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Moya Quiros V, Adham A, Convers P, Lesca G, Mauguiere F, Soulier H, Arzimanoglou A, Bayat A, Braakman H, Camdessanche JP, Casenave P, Chaton L, Chaix Y, Chochoi M, Depienne C, Desportes V, De Ridder J, Dinkelacker V, Gardella E, Kluger GJ, Jung J, Lemesle Martin M, Mancardi MM, Mueller M, Poulat AL, Platzer K, Roubertie A, Stokman MF, Vulto-van Silfhout AT, Wiegand G, Mazzola L. Electro-Clinical Features and Functional Connectivity Analysis in SYN1-Related Epilepsy. Ann Neurol 2024. [PMID: 39177219 DOI: 10.1002/ana.27063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 08/07/2024] [Accepted: 08/07/2024] [Indexed: 08/24/2024]
Abstract
OBJECTIVE There is currently scarce data on the electroclinical characteristics of epilepsy associated with synapsin 1 (SYN1) pathogenic variations. We examined clinical and electro-encephalographic (EEG) features in patients with epilepsy and SYN1 variants, with the aim of identifying a distinctive electroclinical pattern. METHODS In this retrospective multicenter study, we collected and reviewed demographic, genetic, and epilepsy data of 19 male patients with SYN1 variants. Specifically, we analyzed interictal EEG data for all patients, and electro-clinical data from 10 epileptic seizures in 5 patients, using prolonged video-EEG monitoring recordings. Inter-ictal EEG functional connectivity parameters and frequency spectrum of the 10 patients over 12 years of age, were computed and compared with those of 56 age- and sex-matched controls. RESULTS The main electroclinical features of epilepsy in patients with SYN1 were (1) EEG background and organization mainly normal; (2) interictal abnormalities are often rare or not visible on EEG; (3) more than 60% of patients had reflex seizures (cutaneous contact with water and defecation being the main triggers) isolated or associated with spontaneous seizures; (4) electro-clinical semiology of seizures was mainly temporal or temporo-insulo/perisylvian with a notable autonomic component; and (5) ictal EEG showed a characteristic rhythmic theta/delta activity predominating in temporo-perisylvian regions at the beginning of most seizures. Comparing patients with SYN1 to healthy subjects, we observed a shift to lower frequency bands in power spectrum of interictal EEG and an increased connectivity in both temporal regions. INTERPRETATION A distinct epilepsy syndrome emerges in patients with SYN1, with a rather characteristic clinical and EEG pattern suggesting predominant temporo-insular involvement. ANN NEUROL 2024.
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Affiliation(s)
| | - Ahmed Adham
- Physical Medicine and Rehabilitation Department, University Hospital of Saint-Étienne, Saint-Étienne, France
- CEA, LETI, CLINATEC, University Grenoble Alpes, Grenoble, France
| | - Philippe Convers
- Neurology Department, University Hospital, Saint-Etienne, France
- NeuroPain Lab, Lyon Neuroscience Research Centre, CRNL-INSERM U 1028/CNRS UMR 5292, University of Lyon, Lyon, France
| | - Gaetan Lesca
- Department of Genetics, Member of the ERN EpiCARE, Hospices Civils de Lyon, Bron, France
- Institute NeuroMyoGène, Laboratoire Physiopathologie et Génétique du Neurone et du Muscle, CNRS UMR 5261-INSERM U1315, Université de Lyon-Université Claude Bernard Lyon 1, Lyon, France
| | - François Mauguiere
- NeuroPain Lab, Lyon Neuroscience Research Centre, CRNL-INSERM U 1028/CNRS UMR 5292, University of Lyon, Lyon, France
- Department of Functional Neurology and Epileptology, Member of the ERN EpiCARE, Hospices Civils de Lyon, Université de Lyon, Lyon, France
| | - Hugo Soulier
- Neurology Department, University Hospital, Saint-Etienne, France
| | - Alexis Arzimanoglou
- Department of Clinical Epileptology, Sleep Disorders and Functional Pediatric Neurology, coordinating member of the ERN EpiCARE, University Hospitals of Lyon (HCL), Lyon, France
- Sección Epilepsia, Sueño y Neurofisiología, Department of Neurology, coordinating member of the ERN EpiCARE, Hospital Sant Joan de Déu Barcelona, Barcelona, Spain
| | - Allan Bayat
- Institute for Regional Health Services, University of Southern Denmark, Odense, Denmark
- Department of Epilepsy Genetics and Personalized Medicine, Danish Epilepsy Center, Member of the ERN EpiCARE, Dianalund, Denmark
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Genetics, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Hilde Braakman
- Department of Paediatric Neurology, Radboud University Medical Centre, Amalia Children's Hospital, Nijmegen, The Netherlands
| | | | | | - Laurence Chaton
- Department of Neurology, Neurophysiology Unit, CHU Lille, Lille, France
| | - Yves Chaix
- Toulouse NeuroImaging Center, University of Toulouse, INSERM, Université Paul Sabatier, Toulouse, France
- Pediatric Neurology Unit, Children's Hospital, Toulouse-Purpan University Hospital, Toulouse, France
| | - Maxime Chochoi
- Department of Neurology, Neurophysiology Unit, CHU Lille, Lille, France
| | - Christel Depienne
- Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Vincent Desportes
- Hospices Civils de Lyon, Department of Pediatric Neurology, Member of the ERN EpiCARE, Hôpital Femme Mère Enfant, Lyon, France
| | - Jessie De Ridder
- Department of Neurology, Academic Center for Epileptology, Kempenhaeghe, Heeze, The Netherlands
| | - Vera Dinkelacker
- Department of Neurology, University Hospital Strasbourg, Strasbourg, France
| | - Elena Gardella
- Institute for Regional Health Services, University of Southern Denmark, Odense, Denmark
- Department of Epilepsy Genetics and Personalized Medicine, Danish Epilepsy Center, Member of the ERN EpiCARE, Dianalund, Denmark
| | - Gerhard J Kluger
- Schön Klinik Vogtareuth, Center for Pediatric Neurology, Neurorehabilitation and Epileptology, Collaborating Partner of the ERN EpiCARE, PMU, Vogtareuth, Salzburg, Germany
| | - Julien Jung
- Department of Functional Neurology and Epileptology, Member of the ERN EpiCARE, Hospices Civils de Lyon, Université de Lyon, Lyon, France
- Department of Neurology, University Hospital, Lyon Neuroscience Research Center (CRNL), INSERM U1028, CNRS UMR5292, Lyon, France
| | | | - Maria Margherita Mancardi
- Unit of Child Neuropsychiatry, Epilepsy Center, Member of the ERN EpiCARE, Istituto Giannina Gaslini, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health, University of Genoa, Genoa, Italy
| | - Markus Mueller
- Department of Epileptology, Krankenhaus Mara, Bethel Epilepsy Center, Bielefeld University, Bielefeld, Germany
| | - Anne-Lise Poulat
- Hospices Civils de Lyon, Department of Pediatric Neurology, Member of the ERN EpiCARE, Hôpital Femme Mère Enfant, Lyon, France
| | - Konrad Platzer
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Agathe Roubertie
- Department of Pediatric Neurology, INSERM, University Hospital Montpellier, Montpellier, France
| | - Marijn F Stokman
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Gert Wiegand
- Division of Pediatric Neurology, Department of Pediatrics, Asklepios Klinik Nord-Heidberg, Hamburg, Germany
- Department of Pediatric and Adolescent Medicine II (Neuropediatrics, Social Pediatrics), University Medical Centre Schleswig-Holstein, Kiel, Germany
| | - Laure Mazzola
- Neurology Department, University Hospital, Saint-Etienne, France
- NeuroPain Lab, Lyon Neuroscience Research Centre, CRNL-INSERM U 1028/CNRS UMR 5292, University of Lyon, Lyon, France
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Sreenivasan A, Krishna R, Nair PP, Alexander A. Assessment of auditory perception abilities using temporal envelope and fine structure processing in children with self-limited epilepsy with centrotemporal spikes. Epilepsy Res 2023; 196:107204. [PMID: 37591182 DOI: 10.1016/j.eplepsyres.2023.107204] [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: 05/04/2023] [Revised: 08/04/2023] [Accepted: 08/04/2023] [Indexed: 08/19/2023]
Abstract
OBJECTIVES Children with self-limited epilepsy with centrotemporal spikes (SeLECTS) exhibit difficulty processing spoken messages without hearing loss. The temporal envelope and fine structure processing abilities are the fundamental aspects of the normal listening process. There is limited literature on the temporal envelope and fine structure processing in children with SeLECTS. We evaluated the temporal envelope and fine structure processing in children with SeLECTS. DESIGN The study included 35 children with SeLECTS and 50 typically developing children (TDC). The temporal envelope processing was measured using the temporal modulation transfer function (TMTF) and temporal fine structure using the temporal fine structure low-frequency (TFS LF) test. The TMTF was measured for the modulation rates 4, 8, 16, 32, 64 and 128 Hz. The TFS LF was done for 250, 500 and 750 Hz. RESULTS The difference in modulation detection thresholds at 4 Hz was not found to be significant, whereas there was a significant difference in modulation detection thresholds observed for all the other modulation frequencies (p < 0.05) between the children with SeLECTS and TDC. The thresholds at 250, 500 and 750 Hz were higher (poorer) for children with SeLECTS than the TDC and was significant (p < 0.05). CONCLUSIONS The TMTF and TFS LF tests were of practical use in evaluating temporal envelope and fine structure processing abilities in children with SeLECTS. The results suggest that children with SeLECTS have a poor temporal envelope and fine structure processing compared to the TDC.
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Affiliation(s)
- Anuprasad Sreenivasan
- Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Department of ENT, Puducherry, India.
| | - Rajalakshmi Krishna
- School of Rehabilitation and Behavioral Sciences, VMRF(DU), Aarupadai Veedu Medical College and Hospital, Puducherry, India.
| | - Pradeep Pankajakshan Nair
- Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Department of Neurology, Puducherry, India.
| | - Arun Alexander
- Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Department of ENT, Puducherry, India.
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Goad BS, Lee-Messer C, He Z, Porter BE, Baumer FM. Connectivity increases during spikes and spike-free periods in self-limited epilepsy with centrotemporal spikes. Clin Neurophysiol 2022; 144:123-134. [PMID: 36307364 PMCID: PMC10883644 DOI: 10.1016/j.clinph.2022.09.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 09/01/2022] [Accepted: 09/13/2022] [Indexed: 12/26/2022]
Abstract
OBJECTIVE To understand the impact of interictal spikes on brain connectivity in patients with Self-Limited Epilepsy with Centrotemporal Spikes (SeLECTS). METHODS Electroencephalograms from 56 consecutive SeLECTS patients were segmented into periods with and without spikes. Connectivity between electrodes was calculated using the weighted phase lag index. To determine if there are chronic alterations in connectivity in SeLECTS, we compared spike-free connectivity to connectivity in 65 matched controls. To understand the acute impact of spikes, we compared connectivity immediately before, during, and after spikes versus baseline, spike-free connectivity. We explored whether behavioral state, spike laterality, or antiseizure medications affected connectivity. RESULTS Children with SeLECTS had markedly higher connectivity than controls during sleep but not wakefulness, with greatest difference in the right hemisphere. During spikes, connectivity increased globally; before and after spikes, left frontal and bicentral connectivity increased. Right hemisphere connectivity increased more during right-sided than left-sided spikes; left hemisphere connectivity was equally affected by right and left spikes. CONCLUSIONS SeLECTS patient have persistent increased connectivity during sleep; connectivity is further elevated during the spike and perispike periods. SIGNIFICANCE Testing whether increased connectivity impacts cognition or seizure susceptibility in SeLECTS and more severe epilepsies could help determine if spikes should be treated.
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Affiliation(s)
- Beatrice S Goad
- Department of Neurology, Stanford University School of Medicine, Palo Alto, CA, USA.
| | | | - Zihuai He
- Department of Neurology, Stanford University School of Medicine, Palo Alto, CA, USA.
| | - Brenda E Porter
- Department of Neurology, Stanford University School of Medicine, Palo Alto, CA, USA.
| | - Fiona M Baumer
- Department of Neurology, Stanford University School of Medicine, Palo Alto, CA, USA.
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Xu K, Wang F, Geng B, Peng Y, Zhang S, Li P, Chen D, Zeng X, Liu H, Liu P. Abnormal percent amplitude of fluctuation and functional connectivity within and between networks in benign epilepsy with centrotemporal spikes. Epilepsy Res 2022; 185:106989. [DOI: 10.1016/j.eplepsyres.2022.106989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/21/2022] [Accepted: 07/18/2022] [Indexed: 11/25/2022]
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Buchhalter J, Neuray C, Cheng JY, D’Cruz O, Datta AN, Dlugos D, French J, Haubenberger D, Hulihan J, Klein P, Komorowski RW, Kramer L, Lothe A, Nabbout R, Perucca E, der Ark PV. EEG Parameters as Endpoints in Epilepsy Clinical Trials- An Expert Panel Opinion Paper. Epilepsy Res 2022; 187:107028. [DOI: 10.1016/j.eplepsyres.2022.107028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 08/29/2022] [Accepted: 09/26/2022] [Indexed: 11/30/2022]
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Yang Y, Wang F, Andrade-Machado R, De Vito A, Wang J, Zhang T, Liu H. Disrupted functional connectivity patterns of the left inferior frontal gyrus subregions in benign childhood epilepsy with centrotemporal spikes. Transl Pediatr 2022; 11:1552-1561. [PMID: 36247884 PMCID: PMC9561512 DOI: 10.21037/tp-22-270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/26/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Benign epilepsy with centrotemporal spikes (BECTS) is one of the most common pediatric epileptic syndromes. Recent studies have shown that BECTS can lead to significant language dysfunction. Although research supports the role of the left inferior frontal gyrus (LIFG) in BECTS, it is unclear whether the subregions of the LIFG show different change patterns in patients with this syndrome. METHODS Using resting-state functional magnetic resonance imaging (fMRI) data in a group of 49 BECTS patients and 49 healthy controls, we investigated whether the BECTS patients show abnormal connectivity patterns of the LIFG subregions. RESULTS Compared with healthy controls, the BECTS patients exhibited higher connectivity between the following: the inferior frontal sulcus (IFS) and the right anterior cingulate cortex (ACC), and the ventral area 44 (A44v) region and the left hippocampus/parahippocampus. Also, a decreased connectivity was found between the IFS and the left inferior temporal gyrus (ITG). No other significant differences in functional connectivity were found in the other 4 functional subregions of the LIFG in the BECTS. CONCLUSIONS These findings provide evidence for BECTS-related functional connectivity patterns of the LIFG subregions and suggest that different subregions may be involved in different neural circuits associated with language function in the BECTS.
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Affiliation(s)
- Yang Yang
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China.,Department of Radiology, Suining Central Hospital, Suining, China
| | - Fuqin Wang
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - René Andrade-Machado
- Epilepsy Fellow at Children Hospital of Michigan, Detroit Medical Center, Detroit, MI, USA
| | - Andrea De Vito
- Department of Neuroradiology, H. S. Gerardo Monza, Monza, Italy
| | - Jiaojian Wang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Tijiang Zhang
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Heng Liu
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
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Tsai ML, Wang CC, Lee FC, Peng SJ, Chang H, Tseng SH. Resting-State EEG Functional Connectivity in Children with Rolandic Spikes with or without Clinical Seizures. Biomedicines 2022; 10:biomedicines10071553. [PMID: 35884857 PMCID: PMC9312817 DOI: 10.3390/biomedicines10071553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 06/26/2022] [Accepted: 06/28/2022] [Indexed: 11/16/2022] Open
Abstract
Alterations in dynamic brain network function are increasingly recognized in epilepsy. Benign childhood epilepsy with centrotemporal spikes (BECTS), or benign rolandic seizures, is the most common idiopathic focal epilepsy in children. In this study, we analyzed EEG functional connectivity (FC) among children with rolandic spikes with or without clinical seizures as compared to controls, to investigate the relationship between FC and clinical parameters in children with rolandic spikes. The FC analysis based on graph theory and network-based statistics in different frequency bands evaluated global efficiency, clustering coefficient, betweenness centrality, and nodal strength in four frequency bands. Similar to BECTS patients with seizures, children with rolandic spikes without seizures had significantly increased global efficiency, mean clustering coefficient, mean nodal strength, and connectivity strength, specifically in the theta frequency band at almost all proportional thresholds, compared with age-matched controls. Decreased mean betweenness centrality was only present in BECTS patients with seizures. Age at seizure onset was significantly positively associated with the strength of EEG-FC. The decreased function of betweenness centrality was only presented in BECTS patients with clinical seizures, suggesting weaker local connectivity may lower the seizure threshold. These findings may affect treatment policy in children with rolandic spikes.
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Affiliation(s)
- Min-Lan Tsai
- Division of Pediatric Neurology, Department of Pediatrics, Taipei Medical University Hospital, Taipei Medical University, Taipei 110301, Taiwan; (M.-L.T.); (F.-C.L.); (H.C.)
- Department of Pediatrics, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan
| | - Chuang-Chin Wang
- School of Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan;
| | - Feng-Chin Lee
- Division of Pediatric Neurology, Department of Pediatrics, Taipei Medical University Hospital, Taipei Medical University, Taipei 110301, Taiwan; (M.-L.T.); (F.-C.L.); (H.C.)
- Department of Pediatrics, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan
| | - Syu-Jyun Peng
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan
- Correspondence: ; Tel.: +886-2-66382736; Fax: +886-2-27321956
| | - Hsi Chang
- Division of Pediatric Neurology, Department of Pediatrics, Taipei Medical University Hospital, Taipei Medical University, Taipei 110301, Taiwan; (M.-L.T.); (F.-C.L.); (H.C.)
- Department of Pediatrics, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan
| | - Sung-Hui Tseng
- Department of Physical Medicine & Rehabilitation, Taipei Medical University Hospital, Taipei 110301, Taiwan;
- Department of Physical Medicine & Rehabilitation, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan
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Zhang S, Tang J, Huang J, Suo G, Zhou Z, You B, Dai Y, Liu Y. Whole-Brain Dynamic Resting-State Functional Network Analysis in Benign Epilepsy with Centrotemporal Spikes. IEEE J Biomed Health Inform 2022; 26:3813-3821. [PMID: 35380976 DOI: 10.1109/jbhi.2022.3164907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Benign epilepsy with centrotemporal spikes (BECTS), the most common type of epilepsy among children, is considered a network disorder. Both fMRI and EEG source imaging (ESI) studies have indicated that BECTS is associated with static resting-state functional network (SFN) alterations (e.g., decreased global efficiency) in source space. However, we find that the abovementioned alterations are not significant when the SFN calculations are performed in the scalp space using only clinical routine low-density (e.g., 19 channels) EEG recordings (shown in our results). In the context of EEG microstates, it is clear that networks in the scalp space with resting-state EEG recordings dynamically reconfigure in a well-organized way based on different functional states. We are therefore inspired to propose a whole-brain dynamic resting-state functional network (DFN) computation method based on resting-state low-density EEG recordings with four classical microstates in scalp space. Notably, on the one hand, this approach is suitable for clinical conditions, and, on the other hand, the dynamic alternations calculated with a DFN may promote our understanding of how the networks change in BECTS. We analysed the changes in a DFN in six frequency bands (, low, high, and) in patients with BECTS compared to those for healthy controls. Superior to traditional SFNs, the proposed DFN can reveal significant differences between individuals with BECTS and healthy controls (e.g., lower global efficiency), thus matching traditional fMRI and ESI methods in the source space. Our method directly performs DFN computations from low-density EEG recordings and avoids complex ESI computations, making it promising for clinical applications, especially in the outpatient diagnosis stage.
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Jouzizadeh M, Ghaderi AH, Cheraghmakani H, Baghbanian SM, Khanbabaie R. Resting-State Brain Network Deficits in Multiple Sclerosis Participants: Evidence from Electroencephalography and Graph Theoretical Analysis. Brain Connect 2021; 11:359-367. [PMID: 33780635 DOI: 10.1089/brain.2020.0857] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Multiple sclerosis (MS) is a chronic inflammatory disease leading to demyelination and axonal loss in the central nervous system that causes focal lesions of gray and white matter. However, the functional impairments of brain networks in this disease are still unspecified and need to be clearer. Materials and Methods: In the present study, we investigate the resting-state brain network impairments for MS participants in comparison to a normal group using electroencephalography (EEG) and graph theoretical analysis with a source localization method. Thirty-four age- and gender-matched participants from each MS group and normal group participated in this study. We recorded 5 min of EEG in the resting-state eyes open condition for each participant. One min (15 equal 4-sec artifact-free segments) of the EEG signals were selected for each participant, and the Low-Resolution Electromagnetic Tomography software was employed to calculate the functional connectivity among whole cortical regions in six frequency bands (delta, theta, alpha, beta1, beta2, and beta3). Graph theoretical analysis was used to calculate the clustering coefficient (CL), betweenness centrality (BC), shortest path length (SPL), and small-world propensity (SWP) for weighted connectivity matrices. Nonparametric permutation tests were utilized to compare these measures between groups. Results: Significant differences between the MS group and the normal group in the average of BC and SWP were found in the alpha band. The significant differences in the BC were spread over all lobes. Conclusion: These results suggest that the resting-state brain network for the MS group is disrupted in local and global scales, and EEG has the capability of revealing these impairments.
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Affiliation(s)
- Mojtaba Jouzizadeh
- Department of Physics, Babol Noshirvani University of Technology, Babol, Iran
| | - Amir Hossein Ghaderi
- Department of Psychology and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Hamed Cheraghmakani
- Department of Neurology, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | | | - Reza Khanbabaie
- Department of Physics, Babol Noshirvani University of Technology, Babol, Iran.,Department of Physics, I.K. Barber School of Arts and Sciences, University of British Columbia, Kelowna, British Columbia, Canada
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Galaris E, Gallos I, Myatchin I, Lagae L, Siettos C. Electroencephalography source localization analysis in epileptic children during a visual working-memory task. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2020; 36:e3404. [PMID: 33029905 DOI: 10.1002/cnm.3404] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 06/15/2020] [Accepted: 09/28/2020] [Indexed: 06/11/2023]
Abstract
We localize the sources of brain activity of children with epilepsy based on electroencephalograph (EEG) recordings acquired during a visual discrimination working memory task. For the numerical solution of the inverse problem, with the aid of age-specific MRI scans processed from a publicly available database, we use and compare three regularization numerical methods, namely the standardized low resolution brain electromagnetic tomography (sLORETA), the weighted minimum norm estimation (wMNE) and the dynamic statistical parametric mapping (dSPM). We show that all three methods provide the same spatio-temporal patterns of differences between the groups of epileptic and control children. In particular, our analysis reveals statistically significant differences between the two groups in regions of the parietal cortex indicating that these may serve as "biomarkers" for diagnostic purposes and ultimately localized treatment.
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Affiliation(s)
- Evangelos Galaris
- Dipartimento di Matematica e Applicazioni "Renato Caccioppoli", Universita' degli Studi di Napoli Federico II, Napoli, Italy
| | - Ioannis Gallos
- School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Athens, Greece
| | - Ivan Myatchin
- Department of Anesthesiology, Sint-Trudo Regional Hospital, Sint-Truiden, Belgium
| | - Lieven Lagae
- Department of Development and Regeneration, Section Paediatric Neurology, KU Leuven, Leuven, Belgium
| | - Constantinos Siettos
- Dipartimento di Matematica e Applicazioni "Renato Caccioppoli", Universita' degli Studi di Napoli Federico II, Napoli, Italy
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ADHD and ADHD-related neural networks in benign epilepsy with centrotemporal spikes: A systematic review. Epilepsy Behav 2020; 112:107448. [PMID: 32916583 DOI: 10.1016/j.yebeh.2020.107448] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 08/11/2020] [Accepted: 08/11/2020] [Indexed: 11/21/2022]
Abstract
BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) and benign epilepsy with centrotemporal spikes (BECTS or rolandic epilepsy) present with a very high level of comorbidity. We aimed to review the existing literature focusing on two aspects: the possible role of epileptic activity in the damage of ADHD-related neural networks and the clinical approach to patients presenting with both conditions. MATERIAL AND METHODS A systematic review was performed using Sapienza Library System and PubMed. The following search terms have been considered: attention networks, ADHD, attention systems, rolandic epilepsy, benign epilepsy with centrotemporal spikes, centrotemporal spikes epilepsy, and focal epilepsy in children. The target population consisted of patients under 18 years of age diagnosed with either BECTS and ADHD or healthy controls. RESULTS Nine case-control and cohort studies have been selected. The reported prevalence of ADHD in patients with BECTS was around 60%. No clinical correlation was found between the medical records and the presence of ADHD in patients with BECTS, if not due to febrile convulsion (FC). One study showed higher levels of bilateral discharges in patients with severe ADHD. The negative influence of the age at onset of seizures was demonstrated on attention but not on intelligence quotient (IQ). Moreover, the frequency of seizures and the occurrence of discharges during nonrapid eye movement (NREM) sleep were correlated to attention impairment. From a neurobiological point of view, functional connectivity in patients with BECTS and ADHD appears to be disrupted. Two studies reported a specific impairment in selective visual attention, while one study underlined a decreased activation of the dorsal attention network (DAN). Two different studies found that patients with BECTS and comorbid ADHD presented with altered thickness in their magnetic resonance imaging (MRI) scans in the cortical and subcortical regions (including the frontal lobes, lingual-fusiform cortex, cuneus and precuneus, limbic area and pericalcarine cortex among others). This might explain the cognitive and behavioral symptoms such as poor selective visual attention, speech disturbance, and impulsivity. CONCLUSIONS Despite BECTS being considered to have a relative benign course, many studies have documented cognitive and/or behavioral problems in patients diagnosed with this type of epilepsy. In particular, children affected by rolandic epilepsy should receive a complete neuropsychological evaluation at seizure onset considering the high rate of comorbidity with ADHD. A further investigation of the common pathogenic substrate is desirable to better orientate the clinical and therapeutic interventions applied.
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Jiang S, Luo C, Huang Y, Li Z, Chen Y, Li X, Pei H, Wang P, Wang X, Yao D. Altered Static and Dynamic Spontaneous Neural Activity in Drug-Naïve and Drug-Receiving Benign Childhood Epilepsy With Centrotemporal Spikes. Front Hum Neurosci 2020; 14:361. [PMID: 33005141 PMCID: PMC7485420 DOI: 10.3389/fnhum.2020.00361] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 08/07/2020] [Indexed: 11/13/2022] Open
Abstract
The present study aims to investigate intrinsic abnormalities of brain and the effect of antiepileptic treatment on brain activity in Benign childhood epilepsy with centrotemporal spikes (BECTS). Twenty-six drug-naïve patients (DNP) and 22 drug-receiving patients (DRP) with BECTS were collected in this study. Static amplitude of low frequency fluctuation (sALFF) and dynamic ALFF (dALFF) were applied to resting-state fMRI data. Functional connectivity (FC) analysis was further performed for affected regions identified by static and dynamic analysis. One-way analysis of variance and post hoc statistical analyses were performed for between-group differences. Abnormal sALFF and dALFF values were correlated with clinical features of patients. Compared with healthy controls (HC), DNP group demonstrated alterations of sALFF and/or dALFF in medial prefrontal cortex (MPFC), supplementary motor areas (SMA), cerebellum, hippocampus, pallidum and cingulate cortex, in which the values were close to normal in DRP. Notably, sALFF and dALFF showed specific sensitivity in detecting abnormalities in basal ganglia and cerebellum. Additionally, DRP showed additional changes in precuneus, inferior temporal gyrus, superior frontal gyrus and occipital visual cortex. Compared with HC, the DNP showed increased FC in default network and motion-related networks, and the DRP showed decreased FC in default network. The MPFC, hippocampus, SMA, basal ganglia and cerebellum are indicated to be intrinsically affected regions and effective therapeutic targets. And the FC profiles of default and motion-related networks might be potential core indicators for clinical treatment. This study revealed potential neuromodulatory targets and helped understand pathomechanism of BECTS. Static and dynamic analyses should be combined to investigate neuropsychiatric disorders.
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Affiliation(s)
- Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
| | - Yang Huang
- The Clinical Hospital of Chengdu Brain Science Institute, Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhiliang Li
- The Clinical Hospital of Chengdu Brain Science Institute, Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiangkui Li
- The Clinical Hospital of Chengdu Brain Science Institute, Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Haonan Pei
- The Clinical Hospital of Chengdu Brain Science Institute, Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Pingfu Wang
- The Clinical Hospital of Chengdu Brain Science Institute, Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoming Wang
- Department of Neurology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, China
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13
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Hu DK, Mower A, Shrey DW, Lopour BA. Effect of interictal epileptiform discharges on EEG-based functional connectivity networks. Clin Neurophysiol 2020; 131:1087-1098. [PMID: 32199397 DOI: 10.1016/j.clinph.2020.02.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 01/22/2020] [Accepted: 02/04/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Functional connectivity networks (FCNs) based on interictal electroencephalography (EEG) can identify pathological brain networks associated with epilepsy. FCNs are altered by interictal epileptiform discharges (IEDs), but it is unknown whether this is due to the morphology of the IED or the underlying pathological activity. Therefore, we characterized the impact of IEDs on the FCN through simulations and EEG analysis. METHODS We introduced simulated IEDs to sleep EEG recordings of eight healthy controls and analyzed the effect of IED amplitude and rate on the FCN. We then generated FCNs based on epochs with and without IEDs and compared them to the analogous FCNs from eight subjects with infantile spasms (IS), based on 1340 visually marked IEDs. Differences in network structure and strength were assessed. RESULTS IEDs in IS subjects caused increased connectivity strength but no change in network structure. In controls, simulated IEDs with physiological amplitudes and rates did not alter network strength or structure. CONCLUSIONS Increases in connectivity strength in IS subjects are not artifacts caused by the interictal spike waveform and may be related to the underlying pathophysiology of IS. SIGNIFICANCE Dynamic changes in EEG-based FCNs during IEDs may be valuable for identification of pathological networks associated with epilepsy.
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Affiliation(s)
- Derek K Hu
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
| | - Andrew Mower
- Division of Neurology, Children's Hospital Orange County, Orange, CA, USA; Department of Pediatrics, University of California, Irvine, CA, USA
| | - Daniel W Shrey
- Division of Neurology, Children's Hospital Orange County, Orange, CA, USA; Department of Pediatrics, University of California, Irvine, CA, USA
| | - Beth A Lopour
- Department of Biomedical Engineering, University of California, Irvine, CA, USA.
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14
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Vlcek P, Bares M, Novak T, Brunovsky M. Baseline Difference in Quantitative Electroencephalography Variables Between Responders and Non-Responders to Low-Frequency Repetitive Transcranial Magnetic Stimulation in Depression. Front Psychiatry 2020; 11:83. [PMID: 32174854 PMCID: PMC7057228 DOI: 10.3389/fpsyt.2020.00083] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 02/03/2020] [Indexed: 12/13/2022] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depressive disorder, with outcomes approaching 45-55% response and 30-40% remission. Eligible predictors of treatment outcome, however, are still lacking. Few studies have investigated quantitative electroencephalography (QEEG) parameters as predictors of rTMS treatment outcome and none of them have addressed the source localization techniques to predict the response to low-frequency rTMS (LF rTMS). We investigated electrophysiological differences based on scalp EEG data and inverse solution method, exact low-resolution brain electromagnetic tomography (eLORETA), between responders and non-responders to LF rTMS in resting brain activity recorded prior to the treatment. Twenty-five unmedicated depressive patients (mean age of 45.7 years, 20 females) received a 4-week treatment of LF rTMS (1 Hz; 20 sessions per 600 pulses; 100% of the motor threshold) over the right dorsolateral prefrontal cortex. Comparisons between responders (≥50% reduction in Montgomery-Åsberg Depression Rating Scale score) and non-responders were made at baseline for measures of eLORETA current density, spectral absolute power, and inter-hemispheric and intra-hemispheric EEG asymmetry. Responders were found to have lower current source densities in the alpha-2 and beta-1 frequency bands bilaterally (with predominance on the left side) in the inferior, medial, and middle frontal gyrus, precentral gyrus, cingulate gyrus, anterior cingulate, and insula. The most pronounced difference was found in the left middle frontal gyrus for alpha-2 and beta-1 bands (p < 0.05). Using a spectral absolute power analysis, we found a negative correlation between the absolute power in beta and theta frequency bands on the left frontal electrode F7 and the change in depressive symptomatology. None of the selected asymmetries significantly differentiated responders from non-responders in any frequency band. Pre-treatment reduction of alpha-2 and beta-1 sources, but not QEEG asymmetry, was found in patients with major depressive disorder who responded to LF rTMS treatment. Prospective trials with larger groups of subjects are needed to further validate these findings.
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Affiliation(s)
- Premysl Vlcek
- National Institute of Mental Health, Klecany, Czechia.,Third Faculty of Medicine, Charles University, Prague, Czechia
| | - Martin Bares
- National Institute of Mental Health, Klecany, Czechia.,Third Faculty of Medicine, Charles University, Prague, Czechia
| | - Tomas Novak
- National Institute of Mental Health, Klecany, Czechia.,Third Faculty of Medicine, Charles University, Prague, Czechia
| | - Martin Brunovsky
- National Institute of Mental Health, Klecany, Czechia.,Third Faculty of Medicine, Charles University, Prague, Czechia
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15
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Stacey W, Kramer M, Gunnarsdottir K, Gonzalez-Martinez J, Zaghloul K, Inati S, Sarma S, Stiso J, Khambhati AN, Bassett DS, Smith RJ, Liu VB, Lopour BA, Staba R. Emerging roles of network analysis for epilepsy. Epilepsy Res 2020; 159:106255. [PMID: 31855828 PMCID: PMC6990460 DOI: 10.1016/j.eplepsyres.2019.106255] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 12/08/2019] [Indexed: 11/29/2022]
Abstract
In recent years there has been increasing interest in applying network science tools to EEG data. At the 2018 American Epilepsy Society conference in New Orleans, LA, the yearly session of the Engineering and Neurostimulation Special Interest Group focused on emerging, translational technologies to analyze seizure networks. Each speaker demonstrated practical examples of how network tools can be utilized in clinical care and provide additional data to help care for patients with intractable epilepsy. The groups presented advances using tools from functional connectivity, control theory, and graph theory to analyze human EEG data. These tools have great potential to augment clinical interpretation of EEG signals.
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Affiliation(s)
- William Stacey
- Department of Neurology, Department of Biomedical Engineering, University of Michigan, United States.
| | - Mark Kramer
- Department of Mathematics and Statistics, Center of Systems Neuroscience, Boston University, United States
| | | | | | - Kareem Zaghloul
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, NIH, United States
| | - Sara Inati
- Office of the Clinical Director, National Institute of Neurological Disorders and Stroke, NIH, United States
| | - Sridevi Sarma
- Department of Neurology, Department of Biomedical Engineering, University of Michigan, United States
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, United States
| | - Ankit N Khambhati
- Department of Bioengineering, University of Pennsylvania, United States
| | | | - Rachel J Smith
- Department of Biomedical Engineering, University of California, Irvine, United States
| | - Virginia B Liu
- Department of Pediatrics, University of California, Irvine, United States; Department of Child Neurology, Children's Hospital of Orange County, CA, United States
| | - Beth A Lopour
- Department of Biomedical Engineering, University of California, Irvine, United States
| | - Richard Staba
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
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16
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Bear JJ, Chapman KE, Tregellas JR. The epileptic network and cognition: What functional connectivity is teaching us about the childhood epilepsies. Epilepsia 2019; 60:1491-1507. [PMID: 31247129 PMCID: PMC7175745 DOI: 10.1111/epi.16098] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 05/09/2019] [Accepted: 06/05/2019] [Indexed: 12/13/2022]
Abstract
Our objective was to summarize and evaluate the rapidly expanding body of literature studying functional connectivity in childhood epilepsy. In the self-limited childhood epilepsies, awareness of cognitive comorbidities has been steadily increasing, and recent advances in our understanding of the network effects of these disorders promise insights into the underlying neurobiology. We reviewed publications addressing functional connectivity in children with epilepsy with an emphasis on studies of children with self-limited childhood epilepsies. The majority of studies have been published in the past 10 years and predominantly examine childhood epilepsy with centrotemporal spikes and childhood absence epilepsy. Cognitive network alterations are commonly observed across the childhood epilepsies. Some of these effects appear to be nonspecific to epilepsy syndrome or even to category of neurological disorder. Other patterns, such as changes in the connectivity of cortical language areas in childhood epilepsy with centrotemporal spikes, provide clues to the underlying cognitive deficits seen in affected children. The literature to date is dominated by general observations of connectivity patterns without a priori hypotheses. These data-driven studies build an important foundation for hypothesis generation and are already providing useful insights into the neuropathology of the childhood epilepsies. Future work should emphasize hypothesis-driven approaches and rigorous clinical correlations to better understand how the knowledge of network alterations can be applied to guidance and treatment for the children in our clinics.
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Affiliation(s)
- Joshua J Bear
- Department of Pediatrics, Section of Neurology, Children’s Hospital Colorado
- Department of Pediatrics, University of Colorado Anschutz Medical Campus
| | - Kevin E Chapman
- Department of Pediatrics, Section of Neurology, Children’s Hospital Colorado
- Department of Pediatrics, University of Colorado Anschutz Medical Campus
| | - Jason R Tregellas
- Department of Psychiatry, University of Colorado Anschutz Medical Campus
- Research Service, Rocky Mountain Regional VA Medical Center
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17
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Choi HS, Chung YG, Choi SA, Ahn S, Kim H, Yoon S, Hwang H, Kim KJ. Electroencephalographic Resting-State Functional Connectivity of Benign Epilepsy with Centrotemporal Spikes. J Clin Neurol 2019; 15:211-220. [PMID: 30938108 PMCID: PMC6444134 DOI: 10.3988/jcn.2019.15.2.211] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 11/30/2018] [Accepted: 11/30/2018] [Indexed: 12/20/2022] Open
Abstract
Background and Purpose We aimed to reveal resting-state functional connectivity characteristics based on the spike-free waking electroencephalogram (EEG) of benign epilepsy with centrotemporal spikes (BECTS) patients, which usually appears normal in routine visual inspection. Methods Thirty BECTS patients and 30 disease-free and age- and sex-matched controls were included. Eight-second EEG epochs without artifacts were sampled and then bandpass filtered into the delta, theta, lower alpha, upper alpha, and beta bands to construct the association matrix. The weighted phase lag index (wPLI) was used as an association measure for EEG signals. The band-specific connectivity, which was represented as a matrix of wPLI values of all edges, was compared for analyzing the connectivity itself. The global wPLI, characteristic path length (CPL), and mean clustering coefficient were compared. Results The resting-state functional connectivity itself and the network topology differed in the BECTS patients. For the lower-alpha-band and beta-band connectivity, edges that showed significant differences had consistently lower wPLI values compared to the disease-free controls. The global wPLI value was significantly lower for BECTS patients than for the controls in lower-alpha-band connectivity (mean±SD; 0.241±0.034 vs. 0.276±0.054, p=0.024), while the CPL was significantly longer for BECTS in the same frequency band (mean±SD; 4.379±0.574 vs. 3.904±0.695, p=0.04). The resting-state functional connectivity of BECTS showed decreased connectivity, integration, and efficiency compared to controls. Conclusions The connectivity differed significantly between BECTS patients and disease-free controls. In BECTS, global connectivity was significantly decreased and the resting-state functional connectivity showed lower efficiency in the lower alpha band.
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Affiliation(s)
- Hyun Soo Choi
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
| | - Yoon Gi Chung
- Healthcare ICT Research Center, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Sun Ah Choi
- Department of Pediatrics, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Soyeon Ahn
- Division of Medical Statistics, Medical Research Collaborating Center, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Hunmin Kim
- Department of Pediatrics, Seoul National University Bundang Hospital, Seongnam, Korea.
| | - Sungroh Yoon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
| | - Hee Hwang
- Department of Pediatrics, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Ki Joong Kim
- Pediatric Clinical Neuroscience Center, Seoul National University Children's Hospital, Seoul, Korea.,Department of Pediatrics, Seoul National University College of Medicine, Seoul, Korea
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18
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Meng L. A Magnetoencephalography Study of Pediatric Interictal Neuromagnetic Activity Changes and Brain Network Alterations Caused by Epilepsy in the High Frequency (80-1000 Hz). IEEE Trans Neural Syst Rehabil Eng 2019; 27:389-399. [PMID: 30762563 DOI: 10.1109/tnsre.2019.2898683] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
More and more studies propose that high frequency brain signals are promising biomarkers of epileptogenic zone. In this paper, our aim is to investigate the neuromagnetic changes and brain network topological alterations during an interictal period at high frequency ranges (80-1000 Hz) between healthy controls and epileptic patients with Magnetoencephalography. We analyzed neuromagnetic activities with accumulated source imaging, and constructed brain network based on graph theory. Neuromagnetic activity changes and brain network alterations between two groups were analyzed in three frequency bands: ripple (80-250 Hz), fast ripples (FRs, 250-500 Hz), and very high frequency oscillations (VHFO, 500-1000 Hz). We found that epileptic patients showed significantly altered patterns of neuromagnetic source localization and altered brain network patterns. And, we also found that mean functional connectivity and the number of modules from epileptic patients significantly increased in the ripple and FRs bands, and mean clustering coefficient from epileptic patients significantly decreased in the ripple and FRs bands. We also found that the mean functional connectivity was positively correlated with duration of epilepsy in the ripple and VHFO bands, and the number of modules was positively correlated with the duration of epilepsy in the ripple, FRs, and VHFO bands. Our results indicate that epilepsy can alter patients' neuromagnetic activities and brain networks in the high-frequency ranges, and these alterations become more pathological as the duration of epilepsy grows longer.
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19
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Clemens B, Dömötör J, Emri M, Puskás S, Fekete I. Inter-ictal network of focal epilepsy and effects of clinical factors on network activity. Clin Neurophysiol 2018; 130:251-258. [PMID: 30583272 DOI: 10.1016/j.clinph.2018.11.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 11/11/2018] [Accepted: 11/22/2018] [Indexed: 10/27/2022]
Abstract
OBJECTIVE Aim of the study was to explore the inter-ictal, resting-state EEG network in patients with focal epilepsy (FE) and to specify clinical factors that influence network activity. METHODS Functional EEG connectivity (EEGfC) differences were computed between 232 FE patients (FE group) and 77 healthy controls. EEGfC was computed among 23 cortical regions within each hemisphere, for 25 very narrow bands from 1 to 25 Hz. We computed independent effects for six clinical factors on EEGfC in the FE group, by ANOVA and post-hoc t-statistics, corrected for multiple comparisons by false discovery rate method. RESULTS Robust, statistically significant EEGfC differences emerged between the FE and the healthy control groups. Etiology, seizure type, duration of the illness and antiepileptic treatment were independent factors that influenced EEGfC. Statistically significant results occurred selectively in one or a few very narrow bands and outlined networks. Most abnormal EEGfC findings occurred at frequencies that mediate integrative and motor activities. CONCLUSIONS FE patients have abnormal resting-state EEGfC network activity. Clinical factors significantly modify EEGfC. SIGNIFICANCE Delineation of the FE network and modifying factors can open the way for targeted investigations and introduction of EEGfC into epilepsy research and practice.
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Affiliation(s)
- Béla Clemens
- University of Debrecen, Kenézy Gyula University Hospital, Department of Neurology, Bartók Béla út 3., 4031 Debrecen, Hungary
| | - Johanna Dömötör
- University of Debrecen, Kenézy Gyula University Hospital, Department of Neurology, Bartók Béla út 3., 4031 Debrecen, Hungary
| | - Miklós Emri
- University of Debrecen, Department of Medical Imaging, Nagyerdei krt. 98., 4032 Debrecen, Hungary
| | - Szilvia Puskás
- University of Debrecen, Kenézy Gyula University Hospital, Department of Neurology, Bartók Béla út 3., 4031 Debrecen, Hungary.
| | - István Fekete
- University of Debrecen, Medical Center, Department of Neurology, Móricz Zsigmond krt. 22., 4032 Debrecen, Hungary
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20
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Network characteristics in benign epilepsy with centro-temporal spikes patients indicating defective connectivity during spindle sleep: A partial directed coherence study of EEG signals. Clin Neurophysiol 2018; 129:2372-2379. [DOI: 10.1016/j.clinph.2018.09.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 08/31/2018] [Accepted: 09/07/2018] [Indexed: 11/19/2022]
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21
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Ghaderi AH, Andevari MN, Sowman PF. Evidence for a Resting State Network Abnormality in Adults Who Stutter. Front Integr Neurosci 2018; 12:16. [PMID: 29755328 PMCID: PMC5934488 DOI: 10.3389/fnint.2018.00016] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 04/10/2018] [Indexed: 01/21/2023] Open
Abstract
Neural network-based investigations of stuttering have begun to provide a possible integrative account for the large number of brain-based anomalies associated with stuttering. Here we used resting-state EEG to investigate functional brain networks in adults who stutter (AWS). Participants were 19 AWS and 52 age-, and gender-matched normally fluent speakers. EEGs were recorded and connectivity matrices were generated by LORETA in the theta (4-8 Hz), alpha (8-12 Hz), beta1 (12-20 Hz), and beta2 (20-30 Hz) bands. Small-world propensity (SWP), shortest path, and clustering coefficients were computed for weighted graphs. Minimum spanning tree analysis was also performed and measures were compared by non-parametric permutation test. The results show that small-world topology was evident in the functional networks of all participants. Three graph indices (diameter, clustering coefficient, and shortest path) exhibited significant differences between groups in the theta band and one [maximum betweenness centrality (BC)] measure was significantly different between groups in the beta2 band. AWS show higher BC than control in right temporal and inferior frontal areas and lower BC in the right primary motor cortex. Abnormal functional networks during rest state suggest an anomaly of DMN activity in AWS. Furthermore, functional segregation/integration deficits in the theta network are evident in AWS. These deficits reinforce the hypothesis that there is a neural basis for abnormal executive function in AWS. Increased beta2 BC in the right speech-motor related areas confirms previous evidence that right audio-speech areas are over-activated in AWS. Decreased beta2 BC in the right primary motor cortex is discussed in relation to abnormal neural mechanisms associated with time perception in AWS.
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Affiliation(s)
- Amir H. Ghaderi
- Cognitive Neuroscience Laboratory, University of Tabriz, Tabriz, Iran
- Iranian Neuro-wave Laboratory, Center of Isfahan, Isfahan, Iran
| | - Masoud N. Andevari
- Iranian Neuro-wave Laboratory, Center of Isfahan, Isfahan, Iran
- Department of Physics, School of Basic Science, Babol Noshirvani University of Technology, Babol, Iran
| | - Paul F. Sowman
- Department of Cognitive Science, Faculty of Human Sciences, Macquarie University, Sydney, NSW, Australia
- ARC Centre of Excellence in Cognition and its Disorders, Macquarie University, Sydney, NSW, Australia
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22
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Adebimpe A, Bourel-Ponchel E, Wallois F. Identifying neural drivers of benign childhood epilepsy with centrotemporal spikes. NEUROIMAGE-CLINICAL 2017; 17:739-750. [PMID: 29270358 PMCID: PMC5730126 DOI: 10.1016/j.nicl.2017.11.024] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Revised: 10/23/2017] [Accepted: 11/30/2017] [Indexed: 12/23/2022]
Abstract
Epilepsy is a neurological disorder characterized by abnormal electrical discharges in a group of brain cells. Benign childhood epilepsy, which affect children under the age of 12 years, has been reported to contribute to the cognitive impairment of these children, even in the absence of structural abnormalities. Functional connectivity models have been applied to provide a deeper understanding of the processes that control and regulate interictal activity of benign childhood epilepsy. These studies have shown regions of increased connectivity and activity, particularly at the epileptic zone, which is usually the central region around the sensorimotor cortex, and in the immediate regions surrounding the zone and reduced activity in distant regions, such as the frontal lobe and temporal regions. The present study was designed to identify the neural drivers involved in the initiation and propagation of epileptic activity and the causal relationships between brain regions with increased and decreased connectivity and functional activity. We used three different models to identify neural drivers and casual connectivity with dynamic causal modelling (DCM) of EEG data. All models showed that the central region, the source of the epileptic activity, is the major driver of the brain network during interictal discharges. Other regions include the temporoparietal junction and temporal pole. The central region also had influence on the frontal and contralateral hemisphere, which might explain the cognitive deficits observed in these patients. The epileptic source is the major driver of the brain network Other drivers include the temporoparietal junction and temporal pole Epileptic source had influence on the frontal region which might explain the cognitive deficits The right epileptic region drives the left hemisphere during interictal epileptic discharges
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Affiliation(s)
- Azeez Adebimpe
- INSERM UMR 1105, Research Group on Multimodal Analysis of Brain Function, University of Picardy Jules Verne, 80036 Amiens Cedex, France.
| | - Emilie Bourel-Ponchel
- INSERM UMR 1105, Research Group on Multimodal Analysis of Brain Function, University of Picardy Jules Verne, 80036 Amiens Cedex, France; INSERM UMR 1105, EFSN pediatric, Amiens University Hospital, Avenue Laennec, 80054 Amiens Cedex, France
| | - Fabrice Wallois
- INSERM UMR 1105, Research Group on Multimodal Analysis of Brain Function, University of Picardy Jules Verne, 80036 Amiens Cedex, France; INSERM UMR 1105, EFSN pediatric, Amiens University Hospital, Avenue Laennec, 80054 Amiens Cedex, France
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