Dataset Information

What graph theory actually tells us about resting state interictal MEG epileptic activity.

ABSTRACT: Graph theory provides a useful framework to study functional brain networks from neuroimaging data. In epilepsy research, recent findings suggest that it offers unique insight into the fingerprints of this pathology on brain dynamics. Most studies hitherto have focused on seizure activity during focal epilepsy, but less is known about functional epileptic brain networks during interictal activity in frontal focal and generalized epilepsy. Besides, it is not clear yet which measures are most suitable to characterize these networks. To address these issues, we recorded magnetoencephalographic (MEG) data using two orthogonal planar gradiometers from 45 subjects from three groups (15 healthy controls (7 males, 24 ± 6 years), 15 frontal focal (8 male, 32 ± 16 years) and 15 generalized epileptic (6 male, 27 ± 7 years) patients) during interictal resting state with closed eyes. Then, we estimated the total and relative spectral power of the largest principal component of the gradiometers, and the degree of phase synchronization between each sensor site in the frequency range [0.5-40 Hz]. We further calculated a comprehensive battery of 15 graph-theoretic measures and used the affinity propagation clustering algorithm to elucidate the minimum set of them that fully describe these functional brain networks. The results show that differences in spectral power between the control and the other two groups have a distinctive pattern: generalized epilepsy presents higher total power for all frequencies except the alpha band over a widespread set of sensors; frontal focal epilepsy shows higher relative power in the beta band bilaterally in the fronto-central sensors. Moreover, all network indices can be clustered into three groups, whose exemplars are the global network efficiency, the eccentricity and the synchronizability. Again, the patterns of differences were clear: the brain network of the generalized epilepsy patients presented greater efficiency and lower eccentricity than the control subjects for the high frequency bands, without a clear topography. Besides, the frontal focal epileptic patients showed only reduced eccentricity for the theta band over fronto-temporal and central sensors. These outcomes indicate that functional epileptic brain networks are different to those of healthy subjects during interictal stage at rest, with a unique pattern of dissimilarities for each type of epilepsy. Further, when properly selected, three network indices suffice to provide a comprehensive description of these differences. Yet, since such uniqueness in the pattern of differences is also evident in the power spectrum, we conclude that the added value of the graph theory approach in this context should not be overestimated.

SUBMITTER: Niso G

PROVIDER: S-EPMC4475779 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

What graph theory actually tells us about resting state interictal MEG epileptic activity.

Niso Guiomar G Carrasco Sira S Gudín María M Maestú Fernando F Del-Pozo Francisco F Pereda Ernesto E

NeuroImage. Clinical 20150523

Graph theory provides a useful framework to study functional brain networks from neuroimaging data. In epilepsy research, recent findings suggest that it offers unique insight into the fingerprints of this pathology on brain dynamics. Most studies hitherto have focused on seizure activity during focal epilepsy, but less is known about functional epileptic brain networks during interictal activity in frontal focal and generalized epilepsy. Besides, it is not clear yet which measures are most suit ...[more]

PMID: 26106575

Similar Datasets

Project description:Interhemispheric connectivity with resting state MEG has been elusive, and demonstration of the default mode network (DMN) yet more challenging. Recent seed-based MEG analyses have shown interhemispheric connectivity using power envelope correlations. The purpose of this study is to compare graph theoretic maps of brain connectivity generated using MEG with and without signal leakage correction to evaluate for the presence of interhemispheric connectivity. Eight minutes of resting state eyes-open MEG data were obtained in 22 normal male subjects enrolled in an IRB-approved study (ages 16-18). Data were processed using an in-house automated MEG processing pipeline and projected into standard (MNI) source space at 7mm resolution using a scalar beamformer. Mean beta-band amplitude was sampled at 2.5second epochs from the source space time series. Leakage correction was performed in the time domain of the source space beam formed signal prior to amplitude transformation. Graph theoretic voxel-wise source space correlation connectivity analysis was performed for leakage corrected and uncorrected data. Degree maps were thresholded across subjects for the top 20% of connected nodes to identify hubs. Additional degree maps for sensory, visual, motor, and temporal regions were generated to identify interhemispheric connectivity using laterality indices. Hubs for the uncorrected MEG networks were predominantly symmetric and midline, bearing some resemblance to fMRI networks. These included the cingulate cortex, bilateral inferior frontal lobes, bilateral hippocampal formations and bilateral cerebellar hemispheres. These uncorrected networks however, demonstrated little to no interhemispheric connectivity using the ROI-based degree maps. Leakage corrected MEG data identified the DMN, with hubs in the posterior cingulate and biparietal areas. These corrected networks demonstrated robust interhemispheric connectivity for the ROI-based degree maps. Graph theoretic analysis of MEG resting state data without signal leakage correction can demonstrate symmetric networks with some resemblance to fMRI networks. These networks however, are an artifact of high local correlation from signal leakage and lack interhemispheric connectivity. Following signal leakage correction, MEG hubs emerge in the DMN, with strong interhemispheric connectivity.

Project description:Essential tremor (ET) is a neurological disease with both motor and nonmotor manifestations; however, little is known about its underlying brain basis. Furthermore, the overall organization of the brain network in ET remains largely unexplored. We investigated the topological properties of brain functional network, derived from resting-state functional magnetic resonance imaging (MRI) data, in 23 ET patients versus 23 healthy controls. Graph theory analysis was used to assess the functional network organization. At the global level, the functional network of ET patients was characterized by lower small-worldness values than healthy controls-less clustered functionality of the brain. At the regional level, compared with the healthy controls, ET patients showed significantly higher values of global efficiency, cost and degree, and a shorter average path length in the left inferior frontal gyrus (pars opercularis), right inferior temporal gyrus (posterior division and temporo-occipital part), right inferior lateral occipital cortex, left paracingulate, bilateral precuneus bilaterally, left lingual gyrus, right hippocampus, left amygdala, nucleus accumbens bilaterally, and left middle temporal gyrus (posterior part). In addition, ET patients showed significant higher local efficiency and clustering coefficient values in frontal medial cortex bilaterally, subcallosal cortex, posterior cingulate cortex, parahippocampal gyri bilaterally (posterior division), right lingual gyrus, right cerebellar flocculus, right postcentral gyrus, right inferior semilunar lobule of cerebellum and culmen of vermis. Finally, the right intracalcarine cortex and the left orbitofrontal cortex showed a shorter average path length in ET patients, while the left frontal operculum and the right planum polare showed a higher betweenness centrality in ET patients. In conclusion, the efficiency of the overall brain functional network in ET is disrupted. Further, our results support the concept that ET is a disorder that disrupts widespread brain regions, including those outside of the brain regions responsible for tremor.

Project description:Epileptic networks, defined as brain regions involved in epileptic brain activity, have been mapped by functional connectivity in simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) recordings. This technique allows to define brain hemodynamic changes, measured by the Blood Oxygen Level Dependent (BOLD) signal, associated to the interictal epileptic discharges (IED), which together with ictal events constitute a signature of epileptic disease. Given the highly time-varying nature of epileptic activity, a dynamic functional connectivity (dFC) analysis of EEG-fMRI data appears particularly suitable, having the potential to identify transitory features of specific connections in epileptic networks. In the present study, we propose a novel method, defined dFC-EEG, that integrates dFC assessed by fMRI with the information recorded by simultaneous scalp EEG, in order to identify the connections characterised by a dynamic profile correlated with the occurrence of IED, forming the dynamic epileptic subnetwork. Ten patients with drug-resistant focal epilepsy were included, with different aetiology and showing a widespread (or multilobar) BOLD activation, defined as involving at least two distinct clusters, located in two different lobes and/or extended to the hemisphere contralateral to the epileptic focus. The epileptic focus was defined from the IED-related BOLD map. Regions involved in the occurrence of interictal epileptic activity; i.e., forming the epileptic network, were identified by a general linear model considering the timecourse of the fMRI-defined focus as main regressor. dFC between these regions was assessed with a sliding-window approach. dFC timecourses were then correlated with the sliding-window variance of the IED signal (VarIED), to identify connections whose dynamics related to the epileptic activity; i.e., the dynamic epileptic subnetwork. As expected, given the very different clinical picture of each individual, the extent of this subnetwork was highly variable across patients, but was but was reduced of at least 30% with respect to the initially identified epileptic network in 9/10 patients. The connections of the dynamic subnetwork were most commonly close to the epileptic focus, as reflected by the laterality index of the subnetwork connections, reported higher than the one within the original epileptic network. Moreover, the correlation between dFC timecourses and VarIED was predominantly positive, suggesting a strengthening of the dynamic subnetwork associated to the occurrence of IED. The integration of dFC and scalp IED offers a more specific description of the epileptic network, identifying connections strongly influenced by IED. These findings could be relevant in the pre-surgical evaluation for the resection or disconnection of the epileptogenic zone and help in reaching a better post-surgical outcome. This would be particularly important for patients characterised by a widespread pathological brain activity which challenges the surgical intervention.

Dataset Information

What graph theory actually tells us about resting state interictal MEG epileptic activity.

Publications

What graph theory actually tells us about resting state interictal MEG epileptic activity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets