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Miecznikowski KB, Leach J, Rozhkov L, Mangano FT, Skoch J, Krueger DA, Horn PS, Greiner HM. Impact of seizure onset zone and intracranial electroencephalography ictal characteristics on epilepsy surgery outcomes in tuberous sclerosis complex. Epilepsy Res 2024; 205:107422. [PMID: 39121694 DOI: 10.1016/j.eplepsyres.2024.107422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 06/14/2024] [Accepted: 08/05/2024] [Indexed: 08/12/2024]
Abstract
Ninety percent of tuberous sclerosis complex (TSC) patients have seizures, with ∼50 % developing drug refractory epilepsy. Surgical intervention aims to remove the seizure onset zone (SOZ). This retrospective study investigated the relationship of SOZ size, ictal pattern, and extent of resection with surgical outcomes. TSC patients undergoing resective/ablative surgery with >1-year follow-up and adequate imaging were included. Preoperative iEEG data were reviewed to determine ictal pattern and SOZ location. For outcomes, an ILAE score of 1-3 was defined as good and 4-6 as poor. Forty-four patients were included (age 117.4 ± 110.8 months). Of these, 59.1 % achieved a good outcome, while 40.9 % had a poor outcome. Size of SOZ was a significant factor (p = 0.009), with the poor outcome group having a larger SOZ (11.9 ± 6.7 electrode contacts) than the good outcome group (7.3 ± 7.2). SOZ number was significant (p = 0.020); >1 SOZ was associated with poor outcome. These results demonstrate extent of SOZ as a predictor of seizure freedom following epilepsy surgery in a mostly pediatric TSC cohort. We hypothesize that these features represent biomarkers of focality of the epileptogenic zone and can be used to sharpen prognosis for epilepsy surgery outcomes in this cohort.
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Affiliation(s)
| | - James Leach
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
| | - Leonid Rozhkov
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
| | - Francesco T Mangano
- Division of Neurosurgery, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
| | - Jesse Skoch
- Division of Neurosurgery, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
| | - Darcy A Krueger
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
| | - Paul S Horn
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
| | - Hansel M Greiner
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
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Lin J, Smith GC, Gliske SV, Zochowski M, Shedden K, Stacey WC. High frequency oscillation network dynamics predict outcome in non-palliative epilepsy surgery. Brain Commun 2024; 6:fcae032. [PMID: 38384998 PMCID: PMC10881100 DOI: 10.1093/braincomms/fcae032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 12/28/2023] [Accepted: 02/05/2024] [Indexed: 02/23/2024] Open
Abstract
High frequency oscillations are a promising biomarker of outcome in intractable epilepsy. Prior high frequency oscillation work focused on counting high frequency oscillations on individual channels, and it is still unclear how to translate those results into clinical care. We show that high frequency oscillations arise as network discharges that have valuable properties as predictive biomarkers. Here, we develop a tool to predict patient outcome before surgical resection is performed, based on only prospective information. In addition to determining high frequency oscillation rate on every channel, we performed a correlational analysis to evaluate the functional connectivity of high frequency oscillations in 28 patients with intracranial electrodes. We found that high frequency oscillations were often not solitary events on a single channel, but part of a local network discharge. Eigenvector and outcloseness centrality were used to rank channel importance within the connectivity network, then used to compare patient outcome by comparison with the seizure onset zone or a proportion within the proposed resected channels (critical resection percentage). Combining the knowledge of each patient's seizure onset zone resection plan along with our computed high frequency oscillation network centralities and high frequency oscillation rate, we develop a Naïve Bayes model that predicts outcome (positive predictive value: 100%) better than predicting based upon fully resecting the seizure onset zone (positive predictive value: 71%). Surgical margins had a large effect on outcomes: non-palliative patients in whom most of the seizure onset zone was resected ('definitive surgery', ≥ 80% resected) had predictable outcomes, whereas palliative surgeries (<80% resected) were not predictable. These results suggest that the addition of network properties of high frequency oscillations is more accurate in predicting patient outcome than seizure onset zone alone in patients with most of the seizure onset zone removed and offer great promise for informing clinical decisions in surgery for refractory epilepsy.
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Affiliation(s)
- Jack Lin
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Garnett C Smith
- Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Stephen V Gliske
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Michal Zochowski
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Physics and Biophysics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kerby Shedden
- Department of Statistics and Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - William C Stacey
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
- Division of Neurology, Ann Arbor VA Health System, Ann Arbor, MI 48109, USA
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Vila-Vidal M, Pérez Enríquez C, Principe A, Rocamora R, Deco G, Tauste Campo A. Low entropy map of brain oscillatory activity identifies spatially localized events: A new method for automated epilepsy focus prediction. Neuroimage 2019; 208:116410. [PMID: 31785422 DOI: 10.1016/j.neuroimage.2019.116410] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 11/07/2019] [Accepted: 11/26/2019] [Indexed: 10/25/2022] Open
Abstract
The spatial mapping of localized events in brain activity critically depends on the correct identification of the pattern signatures associated with those events. For instance, in the context of epilepsy research, a number of different electrophysiological patterns have been associated with epileptogenic activity. Motivated by the need to define automated seizure focus detectors, we propose a novel data-driven algorithm for the spatial identification of localized events that is based on the following rationale: the distribution of emerging oscillations during confined events across all recording sites is highly non-uniform and can be mapped using a spatial entropy function. By applying this principle to EEG recording obtained from 67 distinct seizure epochs, our method successfully identified the seizure focus on a group of ten drug-resistant temporal lobe epilepsy patients (average sensitivity: 0.94, average specificity: 0.90) together with its characteristic electrophysiological pattern signature. Cross-validation of the method outputs with postresective information revealed the consistency of our findings in long follow-up seizure-free patients. Overall, our methodology provides a reliable computational procedure that might be used as in both experimental and clinical domains to identify the neural populations undergoing an emerging functional or pathological transition.
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Affiliation(s)
- Manel Vila-Vidal
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08005, Barcelona, Spain.
| | | | - Alessandro Principe
- Hospital del Mar Medical Research Institute, 08003, Barcelona, Spain; Epilepsy Monitoring Unit, Department of Neurology, Hospital del Mar, 08003, Barcelona, Spain; Faculty of Health and Life Sciences, Universitat Pompeu Fabra, 08003, Barcelona, Spain
| | - Rodrigo Rocamora
- Hospital del Mar Medical Research Institute, 08003, Barcelona, Spain; Epilepsy Monitoring Unit, Department of Neurology, Hospital del Mar, 08003, Barcelona, Spain; Faculty of Health and Life Sciences, Universitat Pompeu Fabra, 08003, Barcelona, Spain.
| | - Gustavo Deco
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08005, Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats, 08010, Barcelona, Spain
| | - Adrià Tauste Campo
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08005, Barcelona, Spain.
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Tomlinson SB, Wong JN, Conrad EC, Kennedy BC, Marsh ED. Reproducibility of interictal spike propagation in children with refractory epilepsy. Epilepsia 2019; 60:898-910. [PMID: 31006860 PMCID: PMC6488404 DOI: 10.1111/epi.14720] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Revised: 02/11/2019] [Accepted: 03/13/2019] [Indexed: 01/03/2023]
Abstract
OBJECTIVE Interictal spikes are a characteristic feature of invasive electroencephalography (EEG) recordings in children with refractory epilepsy. Spikes frequently co-occur across multiple brain regions with discernable latencies, suggesting that spikes can propagate through distributed neural networks. The purpose of this study was to examine the long-term reproducibility of spike propagation patterns over hours to days of interictal recording. METHODS Twelve children (mean age 13.1 years) were retrospectively studied. A mean ± standard deviation (SD) of 47.2 ± 40.1 hours of interictal EEG recordings were examined per patient (range 17.5-166.5 hours). Interictal recordings were divided into 30-minute segments. Networks were extracted based on the frequency of spike coactivation between pairs of electrodes. For each 30-minute segment, electrodes were assigned a "Degree Preference (DP)" based on the tendency to appear upstream or downstream within propagation sequences. The consistency of DPs across segments ("DP-Stability") was quantified using the Spearman rank correlation. RESULTS Regions exhibited highly stable preferences to appear upstream, intermediate, or downstream in spike propagation sequences. Across networks, the mean ± SD DP-Stability was 0.88 ± 0.07, indicating that propagation patterns observed in 30-minute segments were representative of the patterns observed in the full interictal window. At the group level, regions involved in seizure generation appeared more upstream in spike propagation sequences. SIGNIFICANCE Interictal spike propagation is a highly reproducible output of epileptic networks. These findings shed new light on the spatiotemporal dynamics that may constrain the network mechanisms of refractory epilepsy.
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Affiliation(s)
- Samuel B. Tomlinson
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA
- School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, NY
| | - Jeremy N. Wong
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Erin C. Conrad
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA
| | - Benjamin C. Kennedy
- Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Eric D. Marsh
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA
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Müller M, Schindler K, Goodfellow M, Pollo C, Rummel C, Steimer A. Evaluating resective surgery targets in epilepsy patients: A comparison of quantitative EEG methods. J Neurosci Methods 2018; 305:54-66. [PMID: 29753683 PMCID: PMC6172189 DOI: 10.1016/j.jneumeth.2018.04.021] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Revised: 03/15/2018] [Accepted: 04/29/2018] [Indexed: 01/11/2023]
Abstract
BACKGROUND Quantitative analysis of intracranial EEG is a promising tool to assist clinicians in the planning of resective brain surgery in patients suffering from pharmacoresistant epilepsies. Quantifying the accuracy of such tools, however, is nontrivial as a ground truth to verify predictions about hypothetical resections is missing. NEW METHOD As one possibility to address this, we use customized hypotheses tests to examine the agreement of the methods on a common set of patients. One method uses machine learning techniques to enable the predictive modeling of EEG time series. The other estimates nonlinear interrelation between EEG channels. Both methods were independently shown to distinguish patients with excellent post-surgical outcome (Engel class I) from those without improvement (Engel class IV) when assessing the electrodes associated with the tissue that was actually resected during brain surgery. Using the AND and OR conjunction of both methods we evaluate the performance gain that can be expected when combining them. RESULTS Both methods' assessments correlate strongly positively with the similarity between a hypothetical resection and the corresponding actual resection in class I patients. Moreover, the Spearman rank correlation between the methods' patient rankings is significantly positive. COMPARISON WITH EXISTING METHOD(S) To our best knowledge, this is the first study comparing surgery target assessments from fundamentally differing techniques. CONCLUSIONS Although conceptually completely independent, there is a relation between the predictions obtained from both methods. Their broad consensus supports their application in clinical practice to provide physicians additional information in the process of presurgical evaluation.
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Affiliation(s)
- Michael Müller
- Department of Neurology, Inselspital, Bern University Hospital, University Bern, Bern, Switzerland; Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland.
| | - Kaspar Schindler
- Department of Neurology, Inselspital, Bern University Hospital, University Bern, Bern, Switzerland
| | - Marc Goodfellow
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK; Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, UK; EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK
| | - Claudio Pollo
- Department of Neurosurgery, Inselspital, Bern University Hospital, University Bern, Bern, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
| | - Andreas Steimer
- Department of Neurology, Inselspital, Bern University Hospital, University Bern, Bern, Switzerland
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Detection of recurrent activation patterns across focal seizures: Application to seizure onset zone identification. Clin Neurophysiol 2017; 128:977-985. [DOI: 10.1016/j.clinph.2017.03.040] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Revised: 03/20/2017] [Accepted: 03/23/2017] [Indexed: 11/23/2022]
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Tomlinson SB, Bermudez C, Conley C, Brown MW, Porter BE, Marsh ED. Spatiotemporal Mapping of Interictal Spike Propagation: A Novel Methodology Applied to Pediatric Intracranial EEG Recordings. Front Neurol 2016; 7:229. [PMID: 28066315 PMCID: PMC5165024 DOI: 10.3389/fneur.2016.00229] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 11/30/2016] [Indexed: 12/19/2022] Open
Abstract
Synchronized cortical activity is implicated in both normative cognitive functioning and many neurologic disorders. For epilepsy patients with intractable seizures, irregular synchronization within the epileptogenic zone (EZ) is believed to provide the network substrate through which seizures initiate and propagate. Mapping the EZ prior to epilepsy surgery is critical for detecting seizure networks in order to achieve postsurgical seizure control. However, automated techniques for characterizing epileptic networks have yet to gain traction in the clinical setting. Recent advances in signal processing and spike detection have made it possible to examine the spatiotemporal propagation of interictal spike discharges across the epileptic cortex. In this study, we present a novel methodology for detecting, extracting, and visualizing spike propagation and demonstrate its potential utility as a biomarker for the EZ. Eighteen presurgical intracranial EEG recordings were obtained from pediatric patients ultimately experiencing favorable (i.e., seizure-free, n = 9) or unfavorable (i.e., seizure-persistent, n = 9) surgical outcomes. Novel algorithms were applied to extract multichannel spike discharges and visualize their spatiotemporal propagation. Quantitative analysis of spike propagation was performed using trajectory clustering and spatial autocorrelation techniques. Comparison of interictal propagation patterns revealed an increase in trajectory organization (i.e., spatial autocorrelation) among Sz-Free patients compared with Sz-Persist patients. The pathophysiological basis and clinical implications of these findings are considered.
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Affiliation(s)
- Samuel B Tomlinson
- Department of Pediatrics, Division of Child Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, NY, USA
| | - Camilo Bermudez
- Department of Pediatrics, Division of Child Neurology, Children's Hospital of Philadelphia , Philadelphia, PA , USA
| | - Chiara Conley
- Department of Pediatrics, Division of Child Neurology, Children's Hospital of Philadelphia , Philadelphia, PA , USA
| | - Merritt W Brown
- Department of Pediatrics, Division of Child Neurology, Children's Hospital of Philadelphia , Philadelphia, PA , USA
| | - Brenda E Porter
- Department of Neurology and Neurological Science, Stanford School of Medicine , Palo Alto, CA , USA
| | - Eric D Marsh
- Department of Pediatrics, Division of Child Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Gopinath S, Roy AG, Vinayan KP, Kumar A, Sarma M, Rajeshkannan R, Pillai A. Seizure outcome following primary motor cortex-sparing resective surgery for perirolandic focal cortical dysplasia. Int J Surg 2016; 36:466-476. [DOI: 10.1016/j.ijsu.2015.10.036] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2015] [Accepted: 10/07/2015] [Indexed: 10/22/2022]
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Rummel C, Abela E, Andrzejak RG, Hauf M, Pollo C, Müller M, Weisstanner C, Wiest R, Schindler K. Resected Brain Tissue, Seizure Onset Zone and Quantitative EEG Measures: Towards Prediction of Post-Surgical Seizure Control. PLoS One 2015; 10:e0141023. [PMID: 26513359 PMCID: PMC4626164 DOI: 10.1371/journal.pone.0141023] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2015] [Accepted: 10/02/2015] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Epilepsy surgery is a potentially curative treatment option for pharmacoresistent patients. If non-invasive methods alone do not allow to delineate the epileptogenic brain areas the surgical candidates undergo long-term monitoring with intracranial EEG. Visual EEG analysis is then used to identify the seizure onset zone for targeted resection as a standard procedure. METHODS Despite of its great potential to assess the epileptogenicty of brain tissue, quantitative EEG analysis has not yet found its way into routine clinical practice. To demonstrate that quantitative EEG may yield clinically highly relevant information we retrospectively investigated how post-operative seizure control is associated with four selected EEG measures evaluated in the resected brain tissue and the seizure onset zone. Importantly, the exact spatial location of the intracranial electrodes was determined by coregistration of pre-operative MRI and post-implantation CT and coregistration with post-resection MRI was used to delineate the extent of tissue resection. Using data-driven thresholding, quantitative EEG results were separated into normally contributing and salient channels. RESULTS In patients with favorable post-surgical seizure control a significantly larger fraction of salient channels in three of the four quantitative EEG measures was resected than in patients with unfavorable outcome in terms of seizure control (median over the whole peri-ictal recordings). The same statistics revealed no association with post-operative seizure control when EEG channels contributing to the seizure onset zone were studied. CONCLUSIONS We conclude that quantitative EEG measures provide clinically relevant and objective markers of target tissue, which may be used to optimize epilepsy surgery. The finding that differentiation between favorable and unfavorable outcome was better for the fraction of salient values in the resected brain tissue than in the seizure onset zone is consistent with growing evidence that spatially extended networks might be more relevant for seizure generation, evolution and termination than a single highly localized brain region (i.e. a "focus") where seizures start.
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Affiliation(s)
- Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
| | - Eugenio Abela
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
- Department of Neurology, Inselspital, Bern, Switzerland
| | - Ralph G. Andrzejak
- Universitat Pompeu Fabra, Department of Information and Communication Technologies, Barcelona, Spain
| | - Martinus Hauf
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
- Bethesda Epilepsy Clinic, Tschugg, Switzerland
| | - Claudio Pollo
- Department of Neurosurgery, Inselspital, Bern, Switzerland
| | - Markus Müller
- Centro de Investigaciones en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca, Mexico
- Centro Internacional de Ciencias, Universidad Autónoma de México, Cuernavaca, Mexico
| | - Christian Weisstanner
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
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Szabo GG, Schneider CJ, Soltesz I. Resolution revolution: epilepsy dynamics at the microscale. Curr Opin Neurobiol 2015; 31:239-43. [PMID: 25596364 DOI: 10.1016/j.conb.2014.12.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Revised: 12/29/2014] [Accepted: 12/30/2014] [Indexed: 11/19/2022]
Abstract
Our understanding of the neuronal mechanisms behind epilepsy dynamics has recently advanced due to the application of novel technologies, monitoring hundreds of neurons with single cell resolution. These developments have provided new theories on the relationship between physiological and pathological states, as well as common motifs for the propagation of paroxysmal activity. Although traditional electroencephalogram (EEG) recordings continue to describe normal network oscillations and abnormal epileptic events within and outside of the seizure focus, analysis of epilepsy dynamics at the microscale has found variability in the composition of macroscopically repetitive epileptiform events. These novel results point to heterogeneity in the underlying dynamics of the disorder, highlighting both the need and potential for more specific and targeted therapies.
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Affiliation(s)
- Gergely G Szabo
- Department of Anatomy and Neurobiology, University of California, Irvine, CA 92697, USA
| | - Calvin J Schneider
- Department of Anatomy and Neurobiology, University of California, Irvine, CA 92697, USA
| | - Ivan Soltesz
- Department of Anatomy and Neurobiology, University of California, Irvine, CA 92697, USA.
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