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Dharan AL, Bowden SC, Lai A, Peterson ADH, Cheung MWL, Woldman W, D'Souza WJ. Resting-state functional connectivity in the idiopathic generalized epilepsies: A systematic review and meta-analysis of EEG and MEG studies. Epilepsy Behav 2021; 124:108336. [PMID: 34607215 DOI: 10.1016/j.yebeh.2021.108336] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 09/09/2021] [Accepted: 09/12/2021] [Indexed: 11/20/2022]
Abstract
For idiopathic generalized epilepsies (IGE), brain network analysis is emerging as a biomarker for potential use in clinical care. To determine whether people with IGE show alterations in resting-state brain connectivity compared to healthy controls, and to quantify these differences, we conducted a systematic review and meta-analysis of EEG and magnetoencephalography (MEG) functional connectivity and network studies. The review was conducted according to PRISMA guidelines. Twenty-two studies were eligible for inclusion. Outcomes from individual studies supported hypotheses for interictal, resting-state brain connectivity alterations in IGE patients compared to healthy controls. In contrast, meta-analysis from six studies of common network metrics clustering coefficient, path length, mean degree and nodal strength showed no significant differences between IGE and control groups (effect sizes ranged from -0.151 -1.78). The null findings of the meta-analysis and the heterogeneity of the included studies highlights the importance of developing standardized, validated methodologies for future research. Network neuroscience has significant potential as both a diagnostic and prognostic biomarker in epilepsy, though individual variability in network dynamics needs to be better understood and accounted for.
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Affiliation(s)
- Anita L Dharan
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia.
| | - Stephen C Bowden
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia; Department of Clinical Neurosciences, St Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
| | - Alan Lai
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
| | - Andre D H Peterson
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
| | - Mike W-L Cheung
- Department of Psychology, National University of Singapore, Singapore
| | - Wessel Woldman
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Edgbaston, United Kingdom
| | - Wendyl J D'Souza
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
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Mendoza-Ruiz J, Alonso-Malaver CE, Valderrama M, Rosso OA, Martinez JH. Dynamics in cortical activity revealed by resting-state MEG rhythms. CHAOS (WOODBURY, N.Y.) 2020; 30:123138. [PMID: 33380010 DOI: 10.1063/5.0025189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 11/30/2020] [Indexed: 06/12/2023]
Abstract
The brain is a biophysical system subject to information flows that may be thought of as a many-body architecture with a spatiotemporal dynamics described by its neuronal structures. The oscillatory nature of brain activity allows these structures (nodes) to be described as a set of coupled oscillators forming a network where the node dynamics and that of the network topology can be studied. Quantifying its dynamics at various scales is an issue that claims to be explored for several brain activities, e.g., activity at rest. The resting-state (RS) associates the underlying brain dynamics of healthy subjects that are not actively compromised with sensory or cognitive processes. Studying its dynamics is highly non-trivial but opens the door to understand the general principles of brain functioning, as well as to contrast a passive null condition vs the dynamics of pathologies or non-resting activities. Here, we hypothesize about how the spatiotemporal dynamics of cortical fluctuations could be for healthy subjects at RS. To do that, we retrieve the alphabet that reconstructs the dynamics (entropy-complexity) of magnetoencephalography (MEG) signals. We assemble the cortical connectivity to elicit the dynamics in the network topology. We depict an order relation between entropy and complexity for frequency bands that is ubiquitous for different temporal scales. We unveiled that the posterior cortex conglomerates nodes with both stronger dynamics and high clustering for α band. The existence of an order relation between dynamic properties suggests an emergent phenomenon characteristic of each band. Interestingly, we find the posterior cortex as a domain of dual character that plays a cardinal role in both the dynamics and structure regarding the activity at rest. To the best of our knowledge, this is the first study with MEG involving information theory and network science to better understand the dynamics and structure of brain activity at rest for different bands and scales.
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Affiliation(s)
- J Mendoza-Ruiz
- Department of Statistics, Universidad Nacional de Colombia, Cr 45 #26-85, Bogotá, Colombia
| | - C E Alonso-Malaver
- Department of Statistics, Universidad Nacional de Colombia, Cr 45 #26-85, Bogotá, Colombia
| | - M Valderrama
- Department of Biomedical Engineering, Universidad de los Andes, Cr 1 #18A-12, Bogotá, Colombia
| | - O A Rosso
- Instituto de Física, Universidade Federal de Alagoas (UFAL), BR 104 Norte km 97, 57072-970 Maceió, Alagoas, Brazil
| | - J H Martinez
- Department of Biomedical Engineering, Universidad de los Andes, Cr 1 #18A-12, Bogotá, Colombia
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Follis JL, Lai D. Variability analysis of epileptic EEG using the maximal overlap discrete wavelet transform. Health Inf Sci Syst 2020; 8:26. [PMID: 32999715 DOI: 10.1007/s13755-020-00118-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 09/02/2020] [Indexed: 11/29/2022] Open
Abstract
Purpose To determine if there is a difference in the wavelet variances of seizure and non-seizure channels in the EEG of an epileptic subject. Methods A six-level decomposition was applied using the Maximal Overlap Discrete Wavelet Transform (MODWT). The wavelet variance and 95% CIs were calculated for each level of the decomposition. The number of changes in variance for each level were found using a change-point detection method of Whitcher. The Kruskal-Wallis test was used to determine if there were differences in the median number of change points within channels and across frequency bands (levels). Results No distinctive pattern was found for the wavelet variances to differentiate the seizure and non-seizure channels. The seizure channels tended to have lower variances for each level and overall, but this pattern only held for one of the three seizure channels (RAST4). The median number of change points did not differ between the seizure and non-seizure channels either within each channel or across the frequency bands. Conclusion The use of the MODWT in examining the variances and changes in variance did not show specific patterns which differentiate between seizure and non-seizure channels.
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Affiliation(s)
- Jack L Follis
- Department of Mathematics and Computer Science, University of St. Thomas, 3800 Montrose Boulevard, Houston, TX 77006 USA
| | - Dejian Lai
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, School of Public Health, 1200 Herman Pressler Drive, W-1008, Houston, TX 77030 USA
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Lopes MA, Richardson MP, Abela E, Rummel C, Schindler K, Goodfellow M, Terry JR. An optimal strategy for epilepsy surgery: Disruption of the rich-club? PLoS Comput Biol 2017; 13:e1005637. [PMID: 28817568 PMCID: PMC5560820 DOI: 10.1371/journal.pcbi.1005637] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Accepted: 06/20/2017] [Indexed: 01/05/2023] Open
Abstract
Surgery is a therapeutic option for people with epilepsy whose seizures are not controlled by anti-epilepsy drugs. In pre-surgical planning, an array of data modalities, often including intra-cranial EEG, is used in an attempt to map regions of the brain thought to be crucial for the generation of seizures. These regions are then resected with the hope that the individual is rendered seizure free as a consequence. However, post-operative seizure freedom is currently sub-optimal, suggesting that the pre-surgical assessment may be improved by taking advantage of a mechanistic understanding of seizure generation in large brain networks. Herein we use mathematical models to uncover the relative contribution of regions of the brain to seizure generation and consequently which brain regions should be considered for resection. A critical advantage of this modeling approach is that the effect of different surgical strategies can be predicted and quantitatively compared in advance of surgery. Herein we seek to understand seizure generation in networks with different topologies and study how the removal of different nodes in these networks reduces the occurrence of seizures. Since this a computationally demanding problem, a first step for this aim is to facilitate tractability of this approach for large networks. To do this, we demonstrate that predictions arising from a neural mass model are preserved in a lower dimensional, canonical model that is quicker to simulate. We then use this simpler model to study the emergence of seizures in artificial networks with different topologies, and calculate which nodes should be removed to render the network seizure free. We find that for scale-free and rich-club networks there exist specific nodes that are critical for seizure generation and should therefore be removed, whereas for small-world networks the strategy should instead focus on removing sufficient brain tissue. We demonstrate the validity of our approach by analysing intra-cranial EEG recordings from a database comprising 16 patients who have undergone epilepsy surgery, revealing rich-club structures within the obtained functional networks. We show that the postsurgical outcome for these patients was better when a greater proportion of the rich club was removed, in agreement with our theoretical predictions.
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Affiliation(s)
- Marinho A. Lopes
- Living Systems Institute, University of Exeter, Exeter, United Kingdom
- Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
- * E-mail:
| | - Mark P. Richardson
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
- Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, United Kingdom
| | - Eugenio Abela
- Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, United Kingdom
- Support Center for Advanced Neuroimaging (SCAN), University of Bern, Bern, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University of Bern, Bern, Switzerland
| | | | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter, United Kingdom
- Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
| | - John R. Terry
- Living Systems Institute, University of Exeter, Exeter, United Kingdom
- Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
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A Quantitative Analysis of an EEG Epileptic Record Based on MultiresolutionWavelet Coefficients. ENTROPY 2014. [DOI: 10.3390/e16115976] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures. ENTROPY 2014. [DOI: 10.3390/e16063049] [Citation(s) in RCA: 74] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Adaptive neuro-fuzzy inference system for classification of background EEG signals from ESES patients and controls. ScientificWorldJournal 2014; 2014:140863. [PMID: 24790547 PMCID: PMC3984772 DOI: 10.1155/2014/140863] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2013] [Accepted: 03/18/2014] [Indexed: 11/23/2022] Open
Abstract
Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved.
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Ouyang G, Li J, Liu X, Li X. Dynamic characteristics of absence EEG recordings with multiscale permutation entropy analysis. Epilepsy Res 2012; 104:246-52. [PMID: 23245676 DOI: 10.1016/j.eplepsyres.2012.11.003] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2012] [Revised: 10/01/2012] [Accepted: 11/12/2012] [Indexed: 10/27/2022]
Abstract
Understanding the transition of brain activities towards an absence seizure, called pre-epileptic seizure, is a challenge. In this study, multiscale permutation entropy (MPE) is proposed to describe dynamical characteristics of electroencephalograph (EEG) recordings on different absence seizure states. The classification ability of the MPE measures using linear discriminant analysis is evaluated by a series of experiments. Compared to a traditional multiscale entropy method with 86.1% as its classification accuracy, the classification rate of MPE is 90.6%. Experimental results demonstrate there is a reduction of permutation entropy of EEG from the seizure-free state to the seizure state. Moreover, it is indicated that the dynamical characteristics of EEG data with MPE can identify the differences among seizure-free, pre-seizure and seizure states. This also supports the view that EEG has a detectable change prior to an absence seizure.
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Affiliation(s)
- Gaoxiang Ouyang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
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Abstract
The brain is naturally considered as a network of interacting elements which, when functioning properly, produces an enormous range of dynamic, adaptable behavior. However, when elements of this network fail, pathological changes ensue, including epilepsy, one of the most common brain disorders. This review examines some aspects of cortical network organization that distinguish epileptic cortex from normal brain as well as the dynamics of network activity before and during seizures, focusing primarily on focal seizures. The review is organized around four phases of the seizure: the interictal period, onset, propagation, and termination. For each phase, the authors discuss the most common rhythmic characteristics of macroscopic brain voltage activity and outline the observed functional network features. Although the characteristics of functional networks that support the epileptic seizure remain an area of active research, the prevailing trends point to a complex set of network dynamics between, before, and during seizures.
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Affiliation(s)
- Mark A Kramer
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA.
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Douw L, de Groot M, van Dellen E, Heimans JJ, Ronner HE, Stam CJ, Reijneveld JC. 'Functional connectivity' is a sensitive predictor of epilepsy diagnosis after the first seizure. PLoS One 2010; 5:e10839. [PMID: 20520774 PMCID: PMC2877105 DOI: 10.1371/journal.pone.0010839] [Citation(s) in RCA: 83] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2010] [Accepted: 05/05/2010] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Although epilepsy affects almost 1% of the world population, diagnosis of this debilitating disease is still difficult. The EEG is an important tool for epilepsy diagnosis and classification, but the sensitivity of interictal epileptiform discharges (IEDs) on the first EEG is only 30-50%. Here we investigate whether using 'functional connectivity' can improve the diagnostic sensitivity of the first interictal EEG in the diagnosis of epilepsy. METHODOLOGY/PRINCIPAL FINDINGS Patients were selected from a database with 390 standard EEGs of patients after a first suspected seizure. Patients who were later diagnosed with epilepsy (i.e. > or = two seizures) were compared to matched non-epilepsy patients (with a minimum follow-up of one year). The synchronization likelihood (SL) was used as an index of functional connectivity of the EEG, and average SL per patient was calculated in seven frequency bands. In total, 114 patients were selected. Fifty-seven patients were diagnosed with epilepsy (20 had IEDs on their EEG) and 57 matched patients had other diagnoses. Epilepsy patients had significantly higher SL in the theta band than non-epilepsy patients. Furthermore, theta band SL proved to be a significant predictor of a diagnosis of epilepsy. When only those epilepsy patients without IEDs were considered (n = 74), theta band SL could predict diagnosis with specificity of 76% and sensitivity of 62%. CONCLUSION/SIGNIFICANCE Theta band functional connectivity may be a useful diagnostic tool in diagnosing epilepsy, especially in those patients who do not show IEDs on their first EEG. Our results indicate that epilepsy diagnosis could be improved by using functional connectivity.
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Affiliation(s)
- Linda Douw
- Department of Neurology, VU University Medical Center, Amsterdam, The Netherlands.
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Rosso OA, Mendes A, Berretta R, Rostas JA, Hunter M, Moscato P. Distinguishing childhood absence epilepsy patients from controls by the analysis of their background brain electrical activity (II): A combinatorial optimization approach for electrode selection. J Neurosci Methods 2009; 181:257-67. [DOI: 10.1016/j.jneumeth.2009.04.028] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2009] [Revised: 04/28/2009] [Accepted: 04/30/2009] [Indexed: 11/30/2022]
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