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Characterization of Functional and Structural Integrity in Experimental Focal Epilepsy: Reduced Network Efficiency Coincides with White Matter Changes  [PDF]
Willem M. Otte, Rick M. Dijkhuizen, Maurits P. A. van Meer, Wilhelmina S. van der Hel, Suzanne A. M. W. Verlinde, Onno van Nieuwenhuizen, Max A. Viergever, Cornelis J. Stam, Kees P.J. Braun
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0039078
Abstract: Background Although focal epilepsies are increasingly recognized to affect multiple and remote neural systems, the underlying spatiotemporal pattern and the relationships between recurrent spontaneous seizures, global functional connectivity, and structural integrity remain largely unknown. Methodology/Principal Findings Here we utilized serial resting-state functional MRI, graph-theoretical analysis of complex brain networks and diffusion tensor imaging to characterize the evolution of global network topology, functional connectivity and structural changes in the interictal brain in relation to focal epilepsy in a rat model. Epileptic networks exhibited a more regular functional topology than controls, indicated by a significant increase in shortest path length and clustering coefficient. Interhemispheric functional connectivity in epileptic brains decreased, while intrahemispheric functional connectivity increased. Widespread reductions of fractional anisotropy were found in white matter regions not restricted to the vicinity of the epileptic focus, including the corpus callosum. Conclusions/Significance Our longitudinal study on the pathogenesis of network dynamics in epileptic brains reveals that, despite the locality of the epileptogenic area, epileptic brains differ in their global network topology, connectivity and structural integrity from healthy brains.
Altered Resting State Brain Dynamics in Temporal Lobe Epilepsy Can Be Observed in Spectral Power, Functional Connectivity and Graph Theory Metrics  [PDF]
Maher A. Quraan, Cornelia McCormick, Melanie Cohn, Taufik A. Valiante, Mary Pat McAndrews
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0068609
Abstract: Despite a wealth of EEG epilepsy data that accumulated for over half a century, our ability to understand brain dynamics associated with epilepsy remains limited. Using EEG data from 15 controls and 9 left temporal lobe epilepsy (LTLE) patients, in this study we characterize how the dynamics of the healthy brain differ from the “dynamically balanced” state of the brain of epilepsy patients treated with anti-epileptic drugs in the context of resting state. We show that such differences can be observed in band power, synchronization and network measures, as well as deviations from the small world network (SWN) architecture of the healthy brain. The θ (4–7 Hz) and high α (10–13 Hz) bands showed the biggest deviations from healthy controls across various measures. In particular, patients demonstrated significantly higher power and synchronization than controls in the θ band, but lower synchronization and power in the high α band. Furthermore, differences between controls and patients in graph theory metrics revealed deviations from a SWN architecture. In the θ band epilepsy patients showed deviations toward an orderly network, while in the high α band they deviated toward a random network. These findings show that, despite the focal nature of LTLE, the epileptic brain differs in its global network characteristics from the healthy brain. To our knowledge, this is the only study to encompass power, connectivity and graph theory metrics to investigate the reorganization of resting state functional networks in LTLE patients.
Stability of Synchronization Clusters and Seizurability in Temporal Lobe Epilepsy  [PDF]
Agostina Palmigiano, Jesús Pastor, Rafael García de Sola, Guillermo J. Ortega
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0041799
Abstract: Purpose Identification of critical areas in presurgical evaluations of patients with temporal lobe epilepsy is the most important step prior to resection. According to the “epileptic focus model”, localization of seizure onset zones is the main task to be accomplished. Nevertheless, a significant minority of epileptic patients continue to experience seizures after surgery (even when the focus is correctly located), an observation that is difficult to explain under this approach. However, if attention is shifted from a specific cortical location toward the network properties themselves, then the epileptic network model does allow us to explain unsuccessful surgical outcomes. Methods The intraoperative electrocorticography records of 20 patients with temporal lobe epilepsy were analyzed in search of interictal synchronization clusters. Synchronization was analyzed, and the stability of highly synchronized areas was quantified. Surrogate data were constructed and used to statistically validate the results. Our results show the existence of highly localized and stable synchronization areas in both the lateral and the mesial areas of the temporal lobe ipsilateral to the clinical seizures. Synchronization areas seem to play a central role in the capacity of the epileptic network to generate clinical seizures. Resection of stable synchronization areas is associated with elimination of seizures; nonresection of synchronization clusters is associated with the persistence of seizures after surgery. Discussion We suggest that synchronization clusters and their stability play a central role in the epileptic network, favoring seizure onset and propagation. We further speculate that the stability distribution of these synchronization areas would differentiate normal from pathologic cases.
Disruption of Rolandic Gamma-Band Functional Connectivity by Seizures is Associated with Motor Impairments in Children with Epilepsy  [PDF]
George M. Ibrahim, Tomoyuki Akiyama, Ayako Ochi, Hiroshi Otsubo, Mary Lou Smith, Margot J. Taylor, Elizabeth Donner, James T. Rutka, O. Carter Snead, Sam M. Doesburg
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0039326
Abstract: Although children with epilepsy exhibit numerous neurological and cognitive deficits, the mechanisms underlying these impairments remain unclear. Synchronization of oscillatory neural activity in the gamma frequency range (>30 Hz) is purported to be a mechanism mediating functional integration within neuronal networks supporting cognition, perception and action. Here, we tested the hypothesis that seizure-induced alterations in gamma synchronization are associated with functional deficits. By calculating synchrony among electrodes and performing graph theoretical analysis, we assessed functional connectivity and local network structure of the hand motor area of children with focal epilepsy from intracranial electroencephalographic recordings. A local decrease in inter-electrode phase synchrony in the gamma bands during ictal periods, relative to interictal periods, within the motor cortex was strongly associated with clinical motor weakness. Gamma-band ictal desychronization was a stronger predictor of deficits than the presence of the seizure-onset zone or lesion within the motor cortex. There was a positive correlation between the magnitude of ictal desychronization and impairment of motor dexterity in the contralateral, but not ipsilateral hand. There was no association between ictal desynchronization within the hand motor area and non-motor deficits. This study uniquely demonstrates that seizure-induced disturbances in cortical functional connectivity are associated with network-specific neurological deficits.
Resting-State EEG Source Localization and Functional Connectivity in Schizophrenia-Like Psychosis of Epilepsy  [PDF]
Leonides Canuet, Ryouhei Ishii, Roberto D. Pascual-Marqui, Masao Iwase, Ryu Kurimoto, Yasunori Aoki, Shunichiro Ikeda, Hidetoshi Takahashi, Takayuki Nakahachi, Masatoshi Takeda
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0027863
Abstract: Background It is unclear whether, like in schizophrenia, psychosis-related disruption in connectivity between certain regions, as an index of intrinsic functional disintegration, occurs in schizophrenia-like psychosis of epilepsy (SLPE). In this study, we sought to determine abnormal patterns of resting-state EEG oscillations and functional connectivity in patients with SLPE, compared with nonpsychotic epilepsy patients, and to assess correlations with psychopathological deficits. Methodology/Principal Findings Resting EEG was recorded in 21 patients with focal epilepsy and SLPE and in 21 clinically-matched non-psychotic epilepsy controls. Source current density and functional connectivity were determined using eLORETA software. For connectivity analysis, a novel nonlinear connectivity measure called “lagged phase synchronization” was used. We found increased theta oscillations in regions involved in the default mode network (DMN), namely the medial and lateral parietal cortex bilaterally in the psychotic patients relative to their nonpsychotic counterparts. In addition, patients with psychosis had increased beta temporo-prefrontal connectivity in the hemisphere with predominant seizure focus. This functional connectivity in temporo-prefrontal circuits correlated with positive symptoms. Additionally, there was increased interhemispheric phase synchronization between the auditory cortex of the affected temporal lobe and the Broca's area correlating with auditory hallucination scores. Conclusions/Significance In addition to dysfunction of parietal regions that are part of the DMN, resting-state disrupted connectivity of the medial temporal cortex with prefrontal areas that are either involved in the DMN or implicated in psychopathological dysfunction may be critical to schizophrenia-like psychosis, especially in individuals with temporal lobe epilepsy. This suggests that DMN deficits might be a core neurobiological feature of the disorder, and that abnormalities in theta oscillations and beta phase synchronization represent the underlying neural activity.
Focal Epilepsy Associated with Glioneuronal Tumors  [PDF]
Giulia Loiacono,Chiara Cirillo,Francesco Chiarelli,Alberto Verrotti
ISRN Neurology , 2011, DOI: 10.5402/2011/867503
Abstract: Glioneuronal tumors are an increasingly recognized cause of partial seizures that occur primarily in children and young adults. Focal epilepsy associated with glioneuronal tumors is often resistant to pharmacological treatment. The cellular mechanisms underlying the epileptogenicity of glioneuronal tumors remain largely unknown. The involved mechanisms are certain to be multifactorial and depend on specific tumor histology, integrity of the blood-brain barrier, characteristics of the peritumoral environment, circuit abnormalities, or cellular and molecular defects. Glioneuronal tumors presenting with epilepsy were observed to have relatively benign biological behavior. The completeness of the tumor resection is of paramount importance in avoiding tumor progression and malignant transformation, which are rare in cases of epileptogenic glioneuronal tumors. An evolving understanding of the various mechanisms of tumor-related epileptogenicity may also lead to a more defined surgical objective and effective therapeutic strategies, including antiepileptogenic treatments, to prevent epilepsy in at-risk patients. 1. Introduction Glioneuronal tumors are an increasingly recognized cause of partial seizures that occur primarily in children and young adults [1, 2]. These are tumors with an admixture of glial and neuronal components. Both cell types are thought to be part of the same neoplastic process. Entrapment of preexisting neurons by an infiltrating glioma therefore has to be distinguished from glioneuronal tumors. More well-established examples of glioneuronal tumors include dysembryoplastic neuroepithelial tumors (DNTs) ganglioglioma and desmoplastic infantile ganglioglioma. More recently recognized entities partly included in the latest version of the WHO classification include the rosette-forming tumor of the fourth ventricle the papillary glioneuronal tumor and rosetted glioneuronal tumor/glioneuronal tumor with neuropil-like islands. The glial component in these tumors varies but often resembles either a pilocytic astrocytoma or an infiltrating glioma with astrocytic or oligodendroglial features [3]. Gangliogliomas and DNTs arise most commonly in the temporal lobe and appear to be associated with an increased incidence of cortical dysplasia or neuronal migration abnormalities [1, 4, 5]. Focal epilepsy that is often resistant to pharmacological treatment is a common presenting symptom of glioneuronal tumors [1, 2]. Even though the biological behavior of these tumors is usually benign, especially when patients present only with epilepsy, cases of tumor
Adaptive Synchronization of An Uncertain Complex Dynamical Network  [PDF]
Jin Zhou,Junan Lu,Jinhu Lu
Physics , 2005,
Abstract: This brief paper further investigates the locally and globally adaptive synchronization of an uncertain complex dynamical network. Several network synchronization criteria are deduced. Especially, our hypotheses and designed adaptive controllers for network synchronization are rather simple in form. It is very useful for future practical engineering design. Moreover, numerical simulations are also given to show the effectiveness of our synchronization approaches.
Does sleep deprivation alter functional EEG networks in children with focal epilepsy?  [PDF]
Eric van Diessen,Willem M. Otte,Cornelis J. Stam
Frontiers in Systems Neuroscience , 2014, DOI: 10.3389/fnsys.2014.00067
Abstract: Electroencephalography (EEG) recordings after sleep deprivation increase the diagnostic yield in patients suspected of epilepsy if the routine EEG remains inconclusive. Sleep deprivation is associated with increased interictal EEG abnormalities in patients with epilepsy, but the exact mechanism is unknown. In this feasibility study, we used a network analytical approach to provide novel insights into this clinical observation. The aim was to characterize the effect of sleep deprivation on the interictal functional network organization using a unique dataset of paired routine and sleep deprivation recordings in patients and controls. We included 21 children referred to the first seizure clinic of our center with suspected new onset focal epilepsy in whom a routine interictal and a sleep deprivation EEG (SD-EEG) were performed. Seventeen children, in whom the diagnosis of epilepsy was excluded, served as controls. For both time points weighted functional networks were constructed based on interictal artifact free time-series. Routine and sleep deprivation networks were characterized at different frequency bands using minimum spanning tree (MST) measures (leaf number and diameter) and classical measures of integration (path length) and segregation (clustering coefficient). A significant interaction was found for leaf number and diameter between patients and controls after sleep deprivation: patients showed a shift toward a more path-like MST network whereas controls showed a shift toward a more star-like MST network. This shift in network organization after sleep deprivation in patients is in accordance with previous studies showing a more regular network organization in the ictal state and might relate to the increased epileptiform abnormalities found in patients after sleep deprivation. Larger studies are needed to verify these results. Finally, MST measures were more sensitive in detecting network changes as compared to the classical measures of integration and segregation.
Diagnosis of Epilepsy By Artificial Neural Network  [PDF]
M.I. EL-Gohary,A.S.A. Mohamed,M.M. Dahab,M.A. Ibrahim
Journal of Biological Sciences , 2008,
Abstract: A common feature of epilepsy in EEG signals is an excessive electrical discharge which is appeared as electrical potentials of high amplitudes and frequencies with abrupt onset and rise in amplitude, rythmicity and abnormal synchronization. These potential discharges were termed Seizure patterns. Although several details concerning the cellular basis of these seizure patterns are unknown, numerous experiments led to the general agreement that they reflect a spontaneous and uncontrolled firing of a large number of neurons within a certain region of the brain. Artificial Neural Network (ANN) was proposed in this research as a decision-making tool supported by experimental data to differentiate between healthy and epileptic EEG signals, with accuracy up to 90.2%. This was done by teaching the ANN to perform this function i.e., by Artificial Intelligence (AI) of ANN. The performance of the ANN was calculated for each model`s node to obtain the performance of the node. ANN approach is a powerful tool which is promising to give available results in analysis of bioelectric signals.
Inadequate experimental methods and erroneous epilepsy diagnostic criteria result in confounding acquired focal epilepsy with genetic absence epilepsy  [PDF]
Raimondo D'Ambrosio,Clifford L. Eastman,John W. Miller
Quantitative Biology , 2015,
Abstract: Here we provide a thorough discussion of the study conducted by Rodgers et al. (J Neurosci. 2015; 35(24):9194-204. doi: 10.1523/JNEUROSCI.0919-15.2015) to investigate focal seizures and acquired epileptogenesis induced by head injury in the rat. This manuscript serves as supplementary document for our letter to the Editor to appear in the Journal of Neuroscience. We find that the subject article suffers from poor experimental design, very selective consideration of antecedent literature, and application of inappropriate epilepsy diagnostic criteria which, together, lead to unwarranted conclusions.
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