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Ye H, He C, Hu W, Xiong K, Hu L, Chen C, Xu S, Xu C, Wang Y, Ding Y, Wu Y, Zhang K, Wang S, Wang S. Pre-ictal fluctuation of EEG functional connectivity discriminates seizure phenotypes in mesial temporal lobe epilepsy. Clin Neurophysiol 2023; 151:107-115. [PMID: 37245497 DOI: 10.1016/j.clinph.2023.05.004] [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: 12/23/2022] [Revised: 04/29/2023] [Accepted: 05/10/2023] [Indexed: 05/30/2023]
Abstract
OBJECTIVE We explored whether quantifiable differences between clinical seizures (CSs) and subclinical seizures (SCSs) occur in the pre-ictal state. METHODS We analyzed pre-ictal stereo-electroencephalography (SEEG) retrospectively across mesial temporal lobe epilepsy patients with recorded CSs and SCSs. Power spectral density and functional connectivity (FC) were quantified within and between the seizure onset zone (SOZ) and the early propagation zone (PZ), respectively. To evaluate the fluctuation of neural connectivity, FC variability was computed. Measures were further verified by a logistic regression model to evaluate their classification potentiality through the area under the receiver-operating-characteristics curve (AUC). RESULTS Fifty-four pre-ictal SEEG epochs (27 CSs and 27 SCSs) were selected among 14 patients. Within the SOZ, pre-ictal FC variability of CSs was larger than SCSs in 1-45 Hz during 30 seconds before seizure onset. Pre-ictal FC variability between the SOZ and PZ was larger in SCSs than CSs in 55-80 Hz within 1 minute before onset. Using these two variables, the logistic regression model achieved an AUC of 0.79 when classifying CSs and SCSs. CONCLUSIONS Pre-ictal FC variability within/between epileptic zones, not signal power or FC value, distinguished SCSs from CSs. SIGNIFICANCE Pre-ictal epileptic network stability possibly marks seizure phenotypes, contributing insights into ictogenesis and potentially helping seizure prediction.
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Affiliation(s)
- Hongyi Ye
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Chenmin He
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Kai Xiong
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China
| | - Lingli Hu
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Cong Chen
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Sha Xu
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Cenglin Xu
- Department of Pharmacology, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, Zhejiang Province Key Laboratory of Neurobiology, Basic Medical College, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yi Wang
- Department of Pharmacology, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, Zhejiang Province Key Laboratory of Neurobiology, Basic Medical College, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yao Ding
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yingcai Wu
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shan Wang
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
| | - Shuang Wang
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
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Tanglay O, Dadario NB, Chong EHN, Tang SJ, Young IM, Sughrue ME. Graph Theory Measures and Their Application to Neurosurgical Eloquence. Cancers (Basel) 2023; 15:556. [PMID: 36672504 PMCID: PMC9857081 DOI: 10.3390/cancers15020556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/04/2023] [Accepted: 01/14/2023] [Indexed: 01/18/2023] Open
Abstract
Improving patient safety and preserving eloquent brain are crucial in neurosurgery. Since there is significant clinical variability in post-operative lesions suffered by patients who undergo surgery in the same areas deemed compensable, there is an unknown degree of inter-individual variability in brain 'eloquence'. Advances in connectomic mapping efforts through diffusion tractography allow for utilization of non-invasive imaging and statistical modeling to graphically represent the brain. Extending the definition of brain eloquence to graph theory measures of hubness and centrality may help to improve our understanding of individual variability in brain eloquence and lesion responses. While functional deficits cannot be immediately determined intra-operatively, there has been potential shown by emerging technologies in mapping of hub nodes as an add-on to existing surgical navigation modalities to improve individual surgical outcomes. This review aims to outline and review current research surrounding novel graph theoretical concepts of hubness, centrality, and eloquence and specifically its relevance to brain mapping for pre-operative planning and intra-operative navigation in neurosurgery.
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Affiliation(s)
- Onur Tanglay
- UNSW School of Clinical Medicine, Faulty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW 2000, Australia
| | - Nicholas B. Dadario
- Robert Wood Johnson Medical School, Rutgers University, 125 Paterson St, New Brunswick, NJ 08901, USA
| | - Elizabeth H. N. Chong
- Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Dr, Singapore 117597, Singapore
| | - Si Jie Tang
- School of Medicine, University of California Davis, Sacramento, CA 95817, USA
| | - Isabella M. Young
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW 2000, Australia
| | - Michael E. Sughrue
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW 2000, Australia
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Mao L, Zheng G, Cai Y, Luo W, Zhang Q, Peng W, Ding J, Wang X. Frontotemporal phase lag index correlates with seizure severity in patients with temporal lobe epilepsy. Front Neurol 2022; 13:855842. [DOI: 10.3389/fneur.2022.855842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 10/18/2022] [Indexed: 12/05/2022] Open
Abstract
ObjectivesTo find the brain network indicators correlated with the seizure severity in temporal lobe epilepsy (TLE) by graph theory analysis.MethodsWe enrolled 151 patients with TLE and 36 age- and sex-matched controls with video-EEG monitoring. The 90-s interictal EEG data were acquired. We adopted a network analyzing pipeline based on graph theory to quantify and localize their functional networks, including weighted classical network, minimum spanning tree, community structure, and LORETA. The seizure severities were evaluated using the seizure frequency, drug-resistant epilepsy (DRE), and VA-2 scores.ResultsOur network analysis pipeline showed ipsilateral frontotemporal activation in patients with TLE. The frontotemporal phase lag index (PLI) values increased in the theta band (4–7 Hz), which were elevated in patients with higher seizure severities (P < 0.05). Multivariate linear regression analysis showed that the VA-2 scores were independently correlated with frontotemporal PLI values in the theta band (β = 0.259, P = 0.001) and age of onset (β = −0.215, P = 0.007).SignificanceThis study illustrated that the frontotemporal PLI in the theta band independently correlated with seizure severity in patients with TLE. Our network analysis provided an accessible approach to guide the treatment strategy in routine clinical practice.
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Vagal nerve stimulation cycles alter EEG connectivity in drug-resistant epileptic patients: a study with graph theory metrics. Clin Neurophysiol 2022; 142:59-67. [DOI: 10.1016/j.clinph.2022.07.503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 07/17/2022] [Accepted: 07/28/2022] [Indexed: 11/21/2022]
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Falsaperla R, Vitaliti G, Marino SD, Praticò AD, Mailo J, Spatuzza M, Cilio MR, Foti R, Ruggieri M. Graph theory in paediatric epilepsy: A systematic review. DIALOGUES IN CLINICAL NEUROSCIENCE 2021; 23:3-13. [PMID: 35860177 PMCID: PMC9286734 DOI: 10.1080/19585969.2022.2043128] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Graph theoretical studies have been designed to investigate network topologies during life. Network science and graph theory methods may contribute to a better understanding of brain function, both normal and abnormal, throughout developmental stages. The degree to which childhood epilepsies exert a significant effect on brain network organisation and cognition remains unclear. The hypothesis suggests that the formation of abnormal networks associated with epileptogenesis early in life causes a disruption in normal brain network development and cognition, reflecting abnormalities in later life. Neurological diseases with onset during critical stages of brain maturation, including childhood epilepsy, may threaten this orderly neurodevelopmental process. According to the hypothesis that the formation of abnormal networks associated with epileptogenesis in early life causes a disruption in normal brain network development, it is then mandatory to perform a proper examination of children with new-onset epilepsy early in the disease course and a deep study of their brain network organisation over time. In regards, graph theoretical analysis could add more information. In order to facilitate further development of graph theory in childhood, we performed a systematic review to describe its application in functional dynamic connectivity using electroencephalographic (EEG) analysis, focussing on paediatric epilepsy.
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Affiliation(s)
- Raffaele Falsaperla
- Neonatal Intensive Care Unit, San Marco Hospital, University Hospital Policlinico “G. Rodolico-San Marco", Catania, Italy
- Unit of Pediatrics and Pediatric Emergency, University Hospital Policlinico “G. Rodolico-San Marco", Catania, Italy
| | - Giovanna Vitaliti
- Department of Medical Sciences, Unit of Pediatrics, University of Ferrara, Ferrara, Italy
| | - Simona Domenica Marino
- Unit of Pediatrics and Pediatric Emergency, University Hospital Policlinico “G. Rodolico-San Marco", Catania, Italy
| | - Andrea Domenico Praticò
- Unit of Rare Diseases of the Nervous System in Childhood, Department of Clinical and Experimental Medicine, Section of Pediatrics and Child Neuropsychiatry, University of Catania, Catania, Italy
| | - Janette Mailo
- Division of Pediatric Neurology, University of Alberta, Stollery Children’s Hospital, Edmonton, Alberta, Canada
| | - Michela Spatuzza
- National Council of Research, Institute for Biomedical Research and Innovation (IRIB), Unit of Catania, Catania, Italy
| | - Maria Roberta Cilio
- Institute for Experimental and Clinical Research, Catholic University of Leuven, Brussels, Belgium
| | - Rosario Foti
- Department Chief of Rheumatology Unit, San Marco Hospital, University Hospital Policlinico “G. Rodolico-San Marco", Catania, Italy
| | - Martino Ruggieri
- Unit of Rare Diseases of the Nervous System in Childhood, Department of Clinical and Experimental Medicine, Section of Pediatrics and Child Neuropsychiatry, University of Catania, Catania, Italy
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Spring AM, Pittman DJ, Bessemer R, Federico P. Graph index complexity as a novel surrogate marker of high frequency oscillations in delineating the seizure onset zone. Clin Neurophysiol 2019; 131:78-87. [PMID: 31756595 DOI: 10.1016/j.clinph.2019.09.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 08/09/2019] [Accepted: 09/06/2019] [Indexed: 11/15/2022]
Abstract
OBJECTIVE To investigate the Graph Index Complexity (uGIC) as a marker of high frequency oscillatory (HFO) activity, the seizure onset zone (SOZ), and surgical outcome. METHODS The SOZ, rates of HFOs at two thresholds (broad, strict), and uGIC were determined using EEG data from 41 patients. The correlation between HFOs and uGIC were calculated. HFOs and uGIC were compared within and outside the SOZ. Postsurgical outcome was compared to the colocalization of HFOs and resected SOZ. RESULTS There was significant correlation between uGIC and both broad (r = 0.69, p < 0.0005) and strict HFOs (r = 0.48, p < 0.0005). All were significantly greater within the SOZ overall, but only in 17/41 (strict, uGIC) or 18/41 (broad) patients. HFO markers were significantly greater within the SOZ for 8/15 patients with positive postsurgical outcomes, but not for any patients with negative outcomes (0/5). CONCLUSION The uGIC is a marker of HFO activity, while HFOs and uGIC are markers of the SOZ overall. Colocalization of HFOs and the SOZ has strong positive predictive value for postsurgical outcome, but poor negative predictive value. SIGNIFICANCE The uGIC is an objective surrogate marker of HFO activity independent of identifying discrete HFO events.
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Affiliation(s)
- Aaron M Spring
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, AB, Canada
| | - Daniel J Pittman
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, AB, Canada
| | - Robin Bessemer
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, AB, Canada
| | - Paolo Federico
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, AB, Canada; Department of Radiology, University of Calgary, Calgary, AB, Canada.
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Hippocampal CA1 and cortical interictal oscillations in the pilocarpine model of epilepsy. Brain Res 2019; 1722:146351. [DOI: 10.1016/j.brainres.2019.146351] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 07/17/2019] [Accepted: 07/23/2019] [Indexed: 01/25/2023]
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Vecchio F, Miraglia F, Maria Rossini P. Connectome: Graph theory application in functional brain network architecture. Clin Neurophysiol Pract 2017; 2:206-213. [PMID: 30214997 PMCID: PMC6123924 DOI: 10.1016/j.cnp.2017.09.003] [Citation(s) in RCA: 86] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2017] [Revised: 07/28/2017] [Accepted: 09/06/2017] [Indexed: 12/20/2022] Open
Abstract
Network science and graph theory applications can help in understanding how human cognitive functions are linked to neuronal network structure. The present review focuses on pivotal recent studies regarding graph theory application on functional dynamic connectivity investigated by electroencephalographic (EEG) analysis. Graph analysis applications represent an interesting probe to analyze the distinctive features of real life by focusing on functional connectivity networks. Application of graph theory to patient data might provide more insight into the pathophysiological processes underlying brain disconnection. Graph theory might aid in monitoring the impact of eventual pharmacological and rehabilitative treatments.
Network science and graph theory applications have recently spread widely to help in understanding how human cognitive functions are linked to neuronal network structure, thus providing a conceptual frame that can help in reducing the analytical brain complexity and underlining how network topology can be used to characterize and model vulnerability and resilience to brain disease and dysfunction. The present review focuses on few pivotal recent studies of our research team regarding graph theory application in functional dynamic connectivity investigated by electroencephalographic (EEG) analysis. The article is divided into two parts. The first describes the methodological approach to EEG functional connectivity data analysis. In the second part, network studies of physiological aging and neurological disorders are explored, with a particular focus on epilepsy and neurodegenerative dementias, such as Alzheimer's disease.
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Affiliation(s)
- Fabrizio Vecchio
- Brain Connectivity Laboratory, IRCCS San Raffaele Pisana, Rome, Italy
| | - Francesca Miraglia
- Brain Connectivity Laboratory, IRCCS San Raffaele Pisana, Rome, Italy.,Institute of Neurology, Dept. Geriatrics, Neuroscience & Orthopedics, Catholic University, Policlinic A. Gemelli, Rome, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, IRCCS San Raffaele Pisana, Rome, Italy.,Institute of Neurology, Dept. Geriatrics, Neuroscience & Orthopedics, Catholic University, Policlinic A. Gemelli, Rome, Italy
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Bartolomei F, Lagarde S, Wendling F, McGonigal A, Jirsa V, Guye M, Bénar C. Defining epileptogenic networks: Contribution of SEEG and signal analysis. Epilepsia 2017; 58:1131-1147. [DOI: 10.1111/epi.13791] [Citation(s) in RCA: 262] [Impact Index Per Article: 37.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/10/2017] [Indexed: 12/25/2022]
Affiliation(s)
- Fabrice Bartolomei
- Institut de Neurosciences des Systèmes; Aix Marseille University; Marseille France
- AP-HM; Service de Neurophysiologie Clinique; Hôpital de la Timone; Marseille France
| | - Stanislas Lagarde
- Institut de Neurosciences des Systèmes; Aix Marseille University; Marseille France
- AP-HM; Service de Neurophysiologie Clinique; Hôpital de la Timone; Marseille France
| | - Fabrice Wendling
- U1099; INSERM; Rennes France
- Laboratoire de Traitement du Signal et de l'Image; Université de Rennes 1; Rennes France
| | - Aileen McGonigal
- Institut de Neurosciences des Systèmes; Aix Marseille University; Marseille France
- AP-HM; Service de Neurophysiologie Clinique; Hôpital de la Timone; Marseille France
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes; Aix Marseille University; Marseille France
| | - Maxime Guye
- Centre d'Exploration Métabolique par Résonance Magnétique (CEMEREM); APHM; Hôpitaux de la Timone; Marseille France
| | - Christian Bénar
- Institut de Neurosciences des Systèmes; Aix Marseille University; Marseille France
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