Functional cortical hubs in the eyes-closed resting human brain from an electrophysiological perspective using magnetoencephalography

Brain-wide network analysis of resting-state neuromagnetic data

The present study performed a brain-wide network analysis of resting-state magnetoencephalograms recorded from 53 healthy participants to visualize elaborate brain maps of phase- and amplitude-derived graph-theory metrics at different frequencies. To achieve this, we conducted a vertex-wise computation of threshold-independent graph metrics by combining proportional thresholding and a conjunction analysis and applied them to a correlation analysis of age and brain networks. Source power showed a frequency-dependent cortical distribution. Threshold-independent graph metrics derived from phase- and amplitude-based connectivity showed similar or different distributions depending on frequency. Vertex-wise age-brain correlation maps revealed that source power at the beta band and the amplitude-based degree at the alpha band changed with age in local regions. The present results indicate that a brain-wide analysis of neuromagnetic data has the potential to reveal neurophysiological network features in the human brain in a resting state.

Aberrant Whole-Brain Resting-State Functional Connectivity Architecture in Obsessive-Compulsive Disorder: An EEG Study

Obsessive-compulsive disorder (OCD) is a common neuropsychiatric disorder characterized by intrusive thoughts (obsessions) and repetitive behaviors (compulsions), and few studies have assessed the whole-brain functional connectivity architecture of OCD with electroencephalogram (EEG) during different resting states. Graph theory and network-based statistics (NBS) were employed to examine the neural synchronization and the whole-brain functional connectivity (FC) based on the phase-locking value (PLV) of OCD patients and healthy controls (HCs) during eyes-closed (EC) and eyes-open (EO) states. Compared with HCs, OCD patients exhibited not only decreased global synchronization in terms of phase synchrony but also aberrant global topological properties (decreased average shortest path lengths and normalized shortest path lengths together with increased global efficiencies and normalized clustering coefficients) together with inhibited intra-hemispheric and interhemispheric FCs during rest, which suggested an imbalance between functional integration and segregation of brain networks for OCD patients. Meanwhile, OCD patients had increased global efficiencies and normalized clustering coefficients, but decreased average clustering coefficients and normalized shortest path lengths together with significantly decreased FCs in the alpha band from EC to EO states, which suggested a dynamic switch between highly integrated (EC state) and highly specialized (EO state) modes of information processing. Moreover, the decreased FCs of OCD patients showed obvious hemispheric asymmetry within or between groups during EC and EO states, which might serve as a potential biomarker to classify OCD patients from HCs.

Children with developmental coordination disorder show altered functional connectivity compared to peers

Developmental Coordination Disorder (DCD) is a neurodevelopmental disorder that affects a child's ability to learn motor skills and participate in self-care, educational, and leisure activities. The cause of DCD is unknown, but evidence suggests that children with DCD have atypical brain structure and function. Resting-state MRI assesses functional connectivity by identifying brain regions that have parallel activation during rest. As only a few studies have examined functional connectivity in this population, our objective was to compare whole-brain resting-state functional connectivity of children with DCD and typically-developing children. Using Independent Component Analysis (ICA), we compared functional connectivity of 8-12 year old children with DCD (N = 35) and typically-developing children (N = 23) across 19 networks, controlling for age and sex. Children with DCD demonstrate altered functional connectivity between the sensorimotor network and the posterior cingulate cortex (PCC), precuneus, and the posterior middle temporal gyrus (pMTG) (p < 0.0001). Previous evidence suggests the PCC acts as a link between functionally distinct networks. Our results indicate that ineffective communication between the sensorimotor network and the PCC might play a role in inefficient motor learning seen in DCD. The pMTG acts as hub for action-related information and processing, and its involvement could explain some of the functional difficulties seen in DCD. This study increases our understanding of the neurological differences that characterize this common motor disorder.

Juvenile myoclonic epilepsy shows increased posterior theta, and reduced sensorimotor beta resting connectivity

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We investigated whole brain source space connectivity in JME using across standard MEG frequency bands.

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Connectivity was increased in posterior theta and alpha bands in JME, and decreased in sensorimotor beta band.

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Our findings highlight altered interactions between posterior networks of arousal and attention and the motor system in JME.

Background

Widespread structural and functional brain network changes have been shown in Juvenile Myoclonic Epilepsy (JME) despite normal clinical neuroimaging. We sought to better define these changes using magnetoencephalography (MEG) and source space connectivity analysis for optimal neurophysiological and anatomical localisation.

Methods

We consecutively recruited 26 patients with JME who underwent resting state MEG recording, along with 26 age-and-sex matched controls. Whole brain connectivity was determined through correlation of Automated Anatomical Labelling (AAL) atlas source space MEG timeseries in conventional frequency bands of interest delta (1−4 Hz), theta (4−8 Hz), alpha (8−13 Hz), beta (13−30 Hz) and gamma (40−60 Hz). We used a Linearly Constrained Minimum Variance (LCMV) beamformer to extract voxel wise time series of ‘virtual sensors’ for the desired frequency bands, followed by connectivity analysis using correlation between frequency- and node-specific power fluctuations, for the voxel maxima in each AAL atlas label, correcting for noise, potentially spurious connections and multiple comparisons.

Results

We found increased connectivity in the theta band in posterior brain regions, surviving statistical correction for multiple comparisons (corrected p < 0.05), and decreased connectivity in the beta band in sensorimotor cortex, between right pre- and post- central gyrus (p < 0.05) in JME compared to controls.

Conclusions

Altered resting-state MEG connectivity in JME comprised increased connectivity in posterior theta – the frequency band associated with long range connections affecting attention and arousal - and decreased beta-band sensorimotor connectivity. These findings likely relate to altered regulation of the sensorimotor network and seizure prone states in JME.

Brain mechanisms in motor control during reaching movements: Transition of functional connectivity according to movement states

Understanding how the brain controls movements is a critical issue in neuroscience. The role of brain changes rapidly according to movement states. To elucidate the motor control mechanism of brain, it is essential to investigate the changes in brain network in motor-related regions according to movement states. Therefore, the objective of this study was to investigate the brain network transitions according to movement states. We measured whole brain magnetoencephalography (MEG) signals and extracted source signals in 24 motor-related areas. Functional connectivity and centralities were calculated according to time flow. Our results showed that brain networks differed between states of motor planning and movement. Connectivities between most motor-related areas were increased in the motor-planning state. In contrast, only connectivities with cerebellum and basal ganglia were increased while those of other motor-related areas were decreased during movement. Our results indicate that most processes involved in motor control are completed before movement. Further, brain developed network related to feedback rather than motor decision during movements. Our findings also suggest that neural signals during motor planning might be more predictive than neural signals during movement. They facilitate accurate prediction of movement for brain-machine interfaces and provide insight into brain mechanisms in motor control.

Disrupted Resting State Network of Fibromyalgia in Theta frequency

Fibromyalgia (FM), chronic widespread pain, exhibits spontaneous pain without external stimuli and is associated with altered brain activities during resting state. To understand the topological features of brain network in FM, we employed persistent homology which is a multiple scale network modeling framework not requiring thresholding. Spontaneous magnetoencephalography (MEG) activity was recorded in 19 healthy controls (HCs) and 18 FM patients. Barcode, single linkage dendrogram and single linkage matrix were generated based on the proposed modeling framework. In theta band, the slope of decrease in the number of connected components in barcodes showed steeper in HC, suggesting FM patients had decreased global connectivity. FM patients had reduced connectivity within default mode network, between middle/inferior temporal gyrus and visual cortex. The longer pain duration was correlated with reduced connectivity between inferior temporal gyrus and visual cortex. Our findings demonstrated that the aberrant resting state network could be associated with dysfunction of sensory processing in chronic pain. The spontaneous nature of FM pain may accrue to disruption of resting state network.

Electrophysiological resting-state biomarker for diagnosing mesial temporal lobe epilepsy with hippocampal sclerosis

The main aim of the present study was to evaluate whether resting-state functional connectivity of magnetoencephalography (MEG) signals can differentiate patients with mesial temporal lobe epilepsy (MTLE) from healthy controls (HC) and can differentiate between right and left MTLE as a diagnostic biomarker. To this end, a support vector machine (SVM) method among various machine learning algorithms was employed. We compared resting-state functional networks between 46 MTLE (right MTLE=23; left MTLE=23) patients with histologically proven HS who were free of seizure after surgery, and 46 HC. The optimal SVM group classifier distinguished MTLE patients with a mean accuracy of 95.1% (sensitivity=95.8%; specificity=94.3%). Increased connectivity including the right posterior cingulate gyrus and decreased connectivity including at least one sensory-related resting-state network were key features reflecting the differences between MTLE patients and HC. The optimal SVM model distinguished between right and left MTLE patients with a mean accuracy of 76.2% (sensitivity=76.0%; specificity=76.5%). We showed the potential of electrophysiological resting-state functional connectivity, which reflects brain network reorganization in MTLE patients, as a possible diagnostic biomarker to differentiate MTLE patients from HC and differentiate between right and left MTLE patients.

Multi-Dimensional Dynamics of Human Electromagnetic Brain Activity

Magnetoencephalography (MEG) and electroencephalography (EEG) are invaluable neuroscientific tools for unveiling human neural dynamics in three dimensions (space, time, and frequency), which are associated with a wide variety of perceptions, cognition, and actions. MEG/EEG also provides different categories of neuronal indices including activity magnitude, connectivity, and network properties along the three dimensions. In the last 20 years, interest has increased in inter-regional connectivity and complex network properties assessed by various sophisticated scientific analyses. We herein review the definition, computation, short history, and pros and cons of connectivity and complex network (graph-theory) analyses applied to MEG/EEG signals. We briefly describe recent developments in source reconstruction algorithms essential for source-space connectivity and network analyses. Furthermore, we discuss a relatively novel approach used in MEG/EEG studies to examine the complex dynamics represented by human brain activity. The correct and effective use of these neuronal metrics provides a new insight into the multi-dimensional dynamics of the neural representations of various functions in the complex human brain.

Mapping Individual Brain Networks Using Statistical Similarity in Regional Morphology from MRI

Representing brain morphology as a network has the advantage that the regional morphology of ‘isolated’ structures can be described statistically based on graph theory. However, very few studies have investigated brain morphology from the holistic perspective of complex networks, particularly in individual brains. We proposed a new network framework for individual brain morphology. Technically, in the new network, nodes are defined as regions based on a brain atlas, and edges are estimated using our newly-developed inter-regional relation measure based on regional morphological distributions. This implementation allows nodes in the brain network to be functionally/anatomically homogeneous but different with respect to shape and size. We first demonstrated the new network framework in a healthy sample. Thereafter, we studied the graph-theoretical properties of the networks obtained and compared the results with previous morphological, anatomical, and functional networks. The robustness of the method was assessed via measurement of the reliability of the network metrics using a test-retest dataset. Finally, to illustrate potential applications, the networks were used to measure age-related changes in commonly used network metrics. Results suggest that the proposed method could provide a concise description of brain organization at a network level and be used to investigate interindividual variability in brain morphology from the perspective of complex networks. Furthermore, the method could open a new window into modeling the complexly distributed brain and facilitate the emerging field of human connectomics.

Functional substrate for memory function differences between patients with left and right mesial temporal lobe epilepsy associated with hippocampal sclerosis

OBJECTIVE

Little is known about the functional substrate for memory function differences in patients with left or right mesial temporal lobe epilepsy (mTLE) associated with hippocampal sclerosis (HS) from an electrophysiological perspective. To characterize these differences, we hypothesized that hippocampal theta connectivity in the resting-state might be different between patients with left and right mTLE with HS and be correlated with memory performance.

METHODS

Resting-state hippocampal theta connectivity, identified via whole-brain magnetoencephalography, was evaluated. Connectivity and memory function in 41 patients with mTLE with HS (left mTLE=22; right mTLE=19) were compared with those in 46 age-matched healthy controls and 28 patients with focal cortical dysplasia (FCD) but without HS.

RESULTS

Connectivity between the right hippocampus and the left middle frontal gyrus was significantly stronger in patients with right mTLE than in patients with left mTLE. Moreover, this connectivity was positively correlated with delayed verbal recall and recognition scores in patients with mTLE. Patients with left mTLE had greater delayed recall impairment than patients with right mTLE and FCD. Similarly, delayed recognition performance was worse in patients with left mTLE than in patients with right mTLE and FCD. No significant differences in memory function between patients with right mTLE and FCD were detected. Patients with right mTLE showed significantly stronger hippocampal theta connectivity between the right hippocampus and left middle frontal gyrus than patients with FCD and left mTLE.

CONCLUSION

Our results suggest that right hippocampal-left middle frontal theta connectivity could be a functional substrate that can account for differences in memory function between patients with left and right mTLE. This functional substrate might be related to different compensatory mechanisms against the structural hippocampal lesions in left and right mTLE groups. Given the positive correlation between connectivity and delayed verbal memory function, hemispheric-specific hippocampal-frontal theta connectivity assessment could be useful as an electrophysiological indicator of delayed verbal memory function in patients with mTLE with HS.

Increased Intrinsic Connectivity of the Default Mode Network in Temporal Lobe Epilepsy: Evidence from Resting-State MEG Recordings

The electrophysiological signature of resting state oscillatory functional connectivity within the default mode network (DMN) during spike-free periods in temporal lobe epilepsy (TLE) remains unclear. Using magnetoencephalographic (MEG) recordings, this study investigated how the connectivity within the DMN was altered in TLE, and we examined the effect of lateralized TLE on functional connectivity. Sixteen medically intractable TLE patients and 22 controls participated in this study. Whole-scalp 306-channel MEG epochs without interictal spikes generated from both MEG and EEG data were analyzed using a minimum norm estimate (MNE) and source-based imaginary coherence analysis. With this processing, we obtained the cortical activation and functional connectivity within the DMN. The functional connectivity was increased between DMN and the right medial temporal (MT) region at the delta band and between DMN and the bilateral anterior cingulate cortex (ACC) regions at the theta band. The functional change was associated with the lateralization of TLE. The right TLE showed enhanced DMN connectivity with the right MT while the left TLE demonstrated increased DMN connectivity with the bilateral MT. There was no lateralization effect of TLE upon the DMN connectivity with ACC. These findings suggest that the resting-state functional connectivity within the DMN is reinforced in temporal lobe epilepsy during spike-free periods. Future studies are needed to examine if the altered functional connectivity can be used as a biomarker for treatment responses, cognitive dysfunction and prognosis in patients with TLE.

Mesial temporal lobe epilepsy with hippocampal sclerosis is a network disorder with altered cortical hubs

OBJECTIVE

Electrophysiologic hubs within the large-scale functional networks in mesial temporal lobe epilepsy (mTLE) with hippocampal sclerosis (HS) have not been investigated. We hypothesized that mTLE with HS has different resting-state network hubs in their large-scale functional networks compared to the hubs in healthy controls (HC). We also hypothesized that the hippocampus would be a functional hub in mTLE patients with HS.

METHODS

Resting-state functional networks, identified by using magnetoencephalography (MEG) signals in the theta, alpha, beta, and gamma frequency bands, were evaluated. Networks in 44 mTLE patients with HS (left mTLE = 22; right mTLE = 22) were compared with those in 46 age-matched HC. We investigated betweenness centrality at the source-level MEG network.

RESULTS

The main network hubs were at the pole of the left superior temporal gyrus in the beta band, the pole of the left middle temporal gyrus in the beta and gamma bands, left hippocampus in the theta and alpha bands, and right posterior cingulate gyrus in all four frequency bands in mTLE patients; all of which were different from the main network hubs in HC. Only patients with left mTLE showed profound differences from HC at the left hippocampus in the alpha band.

SIGNIFICANCE

Our analysis of resting-state MEG signals shows that altered electrophysiologic functional hubs in mTLE patients reflect pathophysiologic brain network reorganization. Because we detected network hubs in both hippocampal and extrahippocampal areas, it is probable that mTLE is a large-scale network disorder rather than a focal disorder. The hippocampus was a network hub in left mTLE but not in right mTLE patients, which may be due to intrinsic functional and structural asymmetries between left and right mTLE patients. The evaluation of cortical hubs, even in the spike-free resting-state, could be a clinical diagnostic marker of mTLE with HS.

Directionality of large-scale resting-state brain networks during eyes open and eyes closed conditions

The present study examined directional connections in the brain among resting-state networks (RSNs) when the participant had their eyes open (EO) or had their eyes closed (EC). The resting-state fMRI data were collected from 20 healthy participants (9 males, 20.17 ± 2.74 years) under the EO and EC states. Independent component analysis (ICA) was applied to identify the separated RSNs (i.e., the primary/high-level visual, primary sensory-motor, ventral motor, salience/dorsal attention, and anterior/posterior default-mode networks), and the Gaussian Bayesian network (BN) learning approach was then used to explore the conditional dependencies among these RSNs. The network-to-network directional connections related to EO and EC were depicted, and a support vector machine (SVM) was further employed to identify the directional connection patterns that could effectively discriminate between the two states. The results indicated that the connections among RSNs are directionally connected within a BN during the EO and EC states. The directional connections from the salience network (SN) to the anterior/posterior default-mode networks and the high-level to primary-level visual network were the obvious characteristics of both the EO and EC resting-state BNs. Of the directional connections in BN, the directional connections of the salience and dorsal attention network (DAN) were observed to be discriminative between the EO and EC states. In particular, we noted that the properties of the salience and DANs were in opposite directions. Overall, the present study described the directional connections of RSNs using a BN learning approach during the EO and EC states, and the results suggested that the directionality of the attention systems (i.e., mainly for the salience and the DAN) in resting state might have important roles in switching between the EO and EC conditions.

Abnormal functional brain network in epilepsy patients with focal cortical dysplasia

PURPOSE

Focal cortical dysplasia (FCD) is the second most common pathological entity in surgically treated neocortical focal epilepsy. Despite the recent increase of interest in network approaches derived from graph theory on epilepsy, resting state network analysis of the FCD brain has not been adequately investigated. In this study, we investigated the difference in the resting state functional network between epilepsy patients with FCD and healthy controls using whole-brain magnetoencephalography (MEG) recordings.

METHODS

Global mutual information (MIglob) and global efficiency (Eglob) were calculated for theta (4-7 Hz), alpha (8-12 Hz), beta (13-30 Hz), and gamma (31-45 Hz) bands in 35 epilepsy patients with FCD and 23 healthy controls.

RESULTS

Resting state FCD brains had stronger functional connectivity (MIglob) in the beta and gamma bands and higher functional efficiency (Eglob) in the beta and gamma bands than those of the controls (p<0.05). The MIglob and Eglob values of FCD type I and II brains in the beta band were higher than those of healthy control brains (p<0.05). In the gamma band, the values of FCD type II brains were higher than those of control and FCD type I brains (p<0.05).

CONCLUSIONS

FCD brains had increased functional connectivity in the beta and gamma frequency bands at the resting state compared with those in healthy controls. In addition, patients exhibited different network characteristics depending on the type of FCD. The resting state network analysis could be useful in a clinical setting because we observed network differences even when there was no prominent interictal spike activity.

Positive and negative affective processing exhibit dissociable functional hubs during the viewing of affective pictures

Recent resting-state functional magnetic resonance imaging (fMRI) studies using graph theory metrics have revealed that the functional network of the human brain possesses small-world characteristics and comprises several functional hub regions. However, it is unclear how the affective functional network is organized in the brain during the processing of affective information. In this study, the fMRI data were collected from 25 healthy college students as they viewed a total of 81 positive, neutral, and negative pictures. The results indicated that affective functional networks exhibit weaker small-worldness properties with higher local efficiency, implying that local connections increase during viewing affective pictures. Moreover, positive and negative emotional processing exhibit dissociable functional hubs, emerging mainly in task-positive regions. These functional hubs, which are the centers of information processing, have nodal betweenness centrality values that are at least 1.5 times larger than the average betweenness centrality of the network. Positive affect scores correlated with the betweenness values of the right orbital frontal cortex (OFC) and the right putamen in the positive emotional network; negative affect scores correlated with the betweenness values of the left OFC and the left amygdala in the negative emotional network. The local efficiencies in the left superior and inferior parietal lobe correlated with subsequent arousal ratings of positive and negative pictures, respectively. These observations provide important evidence for the organizational principles of the human brain functional connectome during the processing of affective information.

Preserved high-centrality hubs but efficient network reorganization during eyes-open state compared with eyes-closed resting state: an MEG study

A question to be addressed in the present study is how different the eyes-closed (EC) and eyes-open (EO) resting states are across frequency bands in terms of efficiency and centrality of the brain functional network. We investigated both the global and nodal efficiency and betweenness centrality in the EC and EO resting states from 39 volunteers. Mutual information was used to obtain the functional connectivity for each of the four frequency bands (theta, alpha, beta, and gamma). We showed that the cortical hubs with high betweenness centrality were maintained in the EC and EO resting states. We further showed that these hubs were associated with more than three frequency bands, suggesting that these hubs play an important role in the brain functional network at multiple temporal scales in the resting states. Enhanced global efficiency values were found in the theta and alpha bands in the EO state compared with those in the EC state. Moreover, it turned out that in the EO state the functional network was reorganized to enhance nodal efficiency at the nodes related to both the default mode and the dorsal attention networks and sensory-related resting-state networks. This result suggests that in the EO state the brain functional network was efficiently reorganized, facilitating the adaptation of the brain network to the change in state, which could help in understanding brain disorders that have a disturbance in communication with external environments by using the adaptation ability of brain functional networks.

Functional connectivity networks with and without global signal correction

In functional connectivity analyses in BOLD (blood oxygenation level dependent) fMRI data, there is an ongoing debate on whether to correct global signals in fMRI time series data. Although the discussion has been ongoing in the fMRI community since the early days of fMRI data analyses, this subject has gained renewed attention in recent years due to the surging popularity of functional connectivity analyses, in particular graph theory-based network analyses. However, the impact of correcting (or not correcting) for global signals has not been systematically characterized in the context of network analyses. Thus, in this work, I examined the effect of global signal correction on an fMRI network analysis. In particular, voxel-based resting-state fMRI networks were constructed with and without global signal correction. The resulting functional connectivity networks were compared. Without global signal correction, the distributions of the correlation coefficients were positively biased. I also found that, without global signal correction, nodes along the interhemisphic fissure were highly connected whereas some nodes and subgraphs around white-matter tracts became disconnected from the rest of the network. These results from this study show differences between the networks with or without global signal correction.