Concepts of Connectivity and Human Epileptic Activity (original) (raw)
Physiology of functional and effective networks in epilepsy
Clinical Neurophysiology, 2014
The relationships between functional and structural networks provide insights into brain abnormalities that are observed in epilepsy. Functional and effective connectivity methods have been used to identify the ictal onset zone as well as to characterize the onset, spread, and termination of seizures. Studies of the dynamics of epileptic networks suggest mechanisms that may explain the sudden onset and termination of seizures.
PLoS ONE, 2011
In this study, we aimed to demonstrate whether spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal derived from resting state functional magnetic resonance imaging (fMRI) reflect spontaneous neuronal activity in pathological brain regions as well as in regions spared by epileptiform discharges. This is a crucial issue as coherent fluctuations of fMRI signals between remote brain areas are now widely used to define functional connectivity in physiology and in pathophysiology. We quantified functional connectivity using non-linear measures of cross-correlation between signals obtained from intracerebral EEG (iEEG) and resting-state functional MRI (fMRI) in 5 patients suffering from intractable temporal lobe epilepsy (TLE). Functional connectivity was quantified with both modalities in areas exhibiting different electrophysiological states (epileptic and non affected regions) during the interictal period. Functional connectivity as measured from the iEEG signal was higher in regions affected by electrical epileptiform abnormalities relative to nonaffected areas, whereas an opposite pattern was found for functional connectivity measured from the BOLD signal. Significant negative correlations were found between the functional connectivities of iEEG and BOLD signal when considering all pairs of signals (theta, alpha, beta and broadband) and when considering pairs of signals in regions spared by epileptiform discharges (in broadband signal). This suggests differential effects of epileptic phenomena on electrophysiological and hemodynamic signals and/or an alteration of the neurovascular coupling secondary to pathological plasticity in TLE even in regions spared by epileptiform discharges. In addition, indices of directionality calculated from both modalities were consistent showing that the epileptogenic regions exert a significant influence onto the non epileptic areas during the interictal period. This study shows that functional connectivity measured by iEEG and BOLD signals give complementary but sometimes inconsistent information in TLE.
Progress in Neurobiology, 2014
Today, neuroimaging techniques are frequently used to investigate the integration of functionally specialized brain regions in a network. Functional connectivity, which quantifies the statistical dependencies among the dynamics of simultaneously recorded signals, allows to infer the dynamical interactions of segregated brain regions. In this review we discuss how the functional connectivity patterns obtained from intracranial and scalp electroencephalographic (EEG) recordings reveal information about the dynamics of the epileptic brain and can be used to predict upcoming seizures and to localize the seizure onset zone. The added value of extracting information that is not visibly identifiable in the EEG data using functional connectivity * Corresponding author.
Epileptic Disorders, 2020
The human brain is increasingly seen as a dynamic neural system, the function of which relies on a diverse set of connections between brain regions. To assess these complex dynamical interactions, formalism of complex networks was suggested as one of the most promising tools to offer new insight into the brain's structural and functional organization, with a potential also for clinical implications. Irrespective of the brain mapping technique, modern network approaches have revealed fundamental aspects of normal brain-network organization, such as small-world and scale-free patterns, hierarchical modularity, and the presence of hubs. Moreover, the utility of these approaches, to gain a better understanding of neurological diseases, is of great interest. In the present contribution, we first describe the basic network measures and how the brain networks are constructed on the basis of brain activity data in order to introduce clinical neurologists to this new theoretical paradigm. We then demonstrate how network formalism can be used to detect changes in EEG-based functional connectivity patterns in six paediatric patients with childhood absence epilepsy. Notably, our results do not only indicate enhanced synchronicity during epileptic episodes but also reveal specific spatial changes in the electrical activity of the brain. We argue that the network-based evaluation of functional brain networks can provide clinicians with more detailed insight into the activity of a pathological brain and can also be regarded as a support for objective diagnosis and treatment for various neurological diseases.
Structural and effective connectivity in focal epilepsy
NeuroImage. Clinical, 2018
Patients with medically-refractory focal epilepsy may be candidates for neurosurgery and some may require placement of intracranial EEG electrodes to localise seizure onset. Assessing cerebral responses to single pulse electrical stimulation (SPES) may give diagnostically useful data. SPES produces cortico-cortical evoked potentials (CCEPs), which infer effective brain connectivity. Diffusion-weighted images and tractography may be used to estimate structural brain connectivity. This combination provides the opportunity to observe seizure onset and its propagation throughout the brain, spreading contiguously along the cortex explored with electrodes, or non-contiguously. We analysed CCEPs and diffusion tractography in seven focal epilepsy patients and reconstructed the effective and structural brain networks. We aimed to assess the inter-modal similarity of the networks at a large scale across the cortex, the effective and structural connectivity of the ictal-onset zone, and investi...
Causality within the Epileptic Network: An EEG-fMRI Study Validated by Intracranial EEG
Frontiers in Neurology, 2013
Accurate localization of the Seizure Onset Zone (SOZ) is crucial in patients with drugresistance focal epilepsy. EEG with fMRI recording (EEG-fMRI) has been proposed as a complementary non-invasive tool, which can give useful additional information in the presurgical work-up. However, fMRI maps related to interictal epileptiform activities (IED) often show multiple regions of signal change, or "networks," rather than highly focal ones. Effective connectivity approaches like Dynamic Causal Modeling (DCM) applied to fMRI data potentially offers a framework to address which brain regions drives the generation of seizures and IED within an epileptic network. Here, we present a first attempt to validate DCM on EEG-fMRI data in one patient affected by frontal lobe epilepsy. Pre-surgical EEG-fMRI demonstrated two distinct clusters of blood oxygenation level dependent (BOLD) signal increases linked to IED, one located in the left frontal pole and the other in the ipsilateral dorso-lateral frontal cortex. DCM of the IED-related BOLD signal favored a model corresponding to the left dorso-lateral frontal cortex as driver of changes in the fronto-polar region. The validity of DCM was supported by: (a) the results of two different non-invasive analysis obtained on the same dataset: EEG source imaging (ESI), and "psycho-physiological interaction" analysis; (b) the failure of a first surgical intervention limited to the fronto-polar region; (c) the results of the intracranial EEG monitoring performed after the first surgical intervention confirming a SOZ located over the dorso-lateral frontal cortex.These results add evidence that EEG-fMRI together with advanced methods of BOLD signal analysis is a promising tool that can give relevant information within the epilepsy surgery diagnostic work-up.
Analyzing epileptogenic brain connectivity networks using clinical EEG data
Epileptogenic brain connectivity networks are altered compared to normal ones. Here, we have investigated the properties of epileptogenic networks by applying graph theoretical , statistical and machine learning approaches to the resting state electroencephalography (EEG) recordings obtained from 30 normal volunteers and 51 patients suffering from generalized epilepsy. In the case of epileptic patients, we have found that the brain networks behave like random networks. There is some loss in node connectivity. Hub nodes are more affected during epilepsy. Hence, the epileptogenic networks show less clustering coefficient than normal ones. In addition, we have identified 11 specific regions of brains and ten most significant connections among them as an epileptogenic signature by feature extraction. The ten most significant features are used to classify 81 sample data sets into two classes, i.e., epileptogenic and normal, with 79.01% accuracy. The highly probable eleven regions of human brain according to the positions of electrodes and connections among them may lead to a progress in the clinical treatment of epileptic patients.
Brain topography, 2017
For the first time in research in humans, we used simultaneous icEEG-fMRI to examine the link between connectivity in haemodynamic signals during the resting-state (rs) and connectivity derived from electrophysiological activity in terms of the inter-modal connectivity correlation (IMCC). We quantified IMCC in nine patients with drug-resistant epilepsy (i) within brain networks in 'healthy' non-involved cortical zones (NIZ) and (ii) within brain networks involved in generating seizures and interictal spikes (IZ1) or solely spikes (IZ2). Functional connectivity (h (2) ) estimates for 10 min of resting-state data were obtained between each pair of electrodes within each clinical zone for both icEEG and fMRI. A sliding window approach allowed us to quantify the variability over time of h (2) (vh (2)) as an indicator of connectivity dynamics. We observe significant positive IMCC for h (2) and vh (2), for multiple bands in the NIZ only, with the strongest effect in the lower icEE...
Functional Modularity of Background Activities in Normal and Epileptic Brain Networks
Physical Review Letters, 2010
We analyze the connectivity structure of weighted brain networks extracted from spontaneous magnetoencephalographic (MEG) signals of healthy subjects and epileptic patients (suffering from absence seizures) recorded at rest. We find that, for the activities in the 5-14 Hz range, healthy brains exhibit a sparse connectivity, whereas the brain networks of patients display a rich connectivity with clear modular structure. Our results suggest that modularity plays a key role in the functional organization of brain areas during normal and pathological neural activities at rest. 87.19.le, 87.19.lj From the brain to the Internet and to social groups, the characterization of the connectivity patterns of complex systems has revealed a wiring organization that can be captured neither by regular lattices, nor by random graphs . In neurosciences, it is widely acknowledged that the emergence of several pathological states is accompanied by changes in brain connectivity patterns . Recently, it has been found that functional connectivity patterns obtained from magnetoencephalography (MEG) and electroencephalography (EEG) signals during different pathological and cognitive brain states (including epilepsy) display small-world (SW) properties . Empirical studies have also lead to the hypothesis that brain functions rely on the coordination of a scattered mosaic of functionally specialized brain regions (modules), forming a web-like structure of neural assemblies . Modularity is a key concept in complex networks from RNA structures to social networks . A module is usually defined as a subset of units within a network, such that connections between them are denser than connections with the rest of the network. In biological systems, it is generally acknowledged that modularity results from evolutionary constraints and plays a key role in robustness, flexibility and stability .