Mangor Pedersen

Increased segregation of brain networks in focal epilepsy: An fMRI graph theory finding

Focal epilepsy is conceived of as activating local areas of the brain as well as engaging regional brain networks. Graph theory represents a powerful quantitative framework for investigation of brain networks. Here we investigate whether functional network changes are present in extratemporal focal epilepsy. Task-free functional magnetic resonance imaging data from 15 subjects with extratemporal epilepsy and 26 age and gender matched healthy controls were used for analysis. Local network properties were calculated using local efficiency, clustering coefficient and modularity metrics. Global network properties were assessed with global efficiency and betweenness centrality metrics. Cost-efficiency of the networks at both local and global levels was evaluated by estimating the physical distance between functionally connected nodes, in addition to the overall numbers of connections in the network. Clustering coefficient, local efficiency and modularity were significantly higher in individuals with focal epilepsy than healthy control subjects, while global efficiency and betweenness centrality were not significantly different between the two groups. Local network properties were also highly efficient, at low cost, in focal epilepsy subjects compared to healthy controls. Our results show that functional networks in focal epilepsy are altered in a way that the nodes of the network are more isolated. We postulate that network regularity, or segregation of the nodes of the networks, may be an adaptation that inhibits the conversion of the interictal state to seizures. It remains possible that this may be part of the epileptogenic process or an effect of medications.

Brain regions with abnormal network properties in severe epilepsy of Lennox‐Gastaut phenotype: Multivariate analysis of task‐free fMRI

Lennox‐Gastaut syndrome, and the similar but less tightly defined Lennox‐Gastaut phenotype, describe patients with severe epilepsy, generalized epileptic discharges, and variable intellectual disability. Our previous functional neuroimaging studies suggest that abnormal diffuse association network activity underlies the epileptic discharges of this clinical phenotype. Herein we use a data‐driven multivariate approach to determine the spatial changes in local and global networks of patients with severe epilepsy of the Lennox‐Gastaut phenotype.

Dynamic regional phase synchrony (DRePS): an instantaneous measure of local fMRI connectivity within spatially clustered brain areas

Dynamic functional brain connectivity analysis is a fast expanding field in computational neuroscience research with the promise of elucidating brain network interactions. Sliding temporal window based approaches are commonly used in order to explore dynamic behavior of brain networks in task‐free functional magnetic resonance imaging (fMRI) data. However, the low effective temporal resolution of sliding window methods fail to capture the full dynamics of brain activity at each time point. These also require subjective decisions regarding window size and window overlap. In this study, we introduce dynamic regional phase synchrony (DRePS), a novel analysis approach that measures mean local instantaneous phase coherence within adjacent fMRI voxels. We evaluate the DRePS framework on simulated data showing that the proposed measure is able to estimate synchrony at higher temporal resolution than sliding windows of local connectivity. We applied DRePS analysis to task‐free fMRI data of 20 control subjects, revealing ultra‐slow dynamics of local connectivity in different brain areas. Spatial clustering based on the DRePS feature time series reveals biologically congruent local phase synchrony networks (LPSNs). Taken together, our results demonstrate three main findings. Firstly, DRePS has increased temporal sensitivity compared to sliding window correlation analysis in capturing locally synchronous events. Secondly, DRePS of task‐free fMRI reveals ultra‐slow fluctuations of ∼0.002–0.02 Hz. Lastly, LPSNs provide plausible spatial information about time‐varying brain local phase synchrony. With the DRePS method, we introduce a framework for interrogating brain local connectivity, which can potentially provide biomarkers of human brain function in health and disease. Hum Brain Mapp 37:1970–1985, 2016.

Spontaneous brain network activity: Analysis of its temporal complexity

The brain operates in a complex way. The temporal complexity underlying macroscopic and spontaneous brain network activity is still to be understood. In this study, we explored the brain’s complexity by combining functional connectivity, graph theory, and entropy analyses in 25 healthy people using task-free functional magnetic resonance imaging. We calculated the pairwise instantaneous phase synchrony between 8,192 brain nodes for a total of 200 time points. This resulted in graphs for which time series of clustering coefficients (the “cliquiness” of a node) and participation coefficients (the between-module connectivity of a node) were estimated. For these two network metrics, sample entropy was calculated. The procedure produced a number of results: (1) Entropy is higher for the participation coefficient than for the clustering coefficient. (2) The average clustering coefficient is negatively related to its associated entropy, whereas the average participation coefficient is positively related to its associated entropy. (3) The level of entropy is network-specific to the participation coefficient, but not to the clustering coefficient. High entropy for the participation coefficient was observed in the default-mode, visual, and motor networks. These results were further validated using an independent replication dataset. Our work confirms that brain networks are temporally complex. Entropy is a good candidate metric to explore temporal network alterations in diseases with paroxysmal brain disruptions, including schizophrenia and epilepsy. Author Summary In recent years, connectomics has provided significant insights into the topological complexity of brain networks. However, the temporal complexity of brain networks still remains somewhat poorly understood. In this study we used entropy analysis to demonstrate that the properties of network segregation (the clustering coefficient) and integration (the participation coefficient) are temporally complex, situated between complete order and disorder. Our results also indicated that “segregated network nodes” may attempt to minimize the network’s entropy, whereas “integrated network nodes” require a higher information load, and therefore need to increase entropy. We believe that combining temporal information from functional brain networks and entropy can be used to test the decomplexification theory of disease, especially in neurological and psychiatric conditions characterized by paroxysmal brain abnormalities (e.g., schizophrenia and epilepsy).

Hierarchical disruption in the Bayesian brain: Focal epilepsy and brain networks

In this opinion paper, we describe a combined view of functional and effective brain connectivity along with the free-energy principle for investigating persistent disruptions in brain networks of patients with focal epilepsy. These changes are likely reflected in effective connectivity along the cortical hierarchy and construct the basis of increased local functional connectivity in focal epilepsy. We propose a testable framework based on dynamic causal modelling and functional connectivity analysis with the capacity of explaining commonly observed connectivity changes during interictal periods. We then hypothesise their possible relation with disrupted free-energy minimisation in the Bayesian brain. This may offer a new approach for neuroimaging to specifically develop and address hypotheses regarding the network pathomechanisms underlying epileptic phenotypes.

The dynamics of functional connectivity in neocortical focal epilepsy

Focal epilepsy is characterised by paroxysmal events, reflecting changes in underlying local brain networks. To capture brain network activity at the maximal temporal resolution of the acquired functional magnetic resonance imaging (fMRI) data, we have previously developed a novel analysis framework called Dynamic Regional Phase Synchrony (DRePS). DRePS measures instantaneous mean phase coherence within neighbourhoods of brain voxels. We use it here to examine how the dynamics of the functional connections of regional brain networks are altered in neocortical focal epilepsy. Using task-free fMRI data from 21 subjects with focal epilepsy and 21 healthy controls, we calculated the power spectral density of DRePS, which is a measure of signal variability in local connectivity estimates. Whole-brain averaged power spectral density of DRePS, or signal variability of local connectivity, was significantly higher in epilepsy subjects compared to healthy controls. Maximal increase in DRePS spectral power was seen in bilateral inferior frontal cortices, ipsilateral mid-cingulate gyrus, superior temporal gyrus, caudate head, and contralateral cerebellum. Our results provide further evidence of common brain abnormalities across people with focal epilepsy. We postulate that dynamic changes in specific cortical brain areas may help maintain brain function in the presence of pathological epileptiform network activity in neocortical focal epilepsy.

Further Insight into the Brain's Rich-Club Architecture

The pioneering field of connectomics has the ambitious aim of mapping and understanding the connections of the brain. Brain connectivity can be analyzed at several spatial resolutions: macroscopic (whole-brain), mesoscopic (neuronal populations), and microscopic (single-neuron) scales ([Bullmore and

How small can the epileptogenic region be?: A case in point

Objective: To present a case that demonstrates that seizures and interictal disturbances can be driven by a small area of functionally abnormal cortex. Methods: Two novel functional MRI network analysis methods were used to supplement conventional seizure and lesion localization methods: (1) regional homogeneity to quantify local connectivity, or synchrony, with a resolution of less than 1 cm3 of cortex; and (2) small-worldness to combine information about whole brain network segregation and integration. Results: After a small corticectomy in the dominant supramarginal gyrus (13 × 7 × 6 mm) limited to the area of abnormal local connectivity, and smaller than the PET and SPECT abnormalities, the patient has been seizure-free for 3 years with no language deficit. Whole brain network characteristics normalized (small-worldness) to that of healthy controls. Conclusions: This case demonstrates that small areas of cortex may be highly epileptogenic, drive intractable epilepsy, and disrupt large-scale networks likely to be involved in core cognitive functions.

Spatiotemporal mapping of epileptic spikes using simultaneous EEG-functional MRI

Epileptic spikes occur on the sub-second timescale and are known to involve not only epileptic foci but also large-scale distributed brain networks. There is likely to be a sequence of neural activity in multiple brain regions that occurs within the duration of a single spike, but standard electroencephalography-functional magnetic resonance imaging analyses, which use only the timing of the spikes to model the functional magnetic resonance imaging data, cannot determine the sequence of these activations. Our aim in this study is to temporally resolve these spatial activations to observe the spatiotemporal dynamics of the spike-related neural activity at a sub-second timescale. We studied eight focal epilepsy patients (age 11-42 years, six female) and used amplitude features of the electroencephalogram specific to different spike components (early and late peaks and troughs) to encode temporal information into our functional magnetic resonance imaging models. This enables us to associate each activation with a specific model of each of the spike components to infer the temporal order of these spike-related spatial activations. In seven of eight patients the distributed networks were associated with the late spike component. The focal activations were more variably coupled with time epochs, but tended to precede the distributed network effects. We also found that incorporating electroencephalogram features into the models increased sensitivity and in six patients revealed additional regions unseen in the standard analysis result. This included strong bilateral thalamus activation in two patients. We demonstrate the clinical utility of this approach in a patient who recently underwent a successful surgical resection of the region where we saw enhanced activation using electroencephalogram amplitude information specific to the early spike component. This focal cluster of activation was larger and more precisely tracked the anatomy compared to what was seen using the standard timing-based analysis. Our novel electroencephalography-functional magnetic resonance imaging data fusion approach, which utilizes information based on the single spike variability across all electroencephalogram channels, has the potential to help us better understand epileptic networks and aid in the interpretation of functional magnetic resonance imaging activation maps during treatment planning.

Polymicrogyric Cortex may Predispose to Seizures via Abnormal Network Topology: An fMRI Connectomics Study.

Polymicrogyria is a significant malformation of cortical development with a high incidence of epilepsy and cognitive deficits. Graph theoretic analysis is a useful approach to studying network organization in brain disorders. In this study, we used task‐free functional magnetic resonance imaging (fMRI) data from four patients with polymicrogyria and refractory epilepsy. Gray matter masks from structural MRI data were parcellated into 1,024 network nodes. Functional “connectomes” were obtained based on fMRI time series between the parcellated network nodes; network analysis was conducted using clustering coefficient, path length, node degree, and participation coefficient. These graph metrics were compared between nodes within polymicrogyric cortex and normal brain tissue in contralateral homologous cortical regions. Polymicrogyric nodes showed significantly increased clustering coefficient and characteristic path length. This is the first study using functional connectivity analysis in polymicrogyria—our results indicate a shift toward a regular network topology in polymicrogyric nodes. Regularized network topology has been demonstrated previously in patients with focal epilepsy and during focal seizures. Thus, we postulate that these network alterations predispose to seizures and may be relevant to cognitive deficits in patients with polymicrogyria.