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Feroze N, Karim T, Ostojic K, Mcintyre S, Barnes EH, Lee BC, Dale RC, Gill D, Kothur K. Clinical features associated with epilepsy occurrence, resolution, and drug resistance in children with cerebral palsy: A population-based study. Dev Med Child Neurol 2024; 66:793-803. [PMID: 38059324 DOI: 10.1111/dmcn.15807] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 10/08/2023] [Accepted: 10/17/2023] [Indexed: 12/08/2023]
Abstract
AIM To investigate clinicoradiological features associated with epilepsy, its resolution, and drug resistance in children with cerebral palsy (CP). METHOD Data were gathered from the New South Wales/Australian Capital Territory CP Register, encompassing children with CP born between 2003 and 2015 (n = 1916). Clinical features and the severity of impairments were compared among three groups: children with current epilepsy (n = 604), those with resolved epilepsy by age 5 years (n = 109), and those without epilepsy (n = 1203). Additionally, a subset of the registry cohort attending Children's Hospital Westmead (n = 256) was analysed to compare epilepsy and treatment characteristics between drug-responsive (n = 83) and drug-resistant groups (n = 147) using logistic regression and hierarchical cluster analysis. RESULTS Manual Ability Classification System levels IV and V, intellectual impairment, and vision impairment were found to be associated with epilepsy in children with CP on multivariable analysis (p < 0.01). Moderate to severe intellectual impairment and bilateral spastic CP were independent positive and negative predictors of epilepsy persistence at the age of 5 years respectively (p < 0.05). Microcephaly and multiple seizure types were predictors of drug-resistant epilepsy (area under the receiver operating characteristic curve of 0.83; 95% confidence interval 0.77-0.9). Children with a known genetic cause (14%) and CP epilepsy surgery group (4.3%) formed specific clinical subgroups in CP epilepsy. INTERPRETATION Our study highlights important clinical associations of epilepsy, its resolution, and treatment response in children with CP, providing valuable knowledge to aid in counselling families and identifying distinct prognostic groups for effective medical surveillance and optimal treatment. WHAT THIS PAPER ADDS Severe motor and non-motor impairments in cerebral palsy (CP) increase epilepsy risk. Epilepsy more likely resolves in bilateral spastic and milder CP impairments. Epilepsy in CP often manifests at an early age with multiple seizure types and high drug resistance. Children with a known genetic cause and CP epilepsy surgery group represent distinct clinical subgroups.
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Affiliation(s)
- Nimra Feroze
- The Children's Hospital at Westmead Clinical School, The University of Sydney, Sydney, NSW, Australia
| | - Tasneem Karim
- Cerebral Palsy Alliance Research Institute, Specialty of Child & Adolescent Health, Sydney Medical School, Faculty of Medicine & Health, The University of Sydney, NSW, Australia
| | - Katarina Ostojic
- Cerebral Palsy Alliance Research Institute, Specialty of Child & Adolescent Health, Sydney Medical School, Faculty of Medicine & Health, The University of Sydney, NSW, Australia
| | - Sarah Mcintyre
- Cerebral Palsy Alliance Research Institute, Specialty of Child & Adolescent Health, Sydney Medical School, Faculty of Medicine & Health, The University of Sydney, NSW, Australia
| | - Elizabeth H Barnes
- NHMRC Clinical Trials Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Byoung Chan Lee
- The Children's Hospital at Westmead Clinical School, The University of Sydney, Sydney, NSW, Australia
| | - Russell C Dale
- The Children's Hospital at Westmead Clinical School, The University of Sydney, Sydney, NSW, Australia
- TY Nelson Department of Neurology and Neurosurgery, The Children's Hospital at Westmead, Sydney, NSW, Australia
- Kids Neuroscience Centre, The Children's Hospital at Westmead, Sydney, NSW, Australia
| | - Deepak Gill
- The Children's Hospital at Westmead Clinical School, The University of Sydney, Sydney, NSW, Australia
- TY Nelson Department of Neurology and Neurosurgery, The Children's Hospital at Westmead, Sydney, NSW, Australia
- Kids Neuroscience Centre, The Children's Hospital at Westmead, Sydney, NSW, Australia
| | - Kavitha Kothur
- The Children's Hospital at Westmead Clinical School, The University of Sydney, Sydney, NSW, Australia
- TY Nelson Department of Neurology and Neurosurgery, The Children's Hospital at Westmead, Sydney, NSW, Australia
- Kids Neuroscience Centre, The Children's Hospital at Westmead, Sydney, NSW, Australia
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Widdess-Walsh P. Resting But Not Idle: Insights Into Epilepsy Network Suppression From Intracranial EEG. Epilepsy Curr 2024; 24:25-27. [PMID: 38327528 PMCID: PMC10846507 DOI: 10.1177/15357597231213247] [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] [Indexed: 02/09/2024] Open
Abstract
The Interictal Suppression Hypothesis in Focal Epilepsy: Network-Level Supporting Evidence Johnson GW, Doss DJ, Morgan VL, Paulo DL, Cai LY, Shless JS, Negi AS, Gummadavelli A, Kang H, Reddy SB, Naftel RP, Bick SK, Williams Roberson S, Dawant BM, Wallace MT, Englot DJ. Brain . 2023;146(7):2828-2845. doi:10.1093/brain/awad016 Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states. Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure–function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P -value = 3.13 × 10−13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P -value = 2.5 × 10−3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P -value of 2.6 × 10−12). Structure–function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P -value of 9.76 × 10−21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones. These results suggest that seizure-onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure-onset zones. These findings have implications for the identification of seizure-onset zones using only brief electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative and neuromodulation approaches to improve surgical success rates in those suffering from drug-resistant focal epilepsy.
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Maher C, Tang Z, D’Souza A, Cabezas M, Cai W, Barnett M, Kavehei O, Wang C, Nikpour A. Deep learning distinguishes connectomes from focal epilepsy patients and controls: feasibility and clinical implications. Brain Commun 2023; 5:fcad294. [PMID: 38025275 PMCID: PMC10644981 DOI: 10.1093/braincomms/fcad294] [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: 02/02/2023] [Revised: 08/10/2023] [Accepted: 10/30/2023] [Indexed: 12/01/2023] Open
Abstract
The application of deep learning models to evaluate connectome data is gaining interest in epilepsy research. Deep learning may be a useful initial tool to partition connectome data into network subsets for further analysis. Few prior works have used deep learning to examine structural connectomes from patients with focal epilepsy. We evaluated whether a deep learning model applied to whole-brain connectomes could classify 28 participants with focal epilepsy from 20 controls and identify nodal importance for each group. Participants with epilepsy were further grouped based on whether they had focal seizures that evolved into bilateral tonic-clonic seizures (17 with, 11 without). The trained neural network classified patients from controls with an accuracy of 72.92%, while the seizure subtype groups achieved a classification accuracy of 67.86%. In the patient subgroups, the nodes and edges deemed important for accurate classification were also clinically relevant, indicating the model's interpretability. The current work expands the evidence for the potential of deep learning to extract relevant markers from clinical datasets. Our findings offer a rationale for further research interrogating structural connectomes to obtain features that can be biomarkers and aid the diagnosis of seizure subtypes.
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Affiliation(s)
- Christina Maher
- Faculty of Engineering, School of Biomedical Engineering, The University of Sydney, Sydney, NSW 2050, Australia
- Brain and Mind Centre, The University of Sydney, Sydney, NSW 2050, Australia
| | - Zihao Tang
- Brain and Mind Centre, The University of Sydney, Sydney, NSW 2050, Australia
- Faculty of Engineering, School of Computer Science, The University of Sydney, Sydney, NSW 2050, Australia
| | - Arkiev D’Souza
- Brain and Mind Centre, The University of Sydney, Sydney, NSW 2050, Australia
| | - Mariano Cabezas
- Brain and Mind Centre, The University of Sydney, Sydney, NSW 2050, Australia
| | - Weidong Cai
- Faculty of Engineering, School of Computer Science, The University of Sydney, Sydney, NSW 2050, Australia
| | - Michael Barnett
- Brain and Mind Centre, The University of Sydney, Sydney, NSW 2050, Australia
- Sydney Neuroimaging Analysis Centre, Sydney, NSW 2050, Australia
| | - Omid Kavehei
- Faculty of Engineering, School of Biomedical Engineering, The University of Sydney, Sydney, NSW 2050, Australia
| | - Chenyu Wang
- Brain and Mind Centre, The University of Sydney, Sydney, NSW 2050, Australia
- Sydney Neuroimaging Analysis Centre, Sydney, NSW 2050, Australia
| | - Armin Nikpour
- Faculty of Medicine and Health, Central Clinical School, Sydney, NSW 2050, Australia
- Comprehensive Epilepsy Service and Department of Neurology, Royal Prince Alfred Hospital, Sydney, NSW 2050, Australia
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Schaper FLWVJ, Nordberg J, Cohen AL, Lin C, Hsu J, Horn A, Ferguson MA, Siddiqi SH, Drew W, Soussand L, Winkler AM, Simó M, Bruna J, Rheims S, Guenot M, Bucci M, Nummenmaa L, Staals J, Colon AJ, Ackermans L, Bubrick EJ, Peters JM, Wu O, Rost NS, Grafman J, Blumenfeld H, Temel Y, Rouhl RPW, Joutsa J, Fox MD. Mapping Lesion-Related Epilepsy to a Human Brain Network. JAMA Neurol 2023; 80:891-902. [PMID: 37399040 PMCID: PMC10318550 DOI: 10.1001/jamaneurol.2023.1988] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/03/2023] [Indexed: 07/04/2023]
Abstract
Importance It remains unclear why lesions in some locations cause epilepsy while others do not. Identifying the brain regions or networks associated with epilepsy by mapping these lesions could inform prognosis and guide interventions. Objective To assess whether lesion locations associated with epilepsy map to specific brain regions and networks. Design, Setting, and Participants This case-control study used lesion location and lesion network mapping to identify the brain regions and networks associated with epilepsy in a discovery data set of patients with poststroke epilepsy and control patients with stroke. Patients with stroke lesions and epilepsy (n = 76) or no epilepsy (n = 625) were included. Generalizability to other lesion types was assessed using 4 independent cohorts as validation data sets. The total numbers of patients across all datasets (both discovery and validation datasets) were 347 with epilepsy and 1126 without. Therapeutic relevance was assessed using deep brain stimulation sites that improve seizure control. Data were analyzed from September 2018 through December 2022. All shared patient data were analyzed and included; no patients were excluded. Main Outcomes and Measures Epilepsy or no epilepsy. Results Lesion locations from 76 patients with poststroke epilepsy (39 [51%] male; mean [SD] age, 61.0 [14.6] years; mean [SD] follow-up, 6.7 [2.0] years) and 625 control patients with stroke (366 [59%] male; mean [SD] age, 62.0 [14.1] years; follow-up range, 3-12 months) were included in the discovery data set. Lesions associated with epilepsy occurred in multiple heterogenous locations spanning different lobes and vascular territories. However, these same lesion locations were part of a specific brain network defined by functional connectivity to the basal ganglia and cerebellum. Findings were validated in 4 independent cohorts including 772 patients with brain lesions (271 [35%] with epilepsy; 515 [67%] male; median [IQR] age, 60 [50-70] years; follow-up range, 3-35 years). Lesion connectivity to this brain network was associated with increased risk of epilepsy after stroke (odds ratio [OR], 2.82; 95% CI, 2.02-4.10; P < .001) and across different lesion types (OR, 2.85; 95% CI, 2.23-3.69; P < .001). Deep brain stimulation site connectivity to this same network was associated with improved seizure control (r, 0.63; P < .001) in 30 patients with drug-resistant epilepsy (21 [70%] male; median [IQR] age, 39 [32-46] years; median [IQR] follow-up, 24 [16-30] months). Conclusions and Relevance The findings in this study indicate that lesion-related epilepsy mapped to a human brain network, which could help identify patients at risk of epilepsy after a brain lesion and guide brain stimulation therapies.
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Affiliation(s)
- Frederic L. W. V. J. Schaper
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry and Radiology, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Harvard University, Boston, Massachusetts
- Department of Neurology and School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Janne Nordberg
- Turku Brain and Mind Center, Department of Clinical Neurophysiology, Clinical Neurosciences, Turku University Hospital and University of Turku, Turku, Finland
| | - Alexander L. Cohen
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry and Radiology, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Harvard University, Boston, Massachusetts
- Department of Neurology, Boston Children’s Hospital, Boston, Massachusetts
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Christopher Lin
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry and Radiology, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Harvard University, Boston, Massachusetts
| | - Joey Hsu
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry and Radiology, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Harvard University, Boston, Massachusetts
| | - Andreas Horn
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry and Radiology, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Harvard University, Boston, Massachusetts
| | - Michael A. Ferguson
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry and Radiology, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Harvard University, Boston, Massachusetts
| | - Shan H. Siddiqi
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry and Radiology, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Harvard University, Boston, Massachusetts
| | - William Drew
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry and Radiology, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Harvard University, Boston, Massachusetts
| | - Louis Soussand
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry and Radiology, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Harvard University, Boston, Massachusetts
- Department of Neurology, Boston Children’s Hospital, Boston, Massachusetts
| | - Anderson M. Winkler
- National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
- Department of Human Genetics, University of Texas Rio Grande Valley, Brownsville
| | - Marta Simó
- Neuro-Oncology Unit, Hospital Universitari de Bellvitge - Institut Català d’Oncologia (IDIBELL), L’Hospitalet del Llobregat, Barcelona, Spain
| | - Jordi Bruna
- Neuro-Oncology Unit, Hospital Universitari de Bellvitge - Institut Català d’Oncologia (IDIBELL), L’Hospitalet del Llobregat, Barcelona, Spain
| | - Sylvain Rheims
- Department of Functional Neurology and Epileptology, Lyon Neurosciences Research Center, Hospices Civils de Lyon and University of Lyon, Lyon, France
- Institut national de la santé et de la recherche médicale, Lyon, France
| | - Marc Guenot
- Institut national de la santé et de la recherche médicale, Lyon, France
- Department of Functional Neurosurgery, Hospices Civils de Lyon and University of Lyon, Lyon, France
| | - Marco Bucci
- Turku PET Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Lauri Nummenmaa
- Turku PET Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Department of Psychology, University of Turku, Turku, Finland
| | - Julie Staals
- Department of Neurology and School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Albert J. Colon
- Academic Center for Epileptology Kempenhaeghe/Maastricht University Medical Center, Heeze & Maastricht, the Netherlands
- Department of Epileptology, Centre Hospitalier Universitaire Martinique, Fort-de-France, France
| | - Linda Ackermans
- Department of Neurosurgery and School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Ellen J. Bubrick
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry and Radiology, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Harvard University, Boston, Massachusetts
| | - Jurriaan M. Peters
- Harvard Medical School, Harvard University, Boston, Massachusetts
- Department of Neurology, Boston Children’s Hospital, Boston, Massachusetts
| | - Ona Wu
- Harvard Medical School, Harvard University, Boston, Massachusetts
- Athinoula A Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Natalia S. Rost
- Harvard Medical School, Harvard University, Boston, Massachusetts
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Jordan Grafman
- Cognitive Neuroscience Laboratory, Think + Speak Lab, Shirley Ryan Ability Lab, Chicago, Illinois
- Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Hal Blumenfeld
- Departments of Neurology, Neuroscience and Neurosurgery, Yale School of Medicine, New Haven, Connecticut
| | - Yasin Temel
- Department of Neurosurgery and School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Rob P. W. Rouhl
- Department of Neurology and School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Academic Center for Epileptology Kempenhaeghe/Maastricht University Medical Center, Heeze & Maastricht, the Netherlands
| | - Juho Joutsa
- Turku Brain and Mind Center, Department of Clinical Neurophysiology, Clinical Neurosciences, Turku University Hospital and University of Turku, Turku, Finland
- Turku PET Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Michael D. Fox
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry and Radiology, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Harvard University, Boston, Massachusetts
- Athinoula A Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
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McGonigal A, El Youssef N, Bartolomei F, Giusiano B, Guedj E. Interictal 18F-FDG brain PET metabolism in patients with postictal EEG suppression. Epilepsy Behav 2021; 116:107742. [PMID: 33493809 DOI: 10.1016/j.yebeh.2020.107742] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 12/21/2020] [Accepted: 12/21/2020] [Indexed: 10/22/2022]
Abstract
OBJECTIVE Postictal generalized suppression (PGES) may be associated with SUDEP risk. We aimed to study metabolic changes on 18Fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) in patients with focal to bilateral (generalized) seizures (GTCS) and PGES on stereoelectroencephalography (SEEG). METHODS We analyzed interictal brain metabolism in a group of 19 patients with widespread postictal suppression (PGES+) associated with SEEG-recorded GTCS. This group was compared to 25 patients without widespread suppression (PGES-) as defined by SEEG, matched for epilepsy localization and lateralization. Frequency of GTCS was observed to be higher in the PGES+ group (high risk group for SUDEP). Analysis of metabolic data was performed by statistical parametric mapping (SPM) on the whole-brain, and principal component analysis (PCA) on AAL (automated anatomical labeling) atlas. RESULTS Statistical parametric mapping showed right temporal pole hypometabolism in the PGES+ group (T-score = 3.90; p < 0.001; k = 185), in comparison to the PGES- group. Principal component analysis showed association between the metabolic values of certain regions of interest and PGES+/PGES- groups, confirmed by a significant difference (p < 0.05) in the values of the right dorsal temporal pole and of the left temporal pole between the two groups. Principal component analysis showed two dimensions significantly related to the PGES+/PGES- partition, involving the following regions: right temporal pole, right parahippocampal gyrus, right Rolandic operculum, bilateral paracentral lobule, right precuneus, right thalamus, right caudate and pallidum, bilateral cerebellum, left temporal pole, left Heschl's gyrus, left calcarine region, and left caudate, with loss of connection in PGES+ patients. Metabolic differences were independent of epilepsy localization and lateralization and persisted after correction for GTCS frequency. SIGNIFICANCE Interictal metabolic changes within a predominantly right-sided network involving temporal lobe and connected cortical and subcortical structures were seen in patients with frequent GTCS presenting widespread postictal suppression.
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Affiliation(s)
- Aileen McGonigal
- Clinical Neurophysiology and Epileptology Department, Timone Hospital, Marseille, France; Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.
| | - Nada El Youssef
- Clinical Neurophysiology and Epileptology Department, Timone Hospital, Marseille, France
| | - Fabrice Bartolomei
- Clinical Neurophysiology and Epileptology Department, Timone Hospital, Marseille, France; Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Bernard Giusiano
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France; APHM, Timone Hospital, Public Health Department, Marseille, France
| | - Eric Guedj
- APHM, Timone Hospital, Nuclear Medicine Department, Marseille, France; Aix Marseille Univ, CNRS, Ecole Centrale Marseille, UMR 7249, Institut Fresnel, Marseille, France; Aix Marseille Univ, CERIMED, Marseille, France
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