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Ke M, Hou L, Liu G. The co-activation patterns of multiple brain regions in Juvenile Myoclonic Epilepsy. Cogn Neurodyn 2024; 18:337-347. [PMID: 38699614 PMCID: PMC11061087 DOI: 10.1007/s11571-022-09838-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 06/09/2022] [Accepted: 06/17/2022] [Indexed: 11/03/2022] Open
Abstract
Juvenile myoclonic epilepsy (JME) as an idiopathic generalized epilepsy has been studied by many advanced neuroimaging techniques to elucidate its neuroanatomical basis and pathophysiological mechanisms. In this paper, we used co-activation patterns (CAPs) to explore the differences of dynamic brain activity changes in resting state between JME patients and healthy controls. 27 cases JME patients and 27 cases healthy of fMRI data were collected. The structural image data of the subjects were analyzed by voxel-based morphological analysis, and the regions with gray matter volume atrophy and high voxel were selected as the regions of interest. Further, the mean disease duration was used as boundary to divide the patients' data into the below-average time and the above-average time groups, which were defined as patient disease duration groups. And these data were used to construct CAPs and to compare changes in brain dynamics. It was found that the number of patterns occurrences and the possibility of switching between patterns were smaller than those in the healthy control, which indicated patients with damage to brain regions. For the patient time control group, the number of patterns occurrences and the possibility of switching between patterns were similar, while there was linear regression between the three values and disease duration. Collectively, this study provides important evidence for revealing the key brain regions of JME by studying the transformation between CAPs. Future studies could investigate the effects of receiving treatment on patient dynamic brain activity.
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Affiliation(s)
- Ming Ke
- School of Computer and Communication, Lanzhou University of Technology, 730050 Lanzhou, China
| | - Lei Hou
- School of Computer and Communication, Lanzhou University of Technology, 730050 Lanzhou, China
| | - Guangyao Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, 730030 Lanzhou, China
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Acar D, Ozcelik EU, Baykan B, Bebek N, Demiralp T, Bayram A. Diffusion tensor imaging in photosensitive and nonphotosensitive juvenile myoclonic epilepsy. Seizure 2024; 115:36-43. [PMID: 38183826 DOI: 10.1016/j.seizure.2023.12.015] [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: 05/22/2023] [Revised: 11/27/2023] [Accepted: 12/22/2023] [Indexed: 01/08/2024] Open
Abstract
INTRODUCTION/BACKGROUND Juvenile myoclonic epilepsy (JME) syndrome is known to cause alterations in brain structure and white matter integrity. The study aimed to determine structural white matter changes in patients with JME and to reveal the differences between the photosensitive (PS) and nonphotosensitive (NPS) subgroups by diffusion tensor imaging (DTI) using the tract-based spatial statistics (TBSS) method. METHODS This study included data from 16 PS, 15 NPS patients with JME, and 41 healthy participants. The mean fractional anisotropy (FA) values of these groups were calculated, and comparisons were made via the TBSS method over FA values in the whole-brain and 81 regions of interest (ROI) obtained from the John Hopkins University White Matter Atlas. RESULTS In the whole-brain TBSS analysis, no significant differences in FA values were observed in pairwise comparisons of JME patient group and subgroups with healthy controls (HCs) and in comparison between JME subgroups. In ROI-based TBSS analysis, an increase in FA values of right anterior corona radiata and left corticospinal pathways was found in JME patient group compared with HC group. When comparing JME-PS patients with HCs, an FA increase was observed in the bilateral anterior corona radiata region, whereas when comparing JME-NPS patients with HCs, an FA increase was observed in bilateral corticospinal pathway. Moreover, in subgroup comparison, an increase in FA values was noted in corpus callosum genu region in JME-PS compared with JME-NPS. CONCLUSIONS Our results support the disruption in thalamofrontal white matter integrity in JME, and subgroups and highlight the importance of using different analysis methods to show the underlying microstructural changes.
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Affiliation(s)
- Dilan Acar
- Department of Neuroscience, Aziz Sancar Institute of Experimental Medicine, Istanbul University, Istanbul, Türkiye; Hulusi Behçet Life Sciences Research Laboratory, Istanbul University, Istanbul, Türkiye
| | - Emel Ur Ozcelik
- Departments of Neurology and Clinical Neurophysiology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Türkiye; Department of Neurology, Istanbul Kanuni Sultan Suleyman Training and Research Hospital, University of Health Sciences, Istanbul, Türkiye.
| | - Betül Baykan
- Departments of Neurology and Clinical Neurophysiology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Türkiye; Department of Neurology, Istanbul EMAR Medical Center, Istanbul, Türkiye
| | - Nerses Bebek
- Departments of Neurology and Clinical Neurophysiology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Türkiye
| | - Tamer Demiralp
- Department of Physiology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Türkiye
| | - Ali Bayram
- Department of Neuroscience, Aziz Sancar Institute of Experimental Medicine, Istanbul University, Istanbul, Türkiye; Hulusi Behçet Life Sciences Research Laboratory, Istanbul University, Istanbul, Türkiye
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Luo D, Liu Y, Zhang N, Wang T. Differences in the distribution of triggers among resting state networks in patients with juvenile myoclonic epilepsy explained by network analysis. Front Neurosci 2023; 17:1214687. [PMID: 37859762 PMCID: PMC10582565 DOI: 10.3389/fnins.2023.1214687] [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: 04/30/2023] [Accepted: 08/25/2023] [Indexed: 10/21/2023] Open
Abstract
Background Juvenile myoclonus epilepsy (JME) is an idiopathic generalized epilepsy syndrome. Functional connectivity studies based on graph theory have demonstrated changes in functional connectivity among different brain regions in patients with JME and healthy controls. However, previous studies have not been able to clarify why visual stimulation or increased cognitive load induces epilepsy symptoms in only some patients with JME. Methods This study constructed a small-world network for the visualization of functional connectivity of brain regions in patients with JME, based on system mapping. We used the node reduction method repeatedly to identify the core nodes of the resting brain network of patients with JME. Thereafter, a functional connectivity network of the core brain regions in patients with JME was established, and it was analyzed manually with white matter tracks restriction to explain the differences in symptom distribution in patients with JME. Results Patients with JME had 21 different functional connections in their resting state, and no significant differences in their distribution were noted. The thalamus, cerebellum, basal ganglia, supplementary motor area, visual cortex, and prefrontal lobe were the core brain regions that comprised the functional connectivity network in patients with JME during their resting state. The betweenness centrality of the prefrontal lobe and the visual cortex in the core functional connectivity network of patients with JME was lower than that of the other brain regions. Conclusion The functional connectivity and node importance of brain regions of patients with JME changed dynamically in the resting state. Abnormal discharges originating from the thalamus, cerebellum, basal ganglia, supplementary motor area, visual cortex, and prefrontal cortex are most likely to lead to seizures in patients with JME. Further, the low average value of betweenness centrality of the prefrontal and visual cortices explains why visual stimulation or increased cognitive load can induce epileptic symptoms in only some patients with JME.
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Affiliation(s)
- Dadong Luo
- Department of Neurology, Lanzhou University Second Hospital, Lanzhou, China
- Second School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Yaqing Liu
- Department of Neurology, Lanzhou University Second Hospital, Lanzhou, China
- Second School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Ningning Zhang
- Department of Neurology, Lanzhou University Second Hospital, Lanzhou, China
- Second School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Tiancheng Wang
- Department of Neurology, Lanzhou University Second Hospital, Lanzhou, China
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Elkhateeb N, Olivieri G, Siri B, Boyd S, Stepien KM, Sharma R, Morris AAM, Hartley T, Crowther L, Grunewald S, Cleary M, Mundy H, Chakrapani A, Lachmann R, Murphy E, Santra S, Uudelepp ML, Yeo M, Bernhardt I, Sudakhar S, Chan A, Mills P, Ridout D, Gissen P, Dionisi-Vici C, Baruteau J. Natural history of epilepsy in argininosuccinic aciduria provides new insights into pathophysiology: A retrospective international study. Epilepsia 2023; 64:1612-1626. [PMID: 36994644 DOI: 10.1111/epi.17596] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 03/13/2023] [Accepted: 03/27/2023] [Indexed: 03/31/2023]
Abstract
OBJECTIVE Argininosuccinate lyase (ASL) is integral to the urea cycle, which enables nitrogen wasting and biosynthesis of arginine, a precursor of nitric oxide. Inherited ASL deficiency causes argininosuccinic aciduria, the second most common urea cycle defect and an inherited model of systemic nitric oxide deficiency. Patients present with developmental delay, epilepsy, and movement disorder. Here we aim to characterize epilepsy, a common and neurodebilitating comorbidity in argininosuccinic aciduria. METHODS We conducted a retrospective study in seven tertiary metabolic centers in the UK, Italy, and Canada from 2020 to 2022, to assess the phenotype of epilepsy in argininosuccinic aciduria and correlate it with clinical, biochemical, radiological, and electroencephalographic data. RESULTS Thirty-seven patients, 1-31 years of age, were included. Twenty-two patients (60%) presented with epilepsy. The median age at epilepsy onset was 24 months. Generalized tonic-clonic and focal seizures were most common in early-onset patients, whereas atypical absences were predominant in late-onset patients. Seventeen patients (77%) required antiseizure medications and six (27%) had pharmacoresistant epilepsy. Patients with epilepsy presented with a severe neurodebilitating disease with higher rates of speech delay (p = .04) and autism spectrum disorders (p = .01) and more frequent arginine supplementation (p = .01) compared to patients without epilepsy. Neonatal seizures were not associated with a higher risk of developing epilepsy. Biomarkers of ureagenesis did not differ between epileptic and non-epileptic patients. Epilepsy onset in early infancy (p = .05) and electroencephalographic background asymmetry (p = .0007) were significant predictors of partially controlled or refractory epilepsy. SIGNIFICANCE Epilepsy in argininosuccinic aciduria is frequent, polymorphic, and associated with more frequent neurodevelopmental comorbidities. We identified prognostic factors for pharmacoresistance in epilepsy. This study does not support defective ureagenesis as prominent in the pathophysiology of epilepsy but suggests a role of central dopamine deficiency. A role of arginine in epileptogenesis was not supported and warrants further studies to assess the potential arginine neurotoxicity in argininosuccinic aciduria.
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Affiliation(s)
- Nour Elkhateeb
- Department of Paediatric Metabolic Medicine, Great Ormond Street Hospital for Children NHS Trust, London, UK
- Department of Clinical Genetics, Cambridge University Hospitals, Cambridge, UK
| | - Giorgia Olivieri
- Division of Metabolism, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Barbara Siri
- Division of Metabolism, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Stewart Boyd
- Department of Neurophysiology, Great Ormond Street Hospital for Children NHS Trust, London, UK
| | - Karolina M Stepien
- Mark Holland Metabolic Unit, Adult Inherited Metabolic Diseases Department, Salford Royal NHS Foundation Trust, Salford, UK
| | - Reena Sharma
- Mark Holland Metabolic Unit, Adult Inherited Metabolic Diseases Department, Salford Royal NHS Foundation Trust, Salford, UK
| | - Andrew A M Morris
- Willink Unit, Manchester Centre for Genomic Medicine, Manchester, UK
| | - Thomas Hartley
- Willink Unit, Manchester Centre for Genomic Medicine, Manchester, UK
| | - Laura Crowther
- Willink Unit, Manchester Centre for Genomic Medicine, Manchester, UK
| | - Stephanie Grunewald
- Department of Paediatric Metabolic Medicine, Great Ormond Street Hospital for Children NHS Trust, London, UK
- University College London Great Ormond Street Institute of Child Health, London, UK
- National Institute of Health Research Great Ormond Street Biomedical Research Centre, London, UK
| | - Maureen Cleary
- Department of Paediatric Metabolic Medicine, Great Ormond Street Hospital for Children NHS Trust, London, UK
| | - Helen Mundy
- Evelina London Children's Hospital, St Thomas's Hospital, London, UK
| | - Anupam Chakrapani
- Department of Paediatric Metabolic Medicine, Great Ormond Street Hospital for Children NHS Trust, London, UK
| | - Robin Lachmann
- Charles Dent Metabolic Unit, National Hospital for Neurology and Neurosurgery, London, UK
| | - Elaine Murphy
- Charles Dent Metabolic Unit, National Hospital for Neurology and Neurosurgery, London, UK
| | - Saikat Santra
- Department of Paediatric Metabolic Medicine, Birmingham Children's Hospital, Birmingham, UK
| | - Mari-Liis Uudelepp
- Department of Paediatric Metabolic Medicine, Great Ormond Street Hospital for Children NHS Trust, London, UK
| | - Mildrid Yeo
- Department of Paediatric Metabolic Medicine, Great Ormond Street Hospital for Children NHS Trust, London, UK
| | - Isaac Bernhardt
- Department of Paediatric Metabolic Medicine, Great Ormond Street Hospital for Children NHS Trust, London, UK
| | - Sniya Sudakhar
- Department of Radiology, Great Ormond Street Hospital for Children NHS Trust, London, UK
| | - Alicia Chan
- Department of Medical Genetics, University of Alberta, Edmonton, Alberta, Canada
| | - Philippa Mills
- University College London Great Ormond Street Institute of Child Health, London, UK
| | - Debora Ridout
- Willink Unit, Manchester Centre for Genomic Medicine, Manchester, UK
| | - Paul Gissen
- Department of Paediatric Metabolic Medicine, Great Ormond Street Hospital for Children NHS Trust, London, UK
- University College London Great Ormond Street Institute of Child Health, London, UK
- National Institute of Health Research Great Ormond Street Biomedical Research Centre, London, UK
| | - Carlo Dionisi-Vici
- Division of Metabolism, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Julien Baruteau
- Department of Paediatric Metabolic Medicine, Great Ormond Street Hospital for Children NHS Trust, London, UK
- University College London Great Ormond Street Institute of Child Health, London, UK
- National Institute of Health Research Great Ormond Street Biomedical Research Centre, London, UK
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Zhang B, Chen T, Hao X, Xin M, Liang J. Electroclinical characteristics of photosensitive epilepsy: A retrospective study of 31 Chinese children and literature review. Front Pediatr 2023; 11:994817. [PMID: 36969295 PMCID: PMC10034115 DOI: 10.3389/fped.2023.994817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 01/27/2023] [Indexed: 03/29/2023] Open
Abstract
Objective The objective of this study was to better understand the clinical features of photosensitive epilepsy (PSE) in Chinese children. Methods Thirty-one children with PSE were screened out of 398 children with epilepsy who were consecutively diagnosed by the video-electroencephalogram (VEEG) monitoring method and by using an intermittent photic stimulation (IPS) test. Their EEGs and clinical features were retrospectively analyzed, and their treatment outcomes were followed up. Results PSE accounted for 7.79% (31/398) of children with epilepsy during the observation period in our single epilepsy center. The male to female ratio of PSE was 1:3.43, and the average seizure onset age was 7.8 ± 3.28 years. The highest range of frequency sensitivity of the IPS test for the induction of EEG epileptic discharge or electroclinical seizures was within 10-20 Hz. Electroclinical seizures were induced in 41.94% (13/31) of PSE patients by using the IPS test, while EEG discharge without clinical seizures was induced in 58.06% (18/31) of PSE patients. Among all PSE patients, an IPS-positive reaction in the eye-closure state was induced in 83.87% of patients, and this rate was significantly higher than that in the eye-opened state (41.94%) or eye-closed state (35.48%). (Eye-closure IPS stimulation means: make the subjects close their eyes at the beginning of each stimulation, open their eyes at the end of the stimulation, and close their eyes again at the beginning of the next stimulation, and so on. While Eye-closed IPS stimulation means the stimulation is started after 5 s of eye closure, and the subjects are kept closed throughout the whole process.) The common and effective drugs used for single or combined therapy in PSE children were valproic acid and levetiracetam. Conclusion This study provides some useful information about electroclinical characteristics in a cohort of 31 PSE children. It may be beneficial for pediatric neurologists in terms of paying more attention to PSE and correctly dealing with it.
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Affiliation(s)
- Bo Zhang
- Department of Pediatric Neurology, First Hospital of Jilin University, Changchun, China
- Jilin Provincial Key Laboratory of Pediatric Neurology, Changchun, China
| | - Tianyu Chen
- Department of Pediatric Neurology, First Hospital of Jilin University, Changchun, China
- Department of Pediatrics, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Xiaosheng Hao
- Department of Pediatric Neurology, First Hospital of Jilin University, Changchun, China
- Jilin Provincial Key Laboratory of Pediatric Neurology, Changchun, China
| | - Meiying Xin
- Department of Pediatric Neurology, First Hospital of Jilin University, Changchun, China
- Jilin Provincial Key Laboratory of Pediatric Neurology, Changchun, China
| | - Jianmin Liang
- Department of Pediatric Neurology, First Hospital of Jilin University, Changchun, China
- Jilin Provincial Key Laboratory of Pediatric Neurology, Changchun, China
- Correspondence: Jianmin Liang
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Marapin RS, van der Horn HJ, van der Stouwe AMM, Dalenberg JR, de Jong BM, Tijssen MAJ. Altered brain connectivity in hyperkinetic movement disorders: A review of resting-state fMRI. Neuroimage Clin 2023; 37:103302. [PMID: 36669351 PMCID: PMC9868884 DOI: 10.1016/j.nicl.2022.103302] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 12/21/2022] [Accepted: 12/22/2022] [Indexed: 12/25/2022]
Abstract
BACKGROUND Hyperkinetic movement disorders (HMD) manifest as abnormal and uncontrollable movements. Despite reported involvement of several neural circuits, exact connectivity profiles remain elusive. OBJECTIVES Providing a comprehensive literature review of resting-state brain connectivity alterations using resting-state fMRI (rs-fMRI). We additionally discuss alterations from the perspective of brain networks, as well as correlations between connectivity and clinical measures. METHODS A systematic review was performed according to PRISMA guidelines and searching PubMed until October 2022. Rs-fMRI studies addressing ataxia, chorea, dystonia, myoclonus, tics, tremor, and functional movement disorders (FMD) were included. The standardized mean difference was used to summarize findings per region in the Automated Anatomical Labeling atlas for each phenotype. Furthermore, the activation likelihood estimation meta-analytic method was used to analyze convergence of significant between-group differences per phenotype. Finally, we conducted hierarchical cluster analysis to provide additional insights into commonalities and differences across HMD phenotypes. RESULTS Most articles concerned tremor (51), followed by dystonia (46), tics (19), chorea (12), myoclonus (11), FMD (11), and ataxia (8). Altered resting-state connectivity was found in several brain regions: in ataxia mainly cerebellar areas; for chorea, the caudate nucleus; for dystonia, sensorimotor and basal ganglia regions; for myoclonus, the thalamus and cingulate cortex; in tics, the basal ganglia, cerebellum, insula, and frontal cortex; for tremor, the cerebello-thalamo-cortical circuit; finally, in FMD, frontal, parietal, and cerebellar regions. Both decreased and increased connectivity were found for all HMD. Significant spatial convergence was found for dystonia, FMD, myoclonus, and tremor. Correlations between clinical measures and resting-state connectivity were frequently described. CONCLUSION Key brain regions contributing to functional connectivity changes across HMD often overlap. Possible increases and decreases of functional connections of a specific region emphasize that HMD should be viewed as a network disorder. Despite the complex interplay of physiological and methodological factors, this review serves to gain insight in brain connectivity profiles across HMD phenotypes.
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Affiliation(s)
- Ramesh S Marapin
- University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands; Expertise Center Movement Disorders Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands
| | - Harm J van der Horn
- University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands
| | - A M Madelein van der Stouwe
- University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands; Expertise Center Movement Disorders Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands
| | - Jelle R Dalenberg
- University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands; Expertise Center Movement Disorders Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands
| | - Bauke M de Jong
- University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands
| | - Marina A J Tijssen
- University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands; Expertise Center Movement Disorders Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands.
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Kim KM, Hwang H, Sohn B, Park K, Han K, Ahn SS, Lee W, Chu MK, Heo K, Lee SK. Development and Validation of MRI-Based Radiomics Models for Diagnosing Juvenile Myoclonic Epilepsy. Korean J Radiol 2022; 23:1281-1289. [PMID: 36447416 PMCID: PMC9747272 DOI: 10.3348/kjr.2022.0539] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/21/2022] [Accepted: 09/26/2022] [Indexed: 11/11/2022] Open
Abstract
OBJECTIVE Radiomic modeling using multiple regions of interest in MRI of the brain to diagnose juvenile myoclonic epilepsy (JME) has not yet been investigated. This study aimed to develop and validate radiomics prediction models to distinguish patients with JME from healthy controls (HCs), and to evaluate the feasibility of a radiomics approach using MRI for diagnosing JME. MATERIALS AND METHODS A total of 97 JME patients (25.6 ± 8.5 years; female, 45.5%) and 32 HCs (28.9 ± 11.4 years; female, 50.0%) were randomly split (7:3 ratio) into a training (n = 90) and a test set (n = 39) group. Radiomic features were extracted from 22 regions of interest in the brain using the T1-weighted MRI based on clinical evidence. Predictive models were trained using seven modeling methods, including a light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, with radiomics features in the training set. The performance of the models was validated and compared to the test set. The model with the highest area under the receiver operating curve (AUROC) was chosen, and important features in the model were identified. RESULTS The seven tested radiomics models, including light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, showed AUROC values of 0.817, 0.807, 0.783, 0.779, 0.767, 0.762, and 0.672, respectively. The light gradient boosting machine with the highest AUROC, albeit without statistically significant differences from the other models in pairwise comparisons, had accuracy, precision, recall, and F1 scores of 0.795, 0.818, 0.931, and 0.871, respectively. Radiomic features, including the putamen and ventral diencephalon, were ranked as the most important for suggesting JME. CONCLUSION Radiomic models using MRI were able to differentiate JME from HCs.
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Affiliation(s)
- Kyung Min Kim
- Department of Neurology, Epilepsy Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Heewon Hwang
- Department of Neurology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Beomseok Sohn
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Centre for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.
| | - Kisung Park
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Centre for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.,Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Korea
| | - Kyunghwa Han
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Centre for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Centre for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Wonwoo Lee
- Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, Yongin, Korea
| | - Min Kyung Chu
- Department of Neurology, Epilepsy Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Kyoung Heo
- Department of Neurology, Epilepsy Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Centre for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
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Liu G, Zheng W, Liu H, Guo M, Ma L, Hu W, Ke M, Sun Y, Zhang J, Zhang Z. Aberrant dynamic structure-function relationship of rich-club organization in treatment-naïve newly diagnosed juvenile myoclonic epilepsy. Hum Brain Mapp 2022; 43:3633-3645. [PMID: 35417064 PMCID: PMC9294302 DOI: 10.1002/hbm.25873] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 03/28/2022] [Accepted: 04/03/2022] [Indexed: 11/25/2022] Open
Abstract
Neuroimaging studies have shown that juvenile myoclonic epilepsy (JME) is characterized by impaired brain networks. However, few studies have investigated the potential disruptions in rich‐club organization—a core feature of the brain networks. Moreover, it is unclear how structure–function relationships dynamically change over time in JME. Here, we quantify the anatomical rich‐club organization and dynamic structural and functional connectivity (SC–FC) coupling in 47 treatment‐naïve newly diagnosed patients with JME and 40 matched healthy controls. Dynamic functional network efficiency and its association with SC–FC coupling were also calculated to examine the supporting of structure–function relationship to brain information transfer. The results showed that the anatomical rich‐club organization was disrupted in the patient group, along with decreased connectivity strength among rich‐club hub nodes. Furthermore, reduced SC–FC coupling in rich‐club organization of the patients was found in two functionally independent dynamic states, that is the functional segregation state (State 1) and the strong somatomotor‐cognitive control interaction state (State 5); and the latter was significantly associated with disease severity. In addition, the relationships between SC–FC coupling of hub nodes connections and functional network efficiency in State 1 were found to be absent in patients. The aberrant dynamic SC–FC coupling of rich‐club organization suggests a selective influence of densely interconnected network core in patients with JME at the early phase of the disease, offering new insights and potential biomarkers into the underlying neurodevelopmental basis of behavioral and cognitive impairments observed in JME.
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Affiliation(s)
- Guangyao Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China.,Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Hong Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China.,Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Man Guo
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Laiyang Ma
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China.,Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Wanjun Hu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China.,Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Ming Ke
- College of Computer and Communication, Lanzhou University of Technology, Lanzhou, China
| | - Yu Sun
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.,Zhejiang Lab, Hangzhou, China
| | - Jing Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China.,Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Zhe Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.,School of Physics, Hangzhou Normal University, Hangzhou, China
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Moon JU, Lee JY, Kim KY, Eom TH, Kim YH, Lee IG. Comparative analysis of background EEG activity in juvenile myoclonic epilepsy during valproic acid treatment: a standardized, low-resolution, brain electromagnetic tomography (sLORETA) study. BMC Neurol 2022; 22:48. [PMID: 35139806 PMCID: PMC8827290 DOI: 10.1186/s12883-022-02577-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 01/31/2022] [Indexed: 11/11/2022] Open
Abstract
Background By definition, the background EEG is normal in juvenile myoclonic epilepsy (JME) patients and not accompanied by other developmental and cognitive problems. However, some recent studies using quantitative EEG (qEEG) reported abnormal changes in the background activity. QEEG investigation in patients undergoing anticonvulsant treatment might be a useful approach to explore the electrophysiology and anticonvulsant effects in JME. Methods We investigated background EEG activity changes in patients undergoing valproic acid (VPA) treatment using qEEG analysis in a distributed source model. In 17 children with JME, non-parametric statistical analysis using standardized low-resolution brain electromagnetic tomography was performed to compare the current density distribution of four frequency bands (delta, theta, alpha, and beta) between untreated and treated conditions. Results VPA reduced background EEG activity in the low-frequency (delta-theta) bands across the frontal, parieto-occipital, and limbic lobes (threshold log-F-ratio = ±1.414, p < 0.05; threshold log-F-ratio= ±1.465, p < 0.01). In the delta band, comparative analysis revealed significant current density differences in the occipital, parietal, and limbic lobes. In the theta band, the analysis revealed significant differences in the frontal, occipital, and limbic lobes. The maximal difference was found in the delta band in the cuneus of the left occipital lobe (log-F-ratio = −1.840) and the theta band in the medial frontal gyrus of the left frontal lobe (log-F-ratio = −1.610). Conclusions This study demonstrated the anticonvulsant effects on the neural networks involved in JME. In addition, these findings suggested the focal features and the possibility of functional deficits in patients with JME.
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Affiliation(s)
- Ja-Un Moon
- Department of Pediatrics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Joo-Young Lee
- Department of Pediatrics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Kwang-Yeon Kim
- Department of Pediatrics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Tae-Hoon Eom
- Department of Pediatrics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
| | - Young-Hoon Kim
- Department of Pediatrics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - In-Goo Lee
- Department of Pediatrics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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