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Analysis of Clinical Characteristics, Background, and Paroxysmal Activity in EEG of Patients with Juvenile Myoclonic Epilepsy. Brain Sci 2021; 12:brainsci12010029. [PMID: 35053773 PMCID: PMC8773902 DOI: 10.3390/brainsci12010029] [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: 11/27/2021] [Revised: 12/24/2021] [Accepted: 12/25/2021] [Indexed: 11/17/2022] Open
Abstract
Juvenile myoclonic epilepsy (JME) appears in adolescence with myoclonic, absence, and generalized tonic clonic (GTC) seizures with paroxysmal activity of polyspike and slow wave (PSW), or spike and wave (SW) complexes in EEG. Our aim was to analyze the clinical characteristics, background EEG activity, and paroxysmal events in 41 patients with JME. Background EEG activity was analyzed with visual, quantitative (QEEG), and neurometric parameters. Our JME patients started with absence seizures at 11.4 ± 1.5 years old, myoclonic seizures at 13.6 ± 2.5 years, and GTC seizures at 15.1 ± 0.8 years. The seizures presented in awakening at 7:39 h with sleep deprivation, alcoholic beverage intake, and stress as the most frequent precipitant factors. Paroxysmal activity was of PSW and fast SW complexes with 40.5 ± 62.6 events/hour and a duration of 1.7 s. Right asymmetric paroxysmal activity was present in 68.3% of patients. Background EEG activity was abnormal in 31.7% of patients with visual analysis. With QEEG beta AP (absolute power) increase and AP delta decrease were the most frequent abnormalities found. Spectral analysis showed that 48.7% of patients had normal results, and 26.83% and 24.4% had higher and lower frequencies than 10.156 Hz, respectively. We concluded that, with visual analysis, background EEG activity was abnormal in a few patients and the abnormalities increased when QEEG was used.
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Zhang Z, Liu G, Zheng W, Shi J, Liu H, Sun Y. Altered dynamic effective connectivity of the default mode network in newly diagnosed drug-naïve juvenile myoclonic epilepsy. Neuroimage Clin 2020; 28:102431. [PMID: 32950903 PMCID: PMC7509229 DOI: 10.1016/j.nicl.2020.102431] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 08/08/2020] [Accepted: 09/08/2020] [Indexed: 01/21/2023]
Abstract
Juvenile myoclonic epilepsy (JME) has been repeatedly revealed to be associated with brain dysconnectivity in the default mode network (DMN). However, the implicit assumption of stationary and nondirectional functional connectivity (FC) in most previous resting-state fMRI studies raises an open question of JME-related aberrations in dynamic causal properties of FC. Here, we introduces an empirical method incorporating sliding-window approach and a multivariate Granger causality analysis to investigate, for the first time, the reorganization of dynamic effective connectivity (DEC) in DMN for patients with JME. DEC was obtained from resting-state fMRI of 34 patients with newly diagnosed and drug-naïve JME and 34 matched controls. Through clustering analysis, we found two distinct states that characterize the DEC patterns (i.e., a less frequent, strongly connected state (State 1) and a more frequent, weakly connected state (State 2)). Patients showed altered ECs within DMN subnetworks in the State 2, whereas abnormal ECs between DMN subnetworks were found in the State 1. Furthermore, we observed that the causal influence flows of the medial prefrontal cortex and angular gyrus were altered in a manner of state specificity, and associated with disease severity of patients. Overall, our findings extend the dysconnectivity hypothesis in JME from static to dynamic causal FC and demonstrate that aberrant DEC may underlie abnormal brain function in JME at early phase of illness.
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Affiliation(s)
- Zhe Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Zhejiang, China
| | - Guangyao Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
| | - Weihao Zheng
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Zhejiang, China
| | - Jie Shi
- School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Hong Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
| | - Yu Sun
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Zhejiang, China; Department of Radiology, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Zhejiang, China.
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Abstract
PURPOSE OF REVIEW Seizures can arise in neocortical, thalamocortical, limbic or brainstem networks. Here, we review recent genetic mechanisms implicated in focal and genetic generalized epilepsies (GGEs). RECENT FINDINGS Pathogenic variation in GAP activity toward RAGs 1 (GATOR1) complex genes (i.e., DEPDC5, NPRL2 and NPRL3) mainly result in focal epilepsies. They are associated with high rates of sudden unexpected death in epilepsy and malformations of cortical development (MCD), where "two-hits" in GATOR1-related pathways are also found in MCDs. Large-scale sequencing studies continue to reveal new genetic risk (germline or somatic) variants, and new genes relevant to epileptic encephalopathies (EEs). Genes previously associated with EEs, including GABAA receptor genes, are now known to play a role in both common focal and GGEs in individuals without intellectual disabilities. These findings suggest that there may be a common pathophysiological mechanism in GGEs and focal epilepsies. Finally, polygenic risk scores, based on common genetic variation, offer promise in helping to differentiate between GGEs and common forms of focal epilepsies. Genetic abnormalities are a significant cause of common sporadic epilepsies, epilepsies associated with inflammatory markers, and focal epilepsies with or without MCD. Future studies using genome sequencing may provide more answers to the remaining unresolved epilepsy cases.
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Parsons N, Bowden SC, Vogrin S, D’Souza WJ. Default mode network dysfunction in idiopathic generalised epilepsy. Epilepsy Res 2020; 159:106254. [DOI: 10.1016/j.eplepsyres.2019.106254] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 11/13/2019] [Accepted: 12/07/2019] [Indexed: 12/14/2022]
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Wandschneider B, Hong SJ, Bernhardt BC, Fadaie F, Vollmar C, Koepp MJ, Bernasconi N, Bernasconi A. Developmental MRI markers cosegregate juvenile patients with myoclonic epilepsy and their healthy siblings. Neurology 2019; 93:e1272-e1280. [PMID: 31467252 PMCID: PMC7011863 DOI: 10.1212/wnl.0000000000008173] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2019] [Accepted: 06/07/2019] [Indexed: 12/18/2022] Open
Abstract
OBJECTIVE MRI studies of genetic generalized epilepsies have mainly described group-level changes between patients and healthy controls. To determine the endophenotypic potential of structural MRI in juvenile myoclonic epilepsy (JME), we examined MRI-based cortical morphologic markers in patients and their healthy siblings. METHODS In this prospective, cross-sectional study, we obtained 3T MRI in patients with JME, siblings, and controls. We mapped sulco-gyral complexity and surface area, morphologic markers of brain development, and cortical thickness. Furthermore, we calculated mean geodesic distance, a surrogate marker of cortico-cortical connectivity. RESULTS Compared to controls, patients and siblings showed increased folding complexity and surface area in prefrontal and cingulate cortices. In these regions, they also displayed abnormally increased geodesic distance, suggesting network isolation and decreased efficiency, with strongest effects for limbic, fronto-parietal, and dorsal-attention networks. In areas of findings overlap, we observed strong patient-sibling correlations. Conversely, neocortical thinning was present in patients only and related to disease duration. Patients showed subtle impairment in mental flexibility, a frontal lobe function test, as well as deficits in naming and design learning. Siblings' performance fell between patients and controls. CONCLUSION MRI markers of brain development and connectivity are likely heritable and may thus serve as endophenotypes. The topography of morphologic anomalies and their abnormal structural network integration likely explains cognitive impairments in patients with JME and their siblings. By contrast, cortical atrophy likely represents a marker of disease.
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Affiliation(s)
- Britta Wandschneider
- From the Neuroimaging of Epilepsy Laboratory (B.W., S.-J.H., B.C.B., F.F., N.B., A.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Clinical and Experimental Epilepsy (B.W., C.V., M.J.K.), UCL Institute of Neurology, London, UK; Epilepsy Center, Department of Neurology (C.V.), Klinikum Großhadern, University of Munich, Germany; and Multimodal Imaging and Connectome Analysis Lab (B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Seok-Jun Hong
- From the Neuroimaging of Epilepsy Laboratory (B.W., S.-J.H., B.C.B., F.F., N.B., A.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Clinical and Experimental Epilepsy (B.W., C.V., M.J.K.), UCL Institute of Neurology, London, UK; Epilepsy Center, Department of Neurology (C.V.), Klinikum Großhadern, University of Munich, Germany; and Multimodal Imaging and Connectome Analysis Lab (B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Boris C Bernhardt
- From the Neuroimaging of Epilepsy Laboratory (B.W., S.-J.H., B.C.B., F.F., N.B., A.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Clinical and Experimental Epilepsy (B.W., C.V., M.J.K.), UCL Institute of Neurology, London, UK; Epilepsy Center, Department of Neurology (C.V.), Klinikum Großhadern, University of Munich, Germany; and Multimodal Imaging and Connectome Analysis Lab (B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Fatemeh Fadaie
- From the Neuroimaging of Epilepsy Laboratory (B.W., S.-J.H., B.C.B., F.F., N.B., A.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Clinical and Experimental Epilepsy (B.W., C.V., M.J.K.), UCL Institute of Neurology, London, UK; Epilepsy Center, Department of Neurology (C.V.), Klinikum Großhadern, University of Munich, Germany; and Multimodal Imaging and Connectome Analysis Lab (B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Christian Vollmar
- From the Neuroimaging of Epilepsy Laboratory (B.W., S.-J.H., B.C.B., F.F., N.B., A.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Clinical and Experimental Epilepsy (B.W., C.V., M.J.K.), UCL Institute of Neurology, London, UK; Epilepsy Center, Department of Neurology (C.V.), Klinikum Großhadern, University of Munich, Germany; and Multimodal Imaging and Connectome Analysis Lab (B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Matthias J Koepp
- From the Neuroimaging of Epilepsy Laboratory (B.W., S.-J.H., B.C.B., F.F., N.B., A.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Clinical and Experimental Epilepsy (B.W., C.V., M.J.K.), UCL Institute of Neurology, London, UK; Epilepsy Center, Department of Neurology (C.V.), Klinikum Großhadern, University of Munich, Germany; and Multimodal Imaging and Connectome Analysis Lab (B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Neda Bernasconi
- From the Neuroimaging of Epilepsy Laboratory (B.W., S.-J.H., B.C.B., F.F., N.B., A.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Clinical and Experimental Epilepsy (B.W., C.V., M.J.K.), UCL Institute of Neurology, London, UK; Epilepsy Center, Department of Neurology (C.V.), Klinikum Großhadern, University of Munich, Germany; and Multimodal Imaging and Connectome Analysis Lab (B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | - Andrea Bernasconi
- From the Neuroimaging of Epilepsy Laboratory (B.W., S.-J.H., B.C.B., F.F., N.B., A.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Clinical and Experimental Epilepsy (B.W., C.V., M.J.K.), UCL Institute of Neurology, London, UK; Epilepsy Center, Department of Neurology (C.V.), Klinikum Großhadern, University of Munich, Germany; and Multimodal Imaging and Connectome Analysis Lab (B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
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Qin Y, Jiang S, Zhang Q, Dong L, Jia X, He H, Yao Y, Yang H, Zhang T, Luo C, Yao D. BOLD-fMRI activity informed by network variation of scalp EEG in juvenile myoclonic epilepsy. NEUROIMAGE-CLINICAL 2019; 22:101759. [PMID: 30897433 PMCID: PMC6425117 DOI: 10.1016/j.nicl.2019.101759] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 02/22/2019] [Accepted: 03/10/2019] [Indexed: 01/14/2023]
Abstract
Epilepsy is marked by hypersynchronous bursts of neuronal activity, and seizures can propagate variably to any and all areas, leading to brain network dynamic organization. However, the relationship between the network characteristics of scalp EEG and blood oxygenation level-dependent (BOLD) responses in epilepsy patients is still not well known. In this study, simultaneous EEG and fMRI data were acquired in 18 juvenile myoclonic epilepsy (JME) patients. Then, the adapted directed transfer function (ADTF) values between EEG electrodes were calculated to define the time-varying network. The variation of network information flow within sliding windows was used as a temporal regressor in fMRI analysis to predict the BOLD response. To investigate the EEG-dependent functional coupling among the responding regions, modulatory interactions were analyzed for network variation of scalp EEG and BOLD time courses. The results showed that BOLD activations associated with high network variation were mainly located in the thalamus, cerebellum, precuneus, inferior temporal lobe and sensorimotor-related areas, including the middle cingulate cortex (MCC), supplemental motor area (SMA), and paracentral lobule. BOLD deactivations associated with medium network variation were found in the frontal, parietal, and occipital areas. In addition, modulatory interaction analysis demonstrated predominantly directional negative modulation effects among the thalamus, cerebellum, frontal and sensorimotor-related areas. This study described a novel method to link BOLD response with simultaneous functional network organization of scalp EEG. These findings suggested the validity of predicting epileptic activity using functional connectivity variation between electrodes. The functional coupling among the thalamus, frontal regions, cerebellum and sensorimotor-related regions may be characteristically involved in epilepsy generation and propagation, which provides new insight into the pathophysiological mechanisms and intervene targets for JME.
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Affiliation(s)
- Yun Qin
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Qiqi Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xiaoyan Jia
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yutong Yao
- Faculty of natural science, University of Stirling, Stirling, United Kingdom
| | - Huanghao Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Tao Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China.
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Garcia DDS, Polydoro MS, Alvim MKM, Ishikawa A, Moreira JCV, Nogueira MH, Zanão TA, de Campos BM, Betting LEGG, Cendes F, Yasuda CL. Anxiety and depression symptoms disrupt resting state connectivity in patients with genetic generalized epilepsies. Epilepsia 2019; 60:679-688. [PMID: 30854641 DOI: 10.1111/epi.14687] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 02/12/2019] [Accepted: 02/12/2019] [Indexed: 01/15/2023]
Abstract
OBJECTIVE To analyze the lifetime trajectories in genetic generalized epilepsies (GGEs) and investigate the impact of symptoms of anxiety and depression on resting state functional connectivity (FC). METHODS Seventy-four GGE patients were classified according to the pharmacological response as seizure-free (12 patients), pharmacoresistant (PhR; 14 patients), and fluctuating (FL; 48 patients). Fifty-four subjects completed both the Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI), and 38 also underwent 3-T resting state functional magnetic resonance imaging. These 38 patients were subdivided into a positive group (13 patients with concurrent symptoms of depression and anxiety) and a negative group (21 asymptomatic patients and four with mild anxiety or depression symptoms). For FC analysis of resting state networks, we matched 38 healthy asymptomatic volunteers and used the UF2C toolbox running on MATLAB2017/SPM12. RESULTS The PhR group presented shorter duration of epilepsy (P = 0.016) and follow-up (P < 0.001) compared to the FL group. The PhR group showed higher levels (median = 20) on the BAI and BDI. Myoclonic seizures were the most difficult to control, as 50% of subjects persisted with them at last appointment, compared to generalized tonic-clonic seizures and absence seizures (<40%). Patients with concurrent anxiety and depression symptoms were 7.7 times more likely to exhibit pharmacoresistant seizures, although an increase of 1 year of epilepsy duration was associated with a decrease in the odds of presenting pharmacoresistance by a factor of 0.9. Overall, FC was altered between default mode network (DMN) and visuospatial/dorsal attention. However, only the positive group displayed abnormal FC between DMN and left executive control network, and between salience and visuospatial/dorsal attention. SIGNIFICANCE Our findings may help clinicians to have a better understanding of GGE clinical course and increase attention to the potential relationship of psychopathologies and brain connectivity.
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Affiliation(s)
| | | | - Marina Kutsodontis Machado Alvim
- Laboratory of Neuroimaging, University of Campinas, Campinas, Brazil.,Department of Neurology, University of Campinas, Campinas, Brazil
| | - Akari Ishikawa
- Laboratory of Neuroimaging, University of Campinas, Campinas, Brazil
| | | | | | | | | | | | - Fernando Cendes
- Laboratory of Neuroimaging, University of Campinas, Campinas, Brazil.,Department of Neurology, University of Campinas, Campinas, Brazil
| | - Clarissa L Yasuda
- Laboratory of Neuroimaging, University of Campinas, Campinas, Brazil.,Department of Neurology, University of Campinas, Campinas, Brazil
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Thijs RD, Surges R, O'Brien TJ, Sander JW. Epilepsy in adults. Lancet 2019; 393:689-701. [PMID: 30686584 DOI: 10.1016/s0140-6736(18)32596-0] [Citation(s) in RCA: 948] [Impact Index Per Article: 189.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Revised: 09/08/2018] [Accepted: 10/11/2018] [Indexed: 12/11/2022]
Abstract
Epilepsy is one of the most common serious brain conditions, affecting over 70 million people worldwide. Its incidence has a bimodal distribution with the highest risk in infants and older age groups. Progress in genomic technology is exposing the complex genetic architecture of the common types of epilepsy, and is driving a paradigm shift. Epilepsy is a symptom complex with multiple risk factors and a strong genetic predisposition rather than a condition with a single expression and cause. These advances have resulted in the new classification of epileptic seizures and epilepsies. A detailed clinical history and a reliable eyewitness account of a seizure are the cornerstones of the diagnosis. Ancillary investigations can help to determine cause and prognosis. Advances in brain imaging are helping to identify the structural and functional causes and consequences of the epilepsies. Comorbidities are increasingly recognised as important aetiological and prognostic markers. Antiseizure medication might suppress seizures in up to two-thirds of all individuals but do not alter long-term prognosis. Epilepsy surgery is the most effective way to achieve long-term seizure freedom in selected individuals with drug-resistant focal epilepsy, but it is probably not used enough. With improved understanding of the gradual development of epilepsy, epigenetic determinants, and pharmacogenomics comes the hope for better, disease-modifying, or even curative, pharmacological and non-pharmacological treatment strategies. Other developments are clinical implementation of seizure detection devices and new neuromodulation techniques, including responsive neural stimulation.
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Affiliation(s)
- Roland D Thijs
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, Netherlands; Department of Neurology, Leiden University Medical Centre, Leiden, Netherlands; NIHR University College London Hospitals Biomedical Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Rainer Surges
- Section of Epileptology, Department of Neurology, University Hospital RWTH Aachen, Germany
| | - Terence J O'Brien
- Melbourne Brain Centre, Departments of Medicine and Neurology, Royal Melbourne Hospital, University of Melbourne, VIC, Australia; Departments of Neuroscience and Neurology, Central Clinical School, Monash University, The Alfred Hospital, Melbourne, VIC, Australia
| | - Josemir W Sander
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, Netherlands; NIHR University College London Hospitals Biomedical Research Centre, UCL Queen Square Institute of Neurology, London, UK; Chalfont Centre for Epilepsy, Chalfont St Peter, UK.
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9
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Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies. Nat Commun 2018; 9:5269. [PMID: 30531953 PMCID: PMC6288131 DOI: 10.1038/s41467-018-07524-z] [Citation(s) in RCA: 229] [Impact Index Per Article: 38.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 10/30/2018] [Indexed: 12/16/2022] Open
Abstract
The epilepsies affect around 65 million people worldwide and have a substantial missing heritability component. We report a genome-wide mega-analysis involving 15,212 individuals with epilepsy and 29,677 controls, which reveals 16 genome-wide significant loci, of which 11 are novel. Using various prioritization criteria, we pinpoint the 21 most likely epilepsy genes at these loci, with the majority in genetic generalized epilepsies. These genes have diverse biological functions, including coding for ion-channel subunits, transcription factors and a vitamin-B6 metabolism enzyme. Converging evidence shows that the common variants associated with epilepsy play a role in epigenetic regulation of gene expression in the brain. The results show an enrichment for monogenic epilepsy genes as well as known targets of antiepileptic drugs. Using SNP-based heritability analyses we disentangle both the unique and overlapping genetic basis to seven different epilepsy subtypes. Together, these findings provide leads for epilepsy therapies based on underlying pathophysiology. Epilepsies are common brain disorders and are classified based on clinical phenotyping, imaging and genetics. Here, the authors perform genome-wide association studies for 3 broad and 7 subtypes of epilepsy and identify 16 loci - 11 novel - that are further annotated by eQTL and partitioned heritability analyses.
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Garcia-Ramos C, Dabbs K, Lin JJ, Jones JE, Stafstrom CE, Hsu DA, Meyerand ME, Prabhakaran V, Hermann BP. Progressive dissociation of cortical and subcortical network development in children with new-onset juvenile myoclonic epilepsy. Epilepsia 2018; 59:2086-2095. [PMID: 30281148 PMCID: PMC6334640 DOI: 10.1111/epi.14560] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 08/14/2018] [Accepted: 08/14/2018] [Indexed: 02/05/2023]
Abstract
OBJECTIVE Structural and functional magnetic resonance imaging (MRI) studies have consistently documented cortical and subcortical abnormalities in patients with juvenile myoclonic epilepsy (JME). However, little is known about how these structural abnormalities emerge from the time of epilepsy onset and how network interactions between and within cortical and subcortical regions may diverge in youth with JME compared to typically developing children. METHODS We examined prospective covariations of volumetric differences derived from high-resolution structural MRI during the first 2 years of epilepsy diagnosis in a group of youth with JME (n = 21) compared to healthy controls (n = 22). We indexed developmental brain changes using graph theory by computing network metrics based on the correlation of the cortical and subcortical structural covariance near the time of epilepsy and 2 years later. RESULTS Over 2 years, normally developing children showed modular cortical development and network integration between cortical and subcortical regions. In contrast, children with JME developed a highly correlated and less modular cortical network, which was atypically dissociated from subcortical structures. Furthermore, the JME group also presented higher clustering and lower modularity indices than controls, indicating weaker modules or communities. The local efficiency in JME was higher than controls across the majority of cortical nodes. Regarding network hubs, controls presented a higher number than youth with JME that were spread across the brain with ample representation from the different modules. In contrast, children with JME showed a lower number of hubs that were mainly from one module and comprised mostly subcortical structures. SIGNIFICANCE Youth with JME prospectively developed a network of highly correlated cortical regions dissociated from subcortical structures during the first 2 years after epilepsy onset. The cortical-subcortical network dissociation provides converging insights into the disparate literature of cortical and subcortical abnormalities found in previous studies.
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Affiliation(s)
- Camille Garcia-Ramos
- Departments of Medical Physics,University of Wisconsin
School of Medicine and Public Health, Madison WI
| | - Kevin Dabbs
- Departments of Neurology, University of Wisconsin School of
Medicine and Public Health, Madison WI
| | - Jack J. Lin
- Department of Neurology, University of California, Irvine,
Irvine CA
| | - Jana E. Jones
- Departments of Neurology, University of Wisconsin School of
Medicine and Public Health, Madison WI
| | | | - David A. Hsu
- Departments of Neurology, University of Wisconsin School of
Medicine and Public Health, Madison WI
| | - M. Elizabeth Meyerand
- Departments of Biomedical Engineering, University of
Wisconsin School of Medicine and Public Health, Madison WI
| | - Vivek Prabhakaran
- Departments of Medical Physics,University of Wisconsin
School of Medicine and Public Health, Madison WI
- Departments of Radiology, University of Wisconsin School of
Medicine and Public Health, Madison WI
| | - Bruce P. Hermann
- Departments of Medical Physics,University of Wisconsin
School of Medicine and Public Health, Madison WI
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Dong L, Luo C, Liu X, Jiang S, Li F, Feng H, Li J, Gong D, Yao D. Neuroscience Information Toolbox: An Open Source Toolbox for EEG-fMRI Multimodal Fusion Analysis. Front Neuroinform 2018; 12:56. [PMID: 30197593 PMCID: PMC6117508 DOI: 10.3389/fninf.2018.00056] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Accepted: 08/10/2018] [Indexed: 11/30/2022] Open
Abstract
Recently, scalp electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) multimodal fusion has been pursued in an effort to study human brain function and dysfunction to obtain more comprehensive information on brain activity in which the spatial and temporal resolutions are both satisfactory. However, a more flexible and easy-to-use toolbox for EEG–fMRI multimodal fusion is still lacking. In this study, we therefore developed a freely available and open-source MATLAB graphical user interface toolbox, known as the Neuroscience Information Toolbox (NIT), for EEG–fMRI multimodal fusion analysis. The NIT consists of three modules: (1) the fMRI module, which has batch fMRI preprocessing, nuisance signal removal, bandpass filtering, and calculation of resting-state measures; (2) the EEG module, which includes artifact removal, extracting EEG features (event onset, power, and amplitude), and marking interesting events; and (3) the fusion module, in which fMRI-informed EEG analysis and EEG-informed fMRI analysis are included. The NIT was designed to provide a convenient and easy-to-use toolbox for researchers, especially for novice users. The NIT can be downloaded for free at http://www.neuro.uestc.edu.cn/NIT.html, and detailed information, including the introduction of NIT, user’s manual and example data sets, can also be observed on this website. We hope that the NIT is a promising toolbox for exploring brain information in various EEG and fMRI studies.
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Affiliation(s)
- Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaobo Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongshuo Feng
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jianfu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Diankun Gong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
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12
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Kinay D, Oliver KL, Tüzün E, Damiano JA, Ulusoy C, Andermann E, Hildebrand MS, Bahlo M, Berkovic SF. Evidence of linkage to chromosome 5p13.2-q11.1 in a large inbred family with genetic generalized epilepsy. Epilepsia 2018; 59:e125-e129. [DOI: 10.1111/epi.14506] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/06/2018] [Indexed: 12/16/2022]
Affiliation(s)
- Demet Kinay
- Okmeydani Education and Research Hospital; Istanbul Turkey
| | - Karen L. Oliver
- Epilepsy Research Centre; Austin Health; University of Melbourne; Heidelberg Victoria Australia
- Population Health and Immunity Division; Walter and Eliza Hall Institute of Medical Research; Parkville Victoria Australia
| | - Erdem Tüzün
- Department of Neuroscience; Aziz Sancar Institute of Experimental Medicine; Istanbul University; Istanbul Turkey
| | - John A. Damiano
- Epilepsy Research Centre; Austin Health; University of Melbourne; Heidelberg Victoria Australia
| | - Canan Ulusoy
- Department of Neuroscience; Aziz Sancar Institute of Experimental Medicine; Istanbul University; Istanbul Turkey
| | - Eva Andermann
- Neurogenetics Unit; Montreal Neurological Hospital and Institute; Montreal Quebec Canada
| | - Michael S. Hildebrand
- Epilepsy Research Centre; Austin Health; University of Melbourne; Heidelberg Victoria Australia
| | - Melanie Bahlo
- Population Health and Immunity Division; Walter and Eliza Hall Institute of Medical Research; Parkville Victoria Australia
- Department of Medical Biology; University of Melbourne; Parkville Victoria Australia
| | - Samuel F. Berkovic
- Epilepsy Research Centre; Austin Health; University of Melbourne; Heidelberg Victoria Australia
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13
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Mahesar S, Akbar HF, Abid H, Sana R. Juvenile Myoclonic Epilepsy Presenting with Neurocognitive Impairment: A Case Report. Cureus 2018; 10:e2271. [PMID: 29736355 PMCID: PMC5935437 DOI: 10.7759/cureus.2271] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Juvenile myoclonic epilepsy (JME) is a genetically and clinically diverse disorder which is characterized by myoclonic jerks, usually after awakening from sleep. It affects both genders equally and manifests during the second decade of life. The various precipitating factors include stress, light, sleep deprivation, and alcohol. A history of morning clumsiness supported by typical electroencephalography (EEG) findings, together with a normal clinical examination all point towards a diagnosis of JME. We present the case of a nine-year-old girl who presented with cognitive dysfunction in addition to myoclonic jerks. She had normal brain imaging and her labs were negative for other causes of dementia. Her EEG findings revealed polyspikes with normal background activity. She was treated with antiepileptic drugs (AEDs) for control of seizures.
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Affiliation(s)
- Sarfraz Mahesar
- Department of Neurology, Dow University of Health Sciences (DUHS), Karachi, Pakistan
| | - Hira F Akbar
- Civil Hospital Karachi, Dow University of Health Sciences (DUHS), Karachi, Pakistan
| | - Husnain Abid
- Student, Dow University of Health Sciences (DUHS), Karachi, Pakistan
| | - Rabia Sana
- Civil Hospital Karachi, Dow University of Health Sciences (DUHS), Karachi, Pakistan
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14
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Nuyts S, D'Souza W, Bowden SC, Vogrin SJ. Structural brain abnormalities in genetic generalized epilepsies: A systematic review and meta-analysis. Epilepsia 2017; 58:2025-2037. [DOI: 10.1111/epi.13928] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/26/2017] [Indexed: 01/08/2023]
Affiliation(s)
- Shauni Nuyts
- Department of Psychological Sciences; University of Leuven; Leuven Belgium
- Department of Statistics; University of Leuven; Leuven Belgium
- Melbourne School of Psychological Sciences; University of Melbourne; Parkville Victoria Australia
| | - Wendyl D'Souza
- Department of Medicine; St. Vincent's Hospital; University of Melbourne; Fitzroy Victoria Australia
| | - Stephen C. Bowden
- Melbourne School of Psychological Sciences; University of Melbourne; Parkville Victoria Australia
- Department of Medicine; St. Vincent's Hospital; University of Melbourne; Fitzroy Victoria Australia
| | - Simon J. Vogrin
- Department of Medicine; St. Vincent's Hospital; University of Melbourne; Fitzroy Victoria Australia
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15
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Analysis of variations of correlation dimension and nonlinear interdependence for the prediction of pediatric myoclonic seizures – A preliminary study. Epilepsy Res 2017; 135:102-114. [DOI: 10.1016/j.eplepsyres.2017.06.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Revised: 05/20/2017] [Accepted: 06/16/2017] [Indexed: 01/23/2023]
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16
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Eberhardt O, Topka H. Myoclonic Disorders. Brain Sci 2017; 7:E103. [PMID: 28805718 PMCID: PMC5575623 DOI: 10.3390/brainsci7080103] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 08/06/2017] [Accepted: 08/08/2017] [Indexed: 01/17/2023] Open
Abstract
Few movement disorders seem to make a straightforward approach to diagnosis and treatment more difficult and frustrating than myoclonus, due to its plethora of causes and its variable classifications. Nevertheless, in recent years, exciting advances have been made in the elucidation of the pathophysiology and genetic basis of many disorders presenting with myoclonus. Here, we provide a review of all of the important types of myoclonus encountered in pediatric and adult neurology, with an emphasis on the recent developments that have led to a deeper understanding of this intriguing phenomenon. An up-to-date list of the genetic basis of all major myoclonic disorders is presented. Randomized studies are scarce in myoclonus therapy, but helpful pragmatic approaches at diagnosis as well as treatment have been recently suggested.
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Affiliation(s)
- Olaf Eberhardt
- Klinik für Neurologie, Klinikum Bogenhausen, Städt. Klinikum München GmbH, Englschalkinger Str. 77, 81925 München, Germany.
| | - Helge Topka
- Klinik für Neurologie, Klinikum Bogenhausen, Städt. Klinikum München GmbH, Englschalkinger Str. 77, 81925 München, Germany.
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17
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Raju PK, Satishchandra P, Nayak S, Iyer V, Sinha S, Anand A. Microtubule-associated defects caused by EFHC1
mutations in juvenile myoclonic epilepsy. Hum Mutat 2017; 38:816-826. [DOI: 10.1002/humu.23221] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Revised: 03/21/2017] [Accepted: 03/21/2017] [Indexed: 01/06/2023]
Affiliation(s)
- Praveen K Raju
- Molecular Biology and Genetics Unit, Jawaharlal Nehru Centre for Advanced Scientific Research; Jakkur Bangalore Karnataka India
| | | | - Sourav Nayak
- Molecular Biology and Genetics Unit, Jawaharlal Nehru Centre for Advanced Scientific Research; Jakkur Bangalore Karnataka India
| | - Vishwanathan Iyer
- Department of Neurology; National Institute of Mental Health and Neurosciences; Bangalore Karnataka India
| | - Sanjib Sinha
- Department of Neurology; National Institute of Mental Health and Neurosciences; Bangalore Karnataka India
| | - Anuranjan Anand
- Molecular Biology and Genetics Unit, Jawaharlal Nehru Centre for Advanced Scientific Research; Jakkur Bangalore Karnataka India
- Neuroscience Unit; Jawaharlal Nehru Centre for Advanced Scientific Research; Jakkur Bangalore Karnataka India
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18
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Stopping epilepsy treatment in seizure remission: Good or bad or both? Seizure 2017; 44:157-161. [DOI: 10.1016/j.seizure.2016.09.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Revised: 09/01/2016] [Accepted: 09/04/2016] [Indexed: 11/19/2022] Open
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19
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Dong L, Luo C, Zhu Y, Hou C, Jiang S, Wang P, Biswal BB, Yao D. Complex discharge-affecting networks in juvenile myoclonic epilepsy: A simultaneous EEG-fMRI study. Hum Brain Mapp 2016; 37:3515-29. [PMID: 27159669 DOI: 10.1002/hbm.23256] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Revised: 04/28/2016] [Accepted: 04/29/2016] [Indexed: 02/03/2023] Open
Abstract
Juvenile myoclonic epilepsy (JME) is a common subtype of idiopathic generalized epilepsies (IGEs) and is characterized by myoclonic jerks, tonic-clonic seizures and infrequent absence seizures. The network notion has been proposed to better characterize epilepsy. However, many issues remain not fully understood in JME, such as the associations between discharge-affecting networks and the relationships among resting-state networks. In this project, eigenspace maximal information canonical correlation analysis (emiCCA) and functional network connectivity (FNC) analysis were applied to simultaneous EEG-fMRI data from JME patients. The main findings of our study are as follows: discharge-affecting networks comprising the default model (DMN), self-reference (SRN), basal ganglia (BGN) and frontal networks have linear and nonlinear relationships with epileptic discharge information in JME patients; the DMN, SRN and BGN have dense/specific associations with discharge-affecting networks as well as resting-state networks; and compared with controls, significantly increased FNCs between the salience network (SN) and resting-state networks are found in JME patients. These findings suggest that the BGN, DMN and SRN may play intermediary roles in the modulation and propagation of epileptic discharges. These roles further tend to disturb the switching function of the SN in JME patients. We also postulate that emiCCA and FNC analysis may provide a potential analysis platform to provide insights into our understanding of the pathophysiological mechanism of epilepsy subtypes such as JME. Hum Brain Mapp 37:3515-3529, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Li Dong
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Cheng Luo
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Yutian Zhu
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.,Department of Neurology, Chongzhou People's Hospital, Chengdu, China
| | - Changyue Hou
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Sisi Jiang
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Pu Wang
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.,Department of Neurology, Chongzhou People's Hospital, Chengdu, China
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey
| | - Dezhong Yao
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
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20
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Wight JE, Nguyen V, Medina MT, Patterson C, Durón RM, Molina Y, Lin Y, Martínez‐Juárez IE, Ochoa A, Jara‐Prado A, Tanaka M, Bai D, Aftab S, Bailey JN, Delgado‐Escueta AV. Chromosome loci vary by juvenile myoclonic epilepsy subsyndromes: linkage and haplotype analysis applied to epilepsy and EEG 3.5-6.0 Hz polyspike waves. Mol Genet Genomic Med 2016; 4:197-210. [PMID: 27066514 PMCID: PMC4799870 DOI: 10.1002/mgg3.195] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2015] [Revised: 11/09/2015] [Accepted: 11/12/2015] [Indexed: 12/15/2022] Open
Abstract
Juvenile myoclonic epilepsy (JME), the most common genetic epilepsy, remains enigmatic because it is considered one disease instead of several diseases. We ascertained three large multigenerational/multiplex JME pedigrees from Honduras with differing JME subsyndromes, including Childhood Absence Epilepsy evolving to JME (CAE/JME; pedigree 1), JME with adolescent onset pyknoleptic absence (JME/pA; pedigree 2), and classic JME (cJME; pedigree 3). All phenotypes were validated, including symptomatic persons with various epilepsies, asymptomatic persons with EEG 3.5-6.0 Hz polyspike waves, and asymptomatic persons with normal EEGs. Two-point parametric linkage analyses were performed with 5185 single-nucleotide polymorphisms on individual pedigrees and pooled pedigrees using four diagnostic models based on epilepsy/EEG diagnoses. Haplotype analyses of the entire genome were also performed for each individual. In pedigree 1, haplotyping identified a 34 cM region in 2q21.2-q31.1 cosegregating with all affected members, an area close to 2q14.3 identified by linkage (Z max = 1.77; pedigree 1). In pedigree 2, linkage and haplotyping identified a 44 cM cosegregating region in 13q13.3-q31.2 (Z max = 3.50 at 13q31.1; pooled pedigrees). In pedigree 3, haplotyping identified a 6 cM cosegregating region in 17q12. Possible cosegregation was also identified in 13q14.2 and 1q32 in pedigree 3, although this could not be definitively confirmed due to the presence of uninformative markers in key individuals. Differing chromosome regions identified in specific JME subsyndromes may contain separate JME disease-causing genes, favoring the concept of JME as several distinct diseases. Whole-exome sequencing will likely identify a CAE/JME gene in 2q21.2-2q31.1, a JME/pA gene in 13q13.3-q31.2, and a cJME gene in 17q12.
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Affiliation(s)
- Jenny E. Wight
- Epilepsy Genetics/Genomics LaboratoriesVA GLAHS – West Los AngelesLos AngelesCalifornia
- GENESS International ConsortiumLos AngelesCalifornia
| | - Viet‐Huong Nguyen
- Epilepsy Genetics/Genomics LaboratoriesVA GLAHS – West Los AngelesLos AngelesCalifornia
- GENESS International ConsortiumLos AngelesCalifornia
| | - Marco T. Medina
- GENESS International ConsortiumLos AngelesCalifornia
- National Autonomous University of HondurasTegucigalpaHonduras
| | - Christopher Patterson
- Epilepsy Genetics/Genomics LaboratoriesVA GLAHS – West Los AngelesLos AngelesCalifornia
- GENESS International ConsortiumLos AngelesCalifornia
| | - Reyna M. Durón
- Epilepsy Genetics/Genomics LaboratoriesVA GLAHS – West Los AngelesLos AngelesCalifornia
- GENESS International ConsortiumLos AngelesCalifornia
- National Autonomous University of HondurasTegucigalpaHonduras
- Universidad Tecnológica Centroamericana (UNITEC)TegucigalpaHonduras
- Department of NeurologyDavid Geffen School of Medicine at UCLALos AngelesCalifornia
| | - Yolly Molina
- GENESS International ConsortiumLos AngelesCalifornia
- National Autonomous University of HondurasTegucigalpaHonduras
| | - Yu‐Chen Lin
- Epilepsy Genetics/Genomics LaboratoriesVA GLAHS – West Los AngelesLos AngelesCalifornia
- GENESS International ConsortiumLos AngelesCalifornia
| | - Iris E. Martínez‐Juárez
- GENESS International ConsortiumLos AngelesCalifornia
- National Institute of Neurology and NeurosurgeryMexico CityMexico
| | - Adriana Ochoa
- GENESS International ConsortiumLos AngelesCalifornia
- National Institute of Neurology and NeurosurgeryMexico CityMexico
| | - Aurelio Jara‐Prado
- GENESS International ConsortiumLos AngelesCalifornia
- National Institute of Neurology and NeurosurgeryMexico CityMexico
| | - Miyabi Tanaka
- Epilepsy Genetics/Genomics LaboratoriesVA GLAHS – West Los AngelesLos AngelesCalifornia
- GENESS International ConsortiumLos AngelesCalifornia
- Department of NeurologyDavid Geffen School of Medicine at UCLALos AngelesCalifornia
| | - Dongsheng Bai
- Epilepsy Genetics/Genomics LaboratoriesVA GLAHS – West Los AngelesLos AngelesCalifornia
- GENESS International ConsortiumLos AngelesCalifornia
- Department of NeurologyDavid Geffen School of Medicine at UCLALos AngelesCalifornia
| | - Sumaya Aftab
- Epilepsy Genetics/Genomics LaboratoriesVA GLAHS – West Los AngelesLos AngelesCalifornia
- GENESS International ConsortiumLos AngelesCalifornia
- Department of NeurologyDavid Geffen School of Medicine at UCLALos AngelesCalifornia
| | - Julia N. Bailey
- Epilepsy Genetics/Genomics LaboratoriesVA GLAHS – West Los AngelesLos AngelesCalifornia
- GENESS International ConsortiumLos AngelesCalifornia
- Department of EpidemiologyFielding School of Public Health at UCLALos AngelesCalifornia
| | - Antonio V. Delgado‐Escueta
- Epilepsy Genetics/Genomics LaboratoriesVA GLAHS – West Los AngelesLos AngelesCalifornia
- GENESS International ConsortiumLos AngelesCalifornia
- Department of NeurologyDavid Geffen School of Medicine at UCLALos AngelesCalifornia
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21
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van Klink NEC, Bauer PR, Zijlmans M. Making sense of ripples in generalized epilepsy. Clin Neurophysiol 2016; 127:1759-61. [PMID: 26777056 DOI: 10.1016/j.clinph.2015.12.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Revised: 12/14/2015] [Accepted: 12/15/2015] [Indexed: 11/25/2022]
Affiliation(s)
- N E C van Klink
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, The Netherlands
| | - P R Bauer
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands; NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, London, United Kingdom
| | - M Zijlmans
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, The Netherlands; Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands.
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22
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Knott S, Forty L, Craddock N, Thomas RH. Epilepsy and bipolar disorder. Epilepsy Behav 2015; 52:267-74. [PMID: 26316422 DOI: 10.1016/j.yebeh.2015.07.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2015] [Revised: 06/25/2015] [Accepted: 07/02/2015] [Indexed: 01/30/2023]
Abstract
It is well recognized that mood disorders and epilepsy commonly co-occur. Despite this, our knowledge regarding the relationship between epilepsy and bipolar disorder is limited. Several shared features between the two disorders, such as their episodic nature and potential to run a chronic course, and the efficacy of some antiepileptic medications in the prophylaxis of both disorders, are often cited as evidence of possible shared underlying pathophysiology. The present paper aims to review the bidirectional associations between epilepsy and bipolar disorder, with a focus on epidemiological links, evidence for shared etiology, and the impact of these disorders on both the individual and wider society. Better recognition and understanding of these two complex disorders, along with an integrated clinical approach, are crucial for improved evaluation and management of comorbid epilepsy and mood disorders.
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Affiliation(s)
- Sarah Knott
- MRC Centre for Neuropsychiatric Genetics and Genomics, Haydn Ellis Building, Maindy Road, Cardiff CF24 4HQ, UK.
| | - Liz Forty
- MRC Centre for Neuropsychiatric Genetics and Genomics, Haydn Ellis Building, Maindy Road, Cardiff CF24 4HQ, UK.
| | - Nick Craddock
- MRC Centre for Neuropsychiatric Genetics and Genomics, Haydn Ellis Building, Maindy Road, Cardiff CF24 4HQ, UK.
| | - Rhys H Thomas
- MRC Centre for Neuropsychiatric Genetics and Genomics, Haydn Ellis Building, Maindy Road, Cardiff CF24 4HQ, UK.
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23
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24
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Caeyenberghs K, Powell HWR, Thomas RH, Brindley L, Church C, Evans J, Muthukumaraswamy SD, Jones DK, Hamandi K. Hyperconnectivity in juvenile myoclonic epilepsy: a network analysis. NEUROIMAGE-CLINICAL 2014; 7:98-104. [PMID: 25610771 PMCID: PMC4299970 DOI: 10.1016/j.nicl.2014.11.018] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2014] [Revised: 10/16/2014] [Accepted: 11/24/2014] [Indexed: 12/01/2022]
Abstract
Objective Juvenile myoclonic epilepsy (JME) is a common idiopathic (genetic) generalized epilepsy (IGE) syndrome characterized by impairments in executive and cognitive control, affecting independent living and psychosocial functioning. There is a growing consensus that JME is associated with abnormal function of diffuse brain networks, typically affecting frontal and fronto-thalamic areas. Methods Using diffusion MRI and a graph theoretical analysis, we examined bivariate (network-based statistic) and multivariate (global and local) properties of structural brain networks in patients with JME (N = 34) and matched controls. Neuropsychological assessment was performed in a subgroup of 14 patients. Results Neuropsychometry revealed impaired visual memory and naming in JME patients despite a normal full scale IQ (mean = 98.6). Both JME patients and controls exhibited a small world topology in their white matter networks, with no significant differences in the global multivariate network properties between the groups. The network-based statistic approach identified one subnetwork of hyperconnectivity in the JME group, involving primary motor, parietal and subcortical regions. Finally, there was a significant positive correlation in structural connectivity with cognitive task performance. Conclusions Our findings suggest that structural changes in JME patients are distributed at a network level, beyond the frontal lobes. The identified subnetwork includes key structures in spike wave generation, along with primary motor areas, which may contribute to myoclonic jerks. We conclude that analyzing the affected subnetworks may provide new insights into understanding seizure generation, as well as the cognitive deficits observed in JME patients. Subnetwork of hyperconnectivity in juvenile myoclonic epilepsy Hyperconnectivity in primary motor, parietal and subcortical regions Network-based statistics is a valuable tool for predicting functional cognitive deficits.
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Affiliation(s)
- K Caeyenberghs
- Department of Physical Therapy and Motor Rehabilitation, Faculty of Medicine and Health Sciences, University of Ghent, Ghent, Belgium ; Department of Movement and Sports Sciences, University of Ghent, Ghent, Belgium
| | - H W R Powell
- Department of Neurology, Morriston Hospital, Swansea, United Kingdom ; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - R H Thomas
- Department of Neurology, University Hospital of Wales, Alan Richens Welsh Epilepsy Centre, Cardiff, United Kingdom ; MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Hadyn Ellis Building, Cathays, United Kingdom
| | - L Brindley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - C Church
- Department of Neurology, University Hospital of Wales, Alan Richens Welsh Epilepsy Centre, Cardiff, United Kingdom
| | - J Evans
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - S D Muthukumaraswamy
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - D K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - K Hamandi
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom ; Department of Neurology, University Hospital of Wales, Alan Richens Welsh Epilepsy Centre, Cardiff, United Kingdom
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