1
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Moya Quiros V, Adham A, Convers P, Lesca G, Mauguiere F, Soulier H, Arzimanoglou A, Bayat A, Braakman H, Camdessanche JP, Casenave P, Chaton L, Chaix Y, Chochoi M, Depienne C, Desportes V, De Ridder J, Dinkelacker V, Gardella E, Kluger GJ, Jung J, Lemesle Martin M, Mancardi MM, Mueller M, Poulat AL, Platzer K, Roubertie A, Stokman MF, Vulto-van Silfhout AT, Wiegand G, Mazzola L. Electro-Clinical Features and Functional Connectivity Analysis in SYN1-Related Epilepsy. Ann Neurol 2024. [PMID: 39177219 DOI: 10.1002/ana.27063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 08/07/2024] [Accepted: 08/07/2024] [Indexed: 08/24/2024]
Abstract
OBJECTIVE There is currently scarce data on the electroclinical characteristics of epilepsy associated with synapsin 1 (SYN1) pathogenic variations. We examined clinical and electro-encephalographic (EEG) features in patients with epilepsy and SYN1 variants, with the aim of identifying a distinctive electroclinical pattern. METHODS In this retrospective multicenter study, we collected and reviewed demographic, genetic, and epilepsy data of 19 male patients with SYN1 variants. Specifically, we analyzed interictal EEG data for all patients, and electro-clinical data from 10 epileptic seizures in 5 patients, using prolonged video-EEG monitoring recordings. Inter-ictal EEG functional connectivity parameters and frequency spectrum of the 10 patients over 12 years of age, were computed and compared with those of 56 age- and sex-matched controls. RESULTS The main electroclinical features of epilepsy in patients with SYN1 were (1) EEG background and organization mainly normal; (2) interictal abnormalities are often rare or not visible on EEG; (3) more than 60% of patients had reflex seizures (cutaneous contact with water and defecation being the main triggers) isolated or associated with spontaneous seizures; (4) electro-clinical semiology of seizures was mainly temporal or temporo-insulo/perisylvian with a notable autonomic component; and (5) ictal EEG showed a characteristic rhythmic theta/delta activity predominating in temporo-perisylvian regions at the beginning of most seizures. Comparing patients with SYN1 to healthy subjects, we observed a shift to lower frequency bands in power spectrum of interictal EEG and an increased connectivity in both temporal regions. INTERPRETATION A distinct epilepsy syndrome emerges in patients with SYN1, with a rather characteristic clinical and EEG pattern suggesting predominant temporo-insular involvement. ANN NEUROL 2024.
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Affiliation(s)
| | - Ahmed Adham
- Physical Medicine and Rehabilitation Department, University Hospital of Saint-Étienne, Saint-Étienne, France
- CEA, LETI, CLINATEC, University Grenoble Alpes, Grenoble, France
| | - Philippe Convers
- Neurology Department, University Hospital, Saint-Etienne, France
- NeuroPain Lab, Lyon Neuroscience Research Centre, CRNL-INSERM U 1028/CNRS UMR 5292, University of Lyon, Lyon, France
| | - Gaetan Lesca
- Department of Genetics, Member of the ERN EpiCARE, Hospices Civils de Lyon, Bron, France
- Institute NeuroMyoGène, Laboratoire Physiopathologie et Génétique du Neurone et du Muscle, CNRS UMR 5261-INSERM U1315, Université de Lyon-Université Claude Bernard Lyon 1, Lyon, France
| | - François Mauguiere
- NeuroPain Lab, Lyon Neuroscience Research Centre, CRNL-INSERM U 1028/CNRS UMR 5292, University of Lyon, Lyon, France
- Department of Functional Neurology and Epileptology, Member of the ERN EpiCARE, Hospices Civils de Lyon, Université de Lyon, Lyon, France
| | - Hugo Soulier
- Neurology Department, University Hospital, Saint-Etienne, France
| | - Alexis Arzimanoglou
- Department of Clinical Epileptology, Sleep Disorders and Functional Pediatric Neurology, coordinating member of the ERN EpiCARE, University Hospitals of Lyon (HCL), Lyon, France
- Sección Epilepsia, Sueño y Neurofisiología, Department of Neurology, coordinating member of the ERN EpiCARE, Hospital Sant Joan de Déu Barcelona, Barcelona, Spain
| | - Allan Bayat
- Institute for Regional Health Services, University of Southern Denmark, Odense, Denmark
- Department of Epilepsy Genetics and Personalized Medicine, Danish Epilepsy Center, Member of the ERN EpiCARE, Dianalund, Denmark
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Genetics, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Hilde Braakman
- Department of Paediatric Neurology, Radboud University Medical Centre, Amalia Children's Hospital, Nijmegen, The Netherlands
| | | | | | - Laurence Chaton
- Department of Neurology, Neurophysiology Unit, CHU Lille, Lille, France
| | - Yves Chaix
- Toulouse NeuroImaging Center, University of Toulouse, INSERM, Université Paul Sabatier, Toulouse, France
- Pediatric Neurology Unit, Children's Hospital, Toulouse-Purpan University Hospital, Toulouse, France
| | - Maxime Chochoi
- Department of Neurology, Neurophysiology Unit, CHU Lille, Lille, France
| | - Christel Depienne
- Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Vincent Desportes
- Hospices Civils de Lyon, Department of Pediatric Neurology, Member of the ERN EpiCARE, Hôpital Femme Mère Enfant, Lyon, France
| | - Jessie De Ridder
- Department of Neurology, Academic Center for Epileptology, Kempenhaeghe, Heeze, The Netherlands
| | - Vera Dinkelacker
- Department of Neurology, University Hospital Strasbourg, Strasbourg, France
| | - Elena Gardella
- Institute for Regional Health Services, University of Southern Denmark, Odense, Denmark
- Department of Epilepsy Genetics and Personalized Medicine, Danish Epilepsy Center, Member of the ERN EpiCARE, Dianalund, Denmark
| | - Gerhard J Kluger
- Schön Klinik Vogtareuth, Center for Pediatric Neurology, Neurorehabilitation and Epileptology, Collaborating Partner of the ERN EpiCARE, PMU, Vogtareuth, Salzburg, Germany
| | - Julien Jung
- Department of Functional Neurology and Epileptology, Member of the ERN EpiCARE, Hospices Civils de Lyon, Université de Lyon, Lyon, France
- Department of Neurology, University Hospital, Lyon Neuroscience Research Center (CRNL), INSERM U1028, CNRS UMR5292, Lyon, France
| | | | - Maria Margherita Mancardi
- Unit of Child Neuropsychiatry, Epilepsy Center, Member of the ERN EpiCARE, Istituto Giannina Gaslini, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health, University of Genoa, Genoa, Italy
| | - Markus Mueller
- Department of Epileptology, Krankenhaus Mara, Bethel Epilepsy Center, Bielefeld University, Bielefeld, Germany
| | - Anne-Lise Poulat
- Hospices Civils de Lyon, Department of Pediatric Neurology, Member of the ERN EpiCARE, Hôpital Femme Mère Enfant, Lyon, France
| | - Konrad Platzer
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Agathe Roubertie
- Department of Pediatric Neurology, INSERM, University Hospital Montpellier, Montpellier, France
| | - Marijn F Stokman
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Gert Wiegand
- Division of Pediatric Neurology, Department of Pediatrics, Asklepios Klinik Nord-Heidberg, Hamburg, Germany
- Department of Pediatric and Adolescent Medicine II (Neuropediatrics, Social Pediatrics), University Medical Centre Schleswig-Holstein, Kiel, Germany
| | - Laure Mazzola
- Neurology Department, University Hospital, Saint-Etienne, France
- NeuroPain Lab, Lyon Neuroscience Research Centre, CRNL-INSERM U 1028/CNRS UMR 5292, University of Lyon, Lyon, France
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Gao L, Li X, Li H, Zhang H, Liu X, Yang J. Associations of glymphatic function with structural network and cognition in self-limited epilepsy with centrotemporal spikes. Seizure 2024; 120:104-109. [PMID: 38941800 DOI: 10.1016/j.seizure.2024.06.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 06/09/2024] [Accepted: 06/21/2024] [Indexed: 06/30/2024] Open
Abstract
PURPOSE To investigate glymphatic function by Virchow-Robin space (VRS) counts and volume in patients with newly diagnosed self-limited epilepsy with centrotemporal spikes (SeLECTS) and evaluate its relationship with structural connectivity and cognitive impairment. METHODS Thirty-two children with SeLECTS and thirty-two age- and sex-matched typically developing (TD) children were enrolled in this study. VRS counts and volume were quantified. Structural networks were constructed and the topological metrics were analyzed. Wechsler Intelligence Scale (WISC) was used to assess cognitive function in all participants. Correlation analysis assessed the association between VRS counts and volume, network connectivity, and cognitive impairment. Mediation effects of topological metrics of the structural networks on the relationship between glymphatic function and cognitive impairment were explored. RESULTS Patients with SeLECTS showed a higher VRS counts, VRS volume, and global shortest path length (Lp); they also showed a lower global efficiency (Eg). VRS counts and volume were significantly correlated with full-scale intelligence quotient (FIQ) (r_VRS counts = -0.520, r_VRS volume = -0.639), performance intelligence quotient (PIQ) (r_VRS counts = -0.693, r_VRS volume = -0.597), verbal intelligence quotient (VIQ) (r_VRS counts = -0.713, r_VRS volume = -0.699), Eg (r_VRS counts = -0.499, r_VRS volume = -0.490), and Lp (r_VRS volume = 0.671) in patients with SeLECTS. Eg mediated 24.59% of the effects for the relationship between VRS volume and FIQ. CONCLUSION Glymphatic function may be impaired in SeLECTS reflected by VRS counts and volume. Glymphatic dysfunction may result in cognitive impairment by disrupting structural connectivity in SeLECTS.
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Affiliation(s)
- Lu Gao
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
| | - Xianjun Li
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Huanfa Li
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Hua Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Xiaohong Liu
- Department of Pediatrics, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Jian Yang
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
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Obaid S, Guberman GI, St-Onge E, Campbell E, Edde M, Lamsam L, Bouthillier A, Weil AG, Daducci A, Rheault F, Nguyen DK, Descoteaux M. Progressive remodeling of structural networks following surgery for operculo-insular epilepsy. Front Neurol 2024; 15:1400601. [PMID: 39144703 PMCID: PMC11322451 DOI: 10.3389/fneur.2024.1400601] [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: 03/13/2024] [Accepted: 07/15/2024] [Indexed: 08/16/2024] Open
Abstract
Introduction Operculo-insular epilepsy (OIE) is a rare condition amenable to surgery in well-selected cases. Despite the high rate of neurological complications associated with OIE surgery, most postoperative deficits recover fully and rapidly. We provide insights into this peculiar pattern of functional recovery by investigating the longitudinal reorganization of structural networks after surgery for OIE in 10 patients. Methods Structural T1 and diffusion-weighted MRIs were performed before surgery (t0) and at 6 months (t1) and 12 months (t2) postoperatively. These images were processed with an original, comprehensive structural connectivity pipeline. Using our method, we performed comparisons between the t0 and t1 timepoints and between the t1 and t2 timepoints to characterize the progressive structural remodeling. Results We found a widespread pattern of postoperative changes primarily in the surgical hemisphere, most of which consisted of reductions in connectivity strength (CS) and regional graph theoretic measures (rGTM) that reflect local connectivity. We also observed increases in CS and rGTMs predominantly in regions located near the resection cavity and in the contralateral healthy hemisphere. Finally, most structural changes arose in the first six months following surgery (i.e., between t0 and t1). Discussion To our knowledge, this study provides the first description of postoperative structural connectivity changes following surgery for OIE. The ipsilateral reductions in connectivity unveiled by our analysis may result from the reversal of seizure-related structural alterations following postoperative seizure control. Moreover, the strengthening of connections in peri-resection areas and in the contralateral hemisphere may be compatible with compensatory structural plasticity, a process that could contribute to the recovery of functions seen following operculo-insular resections for focal epilepsy.
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Affiliation(s)
- Sami Obaid
- Department of Neurosciences, University of Montreal, Montreal, QC, Canada
- University of Montreal Hospital Research Center (CRCHUM), Montreal, QC, Canada
- Division of Neurosurgery, Department of Surgery, University of Montreal Hospital Center (CHUM), Montreal, QC, Canada
- Sherbrooke Connectivity Imaging Lab (SCIL), Sherbrooke University, Sherbrooke, QC, Canada
| | - Guido I. Guberman
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Etienne St-Onge
- Department of Computer Science and Engineering, Université du Québec en Outaouais, Montreal, QC, Canada
| | - Emma Campbell
- Department of Psychology, University of Montreal, Montreal, QC, Canada
| | - Manon Edde
- Sherbrooke Connectivity Imaging Lab (SCIL), Sherbrooke University, Sherbrooke, QC, Canada
| | - Layton Lamsam
- Department of Neurosurgery, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Alain Bouthillier
- Division of Neurosurgery, Department of Surgery, University of Montreal Hospital Center (CHUM), Montreal, QC, Canada
| | - Alexander G. Weil
- Department of Neurosciences, University of Montreal, Montreal, QC, Canada
- Division of Pediatric Neurosurgery, Department of Surgery, Sainte Justine Hospital, University of Montreal, Montreal, QC, Canada
| | | | - François Rheault
- Medical Imaging and Neuroimaging (MINi) Lab, Sherbrooke University, Sherbrooke, QC, Canada
| | - Dang K. Nguyen
- Department of Neurosciences, University of Montreal, Montreal, QC, Canada
- University of Montreal Hospital Research Center (CRCHUM), Montreal, QC, Canada
- Division of Neurology, University of Montreal Hospital Center (CHUM), Montreal, QC, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Sherbrooke University, Sherbrooke, QC, Canada
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Wirsich J, Iannotti GR, Ridley B, Shamshiri EA, Sheybani L, Grouiller F, Bartolomei F, Seeck M, Lazeyras F, Ranjeva JP, Guye M, Vulliemoz S. Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy. Netw Neurosci 2024; 8:466-485. [PMID: 38952816 PMCID: PMC11142634 DOI: 10.1162/netn_a_00362] [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: 05/22/2023] [Accepted: 01/17/2024] [Indexed: 07/03/2024] Open
Abstract
Whole-brain functional connectivity networks (connectomes) have been characterized at different scales in humans using EEG and fMRI. Multimodal epileptic networks have also been investigated, but the relationship between EEG and fMRI defined networks on a whole-brain scale is unclear. A unified multimodal connectome description, mapping healthy and pathological networks would close this knowledge gap. Here, we characterize the spatial correlation between the EEG and fMRI connectomes in right and left temporal lobe epilepsy (rTLE/lTLE). From two centers, we acquired resting-state concurrent EEG-fMRI of 35 healthy controls and 34 TLE patients. EEG-fMRI data was projected into the Desikan brain atlas, and functional connectomes from both modalities were correlated. EEG and fMRI connectomes were moderately correlated. This correlation was increased in rTLE when compared to controls for EEG-delta/theta/alpha/beta. Conversely, multimodal correlation in lTLE was decreased in respect to controls for EEG-beta. While the alteration was global in rTLE, in lTLE it was locally linked to the default mode network. The increased multimodal correlation in rTLE and decreased correlation in lTLE suggests a modality-specific lateralized differential reorganization in TLE, which needs to be considered when comparing results from different modalities. Each modality provides distinct information, highlighting the benefit of multimodal assessment in epilepsy.
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Affiliation(s)
- Jonathan Wirsich
- EEG and Epilepsy Unit, Division of Neurology, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Giannina Rita Iannotti
- EEG and Epilepsy Unit, Division of Neurology, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Ben Ridley
- Aix-Marseille Univ, CNRS, CRMBM 7339, Marseille, France
- AP-HM CHU Timone, CEMEREM, Marseille, France
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Elhum A. Shamshiri
- EEG and Epilepsy Unit, Division of Neurology, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Laurent Sheybani
- EEG and Epilepsy Unit, Division of Neurology, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
- UCL Queen Square Institute of Neurology, Queen Square, London, UK
| | - Frédéric Grouiller
- Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland
| | - Fabrice Bartolomei
- Aix-Marseille Univ, INS, INSERM, UMR 1106, Marseille, France
- AP-HM CHU Timone, Service d’épileptologie, Marseille, France
| | - Margitta Seeck
- EEG and Epilepsy Unit, Division of Neurology, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - François Lazeyras
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Jean-Philippe Ranjeva
- Aix-Marseille Univ, CNRS, CRMBM 7339, Marseille, France
- AP-HM CHU Timone, CEMEREM, Marseille, France
| | - Maxime Guye
- Aix-Marseille Univ, CNRS, CRMBM 7339, Marseille, France
- AP-HM CHU Timone, CEMEREM, Marseille, France
| | - Serge Vulliemoz
- EEG and Epilepsy Unit, Division of Neurology, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
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Dam S, Batail JM, Robert GH, Drapier D, Maurel P, Coloigner J. Structural Brain Connectivity and Treatment Improvement in Mood Disorder. Brain Connect 2024; 14:239-251. [PMID: 38534988 DOI: 10.1089/brain.2023.0063] [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] [Indexed: 04/25/2024] Open
Abstract
Background: The treatment of depressive episodes is well established, with clearly demonstrated effectiveness of antidepressants and psychotherapies. However, more than one-third of depressed patients do not respond to treatment. Identifying the brain structural basis of treatment-resistant depression could prevent useless pharmacological prescriptions, adverse events, and lost therapeutic opportunities. Methods: Using diffusion magnetic resonance imaging, we performed structural connectivity analyses on a cohort of 154 patients with mood disorder (MD) and 77 sex- and age-matched healthy control (HC) participants. To assess illness improvement, the patients with MD went through two clinical interviews at baseline and at 6-month follow-up and were classified based on the Clinical Global Impression-Improvement score into improved or not-improved (NI). First, the threshold-free network-based statistics (NBS) was conducted to measure the differences in regional network architecture. Second, nonparametric permutations tests were performed on topological metrics based on graph theory to examine differences in connectome organization. Results: The threshold-free NBS revealed impaired connections involving regions of the basal ganglia in patients with MD compared with HC. Significant increase of local efficiency and clustering coefficient was found in the lingual gyrus, insula, and amygdala in the MD group. Compared with the NI, the improved displayed significantly reduced network integration and segregation, predominately in the default-mode regions, including the precuneus, middle temporal lobe, and rostral anterior cingulate. Conclusions: This study highlights the involvement of regions belonging to the basal ganglia, the fronto-limbic network, and the default mode network, leading to a better understanding of MD disease and its unfavorable outcome.
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Affiliation(s)
- Sébastien Dam
- Univ Rennes, Inria, CNRS, IRISA, INSERM, Empenn U1228 ERL, Rennes, France
| | - Jean-Marie Batail
- Academic Psychiatry Department, Centre Hospitalier Guillaume Régnier, Rennes, France
- CIC 1414, CHU de Rennes, INSERM, Rennes, France
| | - Gabriel H Robert
- Univ Rennes, Inria, CNRS, IRISA, INSERM, Empenn U1228 ERL, Rennes, France
- Academic Psychiatry Department, Centre Hospitalier Guillaume Régnier, Rennes, France
- CIC 1414, CHU de Rennes, INSERM, Rennes, France
| | - Dominique Drapier
- Academic Psychiatry Department, Centre Hospitalier Guillaume Régnier, Rennes, France
- CIC 1414, CHU de Rennes, INSERM, Rennes, France
| | - Pierre Maurel
- Univ Rennes, Inria, CNRS, IRISA, INSERM, Empenn U1228 ERL, Rennes, France
| | - Julie Coloigner
- Univ Rennes, Inria, CNRS, IRISA, INSERM, Empenn U1228 ERL, Rennes, France
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Zimmermann MLM, Breedt LC, Centeno EGZ, Reijneveld JC, Santos FAN, Stam CJ, van Lingen MR, Schoonheim MM, Hillebrand A, Douw L. The relationship between pathological brain activity and functional network connectivity in glioma patients. J Neurooncol 2024; 166:523-533. [PMID: 38308803 PMCID: PMC10876827 DOI: 10.1007/s11060-024-04577-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 01/17/2024] [Indexed: 02/05/2024]
Abstract
PURPOSE Glioma is associated with pathologically high (peri)tumoral brain activity, which relates to faster progression. Functional connectivity is disturbed locally and throughout the entire brain, associating with symptomatology. We, therefore, investigated how local activity and network measures relate to better understand how the intricate relationship between the tumor and the rest of the brain may impact disease and symptom progression. METHODS We obtained magnetoencephalography in 84 de novo glioma patients and 61 matched healthy controls. The offset of the power spectrum, a proxy of neuronal activity, was calculated for 210 cortical regions. We calculated patients' regional deviations in delta, theta and lower alpha network connectivity as compared to controls, using two network measures: clustering coefficient (local connectivity) and eigenvector centrality (integrative connectivity). We then tested group differences in activity and connectivity between (peri)tumoral, contralateral homologue regions, and the rest of the brain. We also correlated regional offset to connectivity. RESULTS As expected, patients' (peri)tumoral activity was pathologically high, and patients showed higher clustering and lower centrality than controls. At the group-level, regionally high activity related to high clustering in controls and patients alike. However, within-patient analyses revealed negative associations between regional deviations in brain activity and clustering, such that pathologically high activity coincided with low network clustering, while regions with 'normal' activity levels showed high network clustering. CONCLUSION Our results indicate that pathological activity and connectivity co-localize in a complex manner in glioma. This insight is relevant to our understanding of disease progression and cognitive symptomatology.
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Affiliation(s)
- Mona L M Zimmermann
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Cancer Center Amsterdam, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Lucas C Breedt
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Eduarda G Z Centeno
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Univ. Bordeaux, CNRS, IMN, UMR 5293, Bordeaux, France
| | - Jaap C Reijneveld
- Department of Neurology, Stichting Epilepsie Instellingen Nederland, Heemstede, The Netherlands
| | - Fernando A N Santos
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Dutch Institute for Emergent Phenomena (DIEP), Institute for Advanced Studies, University of Amsterdam, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Clinical Neurophysiology and MEG Center, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Marike R van Lingen
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Linda Douw
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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Lee DA, Lee HJ, Park KM. Structural connectivity as a predictive factor for responsiveness to levetiracetam treatment in epilepsy. Neuroradiology 2024; 66:93-100. [PMID: 38015213 DOI: 10.1007/s00234-023-03261-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 11/23/2023] [Indexed: 11/29/2023]
Abstract
PURPOSE To investigate whether structural connectivity or glymphatic system function is a potential predictive factor for levetiracetam (LEV) response in patients with newly diagnosed epilepsy. METHODS We enrolled patients with newly diagnosed epilepsy who were administered LEV as initial monotherapy and underwent diffusion tensor imaging (DTI) at diagnosis. We categorized the patients into drug response. We used graph theory to calculate the network measures for structural connectivity based on the DTI scans in patients with epilepsy. Additionally, we evaluated glymphatic system function by calculating the DTI analysis along the perivascular space (DTI-ALPS) index based on DTI scans. RESULTS We enrolled 84 patients with epilepsy. The clinical factors and DTI-ALPS index did not differ between the groups. However, some of the structural connectivity measures significantly differ between the groups. The poor responders exhibited a higher mean clustering coefficient, global efficiency, and small-worldness index than the good responders (p = 0.003, p = 0.048, and p = 0.038, respectively). In the receiver operating characteristic curve analysis, the mean clustering coefficient exhibited the highest performance in predicting the responsiveness to LEV (area under the curve of 0.677). In the multiple logistic regression analysis, the mean clustering coefficient of the structural connectivity measures was the only significant predictor of LEV response (p = 0.014). Furthermore, in the survival analysis, the mean clustering coefficient was the only significant predictor of LEV response (p = 0.026). CONCLUSION We demonstrated that structural connectivity is a potential predictive factor for responsiveness to LEV treatment in patients with newly diagnosed epilepsy.
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Affiliation(s)
- Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Korea
| | - Ho-Joon Lee
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Korea.
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Lu M, Guo Z, Gao Z. Effect of intracranial electrical stimulation on dynamic functional connectivity in medically refractory epilepsy. Front Hum Neurosci 2023; 17:1295326. [PMID: 38178992 PMCID: PMC10765510 DOI: 10.3389/fnhum.2023.1295326] [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: 09/16/2023] [Accepted: 11/21/2023] [Indexed: 01/06/2024] Open
Abstract
Objective The objective of this study was to explore the distributed network effects of intracranial electrical stimulation in patients with medically refractory epilepsy using dynamic functional connectivity (dFC) and graph indicators. Methods The time-varying connectivity patterns of dFC (state-based metrics) as well as topological properties of static functional connectivity (sFC) and dFC (graph indicators) were assessed before and after the intracranial electrical stimulation. The sliding window method and k-means clustering were used for the analysis of dFC states, which were characterized by connectivity strength, occupancy rate, dwell time, and transition. Graph indicators for sFC and dFC were obtained using group statistical tests. Results DFCs were clustered into two connectivity configurations: a strongly connected state (state 1) and a sparsely connected state (state 2). After electrical stimulation, the dwell time and occupancy rate of state 1 decreased, while that of state 2 increased. Connectivity strengths of both state 1 and state 2 decreased. For graph indicators, the clustering coefficient, k-core, global efficiency, and local efficiency of patients showed a significant decrease, but the brain networks of patients exhibited higher modularity after electrical stimulation. Especially, for state 1, there was a significant decrease in functional connectivity strength after stimulation within and between the frontal lobe and temporary lobe, both of which are associated with the seizure onset. Conclusion Our findings demonstrated that intracranial electrical stimulation significantly changed the time-varying connectivity patterns and graph indicators of the brain in patients with medically refractory epilepsy. Specifically, the electrical stimulation decreased functional connectivity strength in both local-level and global-level networks. This might provide a mechanism of understanding for the distributed network effects of intracranial electrical stimulation and extend the knowledge of the pathophysiological network of medically refractory epilepsy.
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Affiliation(s)
- Meili Lu
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China
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9
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Lin Q, Li W, Li Y, Liu P, Zhang Y, Gong Q, Zhou D, An D. Aberrant structural rich club organization in temporal lobe epilepsy with focal to bilateral tonic-clonic seizures. Neuroimage Clin 2023; 40:103536. [PMID: 37944396 PMCID: PMC10663961 DOI: 10.1016/j.nicl.2023.103536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/19/2023] [Accepted: 10/31/2023] [Indexed: 11/12/2023]
Abstract
OBJECTIVE The purpose of this study was to assess the differences of topological characteristic and rich club organization between temporal lobe epilepsy (TLE) patients with focal seizure (FS) only and those with focal to bilateral tonic-clonic seizures (FBTCS). METHODS We recruited 130 unilateral TLE patients, of which 57 patients with FS only and 73 patients with both FS and FBTCS, and 68 age- and gender-matched healthy controls (HC). Whole-brain networks were constructed based on diffusion weighted imaging data. Graph theory was applied to quantify the topological network metrics and rich club organization. Network-based statistic (NBS) analysis was administered to investigate the difference in edge-wise connectivity strength. The non-parametric permutation test was applied to evaluate the differences between groups. Benjamini-Hochberg FDR at the alpha of 5% was carried out for multiple comparations. RESULTS In comparison with HC, both the FS and FBTCS group displayed a significant reduction in whole-brain connectivity strength and global efficiency. The FBTCS group showed lower connectivity strength both in the rich club and feeder connections compared to HC. The FS group had lower connectivity strength in the feeder and local connections compared to HC. NBS analysis revealed a wider range of decreased connectivity strength in the FBTCS group, involving 90% of the rich club regions, mainly affecting temporal-subcortical, frontal-parietal, and frontal-temporal lobe, the majority decreasing connections were between temporal lobe and stratum. While the decreased connectivity strength in the FS group were relatively local, involving 50% of rich club regions, mainly concentrated on the temporal-subcortical lobe. CONCLUSIONS Network integration was reduced in TLE. TLE with FBTCS selectively disrupted the rich club regions, while TLE with FS only were more likely to affect the non-rich club regions, emphasizing the contribution of rich club organization to seizure generalization.
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Affiliation(s)
- Qiuxing Lin
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wei Li
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yuming Li
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Peiwen Liu
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yingying Zhang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Dong Zhou
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Dongmei An
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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10
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Woodfield J, Chin RFM, van Schooneveld MMJ, van den Heuvel M, Bastin ME, Braun KPJ. The association of structural connectome efficiency with cognition in children with epilepsy. Epilepsy Behav 2023; 148:109462. [PMID: 37844437 DOI: 10.1016/j.yebeh.2023.109462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/21/2023] [Accepted: 09/26/2023] [Indexed: 10/18/2023]
Abstract
OBJECTIVE Cognitive impairment is common in children with epilepsy (CWE), but understanding the underlying pathological processes is challenging. We aimed to investigate the association of structural brain network organisation with cognition. METHODS This was a retrospective cohort study of CWE without structural brain abnormalities, comparing whole brain network characteristics between those with cognitive impairment and those with intact cognition. We created structural whole-brain connectomes from anatomical and diffusion tensor magnetic resonance imaging using the number of streamlines and tract-averaged fractional anisotropy. We assessed the differences in average path length and global network efficiency between children with cognitive impairment and those without,using multivariable analyses to account for possible clinical group differences. RESULTS Twenty-eight CWE and cognitive impairment had lower whole brain network global efficiency compared with 34 children with intact cognition (0.54, standard deviation (SD):0.003 vs. 0.56, SD:0.002, p < 0.001), which is equivalent to longer normalized network average path lengths (1.14, SD:0.05 vs. 1.10, SD:0.02, p = 0.003). In multivariable logistic regression cognitive impairment was not significantly associated with age of onset, duration of epilepsy, or number of antiseizure medications, but was independently associated with daily seizures (p = 0.04) and normalized average path length (p = 0.007). CONCLUSIONS Higher structural network average path length and lower global network efficiency may be imaging biomarkers of cognitive impairment in epilepsy. Understanding what leads to changes in structural connectivity could aid identification of modifiable risk factors for cognitive impairment. These findings are only applicable to the specific cohort studied, and further confirmation in other cohorts is required.
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Affiliation(s)
- Julie Woodfield
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Department of Clinical Neurosciences, NHS Lothian, Edinburgh, United Kingdom; Muir Maxwell Epilepsy Centre, University of Edinburgh, Edinburgh, United Kingdom.
| | - Richard F M Chin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Muir Maxwell Epilepsy Centre, University of Edinburgh, Edinburgh, United Kingdom; Royal Hospital for Children and Young People, NHS Lothian, Edinburgh, United Kingdom
| | | | - Martijn van den Heuvel
- Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Kees P J Braun
- Department of Paediatric Neurology, University Medical Center Utrecht, Utrecht, The Netherlands
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11
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Boot EM, Omes QPM, Maaijwee N, Schaapsmeerders P, Arntz RM, Rutten-Jacobs LCA, Kessels RPC, de Leeuw FE, Tuladhar AM. Functional brain connectivity in young adults with post-stroke epilepsy. Brain Commun 2023; 5:fcad277. [PMID: 37953839 PMCID: PMC10639092 DOI: 10.1093/braincomms/fcad277] [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: 08/02/2022] [Revised: 08/07/2023] [Accepted: 10/17/2023] [Indexed: 11/14/2023] Open
Abstract
Approximately 1 in 10 young stroke patients (18-50 years) will develop post-stroke epilepsy, which is associated with cognitive impairment. While previous studies have shown altered brain connectivity in patients with epilepsy, little is however known about the changes in functional brain connectivity in young stroke patients with post-stroke epilepsy and their relationship with cognitive impairment. Therefore, we aimed to investigate whether young ischaemic stroke patients have altered functional networks and whether this alteration is related to cognitive impairment. We included 164 participants with a first-ever cerebral infarction at young age (18-50 years), along with 77 age- and sex-matched controls, from the Follow-Up of Transient Ischemic Attack and Stroke patients and Unelucidated Risk Factor Evaluation study. All participants underwent neuropsychological testing and resting-state functional MRI to generate functional connectivity networks. At follow-up (10.5 years after the index event), 23 participants developed post-stroke epilepsy. Graph theoretical analysis revealed functional network reorganization in participants with post-stroke epilepsy, in whom a weaker (i.e. network strength), less-integrated (i.e. global efficiency) and less-segregated (i.e. clustering coefficient and local efficiency) functional network was observed compared with the participants without post-stroke epilepsy group and the controls (P < 0.05). Regional analysis showed a trend towards decreased clustering coefficient, local efficiency and nodal efficiency in contralesional brain regions, including the caudal anterior cingulate cortex, posterior cingulate cortex, precuneus, superior frontal gyrus and insula in participants with post-stroke epilepsy compared with those without post-stroke epilepsy. Furthermore, participants with post-stroke epilepsy more often had impairment in the processing speed domain than the group without post-stroke epilepsy, in whom the network properties of the precuneus were positively associated with processing speed performance. Our findings suggest that post-stroke epilepsy is associated with functional reorganization of the brain network after stroke that is characterized by a weaker, less-integrated and less-segregated brain network in young ischaemic stroke patients compared with patients without post-stroke epilepsy. The contralesional brain regions, which are mostly considered as hub regions, might be particularly involved in the altered functional network and may contribute to cognitive impairment in post-stroke epilepsy patients. Overall, our findings provide additional evidence for a potential role of disrupted functional network as underlying pathophysiological mechanism for cognitive impairment in patients with post-stroke epilepsy.
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Affiliation(s)
- Esther M Boot
- Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Radboud University Medical Centre, Nijmegen 6525GA, The Netherlands
| | - Quinty P M Omes
- Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Radboud University Medical Centre, Nijmegen 6525GA, The Netherlands
| | - Noortje Maaijwee
- Department of Neurology and Neurorehabilitation, Luzerner Kantonsspital Neurocentre, Luzern 16, Switzerland
| | | | - Renate M Arntz
- Department of Neurology, Medisch Spectrum Twente, Enschede 7500 KA, The Netherlands
| | | | - Roy P C Kessels
- Donders Institute for Brain, Cognition and Behaviour, Department of Psychology, Radboud University, Nijmegen 6525 GD, The Netherlands
- Department of Medical Psychology and Radboudumc Alzheimer Centre, Radboud University Medical Centre, Nijmegen 6525 GA, The Netherlands
- Vincent van Gogh Institute for Psychiatry, Venray 5803 AC, The Netherlands
| | - Frank-Erik de Leeuw
- Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Radboud University Medical Centre, Nijmegen 6525GA, The Netherlands
| | - Anil M Tuladhar
- Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Radboud University Medical Centre, Nijmegen 6525GA, The Netherlands
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12
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Asadi-Pooya AA, Farazdaghi M, Asadi-Pooya H, Fazelian K. Attention deficit hyperactivity disorder in patients with seizures: Functional seizures vs. epilepsy. J Clin Neurosci 2023; 115:20-23. [PMID: 37459827 DOI: 10.1016/j.jocn.2023.07.010] [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: 04/23/2023] [Revised: 06/16/2023] [Accepted: 07/10/2023] [Indexed: 08/23/2023]
Abstract
BACKGROUND We investigated the rates of positive screening for attention deficit-hyperactivity disorder (ADHD) in adults with seizures [i.e., focal epilepsy vs. idiopathic generalized epilepsy (IGE) vs. functional seizures (FS)]. We hypothesized that the rates of positive screening for ADHD are different between these three groups of patients. METHODS This was a cross sectional study. Patients, 19 to 55 years of age, with a diagnosis of IGE, focal epilepsy or FS were investigated at the outpatient epilepsy clinic at Shiraz University of Medical Sciences, Shiraz, Iran, from September 2022 until January 2023 and during their follow-up visits. We used the validated Persian version of Adult ADHD Self-Report Scale (ASRS v1.1)15 to investigate and screen for ADHD in these patients. RESULTS Forty patients with focal epilepsy, 40 with IGE, and 40 with FS were included. Attention deficit-hyperactivity disorder (ADHD) screening was positive in 35% of patients with FS, in 30% of those with focal epilepsy (compared with FS, p = 0.633), and in 10% of patients with IGE (compared with FS, p = 0.007). CONCLUSION Adult patients with functional seizures and those with focal epilepsy are at a high risk of self-reporting experiences that could be characteristic of ADHD. Screening tools [e.g., Adult ADHD Self-Report Scale (ASRS v1.1)] are useful to help clinicians address seizure comorbidities such as ADHD. However, a clinical diagnosis of ADHD should be ascertained in a patient with positive screening.
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Affiliation(s)
- Ali A Asadi-Pooya
- Epilepsy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran; Jefferson Comprehensive Epilepsy Center, Department of Neurology, Thomas Jefferson University, Philadelphia, PA, USA.
| | - Mohsen Farazdaghi
- Epilepsy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Hanieh Asadi-Pooya
- Epilepsy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Khatereh Fazelian
- Epilepsy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
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Abstract
Introduction: Regional hypermetabolism in Alzheimer's disease (AD), especially in the cerebellum, has been consistently observed but often neglected as an artefact produced by the commonly used proportional scaling procedure in the statistical parametric mapping. We hypothesize that the hypermetabolic regions are also important in disease pathology in AD. Methods: Using fluorodeoxyglucose (FDG)-positron emission tomography (PET) images from 88 AD subjects and 88 age-sex matched normal controls (NL) from the publicly available Alzheimer's Disease Neuroimaging Initiative database, we developed a general linear model-based classifier that differentiated AD patients from normal individuals (sensitivity = 87.50%, specificity = 82.95%). We constructed region-region group-wise correlation matrices and evaluated differences in network organization by using the graph theory analysis between AD and control subjects. Results: We confirmed that hypermetabolism found in AD is not an artefact by replicating it using white matter as the reference region. The role of the hypermetabolic regions has been further investigated by using the graph theory. The differences in betweenness centrality (BC) between AD and NL network were correlated with region weights of FDG PET-based AD classifier. In particular, the hypermetabolism in cerebellum was accompanied with higher BC. The brain regions with higher BC in AD network showed a progressive increase in FDG uptake over 2 years in prodromal AD patients (n = 39). Discussion: This study suggests that hypermetabolism found in AD may play an important role in forming the AD-related metabolic network. In particular, hypermetabolic cerebellar regions represent a good candidate for further investigation in altered network organization in AD.
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Affiliation(s)
- Vinay Gupta
- Graduate Program in Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Sciences Centre, Winnipeg, Canada
| | - Samuel Booth
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Sciences Centre, Winnipeg, Canada
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada
| | - Ji Hyun Ko
- Graduate Program in Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Sciences Centre, Winnipeg, Canada
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada
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Lee DA, Lee H, Kim SE, Park KM. Brain networks and epilepsy development in patients with Alzheimer disease. Brain Behav 2023; 13:e3152. [PMID: 37416994 PMCID: PMC10454249 DOI: 10.1002/brb3.3152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/26/2023] [Accepted: 06/26/2023] [Indexed: 07/08/2023] Open
Abstract
INTRODUCTION This study aimed to investigate the association between brain networks and epilepsy development in patients with Alzheimer disease (AD). METHODS We enrolled patients newly diagnosed with AD at our hospital who underwent three-dimensional T1-weighted magnetic resonance imaging at the time of AD diagnosis and included healthy controls. We obtained the cortical, subcortical, and thalamic nuclei structural volumes using FreeSurfer and applied graph theory to obtain the global brain network and intrinsic thalamic network based on the structural volumes using BRAPH. RESULTS We enrolled 25 and 56 patients with AD with and without epilepsy development, respectively. We also included 45 healthy controls. The global brain network differed between the patients with AD and healthy controls. The local efficiency (2.026 vs. 3.185, p = .048) and mean clustering coefficient (0.449 vs. 1.321, p = .024) were lower, whereas the characteristic path length (0.449 vs. 1.321, p = .048) was higher in patients with AD than in healthy controls. Both global and intrinsic thalamic networks were significantly different between AD patients with and without epilepsy development. In the global brain network, local efficiency (1.340 vs. 2.401, p = .045), mean clustering coefficient (0.314 vs. 0.491, p = .045), average degree (27.442 vs. 41.173, p = .045), and assortative coefficient (-0.041 vs. -0.011, p = .045) were lower, whereas the characteristic path length (2.930 vs. 2.118, p = .045) was higher in patients with AD with epilepsy development than in those without. In the intrinsic thalamic network, the mean clustering coefficient (0.646 vs. 0.460, p = .048) was higher, whereas the characteristic path length (1.645 vs. 2.232, p = .048) was lower in patients with AD with epilepsy development than in those without. CONCLUSION We found that the global brain network differs between patients with AD and healthy controls. In addition, we demonstrated significant associations between brain networks (both global brain and intrinsic thalamic networks) and epilepsy development in patients with AD.
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Affiliation(s)
- Dong Ah Lee
- Department of Neurology, Haeundae Paik HospitalInje University College of MedicineBusanRepublic of Korea
| | - Ho‐Joon Lee
- Department of Radiology, Haeundae Paik HospitalInje University College of MedicineBusanRepublic of Korea
| | - Si Eun Kim
- Department of Neurology, Haeundae Paik HospitalInje University College of MedicineBusanRepublic of Korea
| | - Kang Min Park
- Department of Neurology, Haeundae Paik HospitalInje University College of MedicineBusanRepublic of Korea
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15
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Lucas A, Cornblath EJ, Sinha N, Caciagli L, Hadar P, Tranquille A, Stein JM, Das S, Davis KA. Improved Seizure Onset-Zone Lateralization in Temporal Lobe Epilepsy using 7T Resting-State fMRI: A Direct Comparison with 3T. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.06.23291025. [PMID: 37333141 PMCID: PMC10275004 DOI: 10.1101/2023.06.06.23291025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Objective Resting-state functional magnetic resonance imaging (rs-fMRI) at ultra high-field strengths (≥7T) is known to provide superior signal-to-noise and statistical power than comparable acquisitions at lower field strengths. In this study, we aim to provide a direct comparison of the seizure onset-zone (SOZ) lateralizing ability of 7T rs-fMRI and 3T rs-fMRI. Methods We investigated a cohort of 70 temporal lobe epilepsy (TLE) patients. A paired cohort of 19 patients had 3T and 7T rs-fMRI acquisitions for direct comparison between the two field strengths. Forty-three patients had only 3T, and 8 patients had only 7T rs-fMRI acquisitions. We quantified the functional connectivity between the hippocampus and other nodes within the default mode network (DMN) using seed-to-voxel connectivity, and measured how hippocampo-DMN connectivity could inform SOZ lateralization at 7T and 3T field strengths. Results Differences between hippocampo-DMN connectivity ipsilateral and contralateral to the SOZ were significantly higher at 7T (pFDR=0.008) than at 3T (pFDR=0.80) when measured in the same subjects. We found that our ability to lateralize the SOZ, by distinguishing subjects with left TLE from subjects with right TLE, was superior at 7T (AUC = 0.97) than 3T (AUC = 0.68). Our findings were reproduced in extended cohorts of subjects scanned at either 3T or 7T. Our rs-fMRI findings at 7T, but not 3T, are consistent and highly correlated (Spearman Rho=0.65) with clinical FDG-PET lateralizing hypometabolism. Significance We show superior SOZ lateralization in TLE patients when using 7T relative to 3T rs-fMRI, supporting the adoption of high-field strength functional imaging in the epilepsy presurgical evaluation.
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Affiliation(s)
- Alfredo Lucas
- Perelman School of Medicine, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
| | | | | | | | - Peter Hadar
- Department of Neurology, Massachussets General Hospital (work conducted while at the University of Pennsylvania)
| | | | - Joel M Stein
- Department of Radiology, University of Pennsylvania
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Lucas A, Cornblath EJ, Sinha N, Hadar P, Caciagli L, Keller SS, Bonilha L, Shinohara RT, Stein JM, Das S, Gleichgerrcht E, Davis KA. Resting state functional connectivity demonstrates increased segregation in bilateral temporal lobe epilepsy. Epilepsia 2023; 64:1305-1317. [PMID: 36855286 DOI: 10.1111/epi.17565] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 02/26/2023] [Accepted: 02/27/2023] [Indexed: 03/02/2023]
Abstract
OBJECTIVE Temporal lobe epilepsy (TLE) is the most common type of focal epilepsy. An increasingly identified subset of patients with TLE consists of those who show bilaterally independent temporal lobe seizures. The purpose of this study was to leverage network neuroscience to better understand the interictal whole brain network of bilateral TLE (BiTLE). METHODS In this study, using a multicenter resting state functional magnetic resonance imaging (rs-fMRI) data set, we constructed whole-brain functional networks of 19 patients with BiTLE, and compared them to those of 75 patients with unilateral TLE (UTLE). We quantified resting-state, whole-brain topological properties using metrics derived from network theory, including clustering coefficient, global efficiency, participation coefficient, and modularity. For each metric, we computed an average across all brain regions, and iterated this process across network densities. Curves of network density vs each network metric were compared between groups. Finally, we derived a combined metric, which we term the "integration-segregation axis," by combining whole-brain average clustering coefficient and global efficiency curves, and applying principal component analysis (PCA)-based dimensionality reduction. RESULTS Compared to UTLE, BiTLE had decreased global efficiency (p = .031), and decreased whole brain average participation coefficient across a range of network densities (p = .019). Modularity maximization yielded a larger number of smaller communities in BiTLE than in UTLE (p = .020). Differences in network properties separate BiTLE and UTLE along the integration-segregation axis, with regions within the axis having a specificity of up to 0.87 for BiTLE. Along the integration-segregation axis, UTLE patients with poor surgical outcomes were distributed in the same regions as BiTLE, and network metrics confirmed similar patterns of increased segregation in both BiTLE and poor outcome UTLE. SIGNIFICANCE Increased interictal whole-brain network segregation, as measured by rs-fMRI, is specific to BiTLE, as well as poor surgical outcome UTLE, and may assist in non-invasively identifying this patient population prior to intracranial electroencephalography or device implantation.
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Affiliation(s)
- Alfredo Lucas
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Eli J Cornblath
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Nishant Sinha
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Peter Hadar
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Leonardo Bonilha
- Department of Neurology, Emory University, Atlanta, Georgia, USA
| | - Russell T Shinohara
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Joel M Stein
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sandhitsu Das
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ezequiel Gleichgerrcht
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Kathryn A Davis
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Shahabi H, Nair DR, Leahy RM. Multilayer brain networks can identify the epileptogenic zone and seizure dynamics. eLife 2023; 12:e68531. [PMID: 36929752 PMCID: PMC10065796 DOI: 10.7554/elife.68531] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 03/16/2023] [Indexed: 03/18/2023] Open
Abstract
Seizure generation, propagation, and termination occur through spatiotemporal brain networks. In this paper, we demonstrate the significance of large-scale brain interactions in high-frequency (80-200Hz) for the identification of the epileptogenic zone (EZ) and seizure evolution. To incorporate the continuity of neural dynamics, here we have modeled brain connectivity constructed from stereoelectroencephalography (SEEG) data during seizures using multilayer networks. After introducing a new measure of brain connectivity for temporal networks, named multilayer eigenvector centrality (mlEVC), we applied a consensus hierarchical clustering on the developed model to identify the EZ as a cluster of nodes with distinctive brain connectivity in the ictal period. Our algorithm could successfully predict electrodes inside the resected volume as EZ for 88% of participants, who all were seizure-free for at least 12 months after surgery. Our findings illustrated significant and unique desynchronization between EZ and the rest of the brain in the early to mid-seizure. We showed that aging and the duration of epilepsy intensify this desynchronization, which can be the outcome of abnormal neuroplasticity. Additionally, we illustrated that seizures evolve with various network topologies, confirming the existence of different epileptogenic networks in each patient. Our findings suggest not only the importance of early intervention in epilepsy but possible factors that correlate with disease severity. Moreover, by analyzing the propagation patterns of different seizures, we demonstrate the necessity of collecting sufficient data for identifying epileptogenic networks.
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Affiliation(s)
- Hossein Shahabi
- Signal and Image Processing Institute, University of Southern CaliforniaLos AngelesUnited States
| | - Dileep R Nair
- Epilepsy Center, Cleveland Clinic Neurological InstituteClevelandUnited States
| | - Richard M Leahy
- Signal and Image Processing Institute, University of Southern CaliforniaLos AngelesUnited States
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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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19
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Zheng B, Liu DD, Theyel BB, Abdulrazeq H, Kimata AR, Lauro PM, Asaad WF. Thalamic neuromodulation in epilepsy: A primer for emerging circuit-based therapies. Expert Rev Neurother 2023; 23:123-140. [PMID: 36731858 DOI: 10.1080/14737175.2023.2176752] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
INTRODUCTION Epilepsy is a common, often debilitating disease of hyperexcitable neural networks. While medically intractable cases may benefit from surgery, there may be no single, well-localized focus for resection or ablation. In such cases, approaching the disease from a network-based perspective may be beneficial. AREAS COVERED Herein, the authors provide a narrative review of normal thalamic anatomy and physiology and propose general strategies for preventing and/or aborting seizures by modulating this structure. Additionally, they make specific recommendations for targeting the thalamus within different contexts, motivated by a more detailed discussion of its distinct nuclei and their respective connectivity. By describing important principles governing thalamic function and its involvement in seizure networks, the authors aim to provide a primer for those now entering this fast-growing field of thalamic neuromodulation for epilepsy. EXPERT OPINION The thalamus is critically involved with the function of many cortical and subcortical areas, suggesting it may serve as a compelling node for preventing or aborting seizures, and so it has increasingly been targeted for the surgical treatment of epilepsy. As various thalamic neuromodulation strategies for seizure control are developed, there is a need to ground such interventions in a mechanistic, circuit-based framework.
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Affiliation(s)
- Bryan Zheng
- The Warren Alpert Medical School of Brown University, Providence, RI, USA.,Department of Neurosurgery, Rhode Island Hospital, Providence, RI, USA
| | - David D Liu
- The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Brian B Theyel
- Department of Psychiatry, Rhode Island Hospital, Providence, RI, USA.,Department of Neuroscience, Brown University, Providence, RI, USA
| | - Hael Abdulrazeq
- Department of Neurosurgery, Rhode Island Hospital, Providence, RI, USA
| | - Anna R Kimata
- The Warren Alpert Medical School of Brown University, Providence, RI, USA.,Department of Neurosurgery, Rhode Island Hospital, Providence, RI, USA
| | - Peter M Lauro
- The Warren Alpert Medical School of Brown University, Providence, RI, USA.,Department of Neuroscience, Brown University, Providence, RI, USA
| | - Wael F Asaad
- Department of Neurosurgery, Rhode Island Hospital, Providence, RI, USA.,Department of Neuroscience, Brown University, Providence, RI, USA.,The Carney Institute for Brain Science, Brown University, Providence, RI, USA.,The Norman Prince Neurosciences Institute, Rhode Island Hospital, Providence, RI, USA
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20
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Cousyn L, Messaoud RB, Lehongre K, Frazzini V, Lambrecq V, Adam C, Mathon B, Navarro V, Chavez M. Daily resting-state intracranial EEG connectivity for seizure risk forecasts. Epilepsia 2023; 64:e23-e29. [PMID: 36481871 DOI: 10.1111/epi.17480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 12/01/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022]
Abstract
Forecasting seizure risk aims to detect proictal states in which seizures would be more likely to occur. Classical seizure prediction models are trained over long-term electroencephalographic (EEG) recordings to detect specific preictal changes for each seizure, independently of those induced by shifts in states of vigilance. A daily single measure-during a vigilance-controlled period-to estimate the risk of upcoming seizure(s) would be more convenient. Here, we evaluated whether intracranial EEG connectivity (phase-locking value), estimated from daily vigilance-controlled resting-state recordings, could allow distinguishing interictal (no seizure) from preictal (seizure within the next 24 h) states. We also assessed its relevance for daily forecasts of seizure risk using machine learning models. Connectivity in the theta band was found to provide the best prediction performances (area under the curve ≥ .7 in 80% of patients), with accurate daily and prospective probabilistic forecasts (mean Brier score and Brier skill score of .13 and .72, respectively). More efficient ambulatory clinical application could be considered using mobile EEG or chronic implanted devices.
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Affiliation(s)
- Louis Cousyn
- Department of Neurology, Epilepsy Unit, Public Hospital Network of Paris, Pitié-Salpêtrière Hospital, Paris, France
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
- Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France
- Sorbonne University, Paris, France
| | - Rémy Ben Messaoud
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
- INRIA, ARAMIS Project-Team, Paris, France
| | - Katia Lehongre
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
| | - Valerio Frazzini
- Department of Neurology, Epilepsy Unit, Public Hospital Network of Paris, Pitié-Salpêtrière Hospital, Paris, France
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
- Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France
- Sorbonne University, Paris, France
| | - Virginie Lambrecq
- Department of Neurology, Epilepsy Unit, Public Hospital Network of Paris, Pitié-Salpêtrière Hospital, Paris, France
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
- Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France
- Sorbonne University, Paris, France
| | - Claude Adam
- Department of Neurology, Epilepsy Unit, Public Hospital Network of Paris, Pitié-Salpêtrière Hospital, Paris, France
- Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France
| | - Bertrand Mathon
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
- Sorbonne University, Paris, France
- Department of Neurosurgery, Public Hospital Network of Paris, Pitié-Salpêtrière Hospital, Paris, France
| | - Vincent Navarro
- Department of Neurology, Epilepsy Unit, Public Hospital Network of Paris, Pitié-Salpêtrière Hospital, Paris, France
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
- Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France
- Sorbonne University, Paris, France
| | - Mario Chavez
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
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21
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Lemoine É, Neves Briard J, Rioux B, Podbielski R, Nauche B, Toffa D, Keezer M, Lesage F, Nguyen DK, Bou Assi E. Computer-assisted analysis of routine electroencephalogram to identify hidden biomarkers of epilepsy: protocol for a systematic review. BMJ Open 2023; 13:e066932. [PMID: 36693684 PMCID: PMC9884857 DOI: 10.1136/bmjopen-2022-066932] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
INTRODUCTION The diagnosis of epilepsy frequently relies on the visual interpretation of the electroencephalogram (EEG) by a neurologist. The hallmark of epilepsy on EEG is the interictal epileptiform discharge (IED). This marker lacks sensitivity: it is only captured in a small percentage of 30 min routine EEGs in patients with epilepsy. In the past three decades, there has been growing interest in the use of computational methods to analyse the EEG without relying on the detection of IEDs, but none have made it to the clinical practice. We aim to review the diagnostic accuracy of quantitative methods applied to ambulatory EEG analysis to guide the diagnosis and management of epilepsy. METHODS AND ANALYSIS The protocol complies with the recommendations for systematic reviews of diagnostic test accuracy by Cochrane. We will search MEDLINE, EMBASE, EBM reviews, IEEE Explore along with grey literature for articles, conference papers and conference abstracts published after 1961. We will include observational studies that present a computational method to analyse the EEG for the diagnosis of epilepsy in adults or children without relying on the identification of IEDs or seizures. The reference standard is the diagnosis of epilepsy by a physician. We will report the estimated pooled sensitivity and specificity, and receiver operating characteristic area under the curve (ROC AUC) for each marker. If possible, we will perform a meta-analysis of the sensitivity and specificity and ROC AUC for each individual marker. We will assess the risk of bias using an adapted QUADAS-2 tool. We will also describe the algorithms used for signal processing, feature extraction and predictive modelling, and comment on the reproducibility of the different studies. ETHICS AND DISSEMINATION Ethical approval was not required. Findings will be disseminated through peer-reviewed publication and presented at conferences related to this field. PROSPERO REGISTRATION NUMBER CRD42022292261.
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Affiliation(s)
- Émile Lemoine
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- Institute of Biomedical Engineering, Ecole Polytechnique de Montreal, Montreal, Québec, Canada
| | - Joel Neves Briard
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
| | - Bastien Rioux
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
| | - Renata Podbielski
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
| | - Bénédicte Nauche
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
| | - Denahin Toffa
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
| | - Mark Keezer
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
| | - Frédéric Lesage
- Institute of Biomedical Engineering, Ecole Polytechnique de Montreal, Montreal, Québec, Canada
| | - Dang K Nguyen
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
| | - Elie Bou Assi
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
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22
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Fan D, Qi L, Yang Z, Luan G, Wang Q. Putative cause of seizure-induced cognitive alterations: The oscillatory reconfiguration of seizure network. Front Neurosci 2023; 17:1126875. [PMID: 36743804 PMCID: PMC9893114 DOI: 10.3389/fnins.2023.1126875] [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: 12/18/2022] [Accepted: 01/03/2023] [Indexed: 01/21/2023] Open
Abstract
Introduction The dynamic reconfiguration of network oscillations is connected with cognitive processes. Changes in how neural networks and signaling pathways work are crucial to how epilepsy and related conditions develop. Specifically, there is evidence that prolonged or recurrent seizures may induce or exacerbate cognitive impairment. However, it still needs to be determined how the seizure brain configures its functional structure to shape the battle of strong local oscillations vs. slow global oscillations in the network to impair cognitive function. Methods In this paper, we aim to deduce the network mechanisms underlying seizure-induced cognitive impairment by comparing the evolution of strong local oscillations with slow global oscillations and their link to the resting state of healthy controls. Here, we construct a dynamically efficient network of pathological seizures by calculating the synchrony and directionality of information flow between nine patients' SEEG signals. Then, using a pattern-based method, we found hierarchical modules in the brain's functional network and measured the functional balance between the network's local strong and slow global oscillations. Results and discussion According to the findings, a tremendous rise in strong local oscillations during seizures and an increase in slow global oscillations after seizures corresponded to the initiation and recovery of cognitive impairment. Specifically, during the interictal period, local strong and slow global oscillations are in metastable balance, which is the same as a normal cognitive process and can be switched easily. During the pre-ictal period, the two show a bimodal pattern of separate peaks that cannot be easily switched, and some flexibility is lost. During the seizure period, a single-peak pattern with negative peaks is showcased, and the network eventually transitions to a very intense strong local oscillation state. These results shed light on the mechanism behind network oscillations in epilepsy-induced cognitive impairment. On the other hand, the differential (similarity) of oscillatory reorganization between the local (non) epileptogenic network and the global network may be an emergency protective mechanism of the brain, preventing the spread of pathological information flow to more healthy brain regions.
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Affiliation(s)
- Denggui Fan
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China
| | - Lixue Qi
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China
| | - Zecheng Yang
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China
| | - Guoming Luan
- Epilepsy Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China,*Correspondence: Guoming Luan,
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing, China
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23
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Ding Y, Guo K, Wang X, Chen M, Li X, Wu Y. Brain functional connectivity and network characteristics changes after vagus nerve stimulation in patients with refractory epilepsy. Transl Neurosci 2023; 14:20220308. [PMID: 37719745 PMCID: PMC10500639 DOI: 10.1515/tnsci-2022-0308] [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: 05/13/2023] [Revised: 08/05/2023] [Accepted: 08/17/2023] [Indexed: 09/19/2023] Open
Abstract
Objective This study aims to investigate the impact of vagus nerve stimulation (VNS) on the connectivity and small-world metrics of brain functional networks during seizure periods. Methods Ten refractory epilepsy patients underwent video encephalographic monitoring before and after VNS treatment. The 2-min electroencephalogram segment containing the ictal was selected for each participant, resulting in a total of 20 min of seizure data. The weighted phase lag index (wPLI) and small-world metrics were calculated for the whole frequency band and different frequency bands (delta, theta, alpha, beta, and gamma). Finally, the relevant metrics were statistically analyzed, and the false discovery rate was used to correct for differences after multiple comparisons. Results In the whole band, the wPLI was notably enhanced, and the network metrics, including degree (D), clustering coefficient (CC), and global efficiency (GE), increased, while characteristic path length (CPL) decreased (P < 0.01). In different frequency bands, the wPLI between the parieto-occipital and frontal regions was significantly strengthened in the delta and beta bands, while the wPLI within the frontal region and between the frontal and parieto-occipital regions were significantly reduced in the beta and gamma bands (P < 0.01). In the low-frequency band (<13 Hz), the small-world metrics demonstrated significantly increased CC, D, and GE, with a significantly decreased CPL, indicating a more efficient network organization. In contrast, in the gamma band, the GE decreased, and the CPL increased, suggesting a shift toward less efficient network organization. Conclusion VNS treatment can significantly change the wPLI and small-world metrics. These findings contribute to a deeper understanding of the impact of VNS therapy on brain networks and provide objective indicators for evaluating the efficacy of VNS.
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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
| | - Xinjun Wang
- 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
| | - Xinxiao Li
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuehui Wu
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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24
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Gao S, Li N, Wang X, Yu Y, Zhao R, Trigo V, Ramalho NC. An Assessment Framework for the Training of General Practitioners and Specialists Based on EPAs. Front Public Health 2022; 10:896097. [PMID: 35875034 PMCID: PMC9300895 DOI: 10.3389/fpubh.2022.896097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeThe purpose of this study is to explore the practicality and feasibility of EPA (level 4 theory) for establishing medical training and service model in China.MethodWe opted for a mixed qualitative and quantitative research method, considering both explanatory and exploratory sequential designs. The qualitative research comprehended focus groups and interviews conducted with two panels of experts. The quantitative research was conducted to collect data about the applicability of international entrustable professional activities (EPAs) pediatrics standards in the Chinese context by organizing a seminar with a sample of 60 pediatricians. A questionnaire was designed with EPAs and distributed within professional networks. Structural equation modeling and statistical analysis were used to process the data.ResultsIn this study, Medical Service-Groups Model (MSGM) with four levels was successfully established to measure the correlation between specialized and general EPAs. As expected, results showed that specialized EPAs were built on top of general EPAs. There may be a mediating mechanism that general EPAs contribute to the lower level of specialization EPAs. In addition, levels 1 and 2 were primarily needed to lay the groundwork for levels 3 and 4, and these higher levels of EPAs were still the most informative for specialized Gastroenterology EPAs.ConclusionsThe diagnosis and treatment level of primary general practitioners, as the basis of the pediatric medical service chain, affected the clinical disposal ability of specialists. The establishment of MSGM provided a theoretical basis for the linkage training of general practitioners and specialist physicians. In future studies, scholars must explore China's EPAs based on unique national conditions.
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Affiliation(s)
- Shenshen Gao
- Departments of Technology Development and Pediatric, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Research and Development Center of Shanghai Municipal Hospitals, Shanghai Shenkang Hospital Development Center, Shanghai, China
- Department of Human Resources Management and Organizational Behavior, ISCTE Instituto Universitário de Lisboa, Av.das Forcas Armadas, Lisboa, Portugal
| | - Na Li
- Departments of Technology Development and Pediatric, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Tropical Diseases, Key Laboratory of Tropical Translational Medicine of Ministry of Education, NHC Key Laboratory of Tropical Disease Control, The Second Affiliated Hospital of Hainan Medical University, School of Tropical Medicine, Hainan Medical University, Haikou, China
| | - Xinqiong Wang
- Departments of Technology Development and Pediatric, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yi Yu
- Departments of Technology Development and Pediatric, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ren Zhao
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Ren Zhao
| | - Virgínia Trigo
- Department of Human Resources Management and Organizational Behavior, ISCTE Instituto Universitário de Lisboa, Av.das Forcas Armadas, Lisboa, Portugal
- School of Health Services Management, Southern Medical University, Guangzhou, China
- Virgínia Trigo
| | - Nelson Campos Ramalho
- Department of Human Resources Management and Organizational Behavior, ISCTE Instituto Universitário de Lisboa, Av.das Forcas Armadas, Lisboa, Portugal
- School of Health Services Management, Southern Medical University, Guangzhou, China
- *Correspondence: Nelson Campos Ramalho
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25
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Tsai ML, Wang CC, Lee FC, Peng SJ, Chang H, Tseng SH. Resting-State EEG Functional Connectivity in Children with Rolandic Spikes with or without Clinical Seizures. Biomedicines 2022; 10:biomedicines10071553. [PMID: 35884857 PMCID: PMC9312817 DOI: 10.3390/biomedicines10071553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 06/26/2022] [Accepted: 06/28/2022] [Indexed: 11/16/2022] Open
Abstract
Alterations in dynamic brain network function are increasingly recognized in epilepsy. Benign childhood epilepsy with centrotemporal spikes (BECTS), or benign rolandic seizures, is the most common idiopathic focal epilepsy in children. In this study, we analyzed EEG functional connectivity (FC) among children with rolandic spikes with or without clinical seizures as compared to controls, to investigate the relationship between FC and clinical parameters in children with rolandic spikes. The FC analysis based on graph theory and network-based statistics in different frequency bands evaluated global efficiency, clustering coefficient, betweenness centrality, and nodal strength in four frequency bands. Similar to BECTS patients with seizures, children with rolandic spikes without seizures had significantly increased global efficiency, mean clustering coefficient, mean nodal strength, and connectivity strength, specifically in the theta frequency band at almost all proportional thresholds, compared with age-matched controls. Decreased mean betweenness centrality was only present in BECTS patients with seizures. Age at seizure onset was significantly positively associated with the strength of EEG-FC. The decreased function of betweenness centrality was only presented in BECTS patients with clinical seizures, suggesting weaker local connectivity may lower the seizure threshold. These findings may affect treatment policy in children with rolandic spikes.
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Affiliation(s)
- Min-Lan Tsai
- Division of Pediatric Neurology, Department of Pediatrics, Taipei Medical University Hospital, Taipei Medical University, Taipei 110301, Taiwan; (M.-L.T.); (F.-C.L.); (H.C.)
- Department of Pediatrics, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan
| | - Chuang-Chin Wang
- School of Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan;
| | - Feng-Chin Lee
- Division of Pediatric Neurology, Department of Pediatrics, Taipei Medical University Hospital, Taipei Medical University, Taipei 110301, Taiwan; (M.-L.T.); (F.-C.L.); (H.C.)
- Department of Pediatrics, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan
| | - Syu-Jyun Peng
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan
- Correspondence: ; Tel.: +886-2-66382736; Fax: +886-2-27321956
| | - Hsi Chang
- Division of Pediatric Neurology, Department of Pediatrics, Taipei Medical University Hospital, Taipei Medical University, Taipei 110301, Taiwan; (M.-L.T.); (F.-C.L.); (H.C.)
- Department of Pediatrics, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan
| | - Sung-Hui Tseng
- Department of Physical Medicine & Rehabilitation, Taipei Medical University Hospital, Taipei 110301, Taiwan;
- Department of Physical Medicine & Rehabilitation, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan
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26
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Khalife MR, Scott RC, Hernan AE. Mechanisms for Cognitive Impairment in Epilepsy: Moving Beyond Seizures. Front Neurol 2022; 13:878991. [PMID: 35645970 PMCID: PMC9135108 DOI: 10.3389/fneur.2022.878991] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
There has been a major emphasis on defining the role of seizures in the causation of cognitive impairments like memory deficits in epilepsy. Here we focus on an alternative hypothesis behind these deficits, emphasizing the mechanisms of information processing underlying healthy cognition characterized as rate, temporal and population coding. We discuss the role of the underlying etiology of epilepsy in altering neural networks thereby leading to both the propensity for seizures and the associated cognitive impairments. In addition, we address potential treatments that can recover the network function in the context of a diseased brain, thereby improving both seizure and cognitive outcomes simultaneously. This review shows the importance of moving beyond seizures and approaching the deficits from a system-level perspective with the guidance of network neuroscience.
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Affiliation(s)
- Mohamed R. Khalife
- Division of Neuroscience, Nemours Children's Health, Wilmington, DE, United States
- Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
| | - Rod C. Scott
- Division of Neuroscience, Nemours Children's Health, Wilmington, DE, United States
- Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
- Institute of Child Health, Neurosciences Unit University College London, London, United Kingdom
| | - Amanda E. Hernan
- Division of Neuroscience, Nemours Children's Health, Wilmington, DE, United States
- Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
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27
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Guo D, Feng L, Yang Z, Li R, Xiao B, Wen S, Du Y, Deng C, Wang X, Liu D, Xie F. Altered Temporal Variations of Functional Connectivity Associated With Surgical Outcomes in Drug-Resistant Temporal Lobe Epilepsy. Front Neurosci 2022; 16:840481. [PMID: 35516805 PMCID: PMC9063407 DOI: 10.3389/fnins.2022.840481] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 03/03/2022] [Indexed: 11/20/2022] Open
Abstract
Background Currently, more than one-third of patients with drug-resistant temporal lobe epilepsy (TLE) continue to develop seizures after resection surgery. Dynamic functional network connectivity (DFNC) analyses, capturing temporal properties of functional connectivity during MRI acquisition, may help us identify unfavorable surgical outcomes. The purpose of this work was to explore the association of DFNC variations of preoperative resting-state MRI and surgical outcomes in patients with drug-resistant TLE. Methods We evaluated 61 patients with TLE matched for age and gender with 51 healthy controls (HC). Patients with TLE were classified as seizure-free (n = 39) and not seizure-free (n = 16) based on the Engel surgical outcome scale. Six patients were unable to confirm the postoperative status and were not included in the subgroup analysis. The DFNC was calculated using group spatial independent component analysis and the sliding window approach. Results Dynamic functional network connectivity analyses suggested two distinct connectivity “States.” The dynamic connectivity state of patients with TLE was different from HC. TLE subgroup analyses showed not seizure-free (NSF) patients spent significantly more time in State II compared to seizure-free (SF) patients and HC. Further, the number of transitions from State II to State I was significantly lower in NSF patients. SF patients had compensatory enhancement of DFNC strengths between default and dorsal attention network, as well as within the default network. While reduced DFNC strengths of within-network and inter-network were both observed in NSF patients, patients with abnormally temporal properties and more extension DFNC strength alterations were less likely to receive seizure freedom. Conclusions Our study indicates that DFNC could offer a better understanding of dynamic neural impairment mechanisms of drug-resistant TLE functional network, epileptic brain network reorganization, and provide an additional preoperative evaluation support for surgical treatment of drug-resistant TLE.
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Affiliation(s)
- Danni Guo
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Li Feng
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Zhiquan Yang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Rong Li
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Bo Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Shirui Wen
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Yangsa Du
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Chijun Deng
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Xuyang Wang
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Dingyang Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Dingyang Liu,
| | - Fangfang Xie
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
- Fangfang Xie,
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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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30
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Lee DA, Park BS, Ko J, Park SH, Park JH, Kim IH, Lee YJ, Park KM. Glymphatic system function in patients with newly diagnosed focal epilepsy. Brain Behav 2022; 12:e2504. [PMID: 35107879 PMCID: PMC8933756 DOI: 10.1002/brb3.2504] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 11/22/2021] [Accepted: 01/01/2022] [Indexed: 12/28/2022] Open
Abstract
INTRODUCTION The aim of this study was to analyze the glymphatic system function and its relationship with clinical characteristics, global diffusion tensor imaging (DTI) parameters, and global structural connectivity in treatment-naïve patients with newly diagnosed focal epilepsy. METHODS This retrospective single-center study investigated patients with focal epilepsy and healthy controls. All participants underwent routine brain magnetic resonance imaging and DTI. DTI analysis along the perivascular space (DTI-ALPS) was used to evaluate glymphatic system function. We also calculated the measures of global DTI parameters, including whole-brain fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), and performed a graph theoretical network analysis to measure global structural connectivity. RESULTS A total of 109 patients with focal epilepsy and 88 healthy controls were analyzed. There were no significant differences in the DTI-ALPS index (1.67 vs. 1.68, p = 0.861) between the groups. However, statistically significant associations were found between the DTI-ALPS index and age (r = -0.242, p = 0.01), FA (r = 0.257, p = 0.007), MD (r = -0.469, p < 0.001), AD (r = -0.303, p = 0.001), RD (r = -0.434, p < 0.001), and the assortative coefficient (r = 0.230, p = 0.016) in patients with focal epilepsy. CONCLUSION The main finding of this study is that DTI-ALPS index is significantly correlated with global DTI parameters and structural connectivity measures of the brain in patients with focal epilepsy. In addition, DTI-ALPS index decreases with age in these patients. We conclude that the DTI-ALPS index can be used to investigate glymphatic system function in patients with focal epilepsy.
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Affiliation(s)
- Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Bong Soo Park
- Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Junghae Ko
- Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Si Hyung Park
- Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Jin-Han Park
- Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Il Hwan Kim
- Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Yoo Jin Lee
- Department of Internal Medicine, 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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31
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Gleichgerrcht E, Keller SS, Bryant L, Moss H, Kellermann TS, Biswas S, Marson AG, Wilmskoetter J, Jensen JH, Bonilha L. High b-value diffusion tractography: Abnormal axonal network organization associated with medication-refractory epilepsy. Neuroimage 2022; 248:118866. [PMID: 34974117 PMCID: PMC8872809 DOI: 10.1016/j.neuroimage.2021.118866] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 11/17/2021] [Accepted: 12/28/2021] [Indexed: 01/22/2023] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography has played a critical role in characterizing patterns of aberrant brain network reorganization among patients with epilepsy. However, the accuracy of dMRI tractography is hampered by the complex biophysical properties of white matter tissue. High b-value diffusion imaging overcomes this limitation by better isolating axonal pathways. In this study, we introduce tractography derived from fiber ball imaging (FBI), a high b-value approach which excludes non-axonal signals, to identify atypical neuronal networks in patients with epilepsy. Specifically, we compared network properties obtained from multiple diffusion tractography approaches (diffusion tensor imaging, diffusion kurtosis imaging, FBI) in order to assess the pathophysiological relevance of network rearrangement in medication-responsive vs. medication-refractory adults with focal epilepsy. We show that drug-resistant epilepsy is associated with increased global network segregation detected by FBI-based tractography. We propose exploring FBI as a clinically feasible alternative to quantify topological changes that could be used to track disease progression and inform on clinical outcomes.
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Affiliation(s)
| | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK; The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Lorna Bryant
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK
| | - Hunter Moss
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA
| | - Tanja S Kellermann
- Department of Neurology, Medical University of South Carolina, Charleston, SC, USA
| | | | - Anthony G Marson
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK; The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Janina Wilmskoetter
- Department of Neurology, Medical University of South Carolina, Charleston, SC, USA
| | - Jens H Jensen
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA
| | - Leonardo Bonilha
- Department of Neurology, Medical University of South Carolina, Charleston, SC, USA
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32
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West C, Woldman W, Oak K, McLean B, Shankar R. A Review of Network and Computer Analysis of Epileptiform Discharge Free EEG to Characterize and Detect Epilepsy. Clin EEG Neurosci 2022; 53:74-78. [PMID: 33881950 DOI: 10.1177/15500594211008285] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Objectives. There is emerging evidence that network/computer analysis of epileptiform discharge free electroencephalograms (EEGs) can be used to detect epilepsy, improve diagnosis and resource use. Such methods are automated and can be performed on shorter recordings of EEG. We assess the evidence and its strength in the area of seizure detection from network/computer analysis of epileptiform discharge free EEG. Methods. A scoping review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidance was conducted with a literature search of Embase, Medline and PsychINFO. Predesigned inclusion/exclusion criteria were applied to selected articles. Results. The initial search found 3398 articles. After duplicate removal and screening, 591 abstracts were reviewed, 64 articles were selected and read leading to 20 articles meeting the requisite inclusion/exclusion criteria. These were 9 reports and 2 cross-sectional studies using network analysis to compare and/or classify EEG. One review of 17 reports and 10 cross-sectional studies only aimed to classify the EEGs. One cross-sectional study discussed EEG abnormalities associated with autism. Conclusions. Epileptiform discharge free EEG features derived from network/computer analysis differ significantly between people with and without epilepsy. Diagnostic algorithms report high accuracies and could be clinically useful. There is a lack of such research within the intellectual disability (ID) and/or autism populations, where epilepsy is more prevalent and there are additional diagnostic challenges.
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Affiliation(s)
- Caitlin West
- 171002Exeter Medical School, Knowledge Spa, Truro, UK
| | - Wessel Woldman
- Centre for Systems Modelling and Quantitative Biomedicine, 1724University of Birmingham, Birmingham, UK
| | - Katy Oak
- 8028Royal Cornwall Hospitals Trust Truro, Truro, UK
| | | | - Rohit Shankar
- 7491Cornwall Partnership NHS Foundation Trust, Truro, UK.,Cornwall Intellectual Disability Equitable Research (CIDER), University of Plymouth Medical School, Truro, UK
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33
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Slinger G, Otte WM, Braun KPJ, van Diessen E. An updated systematic review and meta-analysis of brain network organization in focal epilepsy: Looking back and forth. Neurosci Biobehav Rev 2021; 132:211-223. [PMID: 34813826 DOI: 10.1016/j.neubiorev.2021.11.028] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 06/23/2021] [Accepted: 11/17/2021] [Indexed: 01/10/2023]
Abstract
Abnormalities of the brain network organization in focal epilepsy have been extensively quantified. However, the extent and directionality of abnormalities are highly variable and subtype insensitive. We conducted meta-analyses to obtain a more accurate and epilepsy type-specific quantification of the interictal global brain network organization in focal epilepsy. By using random-effects models, we estimated differences in average clustering coefficient, average path length, and modularity between patients with focal epilepsy and controls, based on 45 studies with a total sample size of 1,468 patients and 1,021 controls. Structural networks had a significant lower level of integration in patients with epilepsy as compared to controls, with a standardized mean difference of -0.334 (95 % confidence interval -0.631 to -0.038; p-value 0.027). Functional networks did not differ between patients and controls, except for the beta band clustering coefficient. Our meta-analyses show that differences in the brain network organization are not as well defined as individual studies often propose. We discuss potential pitfalls and suggestions to enhance the yield and clinical value of network studies.
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Affiliation(s)
- Geertruida Slinger
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands.
| | - Willem M Otte
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands; Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Kees P J Braun
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Eric van Diessen
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
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34
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Sun J, Li Y, Zhang K, Sun Y, Wang Y, Miao A, Xiang J, Wang X. Frequency-Dependent Dynamics of Functional Connectivity Networks During Seizure Termination in Childhood Absence Epilepsy: A Magnetoencephalography Study. Front Neurol 2021; 12:744749. [PMID: 34759883 PMCID: PMC8573389 DOI: 10.3389/fneur.2021.744749] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 09/21/2021] [Indexed: 12/04/2022] Open
Abstract
Objective: Our aim was to investigate the dynamics of functional connectivity (FC) networks during seizure termination in patients with childhood absence epilepsy (CAE) using magnetoencephalography (MEG) and graph theory (GT) analysis. Methods: MEG data were recorded from 22 drug-naïve patients diagnosed with CAE. FC analysis was performed to evaluate the FC networks in seven frequency bands of the MEG data. GT analysis was used to assess the topological properties of FC networks in different frequency bands. Results: The patterns of FC networks involving the frontal cortex were altered significantly during seizure termination compared with those during the ictal period. Changes in the topological parameters of FC networks were observed in specific frequency bands during seizure termination compared with those in the ictal period. In addition, the connectivity strength at 250–500 Hz during the ictal period was negatively correlated with seizure frequency. Conclusions: FC networks associated with the frontal cortex were involved in the termination of absence seizures. The topological properties of FC networks in different frequency bands could be used as new biomarkers to characterize the dynamics of FC networks related to seizure termination.
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Affiliation(s)
- Jintao Sun
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Yihan Li
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Ke Zhang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Yulei Sun
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Yingfan Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Ailiang Miao
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Jing Xiang
- Division of Neurology, MEG Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Xiaoshan Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
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35
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Sala-Padro J, Miró J, Rodriguez-Fornells A, Rifa-Ros X, Plans G, Santurino M, Falip M, Càmara E. Mapping connectivity fingerprints for presurgical evaluation of temporal lobe epilepsy. BMC Neurol 2021; 21:442. [PMID: 34758783 PMCID: PMC8579661 DOI: 10.1186/s12883-021-02469-1] [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: 03/24/2021] [Accepted: 10/20/2021] [Indexed: 11/30/2022] Open
Abstract
Background Surgery may render temporal lobe epilepsy (TLE) patients seizure-free. However, TLE is a heterogenous entity and surgical prognosis varies between patients. Network-based biomarkers have been shown to be altered in TLE patients and hold promise for classifying TLE subtypes and improving pre-surgical prognosis. The aim of the present study is to investigate a network-based biomarker, the weighted degree of connectivity (wDC), on an individual level, and its relation to TLE subtypes and surgical prognosis. Methods Thirty unilateral TLE patients undergoing the same surgical procedure (anterior temporal resection) and 18 healthy controls were included. All patients were followed-up in the same center for a mean time of 6.85 years and classified as seizure-free (SF) and non seizure-free (non-SF). Using pre-surgical resting state functional MRI, whole brain wDC values for patients and controls were calculated. Then, we divided both temporal lobes in three Regions-of-interest (ROIs) -mesial, pole and lateral- as these areas are known to behave differently in seizure onset and propagation, delimiting different TLE profiles. The wDC values for the defined ROIs of each individual patient were compared with the healthy group. Results After surgery, 14 TLE patients remained SF. As a group, patients had higher wDC than controls in both the temporal pole (p < 0.05) as well as in the mesial regions (p < 0.002) of the to-be-resected temporal lobe. When comparing between SF and non-SF patients, a step-wise binary logistic regression model including all the ROIs, showed that having an increased wDC of the temporal pole (p < 0.05) and the mesial area (p < 0.05) of the to-be-resected temporal lobe was associated with seizure freedom long-term after surgery. Conclusions This study provides a network-based presurgical biomarker that could pave the way towards personalized prediction. In patients with TLE undergoing anterior temporal resections, having an increased wDC at rest could be a signature of the epileptogenic area, and could help identifying those patients who would benefit most from surgery.
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Affiliation(s)
- Jacint Sala-Padro
- Epilepsy Unit, Hospital de Bellvitge, Barcelona, Spain.,Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08097, Barcelona, Spain
| | - Júlia Miró
- Epilepsy Unit, Hospital de Bellvitge, Barcelona, Spain.,Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08097, Barcelona, Spain
| | - Antoni Rodriguez-Fornells
- Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08097, Barcelona, Spain.,Department of Cognition, Development and Educational Science, Campus Bellvitge, University of Barcelona, L'Hospitalet de Llobregat, 08097, Barcelona, Spain.,Catalan Institution for Research and Advanced Studies, ICREA, Barcelona, Spain
| | - Xavier Rifa-Ros
- Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08097, Barcelona, Spain.,Department of Cognition, Development and Educational Science, Campus Bellvitge, University of Barcelona, L'Hospitalet de Llobregat, 08097, Barcelona, Spain
| | - Gerard Plans
- Epilepsy Unit, Hospital de Bellvitge, Barcelona, Spain
| | | | - Mercè Falip
- Epilepsy Unit, Hospital de Bellvitge, Barcelona, Spain
| | - Estela Càmara
- Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08097, Barcelona, Spain. .,Department of Cognition, Development and Educational Science, Campus Bellvitge, University of Barcelona, L'Hospitalet de Llobregat, 08097, Barcelona, Spain.
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36
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Dharan AL, Bowden SC, Lai A, Peterson ADH, Cheung MWL, Woldman W, D'Souza WJ. Resting-state functional connectivity in the idiopathic generalized epilepsies: A systematic review and meta-analysis of EEG and MEG studies. Epilepsy Behav 2021; 124:108336. [PMID: 34607215 DOI: 10.1016/j.yebeh.2021.108336] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 09/09/2021] [Accepted: 09/12/2021] [Indexed: 11/20/2022]
Abstract
For idiopathic generalized epilepsies (IGE), brain network analysis is emerging as a biomarker for potential use in clinical care. To determine whether people with IGE show alterations in resting-state brain connectivity compared to healthy controls, and to quantify these differences, we conducted a systematic review and meta-analysis of EEG and magnetoencephalography (MEG) functional connectivity and network studies. The review was conducted according to PRISMA guidelines. Twenty-two studies were eligible for inclusion. Outcomes from individual studies supported hypotheses for interictal, resting-state brain connectivity alterations in IGE patients compared to healthy controls. In contrast, meta-analysis from six studies of common network metrics clustering coefficient, path length, mean degree and nodal strength showed no significant differences between IGE and control groups (effect sizes ranged from -0.151 -1.78). The null findings of the meta-analysis and the heterogeneity of the included studies highlights the importance of developing standardized, validated methodologies for future research. Network neuroscience has significant potential as both a diagnostic and prognostic biomarker in epilepsy, though individual variability in network dynamics needs to be better understood and accounted for.
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Affiliation(s)
- Anita L Dharan
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia.
| | - Stephen C Bowden
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia; Department of Clinical Neurosciences, St Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
| | - Alan Lai
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
| | - Andre D H Peterson
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
| | - Mike W-L Cheung
- Department of Psychology, National University of Singapore, Singapore
| | - Wessel Woldman
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Edgbaston, United Kingdom
| | - Wendyl J D'Souza
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
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37
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Papadelis C, Perry MS. Localizing the Epileptogenic Zone with Novel Biomarkers. Semin Pediatr Neurol 2021; 39:100919. [PMID: 34620466 PMCID: PMC8501232 DOI: 10.1016/j.spen.2021.100919] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/09/2021] [Accepted: 08/11/2021] [Indexed: 01/01/2023]
Abstract
Several noninvasive methods, such as high-density EEG or magnetoencephalography, are currently used to delineate the epileptogenic zone (EZ) during the presurgical evaluation of patients with drug resistant epilepsy (DRE). Yet, none of these methods can reliably identify the EZ by their own. In most cases a multimodal approach is needed. Challenging cases often require the implantation of intracranial electrodes, either through stereo-taxic EEG or electro-corticography. Recently, a growing body of literature introduces novel biomarkers of epilepsy that can be used for analyzing both invasive as well as noninvasive electrophysiological data. Some of these biomarkers are able to delineate the EZ with high precision, augment the presurgical evaluation, and predict the surgical outcome of patients with DRE undergoing surgery. However, the use of these epilepsy biomarkers in clinical practice is limited. Here, we summarize and discuss the latest technological advances in the presurgical neurophysiological evaluation of children with DRE with emphasis on electric and magnetic source imaging, high frequency oscillations, and functional connectivity.
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Affiliation(s)
- Christos Papadelis
- Jane and John Justin Neurosciences Center, Cook Children's Health Care System, Fort Worth, TX; School of Medicine, Texas Christian University and University of North Texas Health Science Center, Fort Worth, TX; Department of Bioengineering, University of Texas at Arlington, Arlington, TX; Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA.
| | - M Scott Perry
- Jane and John Justin Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
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38
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Pegg EJ, McKavanagh A, Bracewell RM, Chen Y, Das K, Denby C, Kreilkamp BAK, Laiou P, Marson A, Mohanraj R, Taylor JR, Keller SS. Functional network topology in drug resistant and well-controlled idiopathic generalized epilepsy: a resting state functional MRI study. Brain Commun 2021; 3:fcab196. [PMID: 34514400 PMCID: PMC8417840 DOI: 10.1093/braincomms/fcab196] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/18/2021] [Indexed: 11/23/2022] Open
Abstract
Despite an increasing number of drug treatment options for people with idiopathic generalized epilepsy (IGE), drug resistance remains a significant issue and the mechanisms underlying it remain poorly understood. Previous studies have largely focused on potential cellular or genetic explanations for drug resistance. However, epilepsy is understood to be a network disorder and there is a growing body of literature suggesting altered topology of large-scale resting networks in people with epilepsy compared with controls. We hypothesize that network alterations may also play a role in seizure control. The aim of this study was to compare resting state functional network structure between well-controlled IGE (WC-IGE), drug resistant IGE (DR-IGE) and healthy controls. Thirty-three participants with IGE (10 with WC-IGE and 23 with DR-IGE) and 34 controls were included. Resting state functional MRI networks were constructed using the Functional Connectivity Toolbox (CONN). Global graph theoretic network measures of average node strength (an equivalent measure to mean degree in a network that is fully connected), node strength distribution variance, characteristic path length, average clustering coefficient, small-world index and average betweenness centrality were computed. Graphs were constructed separately for positively weighted connections and for absolute values. Individual nodal values of strength and betweenness centrality were also measured and ‘hub nodes’ were compared between groups. Outcome measures were assessed across the three groups and between both groups with IGE and controls. The IGE group as a whole had a higher average node strength, characteristic path length and average betweenness centrality. There were no clear differences between groups according to seizure control. Outcome metrics were sensitive to whether negatively correlated connections were included in network construction. There were no clear differences in the location of ‘hub nodes’ between groups. The results suggest that, irrespective of seizure control, IGE interictal network topology is more regular and has a higher global connectivity compared to controls, with no alteration in hub node locations. These alterations may produce a resting state network that is more vulnerable to transitioning to the seizure state. It is possible that the lack of apparent influence of seizure control on network topology is limited by challenges in classifying drug response. It is also demonstrated that network topological features are influenced by the sign of connectivity weights and therefore future methodological work is warranted to account for anticorrelations in graph theoretic studies.
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Affiliation(s)
- Emily J Pegg
- Department of Neurology, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Salford, UK.,Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Andrea McKavanagh
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | | | - Yachin Chen
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Kumar Das
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | | | - Barbara A K Kreilkamp
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.,Department of Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - Petroula Laiou
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Anthony Marson
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Rajiv Mohanraj
- Department of Neurology, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Salford, UK.,Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Jason R Taylor
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.,Manchester Academic Health Sciences Centre, University of Manchester, Manchester, UK
| | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
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39
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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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40
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Faiman I, Smith S, Hodsoll J, Young AH, Shotbolt P. Resting-state EEG for the diagnosis of idiopathic epilepsy and psychogenic nonepileptic seizures: A systematic review. Epilepsy Behav 2021; 121:108047. [PMID: 34091130 DOI: 10.1016/j.yebeh.2021.108047] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 04/28/2021] [Indexed: 12/17/2022]
Abstract
Quantitative markers extracted from resting-state electroencephalogram (EEG) reveal subtle neurophysiological dynamics which may provide useful information to support the diagnosis of seizure disorders. We performed a systematic review to summarize evidence on markers extracted from interictal, visually normal resting-state EEG in adults with idiopathic epilepsy or psychogenic nonepileptic seizures (PNES). Studies were selected from 5 databases and evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2. 26 studies were identified, 19 focusing on people with epilepsy, 6 on people with PNES, and one comparing epilepsy and PNES directly. Results suggest that oscillations along the theta frequency (4-8 Hz) may have a relevant role in idiopathic epilepsy, whereas in PNES there was no evident trend. However, studies were subject to a number of methodological limitations potentially introducing bias. There was often a lack of appropriate reporting and high heterogeneity. Results were not appropriate for quantitative synthesis. We identify and discuss the challenges that must be addressed for valid resting-state EEG markers of epilepsy and PNES to be developed.
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Affiliation(s)
- Irene Faiman
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, Camberwell, London SE5 8AB, United Kingdom.
| | - Stuart Smith
- Department of Neurophysiology, Great Ormond Street Hospital, Great Ormond Street, London WC1N 3JH, United Kingdom.
| | - John Hodsoll
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, Camberwell, London SE5 8AB, United Kingdom.
| | - Allan H Young
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, Camberwell, London SE5 8AB, United Kingdom; South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, Monks Orchard Road, Beckenham, Kent BR3 3BX, United Kingdom.
| | - Paul Shotbolt
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, Camberwell, London SE5 8AB, United Kingdom.
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41
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An integrative prediction algorithm of drug-refractory epilepsy based on combined clinical-EEG functional connectivity features. J Neurol 2021; 269:1501-1514. [PMID: 34308506 DOI: 10.1007/s00415-021-10718-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 07/15/2021] [Accepted: 07/17/2021] [Indexed: 12/27/2022]
Abstract
OBJECTIVE Although the use of antiepileptic drugs (AEDs) is routine, 30-40% of patients with epilepsy (PWEs) experience drug resistance. Thus, early identification of AED resistance will help optimize treatment regimens and improve patients' prognoses. However, there have been few studies on this topic to date. Here, we try to establish an integrative prediction model of AED resistance for drug-naive PWEs, and to identify the clinical and Electroencephalogram (EEG) factors that affect their outcomes. METHODS One hundred sixty-four PWEs naive to AEDs treated at a tertiary care center from January 2014 to June 2020 were retrospectively analyzed. A total of 113 of these patients were well controlled and 53 were drug refractory with regular AED treatment for more than one year. Eighty clinical characteristics and 684 EEG functional connectivity variables based on phase lag index before drug initiation were identified. Overall, 80% of each group was chosen to establish a support vector machine (SVM) model with ten-fold cross validation, and the other 20% were used to evaluate the model's performance. Absolute weight value was used to rank the features that had impacts on classification. RESULTS An integrative algorithm was modeled to predict AED resistance for drug-naive PWEs by SVM based on clinical characteristics and EEG functional connectivity values. The model had an accuracy of 94% [95% confidence interval (CI) 0.85-1.0], sensitivity of 95% [95% CI 0.82-1.0], specificity of 93% [95% CI 0.77-1.0], and an area under the curve (AUC) of 0.98 [95% CI 0.91-1.0]. The p values of accuracy, sensitivity specificity and AUC were calculated as 0.001, 0.001, 0.01 and 0.001, respectively. The δ band fromT4-FZ and T3-PZ, α band from T3-T6 and β band from F7-CZ and FP2-F3 were the top five EEG features that impacted the SVM classifier. CONCLUSION We constructed an integrative prediction algorithm of AED resistance for drug-naive PWEs. Its utility in clinical settings should be evaluated in the future.
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42
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Changes in the Functional Brain Network of Children Undergoing Repeated Epilepsy Surgery: An EEG Source Connectivity Study. Diagnostics (Basel) 2021; 11:diagnostics11071234. [PMID: 34359317 PMCID: PMC8306224 DOI: 10.3390/diagnostics11071234] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 06/29/2021] [Accepted: 07/05/2021] [Indexed: 11/19/2022] Open
Abstract
About 30% of children with drug-resistant epilepsy (DRE) continue to have seizures after epilepsy surgery. Since epilepsy is increasingly conceptualized as a network disorder, understanding how brain regions interact may be critical for planning re-operation in these patients. We aimed to estimate functional brain connectivity using scalp EEG and its evolution over time in patients who had repeated surgery (RS-group, n = 9) and patients who had one successful surgery (seizure-free, SF-group, n = 12). We analyzed EEGs without epileptiform activity at varying time points (before and after each surgery). We estimated functional connectivity between cortical regions and their relative centrality within the network. We compared the pre- and post-surgical centrality of all the non-resected (untouched) regions (far or adjacent to resection) for each group (using the Wilcoxon signed rank test). In alpha, theta, and beta frequency bands, the post-surgical centrality of the untouched cortical regions increased in the SF group (p < 0.001) whereas they decreased (p < 0.05) or did not change (p > 0.05) in the RS group after failed surgeries; when re-operation was successful, the post-surgical centrality of far regions increased (p < 0.05). Our data suggest that removal of the epileptogenic focus in children with DRE leads to a gain in the network centrality of the untouched areas. In contrast, unaltered or decreased connectivity is seen when seizures persist after surgery.
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Xu N, Shan W, Qi J, Wu J, Wang Q. Presurgical Evaluation of Epilepsy Using Resting-State MEG Functional Connectivity. Front Hum Neurosci 2021; 15:649074. [PMID: 34276321 PMCID: PMC8283278 DOI: 10.3389/fnhum.2021.649074] [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: 01/03/2021] [Accepted: 06/07/2021] [Indexed: 11/21/2022] Open
Abstract
Epilepsy is caused by abnormal electrical discharges (clinically identified by electrophysiological recording) in a specific part of the brain [originating in only one part of the brain, namely, the epileptogenic zone (EZ)]. Epilepsy is now defined as an archetypical hyperexcited neural network disorder. It can be investigated through the network analysis of interictal discharges, ictal discharges, and resting-state functional connectivity. Currently, there is an increasing interest in embedding resting-state connectivity analysis into the preoperative evaluation of epilepsy. Among the various neuroimaging technologies employed to achieve brain functional networks, magnetoencephalography (MEG) with the excellent temporal resolution is an ideal tool for estimating the resting-state connectivity between brain regions, which can reveal network abnormalities in epilepsy. What value does MEG resting-state functional connectivity offer for epileptic presurgical evaluation? Regarding this topic, this paper introduced the origin of MEG and the workflow of constructing source-space functional connectivity based on MEG signals. Resting-state functional connectivity abnormalities correlate with epileptogenic networks, which are defined by the brain regions involved in the production and propagation of epileptic activities. This paper reviewed the evidence of altered epileptic connectivity based on low- or high-frequency oscillations (HFOs) and the evidence of the advantage of using simultaneous MEG and intracranial electroencephalography (iEEG) recordings. More importantly, this review highlighted that MEG-based resting-state functional connectivity has the potential to predict postsurgical outcomes. In conclusion, resting-state MEG functional connectivity has made a substantial progress toward serving as a candidate biomarker included in epileptic presurgical evaluations.
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Affiliation(s)
- Na Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wei Shan
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jing Qi
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jianping Wu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Qun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
- Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neuromodulation, Beijing, China
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44
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Hatlestad-Hall C, Bruña R, Syvertsen MR, Erichsen A, Andersson V, Vecchio F, Miraglia F, Rossini PM, Renvall H, Taubøll E, Maestú F, Haraldsen IH. Source-level EEG and graph theory reveal widespread functional network alterations in focal epilepsy. Clin Neurophysiol 2021; 132:1663-1676. [PMID: 34044189 DOI: 10.1016/j.clinph.2021.04.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 03/19/2021] [Accepted: 04/20/2021] [Indexed: 01/15/2023]
Abstract
OBJECTIVE The hypersynchronous neuronal activity associated with epilepsy causes widespread functional network disruptions extending beyond the epileptogenic zone. This altered network topology is considered a mediator for non-seizure symptoms, such as cognitive impairment. The aim of this study was to investigate functional network alterations in focal epilepsy patients with good seizure control and high quality of life. METHODS We compared twenty-two focal epilepsy patients and sixteen healthy controls on graph metrics derived from functional connectivity of source-level resting-state EEG. Graph metrics were calculated over a range of network densities in five frequency bands. RESULTS We observed a significantly increased small world index in patients relative to controls. On the local level, two left-hemisphere regions displayed a shift towards greater alpha band "hubness". The findings were not mediated by age, sex or education, nor by age of epilepsy onset, duration or focus lateralisation. CONCLUSIONS Widespread functional network alterations are evident in focal epilepsy, even in a cohort characterised by successful anti-seizure medication therapy and high quality of life. These findings might support the position that functional network analysis could hold clinical relevance for epilepsy. SIGNIFICANCE Focal epilepsy is accompanied by global and local functional network aberrancies which might be implied in the sustenance of non-seizure symptoms.
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Affiliation(s)
| | - Ricardo Bruña
- Center for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcón, Spain; Department of Experimental Psychology, Complutense University of Madrid, Pozuelo de Alarcón, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.
| | - Marte Roa Syvertsen
- Department of Neurology, Drammen Hospital, Vestre Viken Health Care Trust, Drammen, Norway.
| | - Aksel Erichsen
- Department of Neurology, Oslo University Hospital, Oslo, Norway; Department of Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
| | | | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy.
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy.
| | - Paolo M Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy.
| | - Hanna Renvall
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Diagnostic Center, Helsinki University Hospital, University of Helsinki and Aalto University School of Science, Helsinki, Finland.
| | - Erik Taubøll
- Department of Neurology, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.
| | - Fernando Maestú
- Center for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcón, Spain; Department of Experimental Psychology, Complutense University of Madrid, Pozuelo de Alarcón, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.
| | - Ira H Haraldsen
- Department of Neurology, Oslo University Hospital, Oslo, Norway.
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45
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Hao J, Luo W, Xie Y, Feng Y, Sun W, Peng W, Zhao J, Zhang P, Ding J, Wang X. Functional Network Alterations as Markers for Predicting the Treatment Outcome of Cathodal Transcranial Direct Current Stimulation in Focal Epilepsy. Front Hum Neurosci 2021; 15:637071. [PMID: 33815082 PMCID: PMC8009991 DOI: 10.3389/fnhum.2021.637071] [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: 12/02/2020] [Accepted: 02/23/2021] [Indexed: 11/13/2022] Open
Abstract
Background and Purpose Transcranial direct current stimulation (tDCS) is an emerging non-invasive neuromodulation technique for focal epilepsy. Because epilepsy is a disease affecting the brain network, our study was aimed to evaluate and predict the treatment outcome of cathodal tDCS (ctDCS) by analyzing the ctDCS-induced functional network alterations. Methods Either the active 5-day, -1.0 mA, 20-min ctDCS or sham ctDCS targeting at the most active interictal epileptiform discharge regions was applied to 27 subjects suffering from focal epilepsy. The functional networks before and after ctDCS were compared employing graph theoretical analysis based on the functional magnetic resonance imaging (fMRI) data. A support vector machine (SVM) prediction model was built to predict the treatment outcome of ctDCS using the graph theoretical measures as markers. Results Our results revealed that the mean clustering coefficient and the global efficiency decreased significantly, as well as the characteristic path length and the mean shortest path length at the stimulation sites in the fMRI functional networks increased significantly after ctDCS only for the patients with response to the active ctDCS (at least 20% reduction rate of seizure frequency). Our prediction model achieved the mean prediction accuracy of 68.3% (mean sensitivity: 70.0%; mean specificity: 67.5%) after the nested cross validation. The mean area under the receiver operating curve was 0.75, which showed good prediction performance. Conclusion The study demonstrated that the response to ctDCS was related to the topological alterations in the functional networks of epilepsy patients detected by fMRI. The graph theoretical measures were promising for clinical prediction of ctDCS treatment outcome.
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Affiliation(s)
- Jiaxin Hao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Wenyi Luo
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yuhai Xie
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yu Feng
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Wei Sun
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Weifeng Peng
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jun Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Puming Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jing Ding
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China
| | - Xin Wang
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology, the Institutes of Brain Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
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46
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Dhaher R, Gruenbaum SE, Sandhu MRS, Ottestad-Hansen S, Tu N, Wang Y, Lee TSW, Deshpande K, Spencer DD, Danbolt NC, Zaveri HP, Eid T. Network-Related Changes in Neurotransmitters and Seizure Propagation During Rodent Epileptogenesis. Neurology 2021; 96:e2261-e2271. [PMID: 33722994 PMCID: PMC8166437 DOI: 10.1212/wnl.0000000000011846] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 01/29/2021] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE To test the hypothesis that glutamate and GABA are linked to the formation of epilepsy networks and the triggering of spontaneous seizures, we examined seizure initiation/propagation characteristics and neurotransmitter levels during epileptogenesis in a translationally relevant rodent model of mesial temporal lobe epilepsy. METHODS The glutamine synthetase (GS) inhibitor methionine sulfoximine was infused into one of the hippocampi in laboratory rats to create a seizure focus. Long-term video-intracranial EEG recordings and brain microdialysis combined with mass spectrometry were used to examine seizure initiation, seizure propagation, and extracellular brain levels of glutamate and GABA. RESULTS All seizures (n = 78 seizures, n = 3 rats) appeared first in the GS-inhibited hippocampus of all animals, followed by propagation to the contralateral hippocampus. Propagation time decreased significantly from 11.65 seconds early in epileptogenesis (weeks 1-2) to 6.82 seconds late in epileptogenesis (weeks 3-4, paired t test, p = 0.025). Baseline extracellular glutamate levels were 11.6-fold higher in the hippocampus of seizure propagation (7.3 µM) vs the hippocampus of seizure onset (0.63 µM, analysis of variance/Fisher least significant difference, p = 0.01), even though the concentrations of the major glutamate transporter proteins excitatory amino acid transporter subtypes 1 and 2 and xCT were unchanged between the brain regions. Finally, extracellular GABA in the seizure focus decreased significantly from baseline several hours before a spontaneous seizure (paired t test/false discovery rate). CONCLUSION The changes in glutamate and GABA suggest novel and potentially important roles of the amino acids in epilepsy network formation and in the initiation and propagation of spontaneous seizures.
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Affiliation(s)
- Roni Dhaher
- From the Departments of Laboratory Medicine (R.D., M.R.S.S., N.T., Y.W., K.D., T.E.), Anesthesiology (S.E.G.), Neurosurgery (D.D.S.), Psychiatry (T.-S.W.L.), and Neurology (H.P.Z.), Yale School of Medicine, New Haven, CT; and Department of Molecular Medicine (S.O.-H., N.C.D.), Division of Anatomy, Institute for Basic Medical Sciences, University of Oslo, Norway
| | - Shaun E Gruenbaum
- From the Departments of Laboratory Medicine (R.D., M.R.S.S., N.T., Y.W., K.D., T.E.), Anesthesiology (S.E.G.), Neurosurgery (D.D.S.), Psychiatry (T.-S.W.L.), and Neurology (H.P.Z.), Yale School of Medicine, New Haven, CT; and Department of Molecular Medicine (S.O.-H., N.C.D.), Division of Anatomy, Institute for Basic Medical Sciences, University of Oslo, Norway
| | - Mani Ratnesh S Sandhu
- From the Departments of Laboratory Medicine (R.D., M.R.S.S., N.T., Y.W., K.D., T.E.), Anesthesiology (S.E.G.), Neurosurgery (D.D.S.), Psychiatry (T.-S.W.L.), and Neurology (H.P.Z.), Yale School of Medicine, New Haven, CT; and Department of Molecular Medicine (S.O.-H., N.C.D.), Division of Anatomy, Institute for Basic Medical Sciences, University of Oslo, Norway
| | - Sigrid Ottestad-Hansen
- From the Departments of Laboratory Medicine (R.D., M.R.S.S., N.T., Y.W., K.D., T.E.), Anesthesiology (S.E.G.), Neurosurgery (D.D.S.), Psychiatry (T.-S.W.L.), and Neurology (H.P.Z.), Yale School of Medicine, New Haven, CT; and Department of Molecular Medicine (S.O.-H., N.C.D.), Division of Anatomy, Institute for Basic Medical Sciences, University of Oslo, Norway
| | - Nathan Tu
- From the Departments of Laboratory Medicine (R.D., M.R.S.S., N.T., Y.W., K.D., T.E.), Anesthesiology (S.E.G.), Neurosurgery (D.D.S.), Psychiatry (T.-S.W.L.), and Neurology (H.P.Z.), Yale School of Medicine, New Haven, CT; and Department of Molecular Medicine (S.O.-H., N.C.D.), Division of Anatomy, Institute for Basic Medical Sciences, University of Oslo, Norway
| | - Yue Wang
- From the Departments of Laboratory Medicine (R.D., M.R.S.S., N.T., Y.W., K.D., T.E.), Anesthesiology (S.E.G.), Neurosurgery (D.D.S.), Psychiatry (T.-S.W.L.), and Neurology (H.P.Z.), Yale School of Medicine, New Haven, CT; and Department of Molecular Medicine (S.O.-H., N.C.D.), Division of Anatomy, Institute for Basic Medical Sciences, University of Oslo, Norway
| | - Tih-Shih W Lee
- From the Departments of Laboratory Medicine (R.D., M.R.S.S., N.T., Y.W., K.D., T.E.), Anesthesiology (S.E.G.), Neurosurgery (D.D.S.), Psychiatry (T.-S.W.L.), and Neurology (H.P.Z.), Yale School of Medicine, New Haven, CT; and Department of Molecular Medicine (S.O.-H., N.C.D.), Division of Anatomy, Institute for Basic Medical Sciences, University of Oslo, Norway
| | - Ketaki Deshpande
- From the Departments of Laboratory Medicine (R.D., M.R.S.S., N.T., Y.W., K.D., T.E.), Anesthesiology (S.E.G.), Neurosurgery (D.D.S.), Psychiatry (T.-S.W.L.), and Neurology (H.P.Z.), Yale School of Medicine, New Haven, CT; and Department of Molecular Medicine (S.O.-H., N.C.D.), Division of Anatomy, Institute for Basic Medical Sciences, University of Oslo, Norway
| | - Dennis D Spencer
- From the Departments of Laboratory Medicine (R.D., M.R.S.S., N.T., Y.W., K.D., T.E.), Anesthesiology (S.E.G.), Neurosurgery (D.D.S.), Psychiatry (T.-S.W.L.), and Neurology (H.P.Z.), Yale School of Medicine, New Haven, CT; and Department of Molecular Medicine (S.O.-H., N.C.D.), Division of Anatomy, Institute for Basic Medical Sciences, University of Oslo, Norway
| | - Niels Christian Danbolt
- From the Departments of Laboratory Medicine (R.D., M.R.S.S., N.T., Y.W., K.D., T.E.), Anesthesiology (S.E.G.), Neurosurgery (D.D.S.), Psychiatry (T.-S.W.L.), and Neurology (H.P.Z.), Yale School of Medicine, New Haven, CT; and Department of Molecular Medicine (S.O.-H., N.C.D.), Division of Anatomy, Institute for Basic Medical Sciences, University of Oslo, Norway
| | - Hitten P Zaveri
- From the Departments of Laboratory Medicine (R.D., M.R.S.S., N.T., Y.W., K.D., T.E.), Anesthesiology (S.E.G.), Neurosurgery (D.D.S.), Psychiatry (T.-S.W.L.), and Neurology (H.P.Z.), Yale School of Medicine, New Haven, CT; and Department of Molecular Medicine (S.O.-H., N.C.D.), Division of Anatomy, Institute for Basic Medical Sciences, University of Oslo, Norway
| | - Tore Eid
- From the Departments of Laboratory Medicine (R.D., M.R.S.S., N.T., Y.W., K.D., T.E.), Anesthesiology (S.E.G.), Neurosurgery (D.D.S.), Psychiatry (T.-S.W.L.), and Neurology (H.P.Z.), Yale School of Medicine, New Haven, CT; and Department of Molecular Medicine (S.O.-H., N.C.D.), Division of Anatomy, Institute for Basic Medical Sciences, University of Oslo, Norway.
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Girardi-Schappo M, Fadaie F, Lee HM, Caldairou B, Sziklas V, Crane J, Bernhardt BC, Bernasconi A, Bernasconi N. Altered communication dynamics reflect cognitive deficits in temporal lobe epilepsy. Epilepsia 2021; 62:1022-1033. [PMID: 33705572 DOI: 10.1111/epi.16864] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 02/15/2021] [Accepted: 02/15/2021] [Indexed: 12/23/2022]
Abstract
OBJECTIVE Although temporal lobe epilepsy (TLE) is recognized as a system-level disorder, little work has investigated pathoconnectomics from a dynamic perspective. By leveraging computational simulations that quantify patterns of information flow across the connectome, we tested the hypothesis that network communication is abnormal in this condition, studied the interplay between hippocampal- and network-level disease effects, and assessed associations with cognition. METHODS We simulated signal spreading via a linear threshold model that temporally evolves on a structural graph derived from diffusion-weighted magnetic resonance imaging (MRI), comparing a homogeneous group of 31 patients with histologically proven hippocampal sclerosis to 31 age- and sex-matched healthy controls. We evaluated the modulatory effects of structural alterations of the neocortex and hippocampus on network dynamics. Furthermore, multivariate statistics addressed the relationship with cognitive parameters. RESULTS We observed a slowing of in- and out-spreading times across multiple areas bilaterally, indexing delayed information flow, with the strongest effects in ipsilateral frontotemporal regions, thalamus, and hippocampus. Effects were markedly reduced when controlling for hippocampal volume but not cortical thickness, underscoring the central role of the hippocampus in whole-brain disease expression. Multivariate analysis associated slower spreading time in frontoparietal, limbic, default mode, and subcortical networks with impairment across tasks tapping into sensorimotor, executive, memory, and verbal abilities. SIGNIFICANCE Moving beyond descriptions of static topology toward the formulation of brain dynamics, our work provides novel insight into structurally mediated network dysfunction and demonstrates that altered whole-brain communication dynamics contribute to common cognitive difficulties in TLE.
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Affiliation(s)
- Mauricio Girardi-Schappo
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Fatemeh Fadaie
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Hyo Min Lee
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Benoit Caldairou
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Viviane Sziklas
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Joelle Crane
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
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48
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Abstract
Epilepsy is characterized by specific alterations in network organization. The main parameters at the basis of epileptogenic network formation are alterations of cortical thickness, development of pathologic hubs, modification of hub distribution, and white matter alterations. The effect is a reinforcement of brain connectivity in both the epileptogenic zone and the propagation zone. Moreover, the epileptogenic network is characterized by some specific neurophysiologic biomarkers that evidence the tendency of the network itself to shift from an interictal state to an ictal one. The recognition of these features is crucial in planning epilepsy surgery.
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49
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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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50
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Pegg EJ, Taylor JR, Laiou P, Richardson M, Mohanraj R. Interictal electroencephalographic functional network topology in drug-resistant and well-controlled idiopathic generalized epilepsy. Epilepsia 2021; 62:492-503. [PMID: 33501642 DOI: 10.1111/epi.16811] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 12/20/2020] [Accepted: 12/22/2020] [Indexed: 01/17/2023]
Abstract
OBJECTIVE The study aim was to compare interictal encephalographic (EEG) functional network topology between people with well-controlled idiopathic generalized epilepsy (WC-IGE) and drug-resistant IGE (DR-IGE). METHODS Nineteen participants with WC-IGE, 18 with DR-IGE, and 20 controls underwent a resting state, 64-channel EEG. An artifact-free epoch was bandpass filtered into the frequency range of high and low extended alpha. Weighted functional connectivity matrices were calculated. Mean degree, degree distribution variance, characteristic path length (L), clustering coefficient, small world index (SWI), and betweenness centrality were measured. A Kruskal-Wallis H-test assessed effects across groups. Where significant differences were found, Bonferroni-corrected Mann-Whitney pairwise comparisons were calculated. RESULTS In the low alpha band (6-9 Hz), there was a significant difference in L at the three-group level (p < .0001). This was lower in controls than both WC-IGE and DR-IGE (p < .0001 for both), with no difference in L between WC-IGE and DR-IGE. Mean degree (p = .031), degree distribution variance (p = .032), and SWI (p = .023) differed across the three groups in the high alpha band (10-12 Hz). Mean degree and degree distribution variance were lower in WC-IGE than controls (p = .029 for both), and SWI was higher in WC-IGE compared with controls (p = .038), with no differences in other pairwise comparisons. SIGNIFICANCE IGE network topology is more regular in the low alpha frequency band, potentially reflecting a more vulnerable structure. WC-IGE network topology is different from controls in the high alpha band. This may reflect drug-induced network changes that have stabilized the WC-IGE network by rendering it less likely to synchronize. These results are of potential importance in advancing the understanding of mechanisms of epilepsy drug resistance and as a possible basis for a biomarker of DR-IGE.
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Affiliation(s)
- Emily J Pegg
- Department of Neurology, Manchester Centre for Clinical Neurosciences, Salford, UK.,Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine, and Health, School of Biological Sciences, University of Manchester, Manchester, UK
| | - Jason R Taylor
- Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine, and Health, School of Biological Sciences, University of Manchester, Manchester, UK.,Manchester Academic Health Sciences Centre, Manchester, UK
| | - Petroula Laiou
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Mark Richardson
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Rajiv Mohanraj
- Department of Neurology, Manchester Centre for Clinical Neurosciences, Salford, UK.,Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine, and Health, School of Biological Sciences, University of Manchester, Manchester, UK
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