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Hinojosa J, Becerra V, Candela-Cantó S, Alamar M, Culebras D, Valencia C, Valera C, Rumiá J, Muchart J, Aparicio J. Extra-temporal pediatric low-grade gliomas and epilepsy. Childs Nerv Syst 2024:10.1007/s00381-024-06573-8. [PMID: 39191974 DOI: 10.1007/s00381-024-06573-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Accepted: 08/11/2024] [Indexed: 08/29/2024]
Abstract
Low-grade gliomas, especially glioneuronal tumors, are a common cause of epilepsy in children. Seizures associated with low-grade pediatric tumors are medically refractory and present a significant burden to patients. Often, morbidity and patients´ quality of life are determined rather by the control of seizures than the oncological process itself and the resolution of epilepsy represents an important part in the treatment of LGGs. The pathogenesis of tumor-related seizures in focal LGG tumors is multifactorial, and mechanisms differ probably among patients and tumor types. Pediatric low-grade tumors associated with epilepsy include a series of neoplasms that have a pure astrocytic or glioneuronal lineage. They are usually benign tumors with a neocortical localization typically in the temporal lobes, but also in other supratentorial locations. Gangliogliomas and dysembryoplastic neuroepithelial tumors (DNET) are the most common entities together with astrocytic gliomas (pilocytic astrocytomas and pleomorphic xanthoastrocytoma) and angiocentric gliomas, and dual pathology is found in up to 40% of glioneuronal tumors. The treatment of low-grade gliomas and associated epilepsy is based mainly on resection and the extent of surgery is the main predictor of postoperative seizure control in patients with a LGG. Long-term epilepsy-associated tumors (LEATs) tend to be well-circumscribed, and therefore, the chances for a complete resection and epilepsy control with a safe approach are very high. New treatments have emerged as alternatives to open microsurgical approaches, including laser thermal ablation or the use of BRAF inhibitors. Future advances in identifying seizure-related biomarkers and molecular tumor pathways will facilitate targeted treatment strategies that will have a deep impact both in oncologic and epilepsy outcomes.
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Affiliation(s)
- José Hinojosa
- Department of Neurosurgery, Hospital Sant Joan de Déu, Pg. de Sant Joan de Déu, 2, 08950, Barcelona, Spain.
- Unit for Epilepsy Surgery, Member of ERN-EpiCARE, Hospital Sant Joan de Déu, Pg. de Sant Joan de Déu, 2, 08950, Barcelona, Spain.
| | - Victoria Becerra
- Department of Neurosurgery, Hospital Sant Joan de Déu, Pg. de Sant Joan de Déu, 2, 08950, Barcelona, Spain
| | - Santiago Candela-Cantó
- Department of Neurosurgery, Hospital Sant Joan de Déu, Pg. de Sant Joan de Déu, 2, 08950, Barcelona, Spain
- Unit for Epilepsy Surgery, Member of ERN-EpiCARE, Hospital Sant Joan de Déu, Pg. de Sant Joan de Déu, 2, 08950, Barcelona, Spain
| | - Mariana Alamar
- Department of Neurosurgery, Hospital Sant Joan de Déu, Pg. de Sant Joan de Déu, 2, 08950, Barcelona, Spain
| | - Diego Culebras
- Department of Neurosurgery, Hospital Sant Joan de Déu, Pg. de Sant Joan de Déu, 2, 08950, Barcelona, Spain
- Unit for Epilepsy Surgery, Member of ERN-EpiCARE, Hospital Sant Joan de Déu, Pg. de Sant Joan de Déu, 2, 08950, Barcelona, Spain
| | - Carlos Valencia
- Department of Neurosurgery, Hospital Sant Joan de Déu, Pg. de Sant Joan de Déu, 2, 08950, Barcelona, Spain
| | - Carlos Valera
- Unit for Epilepsy Surgery, Member of ERN-EpiCARE, Hospital Sant Joan de Déu, Pg. de Sant Joan de Déu, 2, 08950, Barcelona, Spain
| | - Jordi Rumiá
- Department of Neurosurgery, Hospital Sant Joan de Déu, Pg. de Sant Joan de Déu, 2, 08950, Barcelona, Spain
- Department of Neurosurgery, Hospital Clinic Barcelona, C. de Villarroel, 170 08036, Barcelona, Spain
- Unit for Epilepsy Surgery, Member of ERN-EpiCARE, Hospital Sant Joan de Déu, Pg. de Sant Joan de Déu, 2, 08950, Barcelona, Spain
| | - Jordi Muchart
- Department of Neuroradiology, Hospital Sant Joan de Déu, Pg. de Sant Joan de Déu, 2, 08950, Barcelona, Spain
- Unit for Epilepsy Surgery, Member of ERN-EpiCARE, Hospital Sant Joan de Déu, Pg. de Sant Joan de Déu, 2, 08950, Barcelona, Spain
| | - Javier Aparicio
- Unit for Epilepsy Surgery, Member of ERN-EpiCARE, Hospital Sant Joan de Déu, Pg. de Sant Joan de Déu, 2, 08950, Barcelona, Spain
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Bower MR. Review: seizure-related consolidation and the network theory of epilepsy. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1430934. [PMID: 39238837 PMCID: PMC11374659 DOI: 10.3389/fnetp.2024.1430934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 06/25/2024] [Indexed: 09/07/2024]
Abstract
Epilepsy is a complex, multifaceted disease that affects patients in several ways in addition to seizures, including psychological, social, and quality of life issues, but epilepsy is also known to interact with sleep. Seizures often occur at the boundary between sleep and wake, patients with epilepsy often experience disrupted sleep, and the rate of inter-ictal epileptiform discharges increases during non-REM sleep. The Network Theory of Epilepsy did not address a role for sleep, but recent emphasis on the interaction between epilepsy and sleep suggests that post-seizure sleep may also be involved in the process by which seizures arise and become more severe with time ("epileptogenesis") by co-opting processes related to the formation of long-term memories. While it is generally acknowledged that recurrent seizures arise from the aberrant function of neural circuits, it is possible that the progression of epilepsy is aided by normal, physiological function of neural circuits during sleep that are driven by pathological signals. Studies recording multiple, single neurons prior to spontaneous seizures have shown that neural assemblies activated prior to the start of seizures were reactivated during post-seizure sleep, similar to the reactivation of behavioral neural assemblies, which is thought to be involved in the formation of long-term memories, a process known as Memory Consolidation. The reactivation of seizure-related neural assemblies during sleep was thus described as being a component of Seizure-Related Consolidation (SRC). These results further suggest that SRC may viewed as a network-related aspect of epilepsy, even in those seizures that have anatomically restricted neuroanatomical origins. As suggested by the Network Theory of Epilepsy as a means of interfering with ictogenesis, therapies that interfered with SRC may provide some anti-epileptogenic therapeutic benefit, even if the interference targeted structures that were not involved originally in the seizure. Here, we show how the Network Theory of Epilepsy can be expanded to include neural plasticity mechanisms associated with learning by providing an overview of Memory Consolidation, the mechanisms thought to underlie MC, their relation to Seizure-Related Consolidation, and suggesting novel, anti-epileptogenic therapies targeting interference with network activation in epilepsy following seizures during post-seizure sleep.
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Affiliation(s)
- Mark R Bower
- Department of Neurology, Yale University, New Haven, CT, United States
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Vila-Vidal M, Craven-Bartle Corominas F, Gilson M, Zucca R, Principe A, Rocamora R, Deco G, Tauste Campo A. A comparative study between a power and a connectivity sEEG biomarker for seizure-onset zone identification in temporal lobe epilepsy. J Neurosci Methods 2024; 411:110238. [PMID: 39168253 DOI: 10.1016/j.jneumeth.2024.110238] [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: 12/11/2023] [Revised: 07/12/2024] [Accepted: 07/26/2024] [Indexed: 08/23/2024]
Abstract
BACKGROUND Ictal stereo-encephalography (sEEG) biomarkers for seizure onset zone (SOZ) localization can be classified depending on whether they target abnormalities in signal power or functional connectivity between signals, and they may depend on the frequency band and time window at which they are estimated. NEW METHOD This work aimed to compare and optimize the performance of a power and a connectivity-based biomarker to identify SOZ contacts from ictal sEEG recordings. To do so, we used a previously introduced power-based measure, the normalized mean activation (nMA), which quantifies the ictal average power activation. Similarly, we defined the normalized mean strength (nMS), to quantify the ictal mean functional connectivity of every contact with the rest. The optimal frequency bands and time windows were selected based on optimizing AUC and F2-score. RESULTS The analysis was performed on a dataset of 67 seizures from 10 patients with pharmacoresistant temporal lobe epilepsy. Our results suggest that the power-based biomarker generally performs better for the detection of SOZ than the connectivity-based one. However, an equivalent performance level can be achieved when both biomarkers are independently optimized. Optimal performance was achieved in the beta and lower-gamma range for the power biomarker and in the lower- and higher-gamma range for connectivity, both using a 20 or 30 s period after seizure onset. CONCLUSIONS The results of this study highlight the importance of this optimization step over frequency and time windows when comparing different SOZ discrimination biomarkers. This information should be considered when training SOZ classifiers on retrospective patients' data for clinical applications.
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Affiliation(s)
- Manel Vila-Vidal
- Computational Biology and Complex Systems, Department of Physics, Universitat Politècnica de Catalunya, 08028, Barcelona, Spain; Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08005, Barcelona, Spain.
| | - Ferran Craven-Bartle Corominas
- Computational Biology and Complex Systems, Department of Physics, Universitat Politècnica de Catalunya, 08028, Barcelona, Spain
| | - Matthieu Gilson
- Institut de Neurosciences des Systèmes (INS, UMR1106), INSERM-AMU, 13005 Marseille, France
| | - Riccardo Zucca
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08005, Barcelona, Spain; Hospital del Mar Medical Research Institute, 08003, Barcelona, Spain; Donders Centre for Neuroscience, Radboud University, Nijmegen, Netherlands
| | - Alessandro Principe
- Hospital del Mar Medical Research Institute, 08003, Barcelona, Spain; Faculty of Health and Life Sciences, Universitat Pompeu Fabra, 08003, Barcelona, Spain; Epilepsy Monitoring Unit, Department of Neurology, Hospital del Mar, 08003, Barcelona, Spain
| | - Rodrigo Rocamora
- Hospital del Mar Medical Research Institute, 08003, Barcelona, Spain; Faculty of Health and Life Sciences, Universitat Pompeu Fabra, 08003, Barcelona, Spain; Epilepsy Monitoring Unit, Department of Neurology, Hospital del Mar, 08003, Barcelona, Spain.
| | - Gustavo Deco
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08005, Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats, 08010, Barcelona, Spain
| | - Adrià Tauste Campo
- Computational Biology and Complex Systems, Department of Physics, Universitat Politècnica de Catalunya, 08028, Barcelona, Spain
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Yu P, Dong R, Wang X, Tang Y, Liu Y, Wang C, Zhao L. Neuroimaging of motor recovery after ischemic stroke - functional reorganization of motor network. Neuroimage Clin 2024; 43:103636. [PMID: 38950504 PMCID: PMC11267109 DOI: 10.1016/j.nicl.2024.103636] [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: 03/10/2024] [Revised: 06/01/2024] [Accepted: 06/27/2024] [Indexed: 07/03/2024]
Abstract
The long-term motor outcome of acute stroke patients may be correlated to the reorganization of brain motor network. Abundant neuroimaging studies contribute to understand the pathological changes and recovery of motor networks after stroke. In this review, we summarized how current neuroimaging studies have increased understanding of reorganization and plasticity in post stroke motor recovery. Firstly, we discussed the changes in the motor network over time during the motor-activation and resting states, as well as the overall functional integration trend of the motor network. These studies indicate that the motor network undergoes dynamic bilateral hemispheric functional reorganization, as well as a trend towards network randomization. In the second part, we summarized the current study progress in the application of neuroimaging technology to early predict the post-stroke motor outcome. In the third part, we discuss the neuroimaging techniques commonly used in the post-stroke recovery. These methods provide direct or indirect visualization patterns to understand the neural mechanisms of post-stroke motor recovery, opening up new avenues for studying spontaneous and treatment-induced recovery and plasticity after stroke.
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Affiliation(s)
- Pei Yu
- School of Acupuncture and Massage, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Ruoyu Dong
- Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China
| | - Xiao Wang
- School of Acupuncture and Massage, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Yuqi Tang
- School of Acupuncture and Massage, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Yaning Liu
- School of Acupuncture and Massage, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Can Wang
- School of Acupuncture and Massage, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Ling Zhao
- School of Acupuncture and Massage, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
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Lee DA, Lee HJ, Park KM. Structural connectivity as a predictive factor for perampanel response in patients with epilepsy. Seizure 2024; 118:125-131. [PMID: 38701705 DOI: 10.1016/j.seizure.2024.04.026] [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: 01/09/2024] [Revised: 04/05/2024] [Accepted: 04/28/2024] [Indexed: 05/05/2024] Open
Abstract
OBJECTIVES This study aimed to identify clinical characteristics that could predict the response to perampanel (PER) and determine whether structural connectivity is a predictive factor. METHODS We enrolled patients with epilepsy who received PER and were followed-up for a minimum of 12 months. Good PER responders, who were seizure-free or presented with more than 50 % seizure reduction, were classified separately from poor PER responders who had seizure reduction of less than 50 % or non-responders. A graph theoretical analysis was conducted based on diffusion tensor imaging to calculate network measures of structural connectivity among patients with epilepsy. RESULTS 106 patients with epilepsy were enrolled, including 26 good PER responders and 80 poor PER responders. Good PER responders used fewer anti-seizure medications before PER administration compared to those by poor PER responders (3 vs. 4; p = 0.042). Early PER treatment was more common in good PER responders than poor PER responders (46.2 vs. 21.3 %, p = 0.014). Regarding cortical structural connectivity, the global efficiency was higher and characteristic path length was lower in good PER responders than in poor PER responders (0.647 vs. 0.635, p = 0.006; 1.726 vs. 1,759, p = 0.008, respectively). For subcortical structural connectivity, the mean clustering coefficient and small-worldness index were higher in good PER responders than in poor PER responders (0.821 vs. 0.791, p = 0.009; 0.597 vs. 0.560, p = 0.009, respectively). CONCLUSION This study demonstrated that early PER administration can predict a good PER response in patients with epilepsy, and structural connectivity could potentially offer clinical utility in predicting PER response.
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Affiliation(s)
- Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Ho-Joon Lee
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea.
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Russo A, Mazzone S, Landolina L, Colucci R, Baccari F, Fetta A, Boni A, Cordelli DM. Efficacy and Safety of Pulse Intravenous Methylprednisolone in Pediatric Epileptic Encephalopathies: Timing and Networks Consideration. J Clin Med 2024; 13:2497. [PMID: 38731025 PMCID: PMC11084200 DOI: 10.3390/jcm13092497] [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: 03/05/2024] [Revised: 04/10/2024] [Accepted: 04/19/2024] [Indexed: 05/13/2024] Open
Abstract
Background: Epileptic encephalopathies (EE) are characterized by severe drug-resistant seizures, early onset, and unfavorable developmental outcomes. This article discusses the use of intravenous methylprednisolone (IVMP) pulse therapy in pediatric patients with EE to evaluate its efficacy and tolerability. Methods: This is a retrospective study from 2020 to 2023. Inclusion criteria were ≤18 years at the time of IVMP pulse therapy and at least 6 months of follow-up. Efficacy and outcome, defined as seizure reduction > 50% (responder rate), were evaluated at 6 and 9 months of therapy, and 6 months after therapy suspension; quality of life (QoL) was also assessed. Variables predicting positive post-IVMP outcomes were identified using statistical analysis. Results: The study included 21 patients, with a responder rate of 85.7% at 6 and 9 months of therapy, and 80.9% at 6 months after therapy suspension. Variables significantly predicting favorable outcome were etiology (p = 0.0475) and epilepsy type (p = 0.0475), with the best outcome achieved in patients with genetic epilepsy and those with encephalopathy related to electrical status epilepticus during slow-wave sleep (ESES). All patients evidenced improvements in QoL at the last follow-up, with no relevant adverse events reported. Conclusions: Our study confirmed the efficacy and high tolerability of IVMP pulse therapy in pediatric patients with EE. Genetic epilepsy and ESES were positive predictors of a favorable clinical outcome. QOL, EEG tracing, and postural-motor development showed an improving trend as well. IVMP pulse therapy should be considered earlier in patients with EE.
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Affiliation(s)
- Angelo Russo
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
| | - Serena Mazzone
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
| | - Laura Landolina
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
- Dipartimento di Scienze Mediche e Chirurgiche (DIMEC), Università di Bologna, 40126 Bologna, Italy
| | - Roberta Colucci
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
- Dipartimento di Scienze Mediche e Chirurgiche (DIMEC), Università di Bologna, 40126 Bologna, Italy
| | - Flavia Baccari
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOS Epidemiologia e Biostatistica, 40139 Bologna, Italy;
| | - Anna Fetta
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
- Dipartimento di Scienze Mediche e Chirurgiche (DIMEC), Università di Bologna, 40126 Bologna, Italy
| | - Antonella Boni
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
| | - Duccio Maria Cordelli
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
- Dipartimento di Scienze Mediche e Chirurgiche (DIMEC), Università di Bologna, 40126 Bologna, Italy
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Rigoni I, Vorderwülbecke BJ, Carboni M, Roehri N, Spinelli L, Tononi G, Seeck M, Perogamvros L, Vulliémoz S. Network alterations in temporal lobe epilepsy during non-rapid eye movement sleep and wakefulness. Clin Neurophysiol 2024; 159:56-65. [PMID: 38335766 DOI: 10.1016/j.clinph.2024.01.003] [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: 08/09/2023] [Revised: 12/18/2023] [Accepted: 01/02/2024] [Indexed: 02/12/2024]
Abstract
OBJECTIVE Investigate sleep and temporal lobe epilepsy (TLE) effects on brain networks derived from electroencephalography (EEG). METHODS High-density EEG was recorded during non-rapid eye movement (NREM) sleep stage 2 (N2) and wakefulness in 23 patients and healthy controls (HC). Epochs without epileptic discharges were source-reconstructed in 72 brain regions and connectivity was estimated. We calculated network integration and segregation at global (global efficiency, GE; average clustering coefficient, avgCC) and hemispheric level. These were compared between groups across frequency bands and correlated with the individual proportion of wakefulness- or sleep-related seizures. RESULTS At the global level, patients had higher delta GE, delta avgCC and theta avgCC than controls, irrespective of the vigilance state. During wakefulness, theta GE of patients was higher than controls and, for patients, theta GE during wakefulness was higher than during N2. Wake-to-sleep differences in TLE were notable only in the ipsilateral hemisphere. Only measures from wakefulness recordings correlated with the proportion of wakefulness- or sleep-related seizures. CONCLUSIONS TLE network alterations are more prominent during wakefulness and at lower frequencies. Increased integration and segregation suggest a pathological 'small world' configuration with a possible inhibitory role. SIGNIFICANCE Network alterations in TLE occur and are easier to detect during wakefulness.
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Affiliation(s)
- I Rigoni
- EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Switzerland.
| | - B J Vorderwülbecke
- EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Switzerland; Epilepsy-Center Berlin-Brandenburg, Department of Neurology with Experimental Neurology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - M Carboni
- EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Switzerland
| | - N Roehri
- EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Switzerland
| | - L Spinelli
- EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Switzerland
| | - G Tononi
- Department of Psychiatry, University of Wisconsin, Madison, WI, USA
| | - M Seeck
- EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Switzerland
| | - L Perogamvros
- Center for Sleep Medicine, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland
| | - S Vulliémoz
- EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Switzerland
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Kobayashi K, Taylor KN, Shahabi H, Krishnan B, Joshi A, Mackow MJ, Feldman L, Zamzam O, Medani T, Bulacio J, Alexopoulos AV, Najm I, Bingaman W, Leahy RM, Nair DR. Effective connectivity relates seizure outcome to electrode placement in responsive neurostimulation. Brain Commun 2024; 6:fcae035. [PMID: 38390255 PMCID: PMC10882982 DOI: 10.1093/braincomms/fcae035] [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: 09/12/2022] [Revised: 09/06/2023] [Accepted: 02/19/2024] [Indexed: 02/24/2024] Open
Abstract
Responsive neurostimulation is a closed-loop neuromodulation therapy for drug resistant focal epilepsy. Responsive neurostimulation electrodes are placed near ictal onset zones so as to enable detection of epileptiform activity and deliver electrical stimulation. There is no standard approach for determining the optimal placement of responsive neurostimulation electrodes. Clinicians make this determination based on presurgical tests, such as MRI, EEG, magnetoencephalography, ictal single-photon emission computed tomography and intracranial EEG. Currently functional connectivity measures are not being used in determining the placement of responsive neurostimulation electrodes. Cortico-cortical evoked potentials are a measure of effective functional connectivity. Cortico-cortical evoked potentials are generated by direct single-pulse electrical stimulation and can be used to investigate cortico-cortical connections in vivo. We hypothesized that the presence of high amplitude cortico-cortical evoked potentials, recorded during intracranial EEG monitoring, near the eventual responsive neurostimulation contact sites is predictive of better outcomes from its therapy. We retrospectively reviewed 12 patients in whom cortico-cortical evoked potentials were obtained during stereoelectroencephalography evaluation and subsequently underwent responsive neurostimulation therapy. We studied the relationship between cortico-cortical evoked potentials, the eventual responsive neurostimulation electrode locations and seizure reduction. Directional connectivity indicated by cortico-cortical evoked potentials can categorize stereoelectroencephalography electrodes as either receiver nodes/in-degree (an area of greater inward connectivity) or projection nodes/out-degree (greater outward connectivity). The follow-up period for seizure reduction ranged from 1.3-4.8 years (median 2.7) after responsive neurostimulation therapy started. Stereoelectroencephalography electrodes closest to the eventual responsive neurostimulation contact site tended to show larger in-degree cortico-cortical evoked potentials, especially for the early latency cortico-cortical evoked potentials period (10-60 ms period) in six out of 12 patients. Stereoelectroencephalography electrodes closest to the responsive neurostimulation contacts (≤5 mm) also had greater significant out-degree in the early cortico-cortical evoked potentials latency period than those further away (≥10 mm) (P < 0.05). Additionally, significant correlation was noted between in-degree cortico-cortical evoked potentials and greater seizure reduction with responsive neurostimulation therapy at its most effective period (P < 0.05). These findings suggest that functional connectivity determined by cortico-cortical evoked potentials may provide additional information that could help guide the optimal placement of responsive neurostimulation electrodes.
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Affiliation(s)
- Katsuya Kobayashi
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Kenneth N Taylor
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Hossein Shahabi
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90007, USA
| | - Balu Krishnan
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Anand Joshi
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90007, USA
| | - Michael J Mackow
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Lauren Feldman
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Omar Zamzam
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90007, USA
| | - Takfarinas Medani
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90007, USA
| | - Juan Bulacio
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | | | - Imad Najm
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - William Bingaman
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Richard M Leahy
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90007, USA
| | - Dileep R Nair
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
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Bröhl T, Rings T, Pukropski J, von Wrede R, Lehnertz K. The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 3:1338864. [PMID: 38293249 PMCID: PMC10825060 DOI: 10.3389/fnetp.2023.1338864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 12/19/2023] [Indexed: 02/01/2024]
Abstract
Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.
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Affiliation(s)
- Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Jan Pukropski
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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10
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Ding Y, Guo K, Li J, Shan Q, Guo Y, Chen M, Wu Y, Wang X. Alterations in brain network functional connectivity and topological properties in DRE patients. Front Neurol 2023; 14:1238421. [PMID: 38116109 PMCID: PMC10729765 DOI: 10.3389/fneur.2023.1238421] [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: 06/11/2023] [Accepted: 10/20/2023] [Indexed: 12/21/2023] Open
Abstract
Objective The study aimed to find the difference in functional network topology on interictal electroencephalographic (EEG) between patients with drug-resistant epilepsy (DRE) and healthy people. Methods We retrospectively analyzed the medical records as well as EEG data of ten patients with DRE and recruited five sex-age-matched healthy controls (HC group). Each participant remained awake while undergoing video-electroencephalography (vEEG) monitoring. After excluding data that contained abnormal discharges, we screened EEG segments that were free of artifacts and put them together into 20-min segments. The screened data was bandpass filtered to different frequency bands (delta, theta, alpha, beta, and gamma). The weighted phase lag index (wPLI) and the network properties were calculated to evaluate changes in the topology of the functional network. Finally, the results were statistically analyzed, and the false discovery rate (FDR) was used to correct for differences after multiple comparisons. Results In the full frequency band (0.5-45 Hz), the functional connectivity in the DRE group during the interictal period was significantly lower than that in the HC group (p < 0.05). Compared to the HC group, in the full frequency band, the DRE group exhibited significantly decreased clustering coefficient (CC), node degree (D), and global efficiency (GE), while the characteristic path length (CPL) significantly increased (p < 0.05). In the sub-frequency bands, the functional connectivity of the DRE group was significantly lower than that of the HC group in the delta band but higher in the alpha, beta, and gamma bands (p < 0.05). The statistical results of network properties revealed that in the delta band, the DRE group had significantly decreased values for D, CC, and GE, but in the alpha, beta, and gamma bands, these values were significantly increased (p < 0.05). Additionally, the CPL of the DRE group significantly increased in the delta and theta bands but significantly decreased in the alpha, beta, and gamma bands (p < 0.05). Conclusion The topology structure of the functional network in DRE patients was significantly changed compared with healthy people, which was reflected in different frequency bands. It provided a theoretical basis for understanding the pathological network alterations of DRE.
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Affiliation(s)
- Yongqiang Ding
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kunlin Guo
- Henan Key Laboratory of Brain Science and Brain–Computer Interface Technology, School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Jialiang Li
- Department of Neurosurgery, The First People Hospital of Shangqiu, Shangqiu, China
| | - Qiao Shan
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yongkun Guo
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Mingming Chen
- Henan Key Laboratory of Brain Science and Brain–Computer Interface Technology, School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Yuehui Wu
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xinjun Wang
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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11
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Sun Y, Shi Q, Ye M, Miao A. Topological properties and connectivity patterns in brain networks of patients with refractory epilepsy combined with intracranial electrical stimulation. Front Neurosci 2023; 17:1282232. [PMID: 38075280 PMCID: PMC10701286 DOI: 10.3389/fnins.2023.1282232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 11/07/2023] [Indexed: 02/12/2024] Open
Abstract
Objective Although intracranial electrical stimulation has emerged as a treatment option for various diseases, its impact on the properties of brain networks remains challenging due to its invasive nature. The combination of intracranial electrical stimulation and whole-brain functional magnetic resonance imaging (fMRI) in patients with refractory epilepsy (RE) makes it possible to study the network properties associated with electrical stimulation. Thus, our study aimed to investigate the brain network characteristics of RE patients with concurrent electrical stimulation and obtain possible clinical biomarkers. Methods Our study used the GRETNA toolbox, a graph theoretical network analysis toolbox for imaging connectomics, to calculate and analyze the network topological attributes including global measures (small-world parameters and network efficiency) and nodal characteristics. The resting-state fMRI (rs-fMRI) and the fMRI concurrent electrical stimulation (es-fMRI) of RE patients were utilized to make group comparisons with healthy controls to identify the differences in network topology properties. Network properties comparisons before and after electrode implantation in the same patient were used to further analyze stimulus-related changes in network properties. Modular analysis was used to examine connectivity and distribution characteristics in the brain networks of all participants in study. Results Compared to healthy controls, the rs-fMRI and the es-fMRI of RE patients exhibited impaired small-world property and reduced network efficiency. Nodal properties, such as nodal clustering coefficient (NCp), betweenness centrality (Bc), and degree centrality (Dc), exhibited differences between RE patients (including rs-fMRI and es-fMRI) and healthy controls. The network connectivity of RE patients (including rs-fMRI and es-fMRI) showed reduced intra-modular connections in subcortical areas and the occipital lobe, as well as decreased inter-modular connections between frontal and subcortical regions, and parieto-occipital regions compared to healthy controls. The brain networks of es-fMRI showed a relatively weaker small-world structure compared to rs-fMRI. Conclusion The brain networks of RE patients exhibited a reduced small-world property, with a tendency toward random networks. The network connectivity patterns in RE patients exhibited reduced connections between cortical and subcortical regions and enhanced connections among parieto-occipital regions. Electrical stimulation can modulate brain network activity, leading to changes in network connectivity patterns and properties.
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Affiliation(s)
- Yulei Sun
- Department of Neurology, Nanjing BenQ Medical Center, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Qi Shi
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, Jiangsu, China
| | - Min Ye
- Department of Neurology, Nanjing BenQ Medical Center, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Ailiang Miao
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
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12
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Sun C, Geng L, Liu X, Gao Q. Design of Closed-Loop Control Schemes Based on the GA-PID and GA-RBF-PID Algorithms for Brain Dynamic Modulation. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1544. [PMID: 37998236 PMCID: PMC10670460 DOI: 10.3390/e25111544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 11/06/2023] [Accepted: 11/11/2023] [Indexed: 11/25/2023]
Abstract
Neurostimulation can be used to modulate brain dynamics of patients with neuropsychiatric disorders to make abnormal neural oscillations restore to normal. The control schemes proposed on the bases of neural computational models can predict the mechanism of neural oscillations induced by neurostimulation, and then make clinical decisions that are suitable for the patient's condition to ensure better treatment outcomes. The present work proposes two closed-loop control schemes based on the improved incremental proportional integral derivative (PID) algorithms to modulate brain dynamics simulated by Wendling-type coupled neural mass models. The introduction of the genetic algorithm (GA) in traditional incremental PID algorithm aims to overcome the disadvantage that the selection of control parameters depends on the designer's experience, so as to ensure control accuracy. The introduction of the radial basis function (RBF) neural network aims to improve the dynamic performance and stability of the control scheme by adaptively adjusting control parameters. The simulation results show the high accuracy of the closed-loop control schemes based on GA-PID and GA-RBF-PID algorithms for modulation of brain dynamics, and also confirm the superiority of the scheme based on the GA-RBF-PID algorithm in terms of the dynamic performance and stability. This research of making hypotheses and predictions according to model data is expected to improve and perfect the equipment of early intervention and rehabilitation treatment for neuropsychiatric disorders in the biomedical engineering field.
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Affiliation(s)
- Chengxia Sun
- Mechanical and Electrical Engineering College, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China; (C.S.); (L.G.)
| | - Lijun Geng
- Mechanical and Electrical Engineering College, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China; (C.S.); (L.G.)
| | - Xian Liu
- State Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China;
| | - Qing Gao
- State Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China;
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13
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Aiello G, Ledergerber D, Dubcek T, Stieglitz L, Baumann C, Polanìa R, Imbach L. Functional network dynamics between the anterior thalamus and the cortex in deep brain stimulation for epilepsy. Brain 2023; 146:4717-4735. [PMID: 37343140 DOI: 10.1093/brain/awad211] [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: 01/13/2023] [Revised: 05/10/2023] [Accepted: 06/08/2023] [Indexed: 06/23/2023] Open
Abstract
Owing to its unique connectivity profile with cortical brain regions, and its suggested role in the subcortical propagation of seizures, the anterior nucleus of the thalamus (ANT) has been proposed as a key deep brain stimulation (DBS) target in drug-resistant epilepsy. However, the spatio-temporal interaction dynamics of this brain structure, and the functional mechanisms underlying ANT DBS in epilepsy remain unknown. Here, we study how the ANT interacts with the neocortex in vivo in humans and provide a detailed neurofunctional characterization of mechanisms underlying the effectiveness of ANT DBS, aiming at defining intraoperative neural biomarkers of responsiveness to therapy, assessed at 6 months post-implantation as the reduction in seizure frequency. A cohort of 15 patients with drug-resistant epilepsy (n = 6 males, age = 41.6 ± 13.79 years) underwent bilateral ANT DBS implantation. Using intraoperative cortical and ANT simultaneous electrophysiological recordings, we found that the ANT is characterized by high amplitude θ (4-8 Hz) oscillations, mostly in its superior part. The strongest functional connectivity between the ANT and the scalp EEG was also found in the θ band in ipsilateral centro-frontal regions. Upon intraoperative stimulation in the ANT, we found a decrease in higher EEG frequencies (20-70 Hz) and a generalized increase in scalp-to-scalp connectivity. Crucially, we observed that responders to ANT DBS treatment were characterized by higher EEG θ oscillations, higher θ power in the ANT, and stronger ANT-to-scalp θ connectivity, highlighting the crucial role of θ oscillations in the dynamical network characterization of these structures. Our study provides a comprehensive characterization of the interaction dynamic between the ANT and the cortex, delivering crucial information to optimize and predict clinical DBS response in patients with drug-resistant epilepsy.
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Affiliation(s)
- Giovanna Aiello
- Decision Neuroscience Lab, Department of Health Sciences and Technology, ETH Zurich, 8092 Zurich, Switzerland
- Swiss Epilepsy Center (Klinik Lengg), 8008 Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
| | - Debora Ledergerber
- Swiss Epilepsy Center (Klinik Lengg), 8008 Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
| | - Tena Dubcek
- Decision Neuroscience Lab, Department of Health Sciences and Technology, ETH Zurich, 8092 Zurich, Switzerland
- Swiss Epilepsy Center (Klinik Lengg), 8008 Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
| | - Lennart Stieglitz
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Christian Baumann
- Department of Neurology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Rafael Polanìa
- Decision Neuroscience Lab, Department of Health Sciences and Technology, ETH Zurich, 8092 Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
| | - Lukas Imbach
- Swiss Epilepsy Center (Klinik Lengg), 8008 Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
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14
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Yang S, Wu Y, Sun L, Lu Y, Qian K, Kuang H, Meng J, Wu Y. Abnormal Topological Organization of Structural Covariance Networks in Patients with Temporal Lobe Epilepsy Comorbid Sleep Disorder. Brain Sci 2023; 13:1493. [PMID: 37891861 PMCID: PMC10605209 DOI: 10.3390/brainsci13101493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/11/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023] Open
Abstract
OBJECTIVE The structural covariance network (SCN) alterations in patients with temporal lobe epilepsy and comorbid sleep disorder (PWSD) remain poorly understood. This study aimed to investigate changes in SCNs using structural magnetic resonance imaging. METHODS Thirty-four PWSD patients, thirty-three patients with temporal lobe epilepsy without sleep disorder (PWoSD), and seventeen healthy controls underwent high-resolution structural MRI imaging. Subsequently, SCNs were constructed based on gray matter volume and analyzed via graph-theoretical approaches. RESULTS PWSD exhibited significantly increased clustering coefficients, shortest path lengths, transitivity, and local efficiency. In addition, various distributions and numbers of SCN hubs were identified in PWSD. Furthermore, PWSD networks were less robust to random and target attacks than those of healthy controls and PWoSD patients. CONCLUSION This study identifies aberrant SCN changes in PWSD that may be related to the susceptibility of patients with epilepsy to sleep disorders.
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Affiliation(s)
| | | | | | | | | | | | | | - Yuan Wu
- Department of Neurology, The First Affiliated Hospital, Guangxi Medical University, Nanning 530021, China
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15
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Xi Y, Lan Z, Chen Y, Zhang Q, Wu Z, Li G. Patients with epilepsy without cognitive impairment show altered brain networks in multiple frequency bands in an audiovisual integration task. Neurophysiol Clin 2023; 53:102888. [PMID: 37660635 DOI: 10.1016/j.neucli.2023.102888] [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: 02/20/2023] [Revised: 05/30/2023] [Accepted: 06/02/2023] [Indexed: 09/05/2023] Open
Abstract
OBJECTIVES Comorbid cognitive and behavioral deficits are often observed in patients with epilepsy. It is not clear whether the brain networks of patients with epilepsy without cognitive decline differs from that of healthy controls in different frequency bands in the task-state. The purpose of our study was to explore whether epilepsy affects the structure of brain networks associated with cognitive processing, even when patients with epilepsy do not have cognitive impairment. METHODS We designed an audiovisual discrimination task and recorded electroencephalogram (EEG) data from healthy controls and patients with epilepsy. We established constructed time-varying brain networks across the delta, theta, alpha, and beta bands on the task-state EEG data during audiovisual integration processing. RESULTS The results showed changes in the structure of the brain networks in the theta, alpha, and beta bands in patients with epilepsy who had no cognitive deficit. No significant difference in the connectivity strength, clustering coefficient, characteristic path length, or global efficiency was noted between patients and healthy controls. Moreover, the structure of brain networks in patients showed no correlation with the behavioral performance. CONCLUSION The repeated abnormal firing of neurons in the brain of patients with epilepsy may inhibit it from optimizing networks into more efficient structures. Epilepsy might affect decision-making ability by damaging the neural activity in the beta band and preventing its correlation with decision-making behaviors.
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Affiliation(s)
- Yang Xi
- School of Computer Science, Northeast Electric Power University, Jilin 132012, P.R. China.
| | - Zhu Lan
- School of Computer Science, Northeast Electric Power University, Jilin 132012, P.R. China
| | - Ying Chen
- School of Computer Science, Northeast Electric Power University, Jilin 132012, P.R. China
| | - Qiushi Zhang
- School of Computer Science, Northeast Electric Power University, Jilin 132012, P.R. China
| | - Zhenyu Wu
- Department of Orthopedics of Affiliated Hospital of Beihua University, Beihua University, Jilin 132012, P.R. China
| | - Guangjian Li
- Department of Neurology of First Affiliated Hospital of Jilin University, Jilin University, Changchun 130022, P.R. China
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16
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Luo X, Liao J, Liu H, Tang Q, Luo H, Chen X, Ruan J. The micro and macro interactions in acute autoimmune encephalitis: a study of resting-state EEG. Front Neurol 2023; 14:1181629. [PMID: 37360339 PMCID: PMC10285084 DOI: 10.3389/fneur.2023.1181629] [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: 03/08/2023] [Accepted: 05/22/2023] [Indexed: 06/28/2023] Open
Abstract
Objective Early recognition of autoimmune encephalitis (AIE) is often difficult and time-consuming. Understanding how the micro-level (antibodies) and macro-level (EEG) couple with each other may help rapidly diagnose and appropriately treat AIE. However, limited studies focused on brain oscillations involving micro- and macro-interactions in AIE from a neuro-electrophysiological perspective. Here, we investigated brain network oscillations in AIE using Graph theoretical analysis of resting state EEG. Methods AIE Patients (n = 67) were enrolled from June 2018 to June 2022. Each participant underwent a ca.2-hour 19-channel EEG examination. Five 10-second resting state EEG epochs with eyes closed were extracted for each participant. The functional networks based on the channels and Graph theory analysis were carried out. Results Compared with the HC group, significantly decreased FC across whole brain regions at alpha and beta bands were found in AIE patients. In addition, the local efficiency and clustering coefficient of the delta band was higher in AIE patients than in the HC group (P < 0.05). AIE patients had a smaller world index (P < 0.05) and higher shortest path length (P < 0.001) in the alpha band than those of the control group. Also, the AIE patients' global efficiency, local efficiency, and clustering coefficients decreased in the alpha band (P < 0.001). Different types of antibodies (antibodies against ion channels, antibodies against synaptic excitatory receptors, antibodies against synaptic inhibitory receptors, and multiple antibodies positive) showed distinct graph parameters. Moreover, the graph parameters differed in the subgroups by intracranial pressure. Correlation analysis revealed that magnetic resonance imaging abnormalities were related to global efficiency, local efficiency, and clustering coefficients in the theta, alpha, and beta bands, but negatively related to the shortest path length. Conclusion These findings add to our understanding of how brain FC and graph parameters change and how the micro- (antibodies) scales interact with the macro- (scalp EEG) scale in acute AIE. The clinical traits and subtypes of AIE may be suggested by graph properties. Further longitudinal cohort studies are needed to explore the associations between these graph parameters and recovery status, and their possible applications in AIE rehabilitation.
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Affiliation(s)
- Xin Luo
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, China
| | - Jie Liao
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, China
| | - Hong Liu
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, China
| | - Qiulin Tang
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, China
| | - Hua Luo
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, China
| | - Xiu Chen
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, China
| | - Jianghai Ruan
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, China
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17
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Zhu X, Shappell H, Kramer MA, Chu CJ, Kolaczyk ED. Distinguishing between different percolation regimes in noisy dynamic networks with an application to epileptic seizures. PLoS Comput Biol 2023; 19:e1011188. [PMID: 37327238 PMCID: PMC10310035 DOI: 10.1371/journal.pcbi.1011188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/29/2023] [Accepted: 05/17/2023] [Indexed: 06/18/2023] Open
Abstract
In clinical neuroscience, epileptic seizures have been associated with the sudden emergence of coupled activity across the brain. The resulting functional networks-in which edges indicate strong enough coupling between brain regions-are consistent with the notion of percolation, which is a phenomenon in complex networks corresponding to the sudden emergence of a giant connected component. Traditionally, work has concentrated on noise-free percolation with a monotonic process of network growth, but real-world networks are more complex. We develop a class of random graph hidden Markov models (RG-HMMs) for characterizing percolation regimes in noisy, dynamically evolving networks in the presence of edge birth and edge death. This class is used to understand the type of phase transitions undergone in a seizure, and in particular, distinguishing between different percolation regimes in epileptic seizures. We develop a hypothesis testing framework for inferring putative percolation mechanisms. As a necessary precursor, we present an EM algorithm for estimating parameters from a sequence of noisy networks only observed at a longitudinal subsampling of time points. Our results suggest that different types of percolation can occur in human seizures. The type inferred may suggest tailored treatment strategies and provide new insights into the fundamental science of epilepsy.
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Affiliation(s)
- Xiaojing Zhu
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America
| | - Heather Shappell
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Mark A. Kramer
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America
| | - Catherine J. Chu
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Eric D. Kolaczyk
- Department of Mathematics and Statistics, McGill University, Montreal, Quebec, Canada
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18
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Wang K, Xie F, Liu C, Wang G, Zhang M, He J, Tan L, Tang H, Chen F, Xiao B, Song Y, Long L. Shared functional network abnormality in patients with temporal lobe epilepsy and their siblings. CNS Neurosci Ther 2023; 29:1109-1119. [PMID: 36647843 PMCID: PMC10018100 DOI: 10.1111/cns.14087] [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: 07/21/2022] [Revised: 12/07/2022] [Accepted: 12/26/2022] [Indexed: 01/18/2023] Open
Abstract
AIM Temporal lobe epilepsy is a neurological network disease in which genetics played a greater role than previously appreciated. This study aimed to explore shared functional network abnormalities in patients with sporadic temporal lobe epilepsy and their unaffected siblings. METHODS Fifty-eight patients with sporadic temporal lobe epilepsy, 13 unaffected siblings, and 30 healthy controls participated in this cross-sectional study. We examined the task-based whole-brain functional network topology and the effective functional connectivity between networks identified by group-independent component analysis. RESULTS We observed increased global efficiency, decreased clustering coefficiency, and decreased small-worldness in patients and siblings (p < 0.05, false discovery rate-corrected). The effective network connectivity from the ventral attention network to the limbic system was impaired (p < 0.001, false discovery rate-corrected). These features had higher prevalence in unaffected siblings than in normal population and was not correlated with disease burden. In addition, topological abnormalities had a high intraclass correlation between patients and their siblings. CONCLUSION Patients with temporal lobe epilepsy and their unaffected siblings showed shared topological functional disturbance and the effective functional network connectivity impairment. These abnormalities may contribute to the pathogenesis that promotes the susceptibility of seizures and language decline in temporal lobe epilepsy.
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Affiliation(s)
- Kangrun Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,Clinical Research Center for Epileptic disease of Hunan Province, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Fangfang Xie
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Chaorong Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Ge Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Min Zhang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Jialinzi He
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Langzi Tan
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Haiyun Tang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Fenghua Chen
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Bo Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,Clinical Research Center for Epileptic disease of Hunan Province, Xiangya Hospital, Central South University, Changsha, China
| | - Yanmin Song
- Department of Emergency, Xiangya Hospital, Central South University, Changsha, China
| | - Lili Long
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,Clinical Research Center for Epileptic disease of Hunan Province, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
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19
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Lehnertz K, Bröhl T, Wrede RV. Epileptic-network-based prediction and control of seizures in humans. Neurobiol Dis 2023; 181:106098. [PMID: 36997129 DOI: 10.1016/j.nbd.2023.106098] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/08/2023] [Accepted: 03/22/2023] [Indexed: 03/30/2023] Open
Abstract
Epilepsy is now conceptualized as a network disease. The epileptic brain network comprises structurally and functionally connected cortical and subcortical brain regions - spanning lobes and hemispheres -, whose connections and dynamics evolve in time. With this concept, focal and generalized seizures as well as other related pathophysiological phenomena are thought to emerge from, spread via, and be terminated by network vertices and edges that also generate and sustain normal, physiological brain dynamics. Research over the last years has advanced concepts and techniques to identify and characterize the evolving epileptic brain network and its constituents on various spatial and temporal scales. Network-based approaches further our understanding of how seizures emerge from the evolving epileptic brain network, and they provide both novel insights into pre-seizure dynamics and important clues for success or failure of measures for network-based seizure control and prevention. In this review, we summarize the current state of knowledge and address several important challenges that would need to be addressed to move network-based prediction and control of seizures closer to clinical translation.
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Affiliation(s)
- Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127 Bonn, Germany; Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany; Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53175 Bonn, Germany.
| | - Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127 Bonn, Germany; Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127 Bonn, Germany
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Koster LK, Zamyadi R, Yan L, Payne ET, McBain KL, Dunkley BT, Hahn CD. Brain network properties of clinical versus subclinical seizures among critically ill children. Clin Neurophysiol 2023; 149:33-41. [PMID: 36878028 DOI: 10.1016/j.clinph.2023.02.160] [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: 07/01/2022] [Revised: 01/16/2023] [Accepted: 02/05/2023] [Indexed: 02/17/2023]
Abstract
OBJECTIVE Electrographic seizures are common among critically ill children, and have been associated with worse outcomes. Despite their often-widespread cortical representation, most of these seizures remain subclinical, a phenomenon which remains poorly understood. We compared the brain network properties of clinical versus subclinical seizures to gain insight into their relative potential deleterious effects. METHODS Functional connectivity (phase lag index) and graph measures (global efficiency and clustering coefficients) were computed for 2178 electrographic seizures recorded during 48-hours of 19-channel continuous EEG monitoring obtained in 20 comatose children. Frequency-specific group differences in clinical versus subclinical seizures were analyzed using a non-parametric ANCOVA, adjusting for age, sex, medication exposure, treatment intensity and seizures per subject. RESULTS Clinical seizures demonstrated greater functional connectivity than subclinical seizures at alpha frequencies, but less connectivity than subclinical seizures at delta frequencies. Clinical seizures also demonstrated significantly higher median global efficiency than subclinical seizures (p < 0.01), and significantly higher median clustering coefficients across all electrodes at alpha frequencies. CONCLUSIONS Clinical expression of seizures correlates with greater alpha synchronization of distributed brain networks. SIGNIFICANCE The stronger global and local alpha-mediated functional connectivity observed during clinical seizures may indicate greater pathological network recruitment. These observations motivate further studies to investigate whether the clinical expression of seizures may influence their potential to cause secondary brain injury.
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Affiliation(s)
- Laura K Koster
- Division of Neurology, The Hospital for Sick Children and Department of Paediatrics, University of Toronto, Toronto, Canada; Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Canada
| | - Rouzbeh Zamyadi
- Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Canada
| | - Luowei Yan
- Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Canada
| | - Eric T Payne
- Department of Pediatrics, Section of Neurology, Alberta Children's Hospital and University of Calgary, Calgary, Canada
| | - Kristin L McBain
- MAP Centre for Urban Health Solutions, Unity Health Toronto, Toronto, Canada
| | - Benjamin T Dunkley
- Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Canada; Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Canada; Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Cecil D Hahn
- Division of Neurology, The Hospital for Sick Children and Department of Paediatrics, University of Toronto, Toronto, Canada; Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Canada.
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21
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Hu L, Xiong K, Ye L, Yang Y, Chen C, Wang S, Ding Y, Wang Z, Ming W, Zheng Z, Jiang H, Li H, Zhu J, Xu C, Wang Y, Ding M, Chen Z, Wu Y, Wang S. Ictal EEG desynchronization during low-voltage fast activity for prediction of surgical outcomes in focal epilepsy. J Neurosurg 2022:1-10. [PMID: 36681967 DOI: 10.3171/2022.11.jns221469] [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: 06/22/2022] [Accepted: 11/15/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVE The authors investigated alterations in functional connectivity (FC) and EEG power during ictal onset patterns of low-voltage fast activity (LVFA) in drug-resistant focal epilepsy. They hypothesized that such changes would be useful to classify epilepsy surgical outcomes. METHODS In a cohort of 79 patients with drug-resistant focal epilepsy who underwent stereoelectroencephalography (SEEG) evaluation as well as resective surgery, FC changes during the peri-LVFA period were measured using nonlinear regression (h2) and power spectral properties within/between three regions: the seizure onset zone (SOZ), early propagation zone (PZ), and noninvolved zone (NIZ). Desynchronization and power desynchronization h2 indices were calculated to assess the degree of EEG desynchronization during LVFA. Multivariate logistic regression was employed to control for confounding factors. Finally, receiver operating characteristic curves were generated to evaluate the performance of desynchronization indices in predicting surgical outcome. RESULTS Fifty-three patients showed ictal LVFA and distinct zones of the SOZ, PZ, and NIZ. Among them, 39 patients (73.6%) achieved seizure freedom by the final follow-up. EEG desynchronization, measured by h2 analysis, was found in the seizure-free group during LVFA: FC decreased within the SOZ and between regions compared with the pre-LVFA and post-LVFA periods. In contrast, the non-seizure-free group showed no prominent EEG desynchronization. The h2 desynchronization index, but not the power desynchronization index, enabled classification of seizure-free versus non-seizure-free patients after resective surgery. CONCLUSIONS EEG desynchronization during the peri-LVFA period, measured by within-zone and between-zone h2 analysis, may be helpful for identifying patients with favorable postsurgical outcomes and also may potentially improve epileptogenic zone identification in the future.
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Affiliation(s)
- Lingli Hu
- 1Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou
| | - Kai Xiong
- 2School of Computer Science and Technology, Zhejiang University, Hangzhou
| | - Lingqi Ye
- 1Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou
| | - Yuyu Yang
- 1Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou
| | - Cong Chen
- 1Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou
| | - Shan Wang
- 1Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou
| | - Yao Ding
- 1Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou
| | - Zhongjin Wang
- 1Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou
| | - Wenjie Ming
- 1Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou
| | - Zhe Zheng
- 1Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou
| | - Hongjie Jiang
- 1Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou
| | - Hong Li
- 3Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou; and
| | - Junming Zhu
- 1Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou
| | - Cenglin Xu
- 4Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yi Wang
- 4Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Meiping Ding
- 1Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou
| | - Zhong Chen
- 4Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yingcai Wu
- 2School of Computer Science and Technology, Zhejiang University, Hangzhou
| | - Shuang Wang
- 1Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou
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22
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Stone SSD, Park EH, Bolton J, Harini C, Libenson MH, Rotenberg A, Takeoka M, Tsuboyama M, Pearl PL, Madsen JR. Interictal Connectivity Revealed by Granger Analysis of Stereoelectroencephalography: Association With Ictal Onset Zone, Resection, and Outcome. Neurosurgery 2022; 91:583-589. [PMID: 36084171 PMCID: PMC10553068 DOI: 10.1227/neu.0000000000002079] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 05/14/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Stereoelectroencephalography (sEEG) facilitates electrical sampling and evaluation of complex deep-seated, dispersed, and multifocal locations. Granger causality (GC), previously used to study seizure networks using interictal data from subdural grids, may help identify the seizure-onset zone from interictal sEEG recordings. OBJECTIVE To examine whether statistical analysis of interictal sEEG helps identify surgical target sites and whether surgical resection of highly ranked nodes correspond to favorable outcomes. METHODS Ten minutes of extraoperative recordings from sequential patients who underwent sEEG evaluation were analyzed (n = 20). GC maps were compared with clinically defined surgical targets using rank order statistics. Outcomes of patients with focal resection/ablation with median follow-up of 3.6 years were classified as favorable (Engel 1, 2) or poor (Engel 3, 4) to assess their relationship with the removal of highly ranked nodes using the Wilcoxon rank-sum test. RESULTS In 12 of 20 cases, the rankings of contacts (based on the sum of outward connection weights) mapped to the seizure-onset zone showed higher causal node connectivity than predicted by chance ( P ≤ .02). A very low aggregate probability ( P < 10 -18 , n = 20) suggests that causal node connectivity predicts seizure networks. In 8 of 16 with outcome data, causal connectivity in the resection was significantly greater than in the remaining contacts ( P ≤ .05). We found a significant association between favorable outcome and the presence of highly ranked nodes in the resection ( P < .05). CONCLUSION Granger analysis can identify seizure foci from interictal sEEG and correlates highly ranked nodes with favorable outcome, potentially informing surgical decision-making without reliance on ictal recordings.
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Affiliation(s)
- Scellig S. D. Stone
- Epilepsy Surgery Program, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Eun-Hyoung Park
- Epilepsy Surgery Program, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jeffrey Bolton
- Epilepsy Division, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Chellamani Harini
- Epilepsy Division, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Mark H. Libenson
- Epilepsy Division, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Alexander Rotenberg
- Epilepsy Division, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Masanori Takeoka
- Epilepsy Division, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Melissa Tsuboyama
- Epilepsy Division, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Phillip L. Pearl
- Epilepsy Division, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Joseph R. Madsen
- Epilepsy Surgery Program, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Conrad EC, Bernabei JM, Sinha N, Ghosn NJ, Stein JM, Shinohara RT, Litt B. Addressing spatial bias in intracranial EEG functional connectivity analyses for epilepsy surgical planning. J Neural Eng 2022; 19:056019. [PMID: 36084621 PMCID: PMC9590099 DOI: 10.1088/1741-2552/ac90ed] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 08/26/2022] [Accepted: 09/09/2022] [Indexed: 01/25/2023]
Abstract
Objective.To determine the effect of epilepsy on intracranial electroencephalography (EEG) functional connectivity, and the ability of functional connectivity to localize the seizure onset zone (SOZ), controlling for spatial biases.Approach.We analyzed intracranial EEG data from patients with drug-resistant epilepsy admitted for pre-surgical planning. We calculated intracranial EEG functional networks and determined whether changes in functional connectivity lateralized the SOZ using a spatial subsampling method to control for spatial bias. We developed a 'spatial null model' to localize the SOZ electrode using only spatial sampling information, ignoring EEG data. We compared the performance of this spatial null model against models incorporating EEG functional connectivity and interictal spike rates.Main results.About 110 patients were included in the study, although the number of patients differed across analyses. Controlling for spatial sampling, the average connectivity was lower in the SOZ region relative to the same anatomic region in the contralateral hemisphere. A model using intra-hemispheric connectivity accurately lateralized the SOZ (average accuracy 75.5%). A spatial null model incorporating spatial sampling information alone achieved moderate accuracy in classifying SOZ electrodes (mean AUC = 0.70, 95% CI 0.63-0.77). A model incorporating intracranial EEG functional connectivity and spike rate data further outperformed this spatial null model (AUC 0.78,p= 0.002 compared to spatial null model). However, a model incorporating functional connectivity without spike rate data did not significantly outperform the null model (AUC 0.72,p= 0.38).Significance.Intracranial EEG functional connectivity is reduced in the SOZ region, and interictal data predict SOZ electrode localization and laterality, however a predictive model incorporating functional connectivity without interictal spike rates did not significantly outperform a spatial null model. We propose constructing a spatial null model to provide an estimate of the pre-implant hypothesis of the SOZ, and to serve as a benchmark for further machine learning algorithms in order to avoid overestimating model performance because of electrode sampling alone.
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Affiliation(s)
- Erin C Conrad
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - John M Bernabei
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Nishant Sinha
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Nina J Ghosn
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Joel M Stein
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, United States of America
- Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA, United States of America
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Brian Litt
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
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Shen Z, Li G, Fang J, Zhong H, Wang J, Sun Y, Shen X. Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework. SENSORS (BASEL, SWITZERLAND) 2022; 22:5420. [PMID: 35891100 PMCID: PMC9320264 DOI: 10.3390/s22145420] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 07/12/2022] [Accepted: 07/19/2022] [Indexed: 06/15/2023]
Abstract
Although increasing evidences support the notion that psychiatric disorders are associated with abnormal communication between brain regions, scattered studies have investigated brain electrophysiological disconnectivity of patients with generalized anxiety disorder (GAD). To this end, this study intends to develop an analysis framework for automatic GAD detection through incorporating multidimensional EEG feature extraction and machine learning techniques. Specifically, resting-state EEG signals with a duration of 10 min were obtained from 45 patients with GAD and 36 healthy controls (HC). Then, an analysis framework of multidimensional EEG characteristics (including univariate power spectral density (PSD) and fuzzy entropy (FE), and multivariate functional connectivity (FC), which can decode the EEG information from three different dimensions) were introduced for extracting aberrated multidimensional EEG features via statistical inter-group comparisons. These aberrated features were subsequently fused and fed into three previously validated machine learning methods to evaluate classification performance for automatic patient detection. We showed that patients exhibited a significant increase in beta rhythm and decrease in alpha1 rhythm of PSD, together with the reduced long-range FC between frontal and other brain areas in all frequency bands. Moreover, these aberrated features contributed to a very good classification performance with 97.83 ± 0.40% of accuracy, 97.55 ± 0.31% of sensitivity, 97.78 ± 0.36% of specificity, and 97.95 ± 0.17% of F1. These findings corroborate previous hypothesis of disconnectivity in psychiatric disorders and further shed light on distribution patterns of aberrant spatio-spectral EEG characteristics, which may lead to potential application of automatic diagnosis of GAD.
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Affiliation(s)
- Zhongxia Shen
- School of Medicine, Southeast University, Nanjing 210096, China;
- Sleep Medical Center, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou 313000, China
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321017, China; (J.F.); (H.Z.); (J.W.)
- Key Laboratory for Biomedical Engineering of Ministry of Education of China, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China;
| | - Jiaqi Fang
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321017, China; (J.F.); (H.Z.); (J.W.)
| | - Hongyang Zhong
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321017, China; (J.F.); (H.Z.); (J.W.)
| | - Jie Wang
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321017, China; (J.F.); (H.Z.); (J.W.)
| | - Yu Sun
- Key Laboratory for Biomedical Engineering of Ministry of Education of China, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China;
| | - Xinhua Shen
- Sleep Medical Center, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou 313000, China
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25
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Fang S, Li L, Weng S, Guo Y, Zhong Z, Fan X, Jiang T, Wang Y. Contralesional Sensorimotor Network Participates in Motor Functional Compensation in Glioma Patients. Front Oncol 2022; 12:882313. [PMID: 35530325 PMCID: PMC9072743 DOI: 10.3389/fonc.2022.882313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 03/11/2022] [Indexed: 11/18/2022] Open
Abstract
Background Some gliomas in sensorimotor areas induce motor deficits, while some do not. Cortical destruction and reorganization contribute to this phenomenon, but detailed reasons remain unclear. This study investigated the differences of the functional connectivity and topological properties in the contralesional sensorimotor network (cSMN) between patients with motor deficit and those with normal motor function. Methods We retrospectively reviewed 65 patients (32 men) between 2017 and 2020. The patients were divided into four groups based on tumor laterality and preoperative motor status (deficit or non-deficit). Thirty-three healthy controls (18 men) were enrolled after matching for sex, age, and educational status. Graph theoretical measurement was applied to reveal alterations of the topological properties of the cSMN by analyzing resting-state functional MRI. Results The results for patients with different hemispheric gliomas were similar. The clustering coefficient, local efficiency, transitivity, and vulnerability of the cSMN significantly increased in the non-deficit group and decreased in the deficit group compared to the healthy group (p < 0.05). Moreover, the nodes of the motor-related thalamus showed a significantly increased nodal efficiency and nodal local efficiency in the non-deficit group and decreased in the deficit group compared with the healthy group (p < 0.05). Conclusions We posited the existence of two stages of alterations of the preoperative motor status. In the compensatory stage, the cSMN sacrificed stability to acquire high efficiency and to compensate for impaired motor function. With the glioma growing and the motor function being totally damaged, the cSMN returned to a stable state and maintained healthy hemispheric motor function, but with low efficiency.
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Affiliation(s)
- Shengyu Fang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Lianwang Li
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Shimeng Weng
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yuhao Guo
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Zhang Zhong
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xing Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tao Jiang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Research Unit of Accurate Diagnosis, Treatment and Translational Medicine of Brain Tumors, Chinese Academy of Medical Sciences, Beijing, China
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
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26
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Vetkas A, Germann J, Elias G, Loh A, Boutet A, Yamamoto K, Sarica C, Samuel N, Milano V, Fomenko A, Santyr B, Tasserie J, Gwun D, Jung HH, Valiante T, Ibrahim GM, Wennberg R, Kalia SK, Lozano AM. Identifying the neural network for neuromodulation in epilepsy through connectomics and graphs. Brain Commun 2022; 4:fcac092. [PMID: 35611305 PMCID: PMC9123846 DOI: 10.1093/braincomms/fcac092] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/13/2021] [Accepted: 03/31/2022] [Indexed: 02/01/2023] Open
Abstract
Deep brain stimulation is a treatment option for patients with drug-resistant epilepsy. The precise mechanism of neuromodulation in epilepsy is unknown, and biomarkers are needed for optimizing treatment. The aim of this study was to describe the neural network associated with deep brain stimulation targets for epilepsy and to explore its potential application as a novel biomarker for neuromodulation. Using seed-to-voxel functional connectivity maps, weighted by seizure outcomes, brain areas associated with stimulation were identified in normative resting state functional scans of 1000 individuals. To pinpoint specific regions in the normative epilepsy deep brain stimulation network, we examined overlapping areas of functional connectivity between the anterior thalamic nucleus, centromedian thalamic nucleus, hippocampus and less studied epilepsy deep brain stimulation targets. Graph network analysis was used to describe the relationship between regions in the identified network. Furthermore, we examined the associations of the epilepsy deep brain stimulation network with disease pathophysiology, canonical resting state networks and findings from a systematic review of resting state functional MRI studies in epilepsy deep brain stimulation patients. Cortical nodes identified in the normative epilepsy deep brain stimulation network were in the anterior and posterior cingulate, medial frontal and sensorimotor cortices, frontal operculum and bilateral insulae. Subcortical nodes of the network were in the basal ganglia, mesencephalon, basal forebrain and cerebellum. Anterior thalamic nucleus was identified as a central hub in the network with the highest betweenness and closeness values, while centromedian thalamic nucleus and hippocampus showed average centrality values. The caudate nucleus and mammillothalamic tract also displayed high centrality values. The anterior cingulate cortex was identified as an important cortical hub associated with the effect of deep brain stimulation in epilepsy. The neural network of deep brain stimulation targets shared hubs with known epileptic networks and brain regions involved in seizure propagation and generalization. Two cortical clusters identified in the epilepsy deep brain stimulation network included regions corresponding to resting state networks, mainly the default mode and salience networks. Our results were concordant with findings from a systematic review of resting state functional MRI studies in patients with deep brain stimulation for epilepsy. Our findings suggest that the various epilepsy deep brain stimulation targets share a common cortico-subcortical network, which might in part underpin the antiseizure effects of stimulation. Interindividual differences in this network functional connectivity could potentially be used as biomarkers in selection of patients, stimulation parameters and neuromodulation targets.
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Affiliation(s)
- Artur Vetkas
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Neurology clinic, Department of Neurosurgery, Tartu University Hospital, University of Tartu, Tartu, Estonia
| | - Jürgen Germann
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Gavin Elias
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Aaron Loh
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Alexandre Boutet
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Joint Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - Kazuaki Yamamoto
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Can Sarica
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Nardin Samuel
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Vanessa Milano
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Anton Fomenko
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Section of Neurosurgery, Health Sciences Centre, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Brendan Santyr
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Jordy Tasserie
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Dave Gwun
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Hyun Ho Jung
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Taufik Valiante
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Krembil Research Institute, Toronto, Ontario, Canada
- CRANIA, University Health Network and University of Toronto, Toronto, ON, M5G 2A2, Canada
- The KITE Research Institute, University Health Network, Toronto, ON, M5G 2A2, Canada
| | - George M Ibrahim
- Division of Pediatric Neurosurgery, Sick Kids Toronto, University of Toronto, Toronto, ON, Canada
| | - Richard Wennberg
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Krembil Research Institute, Toronto, Ontario, Canada
| | - Suneil K Kalia
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Krembil Research Institute, Toronto, Ontario, Canada
- CRANIA, University Health Network and University of Toronto, Toronto, ON, M5G 2A2, Canada
- The KITE Research Institute, University Health Network, Toronto, ON, M5G 2A2, Canada
| | - Andres M Lozano
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Krembil Research Institute, Toronto, Ontario, Canada
- CRANIA, University Health Network and University of Toronto, Toronto, ON, M5G 2A2, Canada
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Cao M, Vogrin SJ, Peterson ADH, Woods W, Cook MJ, Plummer C. Dynamical Network Models From EEG and MEG for Epilepsy Surgery—A Quantitative Approach. Front Neurol 2022; 13:837893. [PMID: 35422755 PMCID: PMC9001937 DOI: 10.3389/fneur.2022.837893] [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: 12/17/2021] [Accepted: 03/01/2022] [Indexed: 11/16/2022] Open
Abstract
There is an urgent need for more informative quantitative techniques that non-invasively and objectively assess strategies for epilepsy surgery. Invasive intracranial electroencephalography (iEEG) remains the clinical gold standard to investigate the nature of the epileptogenic zone (EZ) before surgical resection. However, there are major limitations of iEEG, such as the limited spatial sampling and the degree of subjectivity inherent in the analysis and clinical interpretation of iEEG data. Recent advances in network analysis and dynamical network modeling provide a novel aspect toward a more objective assessment of the EZ. The advantage of such approaches is that they are data-driven and require less or no human input. Multiple studies have demonstrated success using these approaches when applied to iEEG data in characterizing the EZ and predicting surgical outcomes. However, the limitations of iEEG recordings equally apply to these studies—limited spatial sampling and the implicit assumption that iEEG electrodes, whether strip, grid, depth or stereo EEG (sEEG) arrays, are placed in the correct location. Therefore, it is of interest to clinicians and scientists to see whether the same analysis and modeling techniques can be applied to whole-brain, non-invasive neuroimaging data (from MRI-based techniques) and neurophysiological data (from MEG and scalp EEG recordings), thus removing the limitation of spatial sampling, while safely and objectively characterizing the EZ. This review aims to summarize current state of the art non-invasive methods that inform epilepsy surgery using network analysis and dynamical network models. We also present perspectives on future directions and clinical applications of these promising approaches.
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Affiliation(s)
- Miao Cao
- Center for MRI Research, Peking University, Beijing, China
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - Simon J. Vogrin
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
- School of Health Sciences, Swinburne University of Technology, Melbourne, VIC, Australia
| | - Andre D. H. Peterson
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - William Woods
- School of Health Sciences, Swinburne University of Technology, Melbourne, VIC, Australia
| | - Mark J. Cook
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - Chris Plummer
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
- School of Health Sciences, Swinburne University of Technology, Melbourne, VIC, Australia
- *Correspondence: Chris Plummer
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Fang S, Li L, Weng S, Guo Y, Zhang Z, Wang L, Fan X, Wang Y, Jiang T. Decreasing Shortest Path Length of the Sensorimotor Network Induces Frontal Glioma-Related Epilepsy. Front Oncol 2022; 12:840871. [PMID: 35252008 PMCID: PMC8888886 DOI: 10.3389/fonc.2022.840871] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 01/24/2022] [Indexed: 01/12/2023] Open
Abstract
Background Glioma-related epilepsy (GRE) is a common symptom in patients with prefrontal glioma. Epilepsy onset is associated with functional network alterations. This study investigated alterations of functional networks in patients with prefrontal glioma and GRE. Methods Sixty-five patients with prefrontal lobe gliomas were retrospectively assessed and classified into GRE and non-GRE groups. Additionally, 25 healthy participants were enrolled after matching for general information. Imaging data were acquired within 72 h in pre-operation. The sensorimotor network was used to delineate alterations in functional connectivity (FC) and topological properties. One-way analysis of variance and post-hoc analysis with Bonferroni correction were used to calculate differences of FC and topological properties. Results All significant alterations were solely found in the sensorimotor network. Irrespective of gliomas located in the left or right prefrontal lobes, the edge between medial Brodmann area 6 and caudal ventrolateral Brodmann area 6 decreased FC in the GRE group compared with the non-GRE group [p < 0.0001 (left glioma), p = 0.0002 (right glioma)]. Moreover, the shortest path length decrease was found in the GRE group compared with the non-GRE group [p = 0.0292 (left glioma) and p = 0.0129 (right glioma)]. Conclusions The reduction of FC between the medial BA 6 (supplementary motor area) and caudal ventrolateral BA 6 in the ipsilateral hemisphere and the shortening of the path length of the sensorimotor network were characteristics alterations in patients with GRE onset. These findings fill in the gap which is the relationship between GRE onset and the alterations of functional networks in patients with prefrontal glioma. Significance Statement Glioma related epilepsy is the most common symptom of prefrontal glioma. It is important to identify characteristic alterations in functional networks in patients with GRE. We found that all significant alterations occurred in the sensorimotor network. Moreover, a decreased FC in the supplementary motor area and a shortening of the path’s length are additional characteristics of glioma-related epilepsy. We believe that our findings indicate new directions of research that will contribute to future investigations of glioma-related epilepsy onset.
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Affiliation(s)
- Shengyu Fang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Lianwang Li
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Shimeng Weng
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yuhao Guo
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Zhong Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lei Wang
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xing Fan
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tao Jiang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Research Unit of Accurate Diagnosis, Treatment, and Translational Medicine of Brain Tumors, Chinese Academy of Medical Sciences, Beijing, China
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Prediction of seizure outcome following temporal lobectomy: a magnetoencephalography-based graph theory approach". Seizure 2022; 97:73-81. [DOI: 10.1016/j.seizure.2022.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/12/2022] [Accepted: 03/15/2022] [Indexed: 11/22/2022] Open
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Topological Characteristics Associated with Intraoperative Stimulation Related Epilepsy of Glioma Patients: A DTI Network Study. Brain Sci 2021; 12:brainsci12010060. [PMID: 35053803 PMCID: PMC8774024 DOI: 10.3390/brainsci12010060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 12/26/2021] [Accepted: 12/29/2021] [Indexed: 11/17/2022] Open
Abstract
Background: Awake craniotomy with intraoperative stimulation has been utilized in glioma surgical resection to preserve the quality of life. Epilepsy may occur in 5–20% of cases, leading to severe consequences. This study aimed to discuss the mechanism of intraoperative stimulation-related epilepsy (ISE) using DTI-based graph theoretical analysis. Methods: Twenty patients with motor-area glioma were enrolled and divided into two groups (Ep and nEp) according to the presence of ISE. Additionally, a group of 10 healthy participants matched by age, sex, and years of education was also included. All participants underwent T1, T2, and DTI examinations. Graph theoretical analysis was applied to reveal the topological characteristics of white matter networks. Results: Three connections were found to be significantly lower in at least one weighting in the Ep group. These connections were between A1/2/3truL and A4ulL, A1/2/3truR and A4tR, and A6mL and A6mR. Global efficiency was significantly decreased, while the shortest path length increased in the Ep group in at least one weighting. Ten nodes exhibited significant differences in nodal efficiency and degree centrality analyses. The nodes A6mL and A6mR showed a marked decrease in total four weightings in the Ep group. Conclusions: The hub nodes A6mL and A6mR are disconnected in patients with ISE, causing subsequent lower efficiency of global and regional networks. These findings provide a basis for presurgical assessment of ISE, for which caution should be taken when it involves hub nodes during intraoperative electrical stimulation.
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Vespa S, Heyse J, Stumpp L, Liberati G, Ferrao Santos S, Rooijakkers H, Nonclercq A, Mouraux A, van Mierlo P, El Tahry R. Vagus Nerve Stimulation Elicits Sleep EEG Desynchronization and Network Changes in Responder Patients in Epilepsy. Neurotherapeutics 2021; 18:2623-2638. [PMID: 34668148 PMCID: PMC8804116 DOI: 10.1007/s13311-021-01124-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/06/2021] [Indexed: 12/23/2022] Open
Abstract
Neural desynchronization was shown as a key mechanism of vagus nerve stimulation (VNS) action in epilepsy, and EEG synchronization measures are explored as possible response biomarkers. Since brain functional organization in sleep shows different synchrony and network properties compared to wakefulness, we aimed to explore the effects of acute VNS on EEG-derived measures in the two different states of vigilance. EEG epochs were retrospectively analyzed from twenty-four VNS-treated epileptic patients (11 responders, 13 non-responders) in calm wakefulness and stage N2 sleep. Weighted Phase Lag Index (wPLI) was computed as connectivity measure of synchronization, for VNS OFF and VNS ON conditions. Global efficiency (GE) was computed as a network measure of integration. Ratios OFF/ON were obtained as desynchronization/de-integration index. Values were compared between responders and non-responders, and between EEG states. ROC curve and area-under-the-curve (AUC) analysis was performed for response classification. In responders, stronger VNS-induced theta desynchronization (p < 0.05) and decreased GE (p < 0.05) were found in sleep, but not in wakefulness. Theta sleep wPLI Ratio OFF/ON yielded an AUC of 0.825, and 79% accuracy as a response biomarker if a cut-off value is set at 1.05. Considering all patients, the VNS-induced GE decrease was significantly more important in sleep compared to awake EEG state (p < 0.01). In conclusion, stronger sleep EEG desynchronization in theta band distinguishes responders to VNS therapy from non-responders. VNS-induced reduction of network integration occurs significantly more in sleep than in wakefulness.
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Affiliation(s)
- Simone Vespa
- Institute of Neuroscience (IONS), Université Catholique de Louvain, Avenue Mounier, 53 - 1200, Brussels, Belgium.
| | - Jolan Heyse
- Medical Image and Signal Processing Group (MEDISIP), Ghent University, Ghent, Belgium
| | - Lars Stumpp
- Institute of Neuroscience (IONS), Université Catholique de Louvain, Avenue Mounier, 53 - 1200, Brussels, Belgium
| | - Giulia Liberati
- Institute of Neuroscience (IONS), Université Catholique de Louvain, Avenue Mounier, 53 - 1200, Brussels, Belgium
| | - Susana Ferrao Santos
- Institute of Neuroscience (IONS), Université Catholique de Louvain, Avenue Mounier, 53 - 1200, Brussels, Belgium
- Centre for Refractory Epilepsy, Department of Neurology, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Herbert Rooijakkers
- Centre for Refractory Epilepsy, Department of Neurology, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Antoine Nonclercq
- Bio, Electro and Mechanical Systems (BEAMS), Université Libre de Bruxelles, Brussels, Belgium
| | - André Mouraux
- Institute of Neuroscience (IONS), Université Catholique de Louvain, Avenue Mounier, 53 - 1200, Brussels, Belgium
| | - Pieter van Mierlo
- Medical Image and Signal Processing Group (MEDISIP), Ghent University, Ghent, Belgium
| | - Riëm El Tahry
- Institute of Neuroscience (IONS), Université Catholique de Louvain, Avenue Mounier, 53 - 1200, Brussels, Belgium
- Centre for Refractory Epilepsy, Department of Neurology, Cliniques Universitaires Saint-Luc, Brussels, Belgium
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Structural Connectivity Alterations in Operculo-Insular Epilepsy. Brain Sci 2021; 11:brainsci11081041. [PMID: 34439659 PMCID: PMC8392362 DOI: 10.3390/brainsci11081041] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/27/2021] [Accepted: 08/02/2021] [Indexed: 11/17/2022] Open
Abstract
Operculo-insular epilepsy (OIE) is an under-recognized condition that can mimic temporal and extratemporal epilepsies. Previous studies have revealed structural connectivity changes in the epileptic network of focal epilepsy. However, most reports use the debated streamline-count to quantify ‘connectivity strength’ and rely on standard tracking algorithms. We propose a sophisticated cutting-edge method that is robust to crossing fibers, optimizes cortical coverage, and assigns an accurate microstructure-reflecting quantitative conectivity marker, namely the COMMIT (Convex Optimization Modeling for Microstructure Informed Tractography)-weight. Using our pipeline, we report the connectivity alterations in OIE. COMMIT-weighted matrices were created in all participants (nine patients with OIE, eight patients with temporal lobe epilepsy (TLE), and 22 healthy controls (HC)). In the OIE group, widespread increases in ‘connectivity strength’ were observed bilaterally. In OIE patients, ‘hyperconnections’ were observed between the insula and the pregenual cingulate gyrus (OIE group vs. HC group) and between insular subregions (OIE vs. TLE). Graph theoretic analyses revealed higher connectivity within insular subregions of OIE patients (OIE vs. TLE). We reveal, for the first time, the structural connectivity distribution in OIE. The observed pattern of connectivity in OIE likely reflects a diffuse epileptic network incorporating insular-connected regions and may represent a structural signature and diagnostic biomarker.
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Cao Y, Zhan Y, Du M, Zhao G, Liu Z, Zhou F, He L. Disruption of human brain connectivity networks in patients with cervical spondylotic myelopathy. Quant Imaging Med Surg 2021; 11:3418-3430. [PMID: 34341720 DOI: 10.21037/qims-20-874] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 03/08/2021] [Indexed: 02/05/2023]
Abstract
Background Brain functional plasticity and reorganization in patients with cervical spondylotic myelopathy (CSM) is increasingly being explored and validated. However, specific topological alterations in functional networks and their role in CSM brain functional reorganization remain unclear. This study investigates the topological architecture of intrinsic brain functional networks in CSM patients using graph theory. Methods Functional MRI was conducted on 67 CSM patients and 60 healthy controls (HCs). The topological organization of the whole-brain functional network was then calculated using theoretical graph analysis. The difference in categorical variables between groups was compared using a chi-squared test, while that between continuous variables was evaluated using a two-sample t-test. Nonparametric permutation tests were used to compare network measures between the two groups. Results Small-world architecture in functional brain networks were identified in both CSM patients and HCs. Compared with HCs, CSM patients showed a decreased area under the curve (AUC) of the characteristic path length (FDR q=0.040), clustering coefficient (FDR q=0.037), and normalized characteristic path length (FDR q=0.038) of the network. In contrast, there was an increased AUC of normalized clustering coefficient (FDR q=0.014), small-worldness (FDR q=0.009), and global network efficiency (FDR q=0.027) of the network. In local brain regions, nodal topological properties revealed group differences which were predominantly in the default-mode network (DMN), left postcentral gyrus, bilateral putamen, lingual gyrus, and posterior cingulate gyrus. Conclusions This study reported altered functional topological organization in CSM patients. Decreased nodal centralities in the visual cortex and sensory-motor regions may indicate sensory-motor dysfunction and blurred vision. Furthermore, increased nodal centralities in the cerebellum may be compensatory for sensory-motor dysfunction in CSM, while the increased DMN may indicate increased psychological processing in CSM patients.
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Affiliation(s)
- Yuan Cao
- Department of Radiology, the First Affiliated Hospital of Nanchang University, Nanchang, China.,Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, China.,Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Yaru Zhan
- Department of Radiology, the First Affiliated Hospital of Nanchang University, Nanchang, China.,Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang, China
| | - Miao Du
- College of Electrical Engineering of Sichuan University, Chengdu, China
| | - Guoshu Zhao
- Department of Radiology, the First Affiliated Hospital of Nanchang University, Nanchang, China.,Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang, China
| | - Zhili Liu
- Department of Orthopedic Surgery, the First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Fuqing Zhou
- Department of Radiology, the First Affiliated Hospital of Nanchang University, Nanchang, China.,Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang, China
| | - Laichang He
- Department of Radiology, the First Affiliated Hospital of Nanchang University, Nanchang, China.,Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang, China
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Járdánházy A, Járdánházy T. The effect of photic stimulation alone and in combination with sleep deprivation after a seizure-like event - reappraisal by using linear and nonlinear EEG methods. Neurol Res 2021; 44:104-111. [PMID: 34334110 DOI: 10.1080/01616412.2021.1961186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
ObjectivesThe present study aimed to compare the effectiveness of different provocation tests used for the study of the 'susceptibility to seizure' by quantitative electroencephalography (EEG) analysis.MethodsEight subjects with a history of a seizure-like disturbed consciousness participated in this preliminary study. A routine EEG was carried out with photic stimulation (eyes closed and after eyes open) at the beginning of the investigation. Some days later, a sleep-deprived EEG was recorded with the same protocol. Selected epochs (in eyes closed condition) after the stimulations were analysed with Point(wise) Correlation Dimension (PD2i) and Synchronization Likelihood (SL) methods. The results were compared to those obtained by similar analysis of the resting state (control) epochs with Wilcoxon Signed Rank Test (p ≤ 0.05).ResultsIn our study, significantly lower grand mean PD2i and higher delta SL values were found in sleep-deprived state after stimulation with eyes closed compared to the control. Our results indicated a lower-dimensional, hypersynchronous state of the brain as a consequence of these combined provocations.DiscussionThis may correspond to a possible 'preictal' state of the brain. Accordingly, it is suggested that photic stimulation together with sleep deprivation seems to be more effective in provocation - especially when the stimulation was made with eyes closed.
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35
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da Costa LR, de Campos BM, Alvim MKM, Castellano G. EEG Signal Connectivity for Characterizing Interictal Activity in Patients With Mesial Temporal Lobe Epilepsy. Front Neurol 2021; 12:673559. [PMID: 34354658 PMCID: PMC8334187 DOI: 10.3389/fneur.2021.673559] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 06/08/2021] [Indexed: 12/04/2022] Open
Abstract
Over the last decade, several methods for analysis of epileptiform signals in electroencephalography (EEG) have been proposed. These methods mainly use EEG signal features in either the time or the frequency domain to separate regular, interictal, and ictal brain activity. The aim of this work was to evaluate the feasibility of using functional connectivity (FC) based feature extraction methods for the analysis of epileptiform discharges in EEG signals. These signals were obtained from EEG-fMRI sessions of 10 patients with mesial temporal lobe epilepsy (MTLE) with unilateral hippocampal atrophy. The connectivity functions investigated were motif synchronization, imaginary coherence, and magnitude squared coherence in the alpha, beta, and gamma bands of the EEG. EEG signals were sectioned into 1-s epochs and classified according to (using neurologist markers): activity far from interictal epileptiform discharges (IED), activity immediately before an IED and, finally, mid-IED activity. Connectivity matrices for each epoch for each FC function were built, and graph theory was used to obtain the following metrics: strength, cluster coefficient, betweenness centrality, eigenvector centrality (both local and global), and global efficiency. The statistical distributions of these metrics were compared among the three classes, using ANOVA, for each FC function. We found significant differences in all global (p < 0.001) and local (p < 0.00002) graph metrics of the far class compared with before and mid for motif synchronization on the beta band; local betweenness centrality also pointed to a degree of lateralization on the frontotemporal structures. This analysis demonstrates the potential of FC measures, computed using motif synchronization, for the characterization of epileptiform activity of MTLE patients. This methodology may be helpful in the analysis of EEG-fMRI data applied to epileptic foci localization. Nonetheless, the methods must be tested with a larger sample and with other epileptic phenotypes.
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Affiliation(s)
- Leonardo R da Costa
- Cosmic Rays and Chronology Department, Institute of Physics "Gleb Wataghin", University of Campinas, Campinas, Brazil.,Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Brunno M de Campos
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil.,Neuroimaging Laboratory, School of Medical Sciences, University of Campinas, Campinas, Brazil
| | - Marina K M Alvim
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Gabriela Castellano
- Cosmic Rays and Chronology Department, Institute of Physics "Gleb Wataghin", University of Campinas, Campinas, Brazil.,Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
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Leng X, Qin C, Lin H, Li M, Zhao K, Wang H, Duan F, An J, Wu D, Liu Q, Qiu S. Altered Topological Properties of Static/Dynamic Functional Networks and Cognitive Function After Radiotherapy for Nasopharyngeal Carcinoma Using Resting-State fMRI. Front Neurosci 2021; 15:690743. [PMID: 34335167 PMCID: PMC8316765 DOI: 10.3389/fnins.2021.690743] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 06/21/2021] [Indexed: 12/17/2022] Open
Abstract
Objectives The purpose of this study was to (1) explore the changes in topological properties of static and dynamic brain functional networks after nasopharyngeal carcinoma (NPC) radiotherapy (RT) using rs-fMRI and graph theoretical analysis, (2) explore the correlation between cognitive function and changes in brain function, and (3) add to the understanding of the pathogenesis of radiation brain injury (RBI). Methods Fifty-four patients were divided into 3 groups according to time after RT: PT1 (0–6 months); PT2 (>6 to ≤12 months); and PT3 (>12 months). 29 normal controls (NCs) were included. The subjects’ topological properties were evaluated by graph-theoretic network analysis, the functional connectivity of static functional networks was calculated using network-based statistics, and the dynamic functional network matrix was subjected to cluster analysis. Finally, correlation analyses were conducted to explore the relationship between the altered network parameters and cognitive function. Results Assortativity, hierarchy, and network efficiency were significantly abnormal in the PT1 group compared with the NC or PT3 group. The small-world variance in the PT3 group was smaller than that in NCs. The Nodal ClustCoeff of Postcentral_R in the PT2 group was significantly smaller than that in PT3 and NC groups. Functional connectivities were significantly reduced in the patient groups. Most of the functional connectivities of the middle temporal gyrus (MTG) were shown to be significantly reduced in all three patient groups. Most of the functional connectivities of the insula showed significantly reduced in the PT1 and PT3 groups, and most of the functional connectivities in brain regions such as frontal and parietal lobes showed significantly reduced in the PT2 and PT3 groups. These abnormal functional connectivities were correlated with scores on multiple scales that primarily assessed memory, executive ability, and overall cognitive function. The frequency F of occurrence of various states in each subject differed significantly, and the interaction effect of group and state was significant. Conclusion The disruption of static and dynamic functional network stability, reduced network efficiency and reduced functional connectivity may be potential biomarkers of RBI. Our findings may provide new insights into the pathogenesis of RBI from the perspective of functional networks.
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Affiliation(s)
- Xi Leng
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Chunhong Qin
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Huan Lin
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Mingrui Li
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Kui Zhao
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Hongzhuo Wang
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Fuhong Duan
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jie An
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Donglin Wu
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Qihui Liu
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Shijun Qiu
- Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
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Poor speech recognition, sound localization and reorganization of brain activity in children with unilateral microtia-atresia. Brain Imaging Behav 2021; 16:78-90. [PMID: 34245431 PMCID: PMC8825362 DOI: 10.1007/s11682-021-00478-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/17/2021] [Indexed: 10/31/2022]
Abstract
Microtia-atresia is a congenital malformation of the external ear, often affecting one side and being associated with severe-to-profound unilateral conductive hearing loss (UCHL). Although the impact of unilateral hearing loss (UHL) on speech recognition, sound localization and brain plasticity has been intensively investigated, less is known about the subjects with unilateral microtia-atresia (UMA). Considering these UMA subjects have hearing loss from birth, we hypothesize it has a great effect on brain organization. A questionnaire on speech recognition and spatial listening ability was administered to 40 subjects with UMA and 40 age- and sex-matched controls. UMA subjects showed poorer speech recognition in laboratory and poorer spatial listening ability. However, cognitive scores determined by the Montreal Cognitive Assessment (MoCA) and Wechsler Intelligence Scale for Children (WISC-IV) did not differ significantly in these two groups. The impact of hearing loss in UMA on brain functional organization was examined by comparing resting-state fMRIs (rs-fMRI) in 27 subjects with right-sided UMA and 27 matched controls. UMA subjects had increased nodal betweenness in visual networks and DMN but decreases in auditory and attention networks. These results indicate that UCHL in UMA causes significant abnormalities in brain organization. The impact of UCHL on cognition should be further examined with a battery of tests that are more challenging and better focused on the cognitive networks identified.
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Zhang S, Lan W, Dai W, Wu F, Chen C. An Extended Correlation Dimension of Complex Networks. ENTROPY 2021; 23:e23060710. [PMID: 34205073 PMCID: PMC8229313 DOI: 10.3390/e23060710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/01/2021] [Accepted: 06/01/2021] [Indexed: 11/18/2022]
Abstract
Fractal and self-similarity are important characteristics of complex networks. The correlation dimension is one of the measures implemented to characterize the fractal nature of unweighted structures, but it has not been extended to weighted networks. In this paper, the correlation dimension is extended to the weighted networks. The proposed method uses edge-weights accumulation to obtain scale distances. It can be used not only for weighted networks but also for unweighted networks. We selected six weighted networks, including two synthetic fractal networks and four real-world networks, to validate it. The results show that the proposed method was effective for the fractal scaling analysis of weighted complex networks. Meanwhile, this method was used to analyze the fractal properties of the Newman–Watts (NW) unweighted small-world networks. Compared with other fractal dimensions, the correlation dimension is more suitable for the quantitative analysis of small-world effects.
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Affiliation(s)
- Sheng Zhang
- School of Information Engineering, Nanchang Hangkong University, 696 Fenghe South Avenue, Nanchang 330063, China; (W.D.); (F.W.)
- Correspondence: (S.Z.); (W.L.)
| | - Wenxiang Lan
- School of Information Engineering, Nanchang Hangkong University, 696 Fenghe South Avenue, Nanchang 330063, China; (W.D.); (F.W.)
- Correspondence: (S.Z.); (W.L.)
| | - Weikai Dai
- School of Information Engineering, Nanchang Hangkong University, 696 Fenghe South Avenue, Nanchang 330063, China; (W.D.); (F.W.)
| | - Feng Wu
- School of Information Engineering, Nanchang Hangkong University, 696 Fenghe South Avenue, Nanchang 330063, China; (W.D.); (F.W.)
| | - Caisen Chen
- Military Exercise and Training Center, Academy of Army Armored Force, Beijing 100072, China;
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Zhang M, Li B, Lv X, Liu S, Liu Y, Tang R, Lang Y, Huang Q, He J. Low-Intensity Focused Ultrasound-Mediated Attenuation of Acute Seizure Activity Based on EEG Brain Functional Connectivity. Brain Sci 2021; 11:brainsci11060711. [PMID: 34071964 PMCID: PMC8228165 DOI: 10.3390/brainsci11060711] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 05/18/2021] [Accepted: 05/24/2021] [Indexed: 11/30/2022] Open
Abstract
(1) Background: Ultrasound has been used for noninvasive stimulation and is a promising technique for treating neurological diseases. Epilepsy is a common neurological disorder, that is attributed to uncontrollable abnormal neuronal hyperexcitability. Abnormal synchronized activities can be observed across multiple brain regions during a seizure. (2) Methods: we used low-intensity focused ultrasound (LIFU) to sonicate the brains of epileptic rats, analyzed the EEG functional brain network to explore the effect of LIFU on the epileptic brain network, and continued to explore the mechanism of ultrasound neuromodulation. LIFU was used in the hippocampus of epileptic rats in which a seizure was induced by kainic acid. (3) Results: By comparing the brain network characteristics before and after sonication, we found that LIFU significantly impacted the functional brain network, especially in the low-frequency band. The brain network connection strength across multiple brain regions significantly decreased after sonication compared to the connection strength in the control group. The brain network indicators (the path length, clustering coefficient, small-worldness, local efficiency and global efficiency) all changed significantly in the low-frequency. (4) Conclusions: These results revealed that LIFU could reduce the network connections of epilepsy circuits and change the structure of the brain network at the whole-brain level.
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Affiliation(s)
- Minjian Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; (M.Z.); (B.L.); (S.L.); (Y.L.); (Q.H.); (J.H.)
| | - Bo Li
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; (M.Z.); (B.L.); (S.L.); (Y.L.); (Q.H.); (J.H.)
| | - Xiaodong Lv
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China; (X.L.); (Y.L.)
| | - Sican Liu
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; (M.Z.); (B.L.); (S.L.); (Y.L.); (Q.H.); (J.H.)
| | - Yafei Liu
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; (M.Z.); (B.L.); (S.L.); (Y.L.); (Q.H.); (J.H.)
| | - Rongyu Tang
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China; (X.L.); (Y.L.)
- Correspondence:
| | - Yiran Lang
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China; (X.L.); (Y.L.)
| | - Qiang Huang
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; (M.Z.); (B.L.); (S.L.); (Y.L.); (Q.H.); (J.H.)
| | - Jiping He
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; (M.Z.); (B.L.); (S.L.); (Y.L.); (Q.H.); (J.H.)
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Wachsmuth L, Datunashvili M, Kemper K, Albers F, Lambers H, Lüttjohann A, Kreitz S, Budde T, Faber C. Retrosplenial Cortex Contributes to Network Changes during Seizures in the GAERS Absence Epilepsy Rat Model. Cereb Cortex Commun 2021; 2:tgab023. [PMID: 34296168 PMCID: PMC8263073 DOI: 10.1093/texcom/tgab023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 03/15/2021] [Accepted: 03/16/2021] [Indexed: 11/14/2022] Open
Abstract
Resting state-fMRI was performed to explore brain networks in Genetic Absence Epilepsy Rats from Strasbourg and in nonepileptic controls (NEC) during monitoring of the brain state by simultaneous optical Ca2+-recordings. Graph theoretical analysis allowed for the identification of acute and chronic network changes and revealed preserved small world topology before and after seizure onset. The most prominent acute change in network organization during seizures was the segregation of cortical regions from the remaining brain. Stronger connections between thalamic with limbic regions compared with preseizure state indicated network regularization during seizures. When comparing between strains, intrathalamic connections were prominent in NEC, on local level represented by higher thalamic strengths and hub scores. Subtle differences were observed for retrosplenial cortex (RS), forming more connections beyond cortex in epileptic rats, and showing a tendency to lateralization during seizures. A potential role of RS as hub between subcortical and cortical regions in epilepsy was supported by increased numbers of parvalbumin-positive (PV+) interneurons together with enhanced inhibitory synaptic activity and neuronal excitability in pyramidal neurons. By combining multimodal fMRI data, graph theoretical methods, and electrophysiological recordings, we identified the RS as promising target for modulation of seizure activity and/or comorbidities.
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Affiliation(s)
- Lydia Wachsmuth
- Translational Research Imaging Center, Clinic for Radiology, University Hospital Münster, 48149 Münster, Germany
| | - Maia Datunashvili
- Institute of Physiology I, University of Münster, 48149 Münster, Germany
| | - Katharina Kemper
- Translational Research Imaging Center, Clinic for Radiology, University Hospital Münster, 48149 Münster, Germany
| | - Franziska Albers
- Translational Research Imaging Center, Clinic for Radiology, University Hospital Münster, 48149 Münster, Germany
| | - Henriette Lambers
- Translational Research Imaging Center, Clinic for Radiology, University Hospital Münster, 48149 Münster, Germany
| | - Annika Lüttjohann
- Institute of Physiology I, University of Münster, 48149 Münster, Germany
| | - Silke Kreitz
- Experimental and Clinical Pharmacology and Toxicology, University of Erlangen, 91054 Erlangen, Germany
| | - Thomas Budde
- Institute of Physiology I, University of Münster, 48149 Münster, Germany
| | - Cornelius Faber
- Translational Research Imaging Center, Clinic for Radiology, University Hospital Münster, 48149 Münster, Germany
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Jiang S, Li H, Pei H, Liu L, Li Z, Chen Y, Li X, Li Q, Yao D, Luo C. Connective profiles and antagonism between dynamic and static connectivity underlying generalized epilepsy. Brain Struct Funct 2021; 226:1423-1435. [PMID: 33730218 DOI: 10.1007/s00429-021-02248-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 02/27/2021] [Indexed: 11/28/2022]
Abstract
This study aims to characterize the connective profiles and the coupling relationship between dynamic and static functional connectivity (dFC and sFC) in large-scale brain networks in patients with generalized epilepsy (GE). Functional, structural and diffuse MRI data were collected from 83 patients with GE and 106 matched healthy controls (HC). Resting-state BOLD time course was deconvolved to neural time course using a blind hemodynamic deconvolution method. Then, five connective profiles, including the structural connectivity (SC) and BOLD/neural time course-derived sFC/dFC networks, were constructed based on the proposed whole brain atlas. Network-level weighted correlation probability (NWCP) were proposed to evaluate the association between dFC and sFC. Both the BOLD signal and neural time course showed highly concordant findings and the present study emphasized the consistent findings between two functional approaches. The patients with GE showed hypervariability and enhancement of FC, and notably decreased SC in the subcortical network. Besides, increased dFC, weaker anatomic links, and complex alterations of sFC were observed in the default mode network of GE. Moreover, significantly increased SC and predominantly increased sFC were found in the frontoparietal network. Remarkably, antagonism between dFC and sFC was observed in large-scale networks in HC, while patients with GE showed significantly decreased antagonism in core epileptic networks. In sum, our study revealed distinct connective profiles in different epileptic networks and provided new clues to the brain network mechanism of epilepsy from the perspective of antagonism between dynamic and static functional connectivity.
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Affiliation(s)
- Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Hechun Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Haonan Pei
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Linli Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Zhiliang Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Yan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Xiangkui Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Qifu Li
- Department of Neurology, First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China.,Department of Neurology, First Affiliated Hospital of Hainan Medical University, Haikou, China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China.,High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China. .,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China. .,High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
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42
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Fang S, Zhou C, Wang L, Fan X, Wang Y, Zhang Z, Jiang T. Characteristic Alterations of Network in Patients With Intraoperative Stimulation-Induced Seizures During Awake Craniotomy. Front Neurol 2021; 12:602716. [PMID: 33815243 PMCID: PMC8012772 DOI: 10.3389/fneur.2021.602716] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 02/12/2021] [Indexed: 11/13/2022] Open
Abstract
Background: The use of electrocorticography (ECoG) to avoid intraoperative stimulation-induced seizure (ISS) during awake craniotomy is controversial. Although a standard direct cortical stimulating (DCS) protocol is used to identify the eloquent cortices and subcortical structures, ISS still occurs. Epilepsy is related to alterations in brain networks. In this study, we investigated specific alterations in brain networks in patients with ISS. Methods: Twenty-seven patients with glioma were enrolled and categorized into the ISS and non-ISS groups based on their history of ISS occurrence. A standard DCS protocol was used during awake craniotomy without ECoG supervision. Graph theoretical measurement was used to analyze resting-state functional magnetic resonance imaging data to quantitatively reveal alterations in the functional networks. Results: In the sensorimotor networks, the glioma significantly decreased the functional connectivity (FC) of four edges in the ISS group, which were conversely increased in the non-ISS group after multiple corrections (p < 0.001, threshold of p-value = 0.002). Regarding the topological properties, the sensorimotor network of all participants was classified as a small-world network. Glioma significantly increased global efficiency, nodal efficiency, and the sigma value, as well as decreased the shortest path length in the ISS group compared with the non-ISS group (p < 0.05). Conclusions: The specific alterations indicating patient susceptibility to ISS during DCS increased global and nodal efficiencies and decreased the shortest path length and FC induced by gliomas. If the patient has these specific alterations, ECoG is recommended to monitor after-discharge current during DCS to avoid ISS.
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Affiliation(s)
- Shengyu Fang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Chunyao Zhou
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lei Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xing Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhong Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tao Jiang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Research Unit of Accurate Diagnosis, Treatment, and Translational Medicine of Brain Tumors Chinese (2019RU11), Chinese Academy of Medical Sciences, Beijing, China
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Latini F, Axelson H, Fahlström M, Jemstedt M, Alberius Munkhammar Å, Zetterling M, Ryttlefors M. Role of Preoperative Assessment in Predicting Tumor-Induced Plasticity in Patients with Diffuse Gliomas. J Clin Med 2021; 10:jcm10051108. [PMID: 33799925 PMCID: PMC7961995 DOI: 10.3390/jcm10051108] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 02/28/2021] [Accepted: 03/01/2021] [Indexed: 01/03/2023] Open
Abstract
When diffuse gliomas (DG) affect the brain’s potential to reorganize functional networks, patients can exhibit seizures and/or language/cognitive impairment. The tumor–brain interaction and the individual connectomic organization cannot be predicted preoperatively. We aimed to, first, investigate the relationship between preoperative assessment and intraoperative findings of eloquent tumors in 36 DG operated with awake surgery. Second, we also studied possible mechanisms of tumor-induced brain reorganization in these patients. FLAIR-MRI sequences were used for tumor volume segmentation and the Brain-Grid system (BG) was used as an overlay for infiltration analysis. Neuropsychological (NPS) and/or language assessments were performed in all patients. The distance between eloquent spots and tumor margins was measured. All variables were used for correlation and logistic regression analyses. Eloquent tumors were detected in 75% of the patients with no single variable able to predict this finding. Impaired NPS functions correlated with invasive tumors, crucial location (A4C2S2/A3C2S2-voxels, left opercular-insular/sub-insular region) and higher risk of eloquent tumors. Epilepsy was correlated with larger tumor volumes and infiltrated A4C2S2/A3C2S2 voxels. Language impairment was correlated with infiltrated A3C2S2 voxel. Peritumoral cortical eloquent spots reflected an early compensative mechanism with age as possible influencing factor. Preoperative NPS impairment is linked with high risk of eloquent tumors. A systematic integration of extensive cognitive assessment and advanced neuroimaging can improve our comprehension of the connectomic brain organization at the individual scale and lead to a better oncological/functional balance.
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Affiliation(s)
- Francesco Latini
- Section of Neurosurgery, Department of Neuroscience, Uppsala University, 75185 Uppsala, Sweden; (M.Z.); (M.R.)
- Correspondence: ; Tel.: +46-764-244-653
| | - Hans Axelson
- Section of Clinical Neurophysiology, Department of Neuroscience, Uppsala University, 75185 Uppsala, Sweden;
| | - Markus Fahlström
- Section of Radiology, Department of Surgical Sciences, Uppsala University, 75185 Uppsala, Sweden;
| | - Malin Jemstedt
- Department of Neuroscience, Speech-Language Pathology, Uppsala University, 75185 Uppsala, Sweden;
| | | | - Maria Zetterling
- Section of Neurosurgery, Department of Neuroscience, Uppsala University, 75185 Uppsala, Sweden; (M.Z.); (M.R.)
| | - Mats Ryttlefors
- Section of Neurosurgery, Department of Neuroscience, Uppsala University, 75185 Uppsala, Sweden; (M.Z.); (M.R.)
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Fang S, Wang Y, Jiang T. Epilepsy enhance global efficiency of language networks in right temporal lobe gliomas. CNS Neurosci Ther 2021; 27:363-371. [PMID: 33464718 PMCID: PMC7871790 DOI: 10.1111/cns.13595] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 12/16/2020] [Accepted: 12/17/2020] [Indexed: 12/11/2022] Open
Abstract
AIMS We analyzed the resting state functional magnetic resonance images to investigate the alterations of neural networks in patients with glioma-related epilepsy (GRE). METHODS Fifty-six patients with right temporal lower-grade glioma were divided into GRE (n = 28) and non-GRE groups. Twenty-eight healthy subjects were recruited after matching age, sex, and education level. Sensorimotor, visual, language, and left executive control networks were applied to generate functional connectivity matrices, and their topological properties were investigated. RESULTS No significant alterations in functional connectivity were found. The least significant discovery test revealed differences only in the language network. The shortest path length, clustering coefficient, local efficiency, and vulnerability were greater in the non-GRE group than in the other groups. The nodal efficiencies of two nodes (mirror areas to Broca and Wernicke) were weaker in the non-GRE group than in the other groups. The node of degree centrality (Broca), nodal local efficiency (Wernicke), and nodal clustering coefficient (temporal polar) were greater in the non-GRE group than in the healthy group. CONCLUSION Different tumor locations alter different neural networks. Temporal lobe gliomas in the right hemisphere altered the language network. Glioma itself and GRE altered the network in opposing ways in patients with right temporal glioma.
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Affiliation(s)
- Shengyu Fang
- Beijing Neurosurgical Institute, Capital Medical UniversityBeijingChina
- Department of NeurosurgeryBeijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Yinyan Wang
- Beijing Neurosurgical Institute, Capital Medical UniversityBeijingChina
- Department of NeurosurgeryBeijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Tao Jiang
- Beijing Neurosurgical Institute, Capital Medical UniversityBeijingChina
- Department of NeurosurgeryBeijing Tiantan HospitalCapital Medical UniversityBeijingChina
- Research Unit of Accurate Diagnosis, Treatment, and Translational Medicine of Brain Tumors Chinese Academy of Medical SciencesBeijingChina
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Heiney K, Huse Ramstad O, Fiskum V, Christiansen N, Sandvig A, Nichele S, Sandvig I. Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation. Front Comput Neurosci 2021; 15:611183. [PMID: 33643017 PMCID: PMC7902700 DOI: 10.3389/fncom.2021.611183] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 01/18/2021] [Indexed: 01/03/2023] Open
Abstract
It has been hypothesized that the brain optimizes its capacity for computation by self-organizing to a critical point. The dynamical state of criticality is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified in neuronal networks by observing the behavior of cascades of network activity termed "neuronal avalanches." The dynamic activity that occurs in neuronal networks is closely intertwined with how the elements of the network are connected and how they influence each other's functional activity. In this review, we highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders. First, integrating graph theory into experimental studies on criticality, as is becoming more common in theoretical and modeling studies, would provide insight into the kinds of network structures that support criticality in networks of biological neurons. Furthermore, plasticity mechanisms play a crucial role in shaping these neural structures, both in terms of homeostatic maintenance and learning. Both network structures and plasticity have been studied fairly extensively in theoretical models, but much work remains to bridge the gap between theoretical and experimental findings. Finally, information theoretical approaches can tie in more concrete evidence of a network's computational capabilities. Approaching neural dynamics with all these facets in mind has the potential to provide a greater understanding of what goes wrong in neural disorders. Criticality analysis therefore holds potential to identify disruptions to healthy dynamics, granted that robust methods and approaches are considered.
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Affiliation(s)
- Kristine Heiney
- Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
- Department of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Ola Huse Ramstad
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Vegard Fiskum
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Nicholas Christiansen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Axel Sandvig
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Clinical Neuroscience, Umeå University Hospital, Umeå, Sweden
- Department of Neurology, St. Olav's Hospital, Trondheim, Norway
| | - Stefano Nichele
- Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
- Department of Holistic Systems, Simula Metropolitan, Oslo, Norway
| | - Ioanna Sandvig
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
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Meng Y, Yang S, Chen H, Li J, Xu Q, Zhang Q, Lu G, Zhang Z, Liao W. Systematically disrupted functional gradient of the cortical connectome in generalized epilepsy: Initial discovery and independent sample replication. Neuroimage 2021; 230:117831. [PMID: 33549757 DOI: 10.1016/j.neuroimage.2021.117831] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 01/07/2021] [Accepted: 01/29/2021] [Indexed: 01/03/2023] Open
Abstract
Genetic generalized epilepsy is a network disorder typically involving distributed areas identified by classical neuroanatomy. However, the finer topological relationships in terms of continuous spatial arrangement between these systems are still ambiguous. Connectome gradients provide the topological representations of human macroscale hierarchy in an abstract low-dimensional space by embedding the functional connectome into a set of axes. Leveraging connectome gradients, we systematically scrutinized abnormalities of functional connectome gradient in patients with genetic generalized epilepsy with tonic-clonic seizure (GGE-GTCS, n = 78) compared to healthy controls (HC, n = 85), and further examined the reproducibility across multiple processing configurations and in an independent validation sample (patients with GGE-GTCS, n = 28; HC, n = 31). Our findings demonstrated an extended principal gradient at different spatial scales, network-level and vertex-level, in patients with GGE-GTCS. We found consistent results across processing parameters and in validation sample. The extended principal gradient revealed the excessive functional segregation between unimodal and transmodal systems associated with duration of epilepsy and age at seizure onset in patients. Furthermore, the connectivity profile of regions with abnormal principal gradients verified the disrupted functional hierarchy revealed by gradients. Together, our findings provided a novel view of functional system hierarchy alterations, which facilitated a continuous spatial arrangement of macroscale networks, to increase our understanding of the functional connectome hierarchy in generalized epilepsy.
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Affiliation(s)
- Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P R China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P R China
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P R China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P R China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P R China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P R China.
| | - Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P R China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P R China
| | - Qiang Xu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, P R China
| | - Qirui Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, P R China
| | - Guangming Lu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, P R China
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, P R China.
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P R China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P R China.
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Drenthen GS, Jansen JFA, Gommer E, Gupta L, Hofman PAM, van Kranen-Mastenbroek VH, Hilkman DM, Vlooswijk MCG, Rouhl RPW, Backes WH. Predictive value of functional MRI and EEG in epilepsy diagnosis after a first seizure. Epilepsy Behav 2021; 115:107651. [PMID: 33309424 DOI: 10.1016/j.yebeh.2020.107651] [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: 10/09/2020] [Revised: 11/18/2020] [Accepted: 11/18/2020] [Indexed: 02/07/2023]
Abstract
It is often difficult to predict seizure recurrence in subjects who have suffered a first-ever epileptic seizure. In this study, the predictive value of physiological signals measured using Electroencephalography (EEG) and functional MRI (fMRI) is assessed. In particular those patients developing epilepsy (i.e. a second unprovoked seizure) that were initially evaluated as having a low risk of seizure recurrence are of interest. In total, 26 epilepsy patients, of which 8 were initially evaluated as having a low risk of seizure recurrence (i.e. converters), and 17 subjects with only a single seizure were included. All subjects underwent routine EEG as well as fMRI measurements. For diagnostic classification, features related to the temporal dynamics were determined for both the processed EEG and fMRI data. Subsequently, a logistic regression classifier was trained on epilepsy and first-seizure subjects. The trained model was tested using the clinically relevant converters group. The sensitivity, specificity, and AUC (mean ± SD) of the regression model including metrics from both modalities were 74 ± 19%, 82 ± 18%, and 0.75 ± 0.12, respectively. Positive and negative predictive values (mean ± SD) of the regression model with both EEG and fMRI features are 84 ± 14% and 78 ± 12%. Moreover, this EEG/fMRI model showed significant improvements compared to the clinical diagnosis, whereas the models using metrics from either EEG or fMRI do not reach significance (p > 0.05). Temporal metrics computationally derived from EEG and fMRI time signals may clinically aid and synergistically improve the predictive value in a first-seizure sample.
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Affiliation(s)
- Gerhard S Drenthen
- School for Mental Health and Neuroscience, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands; Department of Electrical Engineering, Eindhoven University of Technology, De Rondom 70, Eindhoven, the Netherlands.
| | - Jacobus F A Jansen
- School for Mental Health and Neuroscience, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands; Department of Electrical Engineering, Eindhoven University of Technology, De Rondom 70, Eindhoven, the Netherlands
| | - Erik Gommer
- Department of Clinical Neurophysiology, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands
| | - Lalit Gupta
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands
| | - Paul A M Hofman
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands
| | | | - Danny M Hilkman
- Department of Clinical Neurophysiology, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands
| | - Marielle C G Vlooswijk
- Department of Neurology, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands
| | - Rob P W Rouhl
- School for Mental Health and Neuroscience, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands; Department of Neurology, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands; Academic Center for Epileptology Kempenhaeghe/MUMC+ Heeze and Maastricht, the Netherlands
| | - Walter H Backes
- School for Mental Health and Neuroscience, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands
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48
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Abstract
PURPOSE OF REVIEW Epilepsy is a dynamical disorder of the brain characterized by sudden, seemingly unpredictable transitions to the ictal state. When and how these transitions occur remain unresolved questions in neurology. RECENT FINDINGS Modelling work based on dynamical systems theory proposed that a slow control parameter is necessary to explain the transition between interictal and ictal states. Recently, converging evidence from chronic EEG datasets unravelled the existence of cycles of epileptic brain activity at multiple timescales - circadian, multidien (over multiple days) and circannual - which could reflect cyclical changes in a slow control parameter. This temporal structure of epilepsy has theoretical implications and argues against the conception of seizures as completely random events. The practical significance of cycles in epilepsy is highlighted by their predictive value in computational models for seizure forecasting. SUMMARY The canonical randomness of seizures is being reconsidered in light of cycles of brain activity discovered through chronic EEG. This paradigm shift motivates development of next-generation devices to track more closely fluctuations in epileptic brain activity that determine time-varying seizure risk.
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Carr SJA, Gershon A, Shafiabadi N, Lhatoo SD, Tatsuoka C, Sahoo SS. An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data. Front Integr Neurosci 2021; 14:491403. [PMID: 33510622 PMCID: PMC7835283 DOI: 10.3389/fnint.2020.491403] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 11/30/2020] [Indexed: 12/22/2022] Open
Abstract
A key area of research in epilepsy neurological disorder is the characterization of epileptic networks as they form and evolve during seizure events. In this paper, we describe the development and application of an integrative workflow to analyze functional and structural connectivity measures during seizure events using stereotactic electroencephalogram (SEEG) and diffusion weighted imaging data (DWI). We computed structural connectivity measures using electrode locations involved in recording SEEG signal data as reference points to filter fiber tracts. We used a new workflow-based tool to compute functional connectivity measures based on non-linear correlation coefficient, which allows the derivation of directed graph structures to represent coupling between signal data. We applied a hierarchical clustering based network analysis method over the functional connectivity data to characterize the organization of brain network into modules using data from 27 events across 8 seizures in a patient with refractory left insula epilepsy. The visualization of hierarchical clustering values as dendrograms shows the formation of connected clusters first within each insulae followed by merging of clusters across the two insula; however, there are clear differences between the network structures and clusters formed across the 8 seizures of the patient. The analysis of structural connectivity measures showed strong connections between contacts of certain electrodes within the same brain hemisphere with higher prevalence in the perisylvian/opercular areas. The combination of imaging and signal modalities for connectivity analysis provides information about a patient-specific dynamical functional network and examines the underlying structural connections that potentially influences the properties of the epileptic network. We also performed statistical analysis of the absolute changes in correlation values across all 8 seizures during a baseline normative time period and different seizure events, which showed decreased correlation values during seizure onset; however, the changes during ictal phases were varied.
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Affiliation(s)
- Sarah J. A. Carr
- Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States
- Neuroimaging Department, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Arthur Gershon
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Nassim Shafiabadi
- Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Samden D. Lhatoo
- Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States
| | - Curtis Tatsuoka
- Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Satya S. Sahoo
- Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
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50
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Zink N, Mückschel M, Beste C. Resting-state EEG Dynamics Reveals Differences in Network Organization and its Fluctuation between Frequency Bands. Neuroscience 2020; 453:43-56. [PMID: 33276088 DOI: 10.1016/j.neuroscience.2020.11.037] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Revised: 11/21/2020] [Accepted: 11/23/2020] [Indexed: 12/24/2022]
Abstract
Functional connectivity in EEG resting-state is not stable but fluctuates considerably. The aim of this study was to investigate how efficient information flows through a network, i.e. how resting-state EEG networks are organized and whether this organization it also subject to fluctuations. Differences of the network organization (small-worldness), degree of clustered connectivity, and path length as an indicator of how information is integrated into the network across time was compared between theta, alpha and beta bands. We show robust differences in network organization (small-worldness) between frequency bands. Fluctuations in network organization were larger in the theta, compared to the alpha and beta frequency. Variation in network organization and not the frequency of fluctuations differs between frequency bands. Furthermore, the degree of clustered connectivity and its modulation across time is the same across frequency bands, but the path length revealed the same modulatory pattern as the small-world metric. It is therefore the interplay of local processing efficiency and global information processing efficiency in the brain that fluctuates in a frequency-specific way. Properties of how information can be integrated is subject to fluctuations in a frequency-specific way in the resting-state. The possible relevance of these resting-state EEG properties is discussed including its clinical relevance.
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Affiliation(s)
- Nicolas Zink
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, United States; Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine of the TU, Dresden, Germany.
| | - Moritz Mückschel
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine of the TU, Dresden, Germany
| | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine of the TU, Dresden, Germany
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