Concepts of Connectivity and Human Epileptic Activity (original) (raw)
Related papers
Epileptic network activity revealed by dynamic functional connectivity in simultaneous EEG-fMRI
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 2014
Recent findings highlighted the non-stationarity of brain functional connectivity (FC) during resting-state functional magnetic resonance imaging (fMRI), encouraging the development of methods allowing to explore brain network dynamics. This appears particularly relevant when dealing with brain diseases involving dynamic neuronal processes, like epilepsy. In this study, we introduce a new method to pinpoint connectivity changes related to epileptic activity by integrating EEG and dynamic FC information. To our knowledge, no previous work has attempted to integrate dFC with the epileptic activity from EEG. The detailed results obtained from the analysis of two patients successfully detected specific patterns of connections/disconnections related to the epileptic activity and highlighted the potential of a dynamic analysis for a better understanding of network organisation in epilepsy.
Patient-specific connectivity pattern of epileptic network in frontal lobe epilepsy
NeuroImage. Clinical, 2014
There is evidence that focal epilepsy may involve the dysfunction of a brain network in addition to the focal region. To delineate the characteristics of this epileptic network, we collected EEG/fMRI data from 23 patients with frontal lobe epilepsy. For each patient, EEG/fMRI analysis was first performed to determine the BOLD response to epileptic spikes. The maximum activation cluster in the frontal lobe was then chosen as the seed to identify the epileptic network in fMRI data. Functional connectivity analysis seeded at the same region was also performed in 63 healthy control subjects. Nine features were used to evaluate the differences of epileptic network patterns in three connection levels between patients and controls. Compared with control subjects, patients showed overall more functional connections between the epileptogenic region and the rest of the brain and higher laterality. However, the significantly increased connections were located in the neighborhood of the seed, b...
Connectivity strength, time lag structure and the epilepsy network in resting-state fMRI
NeuroImage: Clinical
The relationship between the epilepsy network, intrinsic brain networks and hypersynchrony in epilepsy remains incompletely understood. To converge upon a synthesized understanding of these features, we studied two elements of functional connectivity in epilepsy: correlation and time lag structure using resting state fMRI data from both SEEG-defined epileptic brain regions and whole-brain fMRI analysis. Functional connectivity (FC) was analyzed in 15 patients with epilepsy and 36 controls. Correlation strength and time lag were selected to investigate the magnitude of and temporal interdependency across brain regions. Zone-based analysis was carried out investigating directed correlation strength and time lag between both SEEG-defined nodes of the epilepsy network and between the epileptogenic zone and all other brain regions. Findings were compared between patients and controls and against a functional atlas. FC analysis on the nodal and whole brain levels identifies consistent patterns of altered correlation strength and altered time lag architecture in epilepsy patients compared to controls. These patterns include 1) broadly distributed increased strength of correlation between the seizure onset node and the remainder of the brain, 2) decreased time lag within the seizure onset node, and 3) globally increased time lag throughout all regions of the brain not involved in seizure onset or propagation. Comparing the topographic distribution of findings against a functional atlas, all resting state networks were involved to a variable degree. These local and whole brain findings presented here lead us to propose the network steal hypothesis as a possible mechanistic explanation for the non-seizure clinical manifestations of epilepsy.
Parcel-Based Connectivity Analysis of fMRI Data for the Study of Epileptic Seizure Propagation
Brain Topography, 2012
The aim of this work is to improve fMRI Granger Causality Analysis (GCA) by proposing and comparing two strategies for defining the topology of the networks among which cerebral connectivity is measured and to apply fMRI GCA for studying epileptic seizure propagation. The first proposed method is based on information derived from anatomical atlas only; the other one is based on functional information and employs an algorithm of hierarchical clustering applied to fMRI data directly. Both methods were applied to signals recorded during seizures on a group of epileptic subjects and two connectivity matrices were obtained for each patient. The performances of the different parcellation strategies were evaluated in terms of their capability to recover information about the source and the sink of the network (i.e., the starting and the ending point of the seizure propagation). The first method allows to clearly identify the seizure onset in all patients, whereas the network sources are not so immediately recognizable when the second method was used. Nevertheless, results obtained using functional clustering do not contradict those obtained with the anatomical atlas and are able to individuate the main pattern of propagation. In conclusion, the way nodes are defined can influence the easiness of identification of the epileptogenic focus but does not produce contradictory results showing the effectiveness of proposed approach to formulate hypothesis about seizure propagation at least in the early phase of investigation.
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.
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...