anterior cingulate cortex (pACC), and decreases in gamma band connectivity between L-DLPFC/subgenual anterior cingulate cortex (sACC), were correlated with improvements in depressive symptoms.

Conclusions: Our results suggest that non-invasive intervention techniques, such as rTMS, modulate the ongoing activity of depressive circuits targeted for DBS, and that MEG can capture these changes. Gamma oscillations may originate from GABA-mediated inhibition, which increases synchronization of large neuronal populations, possibly leading to increased long-range FC. We postulate that responses to rTMS could provide valuable insights into early evaluation of patient candidates for DBS surgery.

Magnetoencephalography-based identification of functional connectivity network disruption following mild traumatic brain injury

Mild traumatic brain injury (mTBI) leads to long-term cognitive sequelae in a significant portion of patients. Disruption of normal neural communication across functional brain networks may explain the deficits in memory and attention observed after mTBI. In this study, we used magnetoencephalography (MEG) to examine functional connectivity during a resting state in a group of mTBI subjects (n = 9) compared with age-matched control subjects (n = 15). We adopted a data-driven, exploratory analysis in source space using phase locking value across different frequency bands. We observed a significant reduction in functional connectivity in band-specific networks in mTBI compared with control subjects. These networks spanned multiple cortical regions involved in the default mode network (DMN). The DMN is thought to subserve memory and attention during periods when an individual is not engaged in a specific task, and its disruption may lead to cognitive deficits after mTBI. We further applied graph theoretical analysis on the functional connectivity matrices. Our data suggest reduced local efficiency in different brain regions in mTBI patients. In conclusion, MEG can be a potential tool to investigate and detect network alterations in patients with mTBI. The value of MEG to reveal potential neurophysiological biomarkers for mTBI patients warrants further exploration.

Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints

The human brain can be parcellated into diverse anatomical areas. We investigated whether rhythmic brain activity in these areas is characteristic and can be used for automatic classification. To this end, resting-state MEG data of 22 healthy adults was analysed. Power spectra of 1-s long data segments for atlas-defined brain areas were clustered into spectral profiles (“fingerprints”), using k-means and Gaussian mixture (GM) modelling. We demonstrate that individual areas can be identified from these spectral profiles with high accuracy. Our results suggest that each brain area engages in different spectral modes that are characteristic for individual areas. Clustering of brain areas according to similarity of spectral profiles reveals well-known brain networks. Furthermore, we demonstrate task-specific modulations of auditory spectral profiles during auditory processing. These findings have important implications for the classification of regional spectral activity and allow for novel approaches in neuroimaging and neurostimulation in health and disease.

Oscillatory activity in anatomically defined brain areas is organized according to several different spectral modes; these modes are characteristic and can be used for automatic classification.

Quantifying the Test-Retest Reliability of Magnetoencephalography Resting-State Functional Connectivity

The coordinated activity of the resting-state brain can be evaluated with magnetoencephalography (MEG) for distinct brain rhythms by performing source reconstruction to estimate the activities of target brain regions and employing one of the many existent functional connectivity (FC) algorithms. Although this procedure has been applied in a great amount of studies both with healthy and pathological populations, the reliability of such FC estimates is unknown, and this impairs the use of resting-state MEG FC at the individual level. In this study, the test-retest reliability of MEG resting FC was evaluated by exploring both within- and between-subject variability in FC in 16 healthy subjects who underwent three resting-state MEG scans. FC was computed after beamforming source reconstruction with four popular FC metrics: phase-locking value (PLV), phase lag index (PLI), direct envelope correlation (d-ecor), and envelope correlation with leakage correction (lc-ecor). Then, test-restest reliability and within- and between-subject agreement were evaluated with the intraclass correlation coefficient (ICC) and Kendall's W, respectively. Reliability was found to depend on the FC metric, the frequency band, and the specific link. As a general trend, greater test-retest reliability was found for PLV in theta to gamma, and for lc-ecor and d-ecor in beta. Further inspection of the ICC distribution revealed that volume conduction effects could be contributing to high ICC in PLV and d-ecor. In addition, stronger links were found to be more reliable. Overall, this encourages the further use of resting-state MEG FC for individual-level studies, especially with PLV or envelope correlation metrics.

Mid-Task Break Improves Global Integration of Functional Connectivity in Lower Alpha Band

Numerous efforts have been devoted to revealing neurophysiological mechanisms of mental fatigue, aiming to find an effective way to reduce the undesirable fatigue-related outcomes. Until recently, mental fatigue is thought to be related to functional dysconnectivity among brain regions. However, the topological representation of brain functional connectivity altered by mental fatigue is only beginning to be revealed. In the current study, we applied a graph theoretical approach to analyse such topological alterations in the lower alpha band (8~10 Hz) of EEG data from 20 subjects undergoing a two-session experiment, in which one session includes four successive blocks with visual oddball tasks (session 1) whereas a mid-task break was introduced in the middle of four task blocks in the other session (session 2). Phase lag index (PLI) was then employed to measure functional connectivity strengths for all pairs of EEG channels. Behavior and connectivity maps were compared between the first and last task blocks in both sessions. Inverse efficiency scores (IES = reaction time/response accuracy) were significantly increased in the last task block, showing a clear effect of time-on-task in participants. Furthermore, a significant block-by-session interaction was revealed in the IES, suggesting the effectiveness of the mid-task break on maintaining task performance. More importantly, a significant session-independent deficit of global integration and an increase of local segregation were found in the last task block across both sessions, providing further support for the presence of a reshaped topology in functional brain connectivity networks under fatigue state. Moreover, a significant block-by-session interaction was revealed in the characteristic path length, small-worldness, and global efficiency, attributing to the significantly disrupted network topology in session 1 in comparison of the maintained network structure in session 2. Specifically, we found increased nodal betweenness centrality in several channels resided in frontal regions in session 1, resembling the observations of more segregated global architecture under fatigue state. Taken together, our findings provide insights into the substrates of brain functional dysconnectivity patterns for mental fatigue and reiterate the effectiveness of the mid-task break on maintaining brain network efficiency.

Slowing of Hippocampal Activity Correlates with Cognitive Decline in Early Onset Alzheimer's Disease. An MEG Study with Virtual Electrodes

Pathology in Alzheimer's disease (AD) starts in the entorhinal cortex and hippocampus. Because of their deep location, activity from these areas is difficult to record with conventional electro- or magnetoencephalography (EEG/MEG). The purpose of this study was to explore hippocampal activity in AD patients and healthy controls using "virtual MEG electrodes". We used resting-state MEG recordings from 27 early onset AD patients [age 60.6 ± 5.4, 12 females, mini-mental state examination (MMSE) range: 19-28] and 26 cognitively healthy age- and gender-matched controls (age 61.8 ± 5.5, 14 females). Activity was reconstructed using beamformer-based virtual electrodes for 78 cortical regions and 6 hippocampal regions. Group differences in peak frequency and relative power in six frequency bands were identified using permutation testing. For the patients, spearman correlations between the MMSE scores and peak frequency or relative power were calculated. Moreover, receiver operator characteristic curves were plotted to estimate the diagnostic accuracy. We found a lower hippocampal peak frequency in AD compared to controls, which, in the patients, correlated positively with MMSE [r(25) = 0.61; p < 0.01] whereas hippocampal relative theta power correlated negatively with MMSE [r(25) = -0.54; p < 0.01]. Cortical peak frequency was also lower in AD in association areas. Furthermore, cortical peak frequency correlated positively with MMSE [r(25) = 0.43; p < 0.05]. In line with this finding, relative theta power was higher in AD across the cortex, and relative alpha and beta power was lower in more circumscribed areas. The average cortical relative theta power was the best discriminator between AD and controls (sensitivity 82%; specificity 81%). Using beamformer-based virtual electrodes, we were able to detect hippocampal activity in AD. In AD, this hippocampal activity is slowed, and correlates better with cognition than the (slowed) activity in cortical areas. On the other hand, the average cortical relative power in the theta band was shown to be the best diagnostic discriminator. We postulate that this novel approach using virtual electrodes can be used in future research to quantify functional interactions between the hippocampi and cortical areas.

Preoperative evaluation using magnetoencephalography: Experience in 382 epilepsy patients

OBJECTIVE

Identifying epilepsy patients for whom clinical MEG is likely to be beneficial avoids or optimizes burdensome ancillary investigations. We determined whether it could be predicted upfront if MEG would be able to generate a hypothesis about the location of the epileptogenic zone (EZ), and in which patients MEG fails to do so.

METHODS

MEG recordings of 382 epilepsy patients with inconclusive findings regarding EZ localization prior to MEG were acquired for preoperative evaluation. MEG reports were categorized for several demographic, clinical and MEG variables. First, demographic and clinical variables were associated with MEG localization ability for upfront prediction. Second, all variables were compared between patients with and without MEG location in order to characterize patients without MEG location.

RESULTS

Our patient group had often complex etiology and did not contain the (by other means) straightforward and well-localized cases, such as those with concordant tumor and EEG location. For our highly-selected patient group, MEG localization ability cannot be predicted upfront, although the odds of a recording with MEG location were significantly higher in the absence of a tumor and in the presence of widespread MRI abnormalities. Compared to the patients with MEG location, patients without MEG location more often had a tumor, widespread EEG abnormalities, non-lateralizing MEG abnormalities, non-concordant MEG/EEG abnormalities and less often widespread MRI abnormalities or epileptiform MEG activity. In a subgroup of 48 patients with known surgery outcome, more patients with concordant MEG and resection area were seizure-free than patients with discordant results.

CONCLUSIONS

MEG potentially adds information about the location of the EZ even in patients with a complex etiology, and the clinical advice is to not withhold MEG in epilepsy surgery candidates. Providing a hypothesis about the location of the EZ using MEG is difficult in patients with inconclusive EEG and MRI findings, and in the absence of specific epileptiform activity. More refined methods are needed for patients where MEG currently does not contribute to the hypothesis about the location of the EZ.

Regional Patterns of Cortical Phase Synchrony in the Resting State

Synchronized phase estimates between oscillating neuronal signals at the macroscale level reflect coordinated activities between neuronal assemblies. Recent electrophysiological evidence suggests the presence of significant spontaneous phase synchrony within the resting state. The purpose of this study was to investigate phase synchrony, including directional interactions, in resting state subdural electrocorticographic recordings to better characterize patterns of regional phase interactions across the lateral cortical surface during the resting state. We estimated spontaneous phase locking value (PLV) as a measure of functional connectivity, and phase slope index (PSI) as a measure of pseudo-causal phase interactions, across a broad range of canonical frequency bands and the modulation of the amplitude envelope of high gamma (amHG), a band that is believed to best reflect the physiological processes giving rise to the functional magnetic resonance imaging BOLD signal. Long-distance interactions had higher PLVs in slower frequencies (≤theta) than in higher ones (≥beta) with amHG behaving more like slow frequencies, and a general trend of increasing frequency band of significant PLVs when moving across the lateral surface along an anterior-posterior axis. Moreover, there was a strong trend of frontal-to-parietal directional phase synchronization, measured by PSI across multiple frequencies. These findings, which are likely indicative of coordinated and structured spontaneous cortical interactions, are important in the study of time scales and directional nature of resting state functional connectivity, and may ultimately contribute to a better understanding of how spontaneous synchrony is linked to variation in regional architecture across the lateral cortical surface.

Direction of information flow in large-scale resting-state networks is frequency-dependent

Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these interactions, where directionality was inferred from time series of beamformer-reconstructed estimates of neuronal activation, using a recently proposed measure of phase transfer entropy. We observed well-organized posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha1, alpha2, and beta band), dominated by regions in the visual cortex and posterior default mode network. Opposite patterns of anterior-to-posterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network. Many strong information senders in the theta band were also frequent receivers in the alpha2 band, and vice versa. Our results provide evidence that large-scale resting-state patterns of information flow in the human brain form frequency-dependent reentry loops that are dominated by flow from parieto-occipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow.

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.

Task-positive and task-negative networks and their relation to depression: EEG beamformer analysis

Major Depressive Disorder (MDD) has been associated with predominance of the default-mode network (DMN) over the task-positive network (TPN), which is considered a neurobiological base for ruminative responding. It is not known whether this predominance is a signature of the full-blown MDD or it already exists at preclinical stages. Besides, all relevant evidence has been obtained using fMRI, which allows for a precise spatial characterization of resting state networks (RSNs), but their neural correlates remain elusive. Here we show that after leakage correction of beamformer-projected resting EEG time series, seed-based oscillatory-power envelope correlation analysis allows revealing RSNs with significant similarity to respective fMRI RSNs. In a non-clinical sample, depressive symptoms, as measured by the Beck Depression Inventory, are associated with predominance of DMN over TPN connectivity in the right insula and the right temporal lobe in the delta frequency band. These findings imply that in individuals with heightened level of depressive symptoms, emotional circuits are stronger connected with DMN than TPN and should be more easily engaged in self-referential rumination than in responding to environmental challenges. The study's findings are in agreement with fMRI evidence, thus confirming the neural base of the observed in fMRI research effects and showing that implicated in depression neural mechanism may already be in action even at preclinical stages.

EEG-directed connectivity from posterior brain regions is decreased in dementia with Lewy bodies: a comparison with Alzheimer's disease and controls

Directed information flow between brain regions might be disrupted in dementia with Lewy bodies (DLB) and relate to the clinical syndrome of DLB. To investigate this hypothesis, resting-state electroencephalography recordings were obtained in patients with probable DLB and Alzheimer's disease (AD), and controls (N = 66 per group, matched for age and gender). Phase transfer entropy was used to measure directed connectivity in the groups for the theta, alpha, and beta frequency band. A posterior-to-anterior phase transfer entropy gradient, with occipital channels driving the frontal channels, was found in controls in all frequency bands. This posterior-to-anterior gradient was largely lost in DLB in the alpha band (p < 0.05). In the beta band, posterior brain regions were less driving in information flow in AD than in DLB and controls. In conclusion, the common posterior-to-anterior pattern of directed connectivity in controls is disturbed in DLB patients in the alpha band, and in AD patients in the beta band. Disrupted alpha band-directed connectivity may underlie the clinical syndrome of DLB and differentiate between DLB and AD.

Modeling distinct imaging hemodynamics early after TBI: the relationship between signal amplitude and connectivity

Over the past decade, fMRI studies of cognitive change following traumatic brain injury (TBI) have investigated blood oxygen level dependent (BOLD) activity during working memory (WM) performance in individuals in early and chronic phases of recovery. Recently, BOLD fMRI work has largely shifted to focus on WM and resting functional connectivity following TBI. However, fundamental questions in WM remain. Specifically, the effects of injury on the basic relationships between local and interregional functional neuroimaging signals during WM processing early following moderate to severe TBI have not been examined. This study employs a mixed effects model to examine prefrontal cortex and parietal lobe signal change during a WM task, the n-back, and whether there is covariance between regions of high amplitude signal change, (synchrony of elicited activity (SEA) very early following TBI. We also examined whether signal change and SEA differentially predict performance during WM. Overall, percent signal change in the right prefrontal cortex (rPFC) was and important predictor of both reaction time (RT) and SEA in early TBI and matched controls. Right prefrontal cortex (rPFC) percent signal change positively predicted SEA within and between persons regardless of injury status, suggesting that the link between these neurodynamic processes in WM-activated regions remains unaffected even very early after TBI. Additionally, rPFC activity was positively related to RT within and between persons in both groups. Right parietal (rPAR) activity was negatively related to RT within subjects in both groups. Thus, the local signal intensity of the rPFC in TBI appears to be a critical property of network functioning and performance in WM processing and may be a precursor to recruitment observed in chronic samples. The present results suggest that as much research moves toward large scale functional connectivity modeling, it will be essential to develop integrated models of how local and distant neurodynamics promote WM performance after TBI.

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.

•

We introduce a mathematical model to predict fMRI-based RSNs using MEG.

•

Our model is based on a multi-variate Taylor series expansion.

•

The electrophysiological basis of RSNs goes beyond frequency-band specific analysis.

•

RSNs result 1) from multiple frequency bands and cross-frequency coupling.

•

RSNs result 2) from direct and shared electrophysiological connectivity.

Directional patterns of cross frequency phase and amplitude coupling within the resting state mimic patterns of fMRI functional connectivity

Functional imaging investigations into the brain's resting state interactions have yielded a wealth of insight into the intrinsic and dynamic neural architecture supporting cognition and behavior. Electrophysiological studies however have highlighted the fact that synchrony across large-scale cortical systems is composed of spontaneous interactions occurring at timescales beyond the traditional resolution of fMRI, a feature that limits the capacity of fMRI to draw inference on the true directional relationship between network nodes. To approach the question of directionality in resting state signals, we recorded resting state functional MRI (rsfMRI) and electrocorticography (ECoG) from four human subjects undergoing invasive epilepsy monitoring. Using a seed-point based approach, we employed phase-amplitude coupling (PAC) and biPhase Locking Values (bPLV), two measures of cross-frequency coupling (CFC) to explore both outgoing and incoming connections between the seed and all non-seed, site electrodes. We observed robust PAC between a wide range of low-frequency phase and high frequency amplitude estimates. However, significant bPLV, a CFC measure of phase-phase synchrony, was only observed at specific narrow low and high frequency bandwidths. Furthermore, the spatial patterns of outgoing PAC connectivity were most closely associated with the rsfMRI connectivity maps. Our results support the hypothesis that PAC is relatively ubiquitous phenomenon serving as a mechanism for coordinating high-frequency amplitudes across distant neuronal assemblies even in absence of overt task structure. Additionally, we demonstrate that the spatial distribution of a seed-point rsfMRI sensorimotor network is strikingly similar to specific patterns of directional PAC. Specifically, the high frequency activities of distal patches of cortex owning membership in a rsfMRI sensorimotor network were most likely to be entrained to the phase of a low frequency rhythm engendered from the neural populations at the seed-point, suggestive of greater directional coupling from the seed out to the site electrodes.

Closely Spaced MEG Source Localization and Functional Connectivity Analysis Using a New Prewhitening Invariance of Noise Space Algorithm

This paper proposed a prewhitening invariance of noise space (PW-INN) as a new magnetoencephalography (MEG) source analysis method, which is particularly suitable for localizing closely spaced and highly correlated cortical sources under real MEG noise. Conventional source localization methods, such as sLORETA and beamformer, cannot distinguish closely spaced cortical sources, especially under strong intersource correlation. Our previous work proposed an invariance of noise space (INN) method to resolve closely spaced sources, but its performance is seriously degraded under correlated noise between MEG sensors. The proposed PW-INN method largely mitigates the adverse influence of correlated MEG noise by projecting MEG data to a new space defined by the orthogonal complement of dominant eigenvectors of correlated MEG noise. Simulation results showed that PW-INN is superior to INN, sLORETA, and beamformer in terms of localization accuracy for closely spaced and highly correlated sources. Lastly, source connectivity between closely spaced sources can be satisfactorily constructed from source time courses estimated by PW-INN but not from results of other conventional methods. Therefore, the proposed PW-INN method is a promising MEG source analysis to provide a high spatial-temporal characterization of cortical activity and connectivity, which is crucial for basic and clinical research of neural plasticity.

Multimodal description of whole brain connectivity: A comparison of resting state MEG, fMRI, and DWI

Structural and functional connectivity (SC and FC) have received much attention over the last decade, as they offer unique insight into the coordination of brain functioning. They are often assessed independently with three imaging modalities: SC using diffusion‐weighted imaging (DWI), FC using functional magnetic resonance imaging (fMRI), and magnetoencephalography/electroencephalography (MEG/EEG). DWI provides information about white matter organization, allowing the reconstruction of fiber bundles. fMRI uses blood‐oxygenation level‐dependent (BOLD) contrast to indirectly map neuronal activation. MEG and EEG are direct measures of neuronal activity, as they are sensitive to the synchronous inputs in pyramidal neurons. Seminal studies have targeted either the electrophysiological substrate of BOLD or the anatomical basis of FC. However, multimodal comparisons have been scarcely performed, and the relation between SC, fMRI‐FC, and MEG‐FC is still unclear. Here we present a systematic comparison of SC, resting state fMRI‐FC, and MEG‐FC between cortical regions, by evaluating their similarities at three different scales: global network, node, and hub distribution. We obtained strong similarities between the three modalities, especially for the following pairwise combinations: SC and fMRI‐FC; SC and MEG‐FC at theta, alpha, beta and gamma bands; and fMRI‐FC and MEG‐FC in alpha and beta. Furthermore, highest node similarity was found for regions of the default mode network and primary motor cortex, which also presented the highest hubness score. Distance was partially responsible for these similarities since it biased all three connectivity estimates, but not the unique contributor, since similarities remained after controlling for distance. Hum Brain Mapp 37:20–34, 2016. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Measuring electrophysiological connectivity by power envelope correlation: a technical review on MEG methods

The human brain can be divided into multiple areas, each responsible for different aspects of behaviour. Healthy brain function relies upon efficient connectivity between these areas and, in recent years, neuroimaging has been revolutionised by an ability to estimate this connectivity. In this paper we discuss measurement of network connectivity using magnetoencephalography (MEG), a technique capable of imaging electrophysiological brain activity with good (~5 mm) spatial resolution and excellent (~1 ms) temporal resolution. The rich information content of MEG facilitates many disparate measures of connectivity between spatially separate regions and in this paper we discuss a single metric known as power envelope correlation. We review in detail the methodology required to measure power envelope correlation including (i) projection of MEG data into source space, (ii) removing confounds introduced by the MEG inverse problem and (iii) estimation of connectivity itself. In this way, we aim to provide researchers with a description of the key steps required to assess envelope based functional networks, which are thought to represent an intrinsic mode of coupling in the human brain. We highlight the principal findings of the techniques discussed, and furthermore, we show evidence that this method can probe how the brain forms and dissolves multiple transient networks on a rapid timescale in order to support current processing demand. Overall, power envelope correlation offers a unique and verifiable means to gain novel insights into network coordination and is proving to be of significant value in elucidating the neural dynamics of the human connectome in health and disease.

Functional connectivity changes detected with magnetoencephalography after mild traumatic brain injury

Mild traumatic brain injury (mTBI) may affect normal cognition and behavior by disrupting the functional connectivity networks that mediate efficient communication among brain regions. In this study, we analyzed brain connectivity profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 31 mTBI patients and 55 normal controls. We used phase-locking value estimates to compute functional connectivity graphs to quantify frequency-specific couplings between sensors at various frequency bands. Overall, normal controls showed a dense network of strong local connections and a limited number of long-range connections that accounted for approximately 20% of all connections, whereas mTBI patients showed networks characterized by weak local connections and strong long-range connections that accounted for more than 60% of all connections. Comparison of the two distinct general patterns at different frequencies using a tensor representation for the connectivity graphs and tensor subspace analysis for optimal feature extraction showed that mTBI patients could be separated from normal controls with 100% classification accuracy in the alpha band. These encouraging findings support the hypothesis that MEG-based functional connectivity patterns may be used as biomarkers that can provide more accurate diagnoses, help guide treatment, and monitor effectiveness of intervention in mTBI.

•

We analyzed resting state connectivity profiles in 31 mTBI patients and 55 controls.

•

We quantified frequency-specific connectivity couplings using phase-locking values.

•

Normal control networks showed dense local and sparse long-range connections.

•

TBI patient networks showed sparse local and dense long-range connections.

•

Tensor subspace analysis could classify subjects with 100% accuracy in the α band

A geometric correction scheme for spatial leakage effects in MEG/EEG seed-based functional connectivity mapping

Spatial leakage effects are particularly confounding for seed-based investigations of brain networks using source-level electroencephalography (EEG) or magnetoencephalography (MEG). Various methods designed to avoid this issue have been introduced but are limited to particular assumptions about its temporal characteristics. Here, we investigate the usefulness of a model-based geometric correction scheme (GCS) to suppress spatial leakage emanating from the seed location. We analyze its properties theoretically and then assess potential advantages and limitations with simulated and experimental MEG data (resting state and auditory-motor task). To do so, we apply Minimum Norm Estimation (MNE) for source reconstruction and use variation of error parameters, statistical gauging of spatial leakage correction and comparison with signal orthogonalization. Results show that the GCS has a local (i.e., near the seed) effect only, in line with the geometry of MNE spatial leakage, and is able to map spatially all types of brain interactions, including linear correlations eliminated after signal orthogonalization. Furthermore, it is robust against the introduction of forward model errors. On the other hand, the GCS can be affected by local overcorrection effects and seed mislocation. These issues arise with signal orthogonalization too, although significantly less extensively, so the two approaches complement each other. The GCS thus appears to be a valuable addition to the spatial leakage correction toolkits for seed-based FC analyses in source-projected MEG/EEG data.

Incorporating priors for EEG source imaging and connectivity analysis

Electroencephalography source imaging (ESI) is a useful technique to localize the generators from a given scalp electric measurement and to investigate the temporal dynamics of the large-scale neural circuits. By introducing reasonable priors from other modalities, ESI reveals the most probable sources and communication structures at every moment in time. Here, we review the available priors from such techniques as magnetic resonance imaging (MRI), functional MRI (fMRI), and positron emission tomography (PET). The modality's specific contribution is analyzed from the perspective of source reconstruction. For spatial priors, EEG-correlated fMRI, temporally coherent networks (TCNs) and resting-state fMRI are systematically introduced in the ESI. Moreover, the fiber tracking (diffusion tensor imaging, DTI) and neuro-stimulation techniques (transcranial magnetic stimulation, TMS) are also introduced as the potential priors, which can help to draw inferences about the neuroelectric connectivity in the source space. We conclude that combining EEG source imaging with other complementary modalities is a promising approach toward the study of brain networks in cognitive and clinical neurosciences.

The Union of Shortest Path Trees of Functional Brain Networks

Communication between brain regions is still insufficiently understood. Applying concepts from network science has shown to be successful in gaining insight in the functioning of the brain. Recent work has implicated that especially shortest paths in the structural brain network seem to play a major role in the communication within the brain. So far, for the functional brain network, only the average length of the shortest paths has been analyzed. In this article, we propose to construct the union of shortest path trees (USPT) as a new topology for the functional brain network. The minimum spanning tree, which has been successful in a lot of recent studies to comprise important features of the functional brain network, is always included in the USPT. After interpreting the link weights of the functional brain network as communication probabilities, the USPT of this network can be uniquely defined. Using data from magnetoencephalography, we applied the USPT as a method to find differences in the network topology of multiple sclerosis patients and healthy controls. The new concept of the USPT of the functional brain network also allows interesting interpretations and may represent the highways of the brain.

Intrinsic coupling modes in source-reconstructed electroencephalography

Intrinsic coupling of neuronal assemblies constitutes a key feature of ongoing brain activity, yielding the rich spatiotemporal patterns observed in neuroimaging data and putatively supporting cognitive processes. Intrinsic coupling has been investigated in electrophysiological recordings using two types of functional connectivity measures: amplitude and phase coupling. These two coupling modes differ in their likely causes and functions, and have been proposed to provide complementary insights into intrinsic neuronal interactions. Here, we investigate the relationship between amplitude and phase coupling in source-reconstructed electroencephalography (EEG). Volume conduction is a key obstacle for connectivity analysis in EEG-we therefore also test the envelope correlation of orthogonalized signals and the phase lag index. Functional connectivity between six seed source regions (bilateral visual, sensorimotor, and auditory cortices) and all other cortical voxels was computed. For all four measures, coupling between homologous sensory areas in both hemispheres was significantly higher than with other voxels at the same physical distance. The frequency of significant coupling differed between sensory areas: 10 Hz for visual, 30 Hz for auditory, and 40 Hz for sensorimotor cortices. By contrasting envelope correlations and phase locking values, we observed two distinct clusters of voxels showing a different relationship between amplitude and phase coupling. Large clusters contiguous to the seed regions showed an identity (1:1) relationship between amplitude and phase coupling, whereas a cluster located around the contralateral homologous regions showed higher phase than amplitude coupling. These results show a relationship between intrinsic coupling modes that is distinct from the effect of volume conduction.

Frontal-posterior theta oscillations reflect memory retrieval during sentence comprehension

Successful working-memory retrieval requires that items be retained as distinct units. At the neural level, it has been shown that theta-band oscillatory power increases with the number of to-be-distinguished items during working-memory retrieval. Here we hypothesized that during sentence comprehension, verbal-working-memory retrieval demands lead to increased theta power over frontal cortex, supposedly supporting the distinction amongst stored items during verbal-working-memory retrieval. Also, synchronicity may increase between the frontal cortex and the posterior cortex, with the latter supposedly supporting item retention. We operationalized retrieval by using pronouns, which refer to and trigger the retrieval of antecedent nouns from a preceding sentence part. Retrieval demand was systematically varied by changing the pronoun antecedent: Either, it was non-embedded in the preceding main clause, and thus easy-to-retrieve across a single clause boundary, or embedded in the preceding subordinate clause, and thus hard-to-retrieve across a double clause boundary. We combined electroencephalography (EEG), scalp-level time-frequency analysis, source localization, and source-level coherence analysis, observing a frontal-midline and broad left-hemispheric theta-power increase for embedded-antecedent compared to non-embedded-antecedent retrieval. Sources were localized to left-frontal, left-parietal, and bilateral-inferior-temporal cortices. Coherence analyses suggested synchronicity between left-frontal and left-parietal and between left-frontal and right-inferior-temporal cortices. Activity of an array of left-frontal, left-parietal, and bilateral-inferior-temporal cortices may thus assist retrieval during sentence comprehension, potentially indexing the orchestration of item distinction, verbal working memory, and long-term memory. Our results extend prior findings by mapping prior knowledge on the functional role of theta oscillations onto processes genuine to human sentence comprehension.