Physiology of functional and effective networks in epilepsy (original) (raw)
Related papers
Concepts of Connectivity and Human Epileptic Activity
Frontiers in Systems Neuroscience, 2011
We are motivated by the following two perceived needs: firstly, to relate the various measures of connectivity found in the field of epilepsy research to the more general language of functional and effective connectivity as used in neuroscience (neuroimaging) and secondly, to gage the potential benefits of applying state-of-the-art connectivity methods to answer scientific questions raised within the field of human epilepsy research.
Network dynamics of the brain and influence of the epileptic seizure onset zone
The human brain is a dynamic networked system. Patients with partial epileptic seizures have focal regions that periodically diverge from normal brain network dynamics during seizures. We studied the evolution of brain connectivity before, during, and after seizures with graph-theoretic techniques on continuous electrocorticographic (ECoG) recordings (5.4 ± 1.7 d per patient, mean ± SD) from 12 patients with temporal, occipital, or frontal lobe partial onset seizures. Each electrode was considered a node in a graph, and edges between pairs of nodes were weighted by their coherence within a frequency band. The leading eigenvector of the connectivity matrix, which captures network structure, was tracked over time and clustered to uncover a finite set of brain network states. Across patients, we found that (i) the network connectivity is structured and defines a finite set of brain states, (ii) seizures are characterized by a consistent sequence of states, (iii) a subset of nodes is isolated from the network at seizure onset and becomes more connected with the network toward seizure termination, and (iv) the isolated nodes may identify the seizure onset zone with high specificity and sensitivity. To localize a seizure, clinicians visually inspect seizures recorded from multiple intracranial electrode contacts, a time-consuming process that may not always result in definitive localization. We show that network metrics computed from all ECoG channels capture the dynamics of the seizure onset zone as it diverges from normal overall network structure. This suggests that a state space model can be used to help localize the seizure onset zone in ECoG recordings.
Abnormal binding and disruption in large scale networks involved in human partial seizures
There is a marked increase in the amount of electrophysiological and neuroimaging works dealing with the study of large scale brain connectivity in the epileptic brain. Our view of the epileptogenic process in the brain has largely evolved over the last twenty years from the historical concept of " epileptic focus " to a more complex description of " Epileptogenic networks " involved in the genesis and " propagation " of epileptic activities. In particular, a large number of studies have been dedicated to the analysis of intracerebral EEG signals to characterize the dynamic of interactions between brain areas during temporal lobe seizures. These studies have reported that large scale functional connectivity is dramatically altered during seizures, particularly during temporal lobe seizure genesis and development. Dramatic changes in neural synchrony provoked by epileptic rhythms are also responsible for the production of ictal symptoms or changes in patient's behaviour such as automatisms, emotional changes or consciousness alteration. Beside these studies dedicated to seizures, large-scale network connectivity during the interictal state has also been investigated not only to define biomarkers of epileptogenicity but also to better understand the cognitive impairments observed between seizures. Review Approximately 30% of focal epilepsies are resistant to antiepileptic drugs. In this situation , surgical resection of the epileptogenic zone (EZ) is the only therapeutic option able to suppress seizures. The localisation and the definition of the EZ are therefore crucial issues that can be addressed through detailed analysis of anatomo-functional data acquired in epileptic patients during pre-surgical evaluation. From a theoretical viewpoint, the EZ is a highly illustrative example of complex system exhibiting nonlinear dynamics as well as ruptures (more or less abrupt) between these dynamics (typically during the transition from interictal to ictal activity) as reflected by signals directly recorded from involved brain structures. Several reviews dealing with " neural networks " and epilepsy or with synchrony and epilepsy [1,2] are available in the literature. With regard to these reviews, our objectives were more specifically to focus on works studying functional connectivity from stereotactic EEG (SEEG) signals, to propose a general framework (" the epileptogenic
PLOS Computational Biology, 2015
Drug-resistant epilepsy is traditionally characterized by pathologic cortical tissue comprised of seizure-initiating 'foci'. These 'foci' are thought to be embedded within an epileptic network whose functional architecture dynamically reorganizes during seizures through synchronous and asynchronous neurophysiologic processes. Critical to understanding these dynamics is identifying the synchronous connections that link foci to surrounding tissue and investigating how these connections facilitate seizure generation and termination. We use intracranial recordings from neocortical epilepsy patients undergoing pre-surgical evaluation to analyze functional connectivity before and during seizures. We develop and apply a novel technique to track network reconfiguration in time and to parse these reconfiguration dynamics into distinct seizure states, each characterized by unique patterns of network connections that differ in their strength and topography. Our approach suggests that seizures are generated when the synchronous relationships that isolate seizure 'foci' from the surrounding epileptic network are broken down. As seizures progress, foci reappear as isolated subnetworks, marking a shift in network state that may aid seizure termination. Collectively, our observations have important theoretical implications for understanding the spatial involvement of distributed cortical structures in the dynamics of seizure generation, propagation and termination, and have practical significance in determining which circuits to modulate with implantable devices. epileptic networks | seizure focus | network state | synchrony | graph theory | community detection | dynamic network neuroscience Abbreviations: ECoG, electrocorticography Significance Statement. Localization-related epilepsy affects ≈80% of epilepsy patients and is often resistant to medication. The challenge for treating patients is mapping dynamic connectivity between cortical structures in the epileptic network during seizures. While it is well known that whole-brain functional architecture reconfigures during tasks, we hypothesize that epileptic networks reconfigure at the meso-scale leading to seizure initiation, propagation, and termination. We develop new methods to track dynamic network reconfiguration amongst connections of different strength as seizures evolve. Our results indicate that seizure onset is primarily driven by the breakdown of strong connections that re-surge in an isolated focal sub-network as seizures transition to termination. These findings have practical implications for targeting specific connections with implantable, therapeutic devices to control seizures. Footline Author PNAS Issue Date Volume Issue Number 7 arXiv:1407.5105v1 [q-bio.NC]
Large scale brain networks in epilepsy
2008
Two studies of brain networks, performed on interictal intracranial EEGs recorded during the presurgical evaluation of patients with epilepsy, are presented in this report. In the first we examine pairwise relationships between pre-defined brain regions in 12 patients, 6 with medial temporal onset of seizures and 6 with frontal and parietal onset of seizures. We demonstrate that differences, in pairwise relationships between brain regions, allow a distinction of these two groups of patients. In the second study we evaluate short, mid, and long-distance brain connectivity as a function of distance to the seizure onset area in another 2 patients. We demonstrate that the measures of brain connectivity distinguish between brain areas which are close to and far from the seizure onset area. The results of the two studies may help both define large scale brain networks involved in the generation of seizures, and localize the area of seizure onset.
Evolving functional network properties and synchronizability during human epileptic seizures
… Journal of Nonlinear …, 2008
We assess electrical brain dynamics before, during, and after 100 human epileptic seizures with different anatomical onset locations by statistical and spectral properties of functionally defined networks. We observe a concave-like temporal evolution of characteristic path length and cluster coefficient indicative of a movement from a more random toward a more regular and then back toward a more random functional topology. Surprisingly, synchronizability was significantly decreased during the seizure state but increased already prior to seizure end. Our findings underline the high relevance of studying complex systems from the viewpoint of complex networks, which may help to gain deeper insights into the complicated dynamics underlying epileptic seizures.
Network dynamics of the epileptic brain at rest
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2010
Baseline neurodynamics are believed to play an important role in normal brain function. A potentially intrinsic property of the brain is the weak coupling between networks at rest, which enables it to be flexible, adapt, process novel stimuli, and learn. Brain regions become differentially coordinated in response to cognitive task and behavior demands and external stimuli. However, abnormally synchronized resting brain networks may also be associated with different pathologies. We investigated baseline network dynamics in the epileptic brain using information theoretic parameters to quantify coupling and directionality of information flow between different cortical regions. We estimated relative entropy, conditional mutual information and a related measure of directional coupling, from EEGs of patients with epilepsy and healthy subjects. At rest, the healthy brain appears to be characterized by low and non-directional network coupling, whereas the epileptic brain appears to be trans...
Large scale brain networks in epilepsy
Advanced Signal Processing Algorithms, Architectures, and Implementations XVIII, 2008
Two studies of brain networks, performed on interictal intracranial EEGs recorded during the presurgical evaluation of patients with epilepsy, are presented in this report. In the first we examine pairwise relationships between pre-defined brain regions in 12 patients, 6 with medial temporal onset of seizures and 6 with frontal and parietal onset of seizures. We demonstrate that differences, in pairwise relationships between brain regions, allow a distinction of these two groups of patients. In the second study we evaluate short, mid, and long-distance brain connectivity as a function of distance to the seizure onset area in another 2 patients. We demonstrate that the measures of brain connectivity distinguish between brain areas which are close to and far from the seizure onset area. The results of the two studies may help both define large scale brain networks involved in the generation of seizures, and localize the area of seizure onset.
Connectivity Dynamics of Interictal Epileptiform Activity
2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE), 2017
Patterns of interictal epileptiform activities, such as sharp waves, spikes, spike-wave complexes and polyspike-wave complexes are explored in the recorded electroencephalograms (EEG) to gauge the different functional connectivity dynamics and to assess how they could be affected by the type of a seizure. Connectivity measures were represented by the phase synchronization among scalp electrodes that were obtained by adopting a nonlinear data-driven method. These interictal epileptic activities were investigated using a graph theory analysis. The connectivity maps were compared by considering the number of connections in four main brain regions (anterior region, posterior region, left hemisphere and right hemisphere). Results revealed interesting and different network topology for the connectivity maps. Besides, a relationship between the connectivity patterns of the recorded epileptic activities and the types of seizures was observed. This relationship was statistically confirmed by analysis of variance (ANOVA) that denoted a significant difference among connectivity patterns of sharp waves and spike activities, which were seen in focal epilepsy, in contrast to the spike-wave and polyspike-wave complexes that were associated with generalized epilepsy (). These results augment the prospects for diagnosis and enhance the recognition of the disease type via EEG-based connectivity maps.