Review article
Epilepsy as a disease affecting neural networks: a neurophysiological perspective
Epilepsia como una enfermedad de redes neuronales. Un punto de vista neurofisiológico
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San-Juan, D.A. Rodríguez-Méndez" "autores" => array:2 [ 0 => array:4 [ "nombre" => "D." "apellidos" => "San-Juan" "email" => array:1 [ 0 => "dsanjuan@innn.edu.mx" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "D.A." "apellidos" => "Rodríguez-Méndez" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] ] "afiliaciones" => array:2 [ 0 => array:3 [ "entidad" => "Departamento de Investigación Clínica, Instituto Nacional de Neurología y Neurocirugía, Ciudad de México, Mexico" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Facultad de Ciencias, Universidad Autónoma del Estado de México, Toluca de Lerdo, Mexico" "etiqueta" => "b" "identificador" => "aff0010" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Epilepsia como una enfermedad de redes neuronales. Un punto de vista neurofisiológico" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:8 [ "identificador" => "fig0020" "etiqueta" => "Figure 4" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr4.jpeg" "Alto" => 2213 "Ancho" => 3500 "Tamanyo" => 1006661 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0020" "detalle" => "Figure " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">EEG recording of typical tonic-clonic seizures. A) Interictal and preictal periods. B) Ictal period. C) Ictal and postictal periods.</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Epilepsy is a chronic disorder of the central nervous system that affects individuals of all ages and geographical origins.<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> According to the Global Burden of Disease Study 2016, epilepsy accounts for 0.5% of disability-adjusted life-years for all diseases and injuries and 5.0% of disability-adjusted life-years for neurological disorders. In 2016, epilepsy (idiopathic and secondary) was estimated to affect 45.9 million individuals (range, 39.9-54.6).<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a></p><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Definition of epilepsy and epileptic seizure</span><p id="par0010" class="elsevierStylePara elsevierViewall">Epilepsy is a disorder of the brain characterised by an enduring predisposition to generate epileptic seizures, which has neurobiological, cognitive, psychological, and social consequences.<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> A seizure is defined as a transient occurrence of signs or symptoms caused by excessive or synchronous neuronal activity in the brain.<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">Epilepsy is a disease of the brain meeting either of the following criteria: <span class="elsevierStyleItalic">1)</span> at least 2 unprovoked (or reflex) seizures occurring less than 24 hours apart, or <span class="elsevierStyleItalic">2)</span> an unprovoked (or reflex) seizure and a probability of further seizures similar to the general recurrence risk (at least 60%) following 2 unprovoked seizures occurring over the next 10 years.<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">The International League Against Epilepsy classification has recently been updated to reflect the current understanding of epilepsy and its pathophysiological mechanisms, according to the latest scientific evidence.<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> Once seizure type is established, the following step is to diagnose the epilepsy type: focal, generalised, combined (focal and generalised), or unknown.<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> This classification is based on a complex neural network of neurophysiological and neuroanatomical data from neurons and brain lobes and hemispheres.<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a></p></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">The epileptogenic zone</span><p id="par0025" class="elsevierStylePara elsevierViewall">In 1993, Lüders et al. defined the epileptogenic zone as “area of cortex that is necessary and sufficient for initiating seizures and whose removal (or disconnection) is necessary for complete abolition of seizures” (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>).<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a> This concept is relevant only in the context of the definition of 5 cortical zones proposed for preoperative assessment:<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005"><span class="elsevierStyleLabel">1.</span><p id="par0030" class="elsevierStylePara elsevierViewall">Irritative zone: area of cortex that generates interictal epileptiform activity;</p></li><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">2.</span><p id="par0035" class="elsevierStylePara elsevierViewall">Seizure-onset zone: area of the cortex where clinical seizures initiate;</p></li><li class="elsevierStyleListItem" id="lsti0015"><span class="elsevierStyleLabel">3.</span><p id="par0040" class="elsevierStylePara elsevierViewall">Symptomatogenic zone: area of cortex that, when activated, produces the initial ictal signs or symptoms;</p></li><li class="elsevierStyleListItem" id="lsti0020"><span class="elsevierStyleLabel">4.</span><p id="par0045" class="elsevierStylePara elsevierViewall">Epileptogenic lesion: macroscopic lesion which is causative of the epileptic seizures because the lesion itself is epileptogenic or by secondary hyperexcitability; and</p></li><li class="elsevierStyleListItem" id="lsti0025"><span class="elsevierStyleLabel">5.</span><p id="par0050" class="elsevierStylePara elsevierViewall">Functional deficit zone: area of cortex that is not functioning normally in the interictal period.7</p></li></ul></p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0055" class="elsevierStylePara elsevierViewall">However, direct preoperative measurement of the epileptogenic zone is not possible; rather, its delineation is a purely conceptual exercise incorporating data from a wide range of tests and components of a preoperative evaluation.<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a></p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Epilepsy as an abnormal neural network</span><p id="par0060" class="elsevierStylePara elsevierViewall">In 2002, Spencer defined epilepsy as a disorder of neural networks, which constitute a functionally and anatomically connected and bilaterally represented set of cortical and subcortical structures in which activity in any one part affects activity in the others.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> This approach is essential for some therapies, such as deep brain stimulation,<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> vagus nerve stimulation,<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a> or responsive neurostimulation<a class="elsevierStyleCrossRef" href="#bib0055"><span class="elsevierStyleSup">11</span></a>; these treatments, targeting any region of the neural network, should in theory be as effective as those targeting the specific “focus” of epileptic activity.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> It is also a key factor in identifying the anatomical distribution of epileptogenic processes, including epilepsy surgery.<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a></p><p id="par0065" class="elsevierStylePara elsevierViewall">In humans, the study of epilepsy as a network disorder is based on neuroimaging (MRI, single-photon emission CT, positron emission tomography)<a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">13</span></a> and neurophysiological studies (EEG, transcranial magnetic stimulation, magnetoencephalography [MEG]), which have varying spatial and contrast resolutions and present different advantages and limitations for the structural or functional analysis of brain regions.<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">14</span></a> Some of these techniques are invasive (intraoperative electrocorticography, stereo EEG, depth electrodes) but present limited spatial resolution.<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a> All studies aim to evaluate ictal (epileptic seizures) or interictal states (interval between epileptic seizures) to identify the epileptogenic areas involved in each type of epilepsy.<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a></p></span></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Methods for recording and analysis of epileptic networks using electroencephalography signals</span><p id="par0070" class="elsevierStylePara elsevierViewall">Several methods have been developed to analyse epileptic networks using frequency or time-frequency analysis of EEG signals (recorded through invasive<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">17</span></a> or non-invasive procedures<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a>), epileptogenicity maps,<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a> or the epileptogenicity index.<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a> In the following sections, we describe some general principles and their applications in epileptic neural networks.</p><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Connectivity analysis</span><p id="par0075" class="elsevierStylePara elsevierViewall">This approach is based on mathematical estimations of the relationship between 2 signals from different parts of the brain.<a class="elsevierStyleCrossRefs" href="#bib0095"><span class="elsevierStyleSup">19,21,22</span></a> Connectivity analysis enables the analysis of neuroimaging and neurophysiological data obtained during the ictal and interictal periods.<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">23</span></a> For example, dynamic coupling of functional MRI (fMRI) and interictal EEG has shown a strong, time-dependent association between fMRI local connectivity and interictal EEG in focal epilepsy.<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">23</span></a> Coupling of both techniques usually identifies more consistent epileptic networks than either technique alone.<a class="elsevierStyleCrossRefs" href="#bib0115"><span class="elsevierStyleSup">23–25</span></a> Several linear (coherence, linear regression analysis) and nonlinear statistics (mutual information, nonlinear regression analysis, similarity indices based on state space trajectories reconstructed from observed signals) have been used.<a class="elsevierStyleCrossRefs" href="#bib0130"><span class="elsevierStyleSup">26,27</span></a> Some studies have shown that nonlinear methods (nonlinear correlation coefficient, transfer entropy, phase synchronisation, and mutual information) provide a more efficient measure of connectivity than linear methods (eg, cross-correlation coefficient) using interictal epileptiform activity recorded with non-invasive dense EEG.<a class="elsevierStyleCrossRefs" href="#bib0130"><span class="elsevierStyleSup">26,28</span></a> Correlations between time series may be estimated directly using time-domain features, frequency-domain features, or wavelets, in which time resolution may be adapted as a function of frequency.<a class="elsevierStyleCrossRefs" href="#bib0145"><span class="elsevierStyleSup">29,30</span></a></p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Causality analysis</span><p id="par0080" class="elsevierStylePara elsevierViewall">Causal relationships and the direction of information flow within a multivariate time series have been thoroughly studied under the concept of Granger causality.<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">31</span></a> This approach has relevant applications in neuroscience in general, and in the study of epilepsy in particular.<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">32</span></a> Granger causality is a statistical concept based on prediction, according to which if a signal X1 “Granger-causes” (or “G-causes”) a signal X2, then past values of X1 should contain information that helps predict X2 beyond the information contained in past values of X2 alone. The mathematical formula is based on linear regression modelling of stochastic processes.<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">31</span></a> The definition of Granger causality has also been applied to nonlinear cases in the time and frequency domains, based on non-parametric statistics.<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">32</span></a> Two of these techniques are the direct directed transfer function, which estimates the relationships between direct and indirect flows,<a class="elsevierStyleCrossRef" href="#bib0165"><span class="elsevierStyleSup">33</span></a> and partial directed coherence, a Granger causality measure in the frequency domain.<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">32</span></a> Granger causality may help to localise seizure networks based on interictal data, as demonstrated in a study including 25 patients with focal epilepsy.<a class="elsevierStyleCrossRef" href="#bib0170"><span class="elsevierStyleSup">34</span></a></p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Graph theory<span class="elsevierStyleBold">–</span>based analysis</span><p id="par0085" class="elsevierStylePara elsevierViewall">The origins of graph theory date to 1735, when Leonhard Euler solved the classic Königsberg bridge problem, concerning whether it is possible to take a walk through the town of Königsberg (now Kaliningrad) that crossed each of the 7 bridges once and only once. Euler used the terms “node” for landmasses and “edge” for bridges, and proved that the problem has no solution. In graph theory, a graph G is defined as a set of vertices (nodes) and edges connecting them. In a graph G = (V, E), for example, V denotes a finite set of vertices and E denotes the set of edges connecting those vertices, with E being a subset of the Cartesian product V × V (pairs of different nodes). G is used to represent the conditional dependencies between nodes; more specifically, each edge or connection between nodes represents a conditional dependency. This approach enables structural and functional analyses of varying levels of complexity. Depending on its application in neurology, the network may be modelled by directed or undirected edges, although graphical models of brain networks frequently use undirected edges since most brain connections are reciprocal.<a class="elsevierStyleCrossRefs" href="#bib0175"><span class="elsevierStyleSup">35,36</span></a></p><p id="par0090" class="elsevierStylePara elsevierViewall">The clustering coefficient (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>) and characteristic path length (<a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a>) are 2 of the most frequently used parameters in graph theory. The clustering coefficient measures the density of connections surrounding a given node; it is linked to local network efficiency and quantifies the robustness of a network against the loss of a given node.<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">37</span></a> The overall clustering coefficient is the average clustering coefficient across all nodes in the network. <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a> compares a node with a high clustering coefficient against a node with a low clustering coefficient. The characteristic path length quantifies the average value of the shortest paths between all pairs of nodes in the network. Networks with a short characteristic path length are considered globally efficient.<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">38</span></a> The interaction between clustering and path length may be used to characterise the global topological properties of a network. Regular lattice networks present high clustering and path length values, while random networks show low clustering and path length. Small-world networks present high clustering coefficients and low path length and are therefore considered both locally and globally efficient.<a class="elsevierStyleCrossRefs" href="#bib0105"><span class="elsevierStyleSup">21,38</span></a> On an intermediary scale, we may assess communities of nodes, or modules; nodes within a module are more densely connected to one another than to nodes in other modules.<a class="elsevierStyleCrossRefs" href="#bib0195"><span class="elsevierStyleSup">39,40</span></a></p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><elsevierMultimedia ident="fig0015"></elsevierMultimedia><p id="par0095" class="elsevierStylePara elsevierViewall">Preliminary studies provide evidence that focal epilepsy is associated with alterations in brain network topology.<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">38</span></a> The graph theory may be used in the interictal,<a class="elsevierStyleCrossRef" href="#bib0205"><span class="elsevierStyleSup">41</span></a> ictal,<a class="elsevierStyleCrossRef" href="#bib0210"><span class="elsevierStyleSup">42</span></a> and preictal periods.<a class="elsevierStyleCrossRef" href="#bib0215"><span class="elsevierStyleSup">43</span></a> One of the main advantages of the graph theory is that it simplifies the complex interactions between neuroimaging and neurophysiological data; however, our ability to interpret the results is still limited.<a class="elsevierStyleCrossRefs" href="#bib0175"><span class="elsevierStyleSup">35,36,38</span></a></p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Neurophysiological networks in epileptic seizures</span><p id="par0100" class="elsevierStylePara elsevierViewall">Focal and generalised epilepsy present different EEG patterns during the interictal and ictal periods (<a class="elsevierStyleCrossRef" href="#fig0020">Fig. 4</a>).<a class="elsevierStyleCrossRef" href="#bib0220"><span class="elsevierStyleSup">44</span></a> Recent studies have used wide-band EEG to measure high-frequency oscillations (80-600 Hz).<a class="elsevierStyleCrossRefs" href="#bib0225"><span class="elsevierStyleSup">45,46</span></a> These are linked to a wide range of physiological and pathological events; fast ripples (250-600 Hz), in particular, seem to reflect pathological processes associated with epileptogenicity.<a class="elsevierStyleCrossRef" href="#bib0230"><span class="elsevierStyleSup">46</span></a></p><elsevierMultimedia ident="fig0020"></elsevierMultimedia><p id="par0105" class="elsevierStylePara elsevierViewall">The most common stereo-EEG pattern during ictal events is low-voltage fast discharges, which are frequently preceded by spikes or trains of slow-wave complexes.<a class="elsevierStyleCrossRef" href="#bib0235"><span class="elsevierStyleSup">47</span></a> The frequency of low-voltage fast discharges range from beta ranges (15-30 Hz; eg, mesial temporal lobe epilepsy [TLE]) to higher frequencies (gamma range, 30-100 Hz; eg, neocortical seizures).<a class="elsevierStyleCrossRefs" href="#bib0235"><span class="elsevierStyleSup">47,48</span></a> At the beginning of a seizure, stereo-EEG may be used to calculate the epileptogenicity index, which combines an analysis of both spectral and temporal parameters and is linked to the propensity of brain area to generate fast discharges (12.4-97 Hz), with a view to predicting the initial seizure-onset zone.<a class="elsevierStyleCrossRefs" href="#bib0235"><span class="elsevierStyleSup">47,49</span></a> Neuroimaging data can be used to create parametric statistical maps of high-frequency oscillations at seizure onset; these are called epileptogenicity maps.<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">21</span></a> For example, in a study using stereo-EEG, Aubert et al.<a class="elsevierStyleCrossRef" href="#bib0250"><span class="elsevierStyleSup">50</span></a> used the original calculation of the epileptogenicity index in 36 patients with malformations of cortical development, finding that 30% presented a strictly focal epileptogenic zone organisation, whereas the rest presented more than one epileptogenic zone, with a bilateral organisation. Another study compared 15 patients with bilateral mesial TLE against 15 patients with unilateral mesial TLE, and found that the latter group more frequently presented maximal epileptogenicity in hippocampal structures, whereas patients with bilateral mesial TLE presented maximal values in subhippocampal areas (entorhinal cortex, temporal pole, parahippocampal cortex). Another study found that the epileptogenicity index is higher in patients with neocortical TLE and normal MRI findings than in those with hippocampal sclerosis.<a class="elsevierStyleCrossRef" href="#bib0255"><span class="elsevierStyleSup">52</span></a> This is also true for such other focal epilepsies as frontal lobe,<a class="elsevierStyleCrossRef" href="#bib0260"><span class="elsevierStyleSup">53</span></a> parietal lobe,<a class="elsevierStyleCrossRef" href="#bib0265"><span class="elsevierStyleSup">54</span></a> and occipital lobe epilepsy.<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">55</span></a></p><p id="par0110" class="elsevierStylePara elsevierViewall">The postoperative prognosis of TLE is associated with the number of epileptogenic zones, while focal epilepsies present better prognosis.<a class="elsevierStyleCrossRefs" href="#bib0250"><span class="elsevierStyleSup">50,52</span></a> The epileptogenicity index may also be used with MEG signals. For example, Bouet et al.<a class="elsevierStyleCrossRef" href="#bib0275"><span class="elsevierStyleSup">56</span></a> studied the spatial relationship between MEG spiking volume, seizure-onset zone location as determined with the epileptogenic index, and lesion volume delineated on brain MRI in 11 patients with focal cortical dysplasia. Stereo-EEG demonstrated a significant correlation between MEG spiking activity and the epileptogenicity index in 8 patients, 7 of whom presented good surgical outcomes. Recently, stereo-EEG and the epileptogenicity index have been used for patient reassessment after epilepsy surgery failure.<a class="elsevierStyleCrossRef" href="#bib0280"><span class="elsevierStyleSup">57</span></a></p><p id="par0115" class="elsevierStylePara elsevierViewall">Some computational models provide a framework for studying the influence of neural networks and local tissue properties, and exploring alternative resection strategies.<a class="elsevierStyleCrossRef" href="#bib0285"><span class="elsevierStyleSup">58</span></a> However, these models must be validated in vivo in animal models of epilepsy or in the clinical setting.</p></span></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Functional connectivity during epileptic seizures</span><p id="par0120" class="elsevierStylePara elsevierViewall">As mentioned earlier, an epileptic seizure is currently defined as excessive or synchronous neuronal activity in the brain.<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> Brain structures present marked desynchronisation at seizure onset,<a class="elsevierStyleCrossRef" href="#bib0290"><span class="elsevierStyleSup">59</span></a> but synchronisation increases during seizure propagation and termination.<a class="elsevierStyleCrossRefs" href="#bib0185"><span class="elsevierStyleSup">37,60</span></a> The available information on this topic is limited to TLE, as this is the most frequent indication for presurgical assessment.<a class="elsevierStyleCrossRefs" href="#bib0070"><span class="elsevierStyleSup">14,48</span></a> These findings have been observed in patients with other focal epilepsies affecting the frontal and occipital lobes,<a class="elsevierStyleCrossRef" href="#bib0295"><span class="elsevierStyleSup">60</span></a> and may be more common than in studies using standard EEG.<a class="elsevierStyleCrossRef" href="#bib0300"><span class="elsevierStyleSup">61</span></a></p><p id="par0125" class="elsevierStylePara elsevierViewall">Animal and human studies have shown that the seizure-onset zone is functionally organised in small clusters of neurons, which present synchronous firing in tiny microdomains (< 1 mm diameter) during epileptogenesis and ictogenesis.<a class="elsevierStyleCrossRef" href="#bib0290"><span class="elsevierStyleSup">59</span></a> However, the exact role of these microseizures in seizure genesis and propagation is yet to be understood. For example, rather than propagating outwards from a restricted cortical site, epileptic seizures may be generated from clusters of spatially distant microdomains.<a class="elsevierStyleCrossRef" href="#bib0305"><span class="elsevierStyleSup">62</span></a></p><p id="par0130" class="elsevierStylePara elsevierViewall">During temporal lobe seizures, a strong correlation in epileptic activity has been observed between the thalamus and temporal lobe structures, which may manifest as dystonic posturing. Neural networks can be used to predict surgical outcomes. In a case-control study by Morgan et al.,<a class="elsevierStyleCrossRef" href="#bib0315"><span class="elsevierStyleSup">65</span></a> including 26 presurgical patients with mesial TLE and 44 healthy controls, 3T MRI was used for functional and structural connectivity mapping across an 8-region network of seizure propagation, including the hippocampus, insula, thalamus, precuneus and mid cingulate (<a class="elsevierStyleCrossRef" href="#fig0025">Fig. 5</a>). The authors confirmed that whole-network propagation connectivity patterns were consistent with the mesial TLE model predicting surgical failure.<a class="elsevierStyleCrossRefs" href="#bib0310"><span class="elsevierStyleSup">64,65</span></a> In any case, other studies of patients with mesial TLE have shown that the basal ganglia do not generate specific EEG epileptic activity; despite this, they do reflect changes in the distribution of the ictal epileptic activity.<a class="elsevierStyleCrossRef" href="#bib0320"><span class="elsevierStyleSup">66</span></a></p><elsevierMultimedia ident="fig0025"></elsevierMultimedia><p id="par0135" class="elsevierStylePara elsevierViewall">Interictal epileptiform discharges are biomarkers of epilepsy; although they usually indicate the irritative zone, on occasion they may also indicate the epileptogenic zone and other cortical zones defined in presurgical evaluation.<a class="elsevierStyleCrossRefs" href="#bib0035"><span class="elsevierStyleSup">7,67</span></a> Several studies into TLE<a class="elsevierStyleCrossRef" href="#bib0330"><span class="elsevierStyleSup">68</span></a> and extratemporal lobe epilepsy<a class="elsevierStyleCrossRefs" href="#bib0335"><span class="elsevierStyleSup">69,70</span></a> have observed complex interictal distributions between the medial and lateral cortices or between cortical and subcortical areas.<a class="elsevierStyleCrossRef" href="#bib0340"><span class="elsevierStyleSup">70</span></a> Brain regions with interictal epileptiform discharges frequently show increased interictal synchrony in structures located in the epileptogenic zone in patients with mesial TLE as compared with patients with neocortical epilepsies, and are linked to the duration of epilepsy.<a class="elsevierStyleCrossRef" href="#bib0345"><span class="elsevierStyleSup">71</span></a> These patterns of functional connectivity in interictal epileptiform discharges may be useful in understanding the action mechanisms of such neuromodulatory therapies as vagus nerve stimulation<a class="elsevierStyleCrossRef" href="#bib0350"><span class="elsevierStyleSup">72</span></a> or deep brain stimulation. In a small study including 6 patients with epilepsy, stereo EEG was performed during vagus nerve stimulation. Functional connectivity, analysed with deep electrodes, was found to decrease during treatment, and was linked to patient response; this suggests that the effect of vagus nerve stimulation may be linked to this mechanism. Furthermore, during deep brain stimulation of the anterior thalamic nucleus, the authors observed increased resting-state network connectivity in responders; the working hypothesis to explain this was that this network increases the threshold for seizure propagation.<a class="elsevierStyleCrossRef" href="#bib0355"><span class="elsevierStyleSup">73</span></a></p><p id="par0140" class="elsevierStylePara elsevierViewall">Caution should be exercised when extrapolating these findings to other types of epilepsy or epileptic syndromes. Several region-specific seizure-onset patterns are known, but they may involve different cellular, network, and synchronisation mechanisms.<a class="elsevierStyleCrossRefs" href="#bib0230"><span class="elsevierStyleSup">46,59</span></a> For example, increased functional connectivity has been detected in specific cortical regions in untreated patients with juvenile myoclonic epilepsy. This interictal abnormality is increased in the preictal state. Nodal graph statistics revealed abnormal neuronal dynamics in the cortical area identified as the seizure-onset zone in this type of generalised epilepsy.<a class="elsevierStyleCrossRef" href="#bib0360"><span class="elsevierStyleSup">74</span></a></p><p id="par0145" class="elsevierStylePara elsevierViewall">The complex relationships between epilepsy, epilepsy aetiology, antiepileptic agents, and epilepsy duration are yet to be fully understood. However, analysis of epileptic networks may be more relevant in non-lesional epilepsies<a class="elsevierStyleCrossRef" href="#bib0365"><span class="elsevierStyleSup">75</span></a> or in such critical conditions as status epilepticus.<a class="elsevierStyleCrossRef" href="#bib0370"><span class="elsevierStyleSup">76</span></a></p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Author contributions</span><p id="par0150" class="elsevierStylePara elsevierViewall">All authors made substantial contributions to the study and approved the manuscript for publication.</p></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Funding</span><p id="par0155" class="elsevierStylePara elsevierViewall">None.</p></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">Conflicts of interest</span><p id="par0160" class="elsevierStylePara elsevierViewall">The authors have no financial or commercial relationships that could create conflicts of interest with regard to this article.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:12 [ 0 => array:3 [ "identificador" => "xres1867658" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Objectives" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1622532" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres1867657" "titulo" => "Resumen" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Introducción" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Objetivo" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Desarroll" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec1622533" "titulo" => "Palabras clave" ] 4 => array:3 [ "identificador" => "sec0005" "titulo" => "Introduction" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0010" "titulo" => "Definition of epilepsy and epileptic seizure" ] 1 => array:2 [ "identificador" => "sec0015" "titulo" => "The epileptogenic zone" ] 2 => array:2 [ "identificador" => "sec0020" "titulo" => "Epilepsy as an abnormal neural network" ] ] ] 5 => array:3 [ "identificador" => "sec0025" "titulo" => "Methods for recording and analysis of epileptic networks using electroencephalography signals" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "sec0030" "titulo" => "Connectivity analysis" ] 1 => array:2 [ "identificador" => "sec0035" "titulo" => "Causality analysis" ] 2 => array:2 [ "identificador" => "sec0040" "titulo" => "Graph theory–based analysis" ] 3 => array:2 [ "identificador" => "sec0045" "titulo" => "Neurophysiological networks in epileptic seizures" ] ] ] 6 => array:2 [ "identificador" => "sec0050" "titulo" => "Functional connectivity during epileptic seizures" ] 7 => array:2 [ "identificador" => "sec0055" "titulo" => "Author contributions" ] 8 => array:2 [ "identificador" => "sec0060" "titulo" => "Funding" ] 9 => array:2 [ "identificador" => "sec0065" "titulo" => "Conflicts of interest" ] 10 => array:2 [ "identificador" => "xack658128" "titulo" => "Acknowledgements" ] 11 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2020-01-28" "fechaAceptado" => "2020-06-12" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1622532" "palabras" => array:6 [ 0 => "Epilepsy" 1 => "Networks" 2 => "Connectome" 3 => "EEG" 4 => "Neurons" 5 => "Epilepsy surgery" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec1622533" "palabras" => array:6 [ 0 => "Epilepsia" 1 => "Redes" 2 => "Conectoma" 3 => "EEG" 4 => "Neuronal" 5 => "Cirugía de epilepsia" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Introduction</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">The brain is a series of networks of functionally and anatomically connected, bilaterally represented structures; in epilepsy, activity of any part of the brain affects activity in the other parts. This is relevant for understanding the pathophysiology, diagnosis, and prognosis of the disease.</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Objectives</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">In this study, we present a state-of-the-art review of the neurophysiological view of epilepsy as a disease affecting neural networks.</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">We describe the basic and advanced principles of epilepsy as a disease affecting neural networks, based on the use of different clinical and mathematical techniques from a neurophysiological perspective, and signal the limitations of these findings in the clinical context.</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Epilepsy is a disease affecting complex neural networks. Understanding these will enable better management and prognostic confidence.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Objectives" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] "es" => array:3 [ "titulo" => "Resumen" "resumen" => "<span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Introducción</span><p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">La epilepsia es un conjunto de redes de estructuras cerebrales representadas bilateralmente, que están funcional y anatómicamente conectadas, en la epilepsia, la actividad de cualquier parte del cerebro afecta la actividad de las demás. Esto es relevante para el entendimiento de la fisiopatología, etiología, el diagnóstico y la prognosis de esta enfermedad.</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Objetivo</span><p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Revisar el estado del arte en cuanto al entendimiento de la visión neurofisiológica de la epilepsia como una enfermedad de redes neuronales.</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Desarroll</span><p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Se describen los principios básicos y avanzados de la epilepsia como enfermedad de redes neuronales usando distintos métodos clínicos y matemáticos con una visión neurofisiológica, indicando las limitaciones de estos hallazgos en el contexto clínico.</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusiones</span><p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">La epilepsia es una enfermedad de redes neuronales complejas cuyo entendimiento permitirá mejorar los tratamientos disponibles y la certeza pronostica.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Introducción" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Objetivo" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Desarroll" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] ] "multimedia" => array:5 [ 0 => array:8 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1351 "Ancho" => 1708 "Tamanyo" => 194115 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0005" "detalle" => "Figure " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Overlap of cortical zones related to epileptic seizures.</p>" ] ] 1 => array:8 [ "identificador" => "fig0010" "etiqueta" => "Figure 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 959 "Ancho" => 2509 "Tamanyo" => 127610 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0010" "detalle" => "Figure " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Calculation of the clustering coefficient (C). The clustering coefficient of a given node (shaded circle) is the proportion of established connections (thick continuous lines) between its neighbours (white circles) over all possible connections (dashed lines). In the examples presented here, the clustering coefficient is 1 in the first network, as all possible connections are established; 1/3 in the second network, since only 1 of 3 possible connections is established; and 0 in the third, as the neighbouring nodes are not connected.</p>" ] ] 2 => array:8 [ "identificador" => "fig0015" "etiqueta" => "Figure 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 878 "Ancho" => 1718 "Tamanyo" => 62547 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0015" "detalle" => "Figure " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Calculation of the characteristic path length (L). The characteristic path length is defined as the average distance between all node pairs in a graph. In the example presented here, we measured the distance between the 15 possible pairs of nodes (total distance of 27) and divided it by the number of pairs (15).</p>" ] ] 3 => array:8 [ "identificador" => "fig0020" "etiqueta" => "Figure 4" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr4.jpeg" "Alto" => 2213 "Ancho" => 3500 "Tamanyo" => 1006661 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0020" "detalle" => "Figure " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">EEG recording of typical tonic-clonic seizures. A) Interictal and preictal periods. B) Ictal period. C) Ictal and postictal periods.</p>" ] ] 4 => array:8 [ "identificador" => "fig0025" "etiqueta" => "Figure 5" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr5.jpeg" "Alto" => 2699 "Ancho" => 2925 "Tamanyo" => 846316 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0025" "detalle" => "Figure " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Connectome of the left frontomedial thalamus (green arrow). The image shows the links to different connected areas. Clockwise, from the green arrow: the hippocampus, precuneus, cingulate, and insula (orange arrow). 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| Year/Month | Html | Total | |
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