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Kanai S, Oguri M, Okanishi T, Miyamoto Y, Maeda M, Yazaki K, Matsuura R, Tozawa T, Sakuma S, Chiyonobu T, Hamano SI, Maegaki Y. Predictive modeling based on functional connectivity of interictal scalp EEG for infantile epileptic spasms syndrome. Clin Neurophysiol 2024; 167:37-48. [PMID: 39265289 DOI: 10.1016/j.clinph.2024.08.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 08/20/2024] [Accepted: 08/24/2024] [Indexed: 09/14/2024]
Abstract
OBJECTIVE This study aims to delineate the electrophysiological variances between patients with infantile epileptic spasms syndrome (IESS) and healthy controls and to devise a predictive model for long-term seizure outcomes. METHODS The cohort consisted of 30 individuals in the seizure-free group, 23 in the seizure-residual group, and 20 in the control group. We conducted a comprehensive analysis of pretreatment electroencephalography, including the relative power spectrum (rPS), weighted phase-lag index (wPLI), and network metrics. Follow-up EEGs at 2 years of age were also analyzed to elucidate physiological changes among groups. RESULTS Infants in the seizure-residual group exhibited increased rPS in theta and alpha bands at IESS onset compared to the other groups (all p < 0.0001). The control group showed higher rPS in fast frequency bands, indicating potentially enhanced cognitive function. The seizure-free group presented increased wPLI across all frequency bands (all p < 0.0001). Our predictive model utilizing wPLI anticipated long-term outcomes at IESS onset (area under the curve 0.75). CONCLUSION Our findings demonstrated an initial "hypersynchronous state" in the seizure-free group, which was ameliorated following successful treatment. SIGNIFICANCE This study provides a predictive model utilizing functional connectivity and insights into the diverse electrophysiology observed among outcome groups of IESS.
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Affiliation(s)
- Sotaro Kanai
- Division of Child Neurology, Institute of Neurological Sciences, Faculty of Medicine, Tottori University, 86 Nishi-cho, Yonago 683-8503, Japan.
| | - Masayoshi Oguri
- Department of Medical Technology, Kagawa Prefectural University of Health Sciences, 281-1 Mure-cho, Takamatsu 761-0123, Japan
| | - Tohru Okanishi
- Division of Child Neurology, Institute of Neurological Sciences, Faculty of Medicine, Tottori University, 86 Nishi-cho, Yonago 683-8503, Japan
| | - Yosuke Miyamoto
- Department of Pediatrics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kawaramachi Hirokoji, Kamigyo-ku, Kyoto 602-8566, Japan
| | - Masanori Maeda
- Department of Pediatrics, Wakayama Medical University, 811-1 Kimiidera, Wakayama 641-8509, Japan
| | - Kotaro Yazaki
- Department of Pediatrics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan
| | - Ryuki Matsuura
- Division of Neurology, Saitama Children's Medical Center, 1-2 Shintoshin, Chuo-ku, Saitama 330-8777, Japan
| | - Takenori Tozawa
- Department of Pediatrics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kawaramachi Hirokoji, Kamigyo-ku, Kyoto 602-8566, Japan
| | - Satoru Sakuma
- Department of Pediatrics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan
| | - Tomohiro Chiyonobu
- Department of Pediatrics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kawaramachi Hirokoji, Kamigyo-ku, Kyoto 602-8566, Japan
| | - Shin-Ichiro Hamano
- Division of Neurology, Saitama Children's Medical Center, 1-2 Shintoshin, Chuo-ku, Saitama 330-8777, Japan
| | - Yoshihiro Maegaki
- Division of Child Neurology, Institute of Neurological Sciences, Faculty of Medicine, Tottori University, 86 Nishi-cho, Yonago 683-8503, Japan
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Sun Y, Chen X. Epileptic EEG Signal Detection Using Variational Modal Decomposition and Improved Grey Wolf Algorithm. SENSORS (BASEL, SWITZERLAND) 2023; 23:8078. [PMID: 37836909 PMCID: PMC10575143 DOI: 10.3390/s23198078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 09/16/2023] [Accepted: 09/18/2023] [Indexed: 10/15/2023]
Abstract
Epilepsy does great harm to the human body, and even threatens human life when it is serious. Therefore, research focused on the diagnosis and treatment of epilepsy holds paramount clinical significance. In this paper, we utilized variational modal decomposition (VMD) and an enhanced grey wolf algorithm to detect epileptic electroencephalogram (EEG) signals. Data were extracted from each patient's preseizure period and seizure period of 200 s each, with every 2 s as a segment, meaning 100 data points could be obtained for each patient's health period as well as 100 data points for each patient's epilepsy period. Variational modal decomposition (VMD) was used to obtain the corresponding intrinsic modal function (VMF) of the data. Then, the differential entropy (DE) and high frequency detection (HFD) of each VMF were extracted as features. The improved grey wolf algorithm is adopted for a selected channel to improve the maximum value of the channel. Finally, the EEG signal samples were classified using a support vector machine (SVM) classifier to achieve the accurate detection of epilepsy EEG signals. Experimental results show that the accuracy, sensitivity and specificity of the proposed method can reach 98.3%, 98.9% and 98.5%, respectively. The proposed algorithm in this paper can be used as an index to detect epileptic seizures and has certain guiding significance for the early diagnosis and effective treatment of epileptic patients.
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Affiliation(s)
- Yongxin Sun
- College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China
- College of Physics and Electronic Information, Baicheng Normal University, Baicheng 137099, China
| | - Xiaojuan Chen
- College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China
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3
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Sarkisova K, van Luijtelaar G. The impact of early-life environment on absence epilepsy and neuropsychiatric comorbidities. IBRO Neurosci Rep 2022; 13:436-468. [PMID: 36386598 PMCID: PMC9649966 DOI: 10.1016/j.ibneur.2022.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/30/2022] [Accepted: 10/31/2022] [Indexed: 11/11/2022] Open
Abstract
This review discusses the long-term effects of early-life environment on epileptogenesis, epilepsy, and neuropsychiatric comorbidities with an emphasis on the absence epilepsy. The WAG/Rij rat strain is a well-validated genetic model of absence epilepsy with mild depression-like (dysthymia) comorbidity. Although pathologic phenotype in WAG/Rij rats is genetically determined, convincing evidence presented in this review suggests that the absence epilepsy and depression-like comorbidity in WAG/Rij rats may be governed by early-life events, such as prenatal drug exposure, early-life stress, neonatal maternal separation, neonatal handling, maternal care, environmental enrichment, neonatal sensory impairments, neonatal tactile stimulation, and maternal diet. The data, as presented here, indicate that some early environmental events can promote and accelerate the development of absence seizures and their neuropsychiatric comorbidities, while others may exert anti-epileptogenic and disease-modifying effects. The early environment can lead to phenotypic alterations in offspring due to epigenetic modifications of gene expression, which may have maladaptive consequences or represent a therapeutic value. Targeting DNA methylation with a maternal methyl-enriched diet during the perinatal period appears to be a new preventive epigenetic anti-absence therapy. A number of caveats related to the maternal methyl-enriched diet and prospects for future research are discussed.
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Affiliation(s)
- Karine Sarkisova
- Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences, Butlerova str. 5a, Moscow 117485, Russia
| | - Gilles van Luijtelaar
- Donders Institute for Brain, Cognition, and Behavior, Donders Center for Cognition, Radboud University, Nijmegen, PO Box 9104, 6500 HE Nijmegen, the Netherlands
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4
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Hybrid machine learning method for a connectivity-based epilepsy diagnosis with resting-state EEG. Med Biol Eng Comput 2022; 60:1675-1689. [DOI: 10.1007/s11517-022-02560-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/25/2022] [Indexed: 10/18/2022]
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5
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Vogel S, Kaltenhäuser M, Kim C, Müller-Voggel N, Rössler K, Dörfler A, Schwab S, Hamer H, Buchfelder M, Rampp S. MEG Node Degree Differences in Patients with Focal Epilepsy vs. Controls-Influence of Experimental Conditions. Brain Sci 2021; 11:1590. [PMID: 34942895 PMCID: PMC8699109 DOI: 10.3390/brainsci11121590] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/25/2021] [Accepted: 11/27/2021] [Indexed: 11/16/2022] Open
Abstract
Drug-resistant epilepsy can be most limiting for patients, and surgery represents a viable therapy option. With the growing research on the human connectome and the evidence of epilepsy being a network disorder, connectivity analysis may be able to contribute to our understanding of epilepsy and may be potentially developed into clinical applications. In this magnetoencephalographic study, we determined the whole-brain node degree of connectivity levels in patients and controls. Resting-state activity was measured at five frequency bands in 15 healthy controls and 15 patients with focal epilepsy of different etiologies. The whole-brain all-to-all imaginary part of coherence in source space was then calculated. Node degree was determined and parcellated and was used for further statistical evaluation. In comparison to controls, we found a significantly higher overall node degree in patients with lesional and non-lesional epilepsy. Furthermore, we examined the conditions of high/reduced vigilance and open/closed eyes in controls, to analyze whether patient node degree levels can be achieved. We evaluated intraclass-correlation statistics (ICC) to evaluate the reproducibility. Connectivity and specifically node degree analysis could present new tools for one of the most common neurological diseases, with potential applications in epilepsy diagnostics.
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Affiliation(s)
- Stephan Vogel
- Department of Neurosurgery, University Hospital Erlangen, 91054 Erlangen, Germany; (M.K.); (C.K.); (N.M.-V.); (M.B.); (S.R.)
- Friedrich Alexander University Erlangen Nürnberg (FAU), 91054 Erlangen, Germany
| | - Martin Kaltenhäuser
- Department of Neurosurgery, University Hospital Erlangen, 91054 Erlangen, Germany; (M.K.); (C.K.); (N.M.-V.); (M.B.); (S.R.)
| | - Cora Kim
- Department of Neurosurgery, University Hospital Erlangen, 91054 Erlangen, Germany; (M.K.); (C.K.); (N.M.-V.); (M.B.); (S.R.)
| | - Nadia Müller-Voggel
- Department of Neurosurgery, University Hospital Erlangen, 91054 Erlangen, Germany; (M.K.); (C.K.); (N.M.-V.); (M.B.); (S.R.)
| | - Karl Rössler
- Department of Neurosurgery, Medical University Vienna, 1090 Vienna, Austria;
| | - Arnd Dörfler
- Department of Neuroradiology, University Hospital Erlangen, 91054 Erlangen, Germany;
| | - Stefan Schwab
- Department of Neurology, University Hospital Erlangen, 91054 Erlangen, Germany; (S.S.); (H.H.)
| | - Hajo Hamer
- Department of Neurology, University Hospital Erlangen, 91054 Erlangen, Germany; (S.S.); (H.H.)
| | - Michael Buchfelder
- Department of Neurosurgery, University Hospital Erlangen, 91054 Erlangen, Germany; (M.K.); (C.K.); (N.M.-V.); (M.B.); (S.R.)
| | - Stefan Rampp
- Department of Neurosurgery, University Hospital Erlangen, 91054 Erlangen, Germany; (M.K.); (C.K.); (N.M.-V.); (M.B.); (S.R.)
- Department of Neurosurgery, University Hospital Halle (Saale), 06120 Halle (Saale), Germany
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6
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Slinger G, Otte WM, Braun KPJ, van Diessen E. An updated systematic review and meta-analysis of brain network organization in focal epilepsy: Looking back and forth. Neurosci Biobehav Rev 2021; 132:211-223. [PMID: 34813826 DOI: 10.1016/j.neubiorev.2021.11.028] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 06/23/2021] [Accepted: 11/17/2021] [Indexed: 01/10/2023]
Abstract
Abnormalities of the brain network organization in focal epilepsy have been extensively quantified. However, the extent and directionality of abnormalities are highly variable and subtype insensitive. We conducted meta-analyses to obtain a more accurate and epilepsy type-specific quantification of the interictal global brain network organization in focal epilepsy. By using random-effects models, we estimated differences in average clustering coefficient, average path length, and modularity between patients with focal epilepsy and controls, based on 45 studies with a total sample size of 1,468 patients and 1,021 controls. Structural networks had a significant lower level of integration in patients with epilepsy as compared to controls, with a standardized mean difference of -0.334 (95 % confidence interval -0.631 to -0.038; p-value 0.027). Functional networks did not differ between patients and controls, except for the beta band clustering coefficient. Our meta-analyses show that differences in the brain network organization are not as well defined as individual studies often propose. We discuss potential pitfalls and suggestions to enhance the yield and clinical value of network studies.
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Affiliation(s)
- Geertruida Slinger
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands.
| | - Willem M Otte
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands; Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Kees P J Braun
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Eric van Diessen
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
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7
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Hirosawa T, An KM, Soma D, Shiota Y, Sano M, Kameya M, Hino S, Naito N, Tanaka S, Yaoi K, Iwasaki S, Yoshimura Y, Kikuchi M. Epileptiform discharges relate to altered functional brain networks in autism spectrum disorders. Brain Commun 2021; 3:fcab184. [PMID: 34541529 PMCID: PMC8440646 DOI: 10.1093/braincomms/fcab184] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 05/23/2021] [Accepted: 06/22/2021] [Indexed: 11/13/2022] Open
Abstract
Many individuals with autism spectrum disorders have comorbid epilepsy. Even in the absence of observable seizures, interictal epileptiform discharges are common in individuals with autism spectrum disorders. However, how these interictal epileptiform discharges are related to autistic symptomatology remains unclear. This study used magnetoencephalography to investigate the relation between interictal epileptiform discharges and altered functional brain networks in children with autism spectrum disorders. Instead of particularly addressing individual brain regions, we specifically examine network properties. For this case-control study, we analysed 70 children with autism spectrum disorders (52 boys, 18 girls, 38-92 months old) and 19 typically developing children (16 boys, 3 girls, 48-88 months old). After assessing the participants' social reciprocity using the Social Responsiveness Scale, we constructed graphs of functional brain networks from frequency band separated task-free magnetoencephalography recordings. Nodes corresponded to Desikan-Killiany atlas-based 68 brain regions. Edges corresponded to phase lag index values between pairs of brain regions. To elucidate the effects of the existence of interictal epileptiform discharges on graph metrics, we matched each of three pairs from three groups (typically developing children, children with autism spectrum disorders who had interictal epileptiform discharges and those who did not) in terms of age and sex. We used a coarsened exact matching algorithm and applied adjusted regression analysis. We also investigated the relation between social reciprocity and the graph metric. Results show that, in children with autism spectrum disorders, the average clustering coefficient in the theta band was significantly higher in children who had interictal epileptiform discharges. Moreover, children with autism spectrum disorders who had no interictal epileptiform discharges had a significantly lower average clustering coefficient in the theta band than typically developing children had. However, the difference between typically developing children and children with autism spectrum disorder who had interictal epileptiform discharges was not significant. Furthermore, the higher average clustering coefficient in the theta band corresponded to severe autistic symptoms in children with autism spectrum disorder who had interictal epileptiform discharges. However, the association was not significant in children with autism spectrum disorders who had no interictal epileptiform discharge. In conclusion, results demonstrate that alteration of functional brain networks in children with autism spectrum disorders depends on the existence of interictal epileptiform discharges. Interictal epileptiform discharges might 'normalize' the deviation of altered brain networks in autism spectrum disorders, increasing the clustering coefficient. However, when the effect exceeds tolerance, it actually exacerbates autistic symptoms.
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Affiliation(s)
- Tetsu Hirosawa
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa 920-0934, Japan
- Research Center for Child Mental Development, Kanazawa University, Kanazawa 920-8641, Japan
- Division of Socio-Cognitive-Neuroscience, Department of Child Development United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Kanazawa 920-8640, Japan
| | - Kyung-min An
- Research Center for Child Mental Development, Kanazawa University, Kanazawa 920-8641, Japan
- Division of Socio-Cognitive-Neuroscience, Department of Child Development United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Kanazawa 920-8640, Japan
| | - Daiki Soma
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa 920-0934, Japan
| | - Yuka Shiota
- Research Center for Child Mental Development, Kanazawa University, Kanazawa 920-8641, Japan
- Division of Socio-Cognitive-Neuroscience, Department of Child Development United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Kanazawa 920-8640, Japan
| | - Masuhiko Sano
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa 920-0934, Japan
| | - Masafumi Kameya
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa 920-0934, Japan
| | - Shoryoku Hino
- Department of Neuropsychiatry, Ishikawa Prefectural Takamatsu Hospital, Ishikawa 929-1214, Japan
| | - Nobushige Naito
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa 920-0934, Japan
| | - Sanae Tanaka
- Research Center for Child Mental Development, Kanazawa University, Kanazawa 920-8641, Japan
- Division of Socio-Cognitive-Neuroscience, Department of Child Development United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Kanazawa 920-8640, Japan
| | - Ken Yaoi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa 920-8641, Japan
- Division of Socio-Cognitive-Neuroscience, Department of Child Development United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Kanazawa 920-8640, Japan
| | - Sumie Iwasaki
- Research Center for Child Mental Development, Kanazawa University, Kanazawa 920-8641, Japan
| | - Yuko Yoshimura
- Research Center for Child Mental Development, Kanazawa University, Kanazawa 920-8641, Japan
- Division of Socio-Cognitive-Neuroscience, Department of Child Development United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Kanazawa 920-8640, Japan
- Faculty of Education, Institute of Human and Social Sciences, Kanazawa University, Kanazawa 920-1164, Japan
| | - Mitsuru Kikuchi
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa 920-0934, Japan
- Research Center for Child Mental Development, Kanazawa University, Kanazawa 920-8641, Japan
- Division of Socio-Cognitive-Neuroscience, Department of Child Development United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Kanazawa 920-8640, Japan
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Li H, Zhang Q, Lin Z, Gao F. Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network. Brain Sci 2021; 11:1066. [PMID: 34439685 PMCID: PMC8392428 DOI: 10.3390/brainsci11081066] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 08/05/2021] [Accepted: 08/11/2021] [Indexed: 11/23/2022] Open
Abstract
Epilepsy is a chronic neurological disorder which can affect 65 million patients worldwide. Recently, network based analyses have been of great help in the investigation of seizures. Now graph theory is commonly applied to analyze functional brain networks, but functional brain networks are dynamic. Methods based on graph theory find it difficult to reflect the dynamic changes of functional brain network. In this paper, an approach to extracting features from brain functional networks is presented. Dynamic functional brain networks can be obtained by stacking multiple functional brain networks on the time axis. Then, a tensor decomposition method is used to extract features, and an ELM classifier is introduced to complete epilepsy prediction. In the prediction of epilepsy, the accuracy and F1 score of the feature extracted by tensor decomposition are higher than the degree and clustering coefficient. The features extracted from the dynamic functional brain network by tensor decomposition show better and more comprehensive performance than degree and clustering coefficient in epilepsy prediction.
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Affiliation(s)
| | - Qizhong Zhang
- Institute of Intelligent Control and Robotics, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; (H.L.); (Z.L.); (F.G.)
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9
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Li Y, Zhu H, Chen Q, Yang L, Bao X, Chen F, Ma H, Xu H, Luo L, Zhang R. Evaluation of Brain Network Properties in Patients with MRI-Negative Temporal Lobe Epilepsy: An MEG Study. Brain Topogr 2021; 34:618-631. [PMID: 34173926 DOI: 10.1007/s10548-021-00856-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 06/13/2021] [Indexed: 11/25/2022]
Abstract
Abnormal functional brain networks of temporal lobe epilepsy (TLE) patients with structural abnormalities may partially reflect structural lesions rather than either TLE per se or functional compensatory processes. In this study, we sought to investigate the brain-network properties of intractable TLE patients apart from the effects of structural abnormalities. The brain network properties of 20 left and 23 right MRI-negative TLE patients and 22 healthy controls were evaluated using magnetoencephalographic recordings in six main frequency bands. A slowing of oscillatory brain activity was observed for the left or right TLE group vs. healthy controls. The TLE groups presented significantly increased functional connectivity in the delta, theta, lower alpha and beta bands, and significantly greater values in the normalized clustering coefficient and path length, and significantly smaller values in the weighted small-world measure in the theta band when compared to healthy controls. Alterations in global and regional band powers can be attributed to spectral slowing in TLE patients. The brain networks of TLE patients displayed abnormally high synchronization in multi-frequency bands and shifted toward a more regular architecture with worse network efficiency in the theta band. Without the contamination of structural lesions, these significant findings can be helpful for better understanding of the pathophysiological mechanism of TLE. The theta band can be considered as a preferred frequency band for investigating the brain-network dysfunction of MRI-negative intractable TLE patients.
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Affiliation(s)
- Yuejun Li
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
- Department of Magnetoencephalography, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Haitao Zhu
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Qiqi Chen
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
- Department of Magnetoencephalography, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Lu Yang
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Xincai Bao
- Library of Xinxiang Medical University, Xinxiang, 453003, Henan, China
| | - Fangqing Chen
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Haiyan Ma
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Honghao Xu
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Lei Luo
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Rui Zhang
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China.
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10
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Derks J, Kulik SD, Numan T, de Witt Hamer PC, Noske DP, Klein M, Geurts JJG, Reijneveld JC, Stam CJ, Schoonheim MM, Hillebrand A, Douw L. Understanding Global Brain Network Alterations in Glioma Patients. Brain Connect 2021; 11:865-874. [PMID: 33947274 DOI: 10.1089/brain.2020.0801] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Introduction: Glioma patients show increased global brain network clustering related to poorer cognition and epilepsy. However, it is unclear whether this increase is spatially widespread, localized in the (peri)tumor region only, or decreases with distance from the tumor. Materials and Methods: Weighted global and local brain network clustering was determined in 71 glioma patients and 53 controls by using magnetoencephalography. Tumor clustering was determined by averaging local clustering of regions overlapping with the tumor, and vice versa for non-tumor regions. Euclidean distance was determined from the tumor centroid to the centroids of other regions. Results: Patients showed higher global clustering compared with controls. Clustering of tumor and non-tumor regions did not differ, and local clustering was not associated with distance from the tumor. Post hoc analyses revealed that in the patient group, tumors were located more often in regions with higher clustering in controls, but it seemed that tumors of patients with high global clustering were located more often in regions with lower clustering in controls. Conclusions: Glioma patients show non-local network disturbances. Tumors of patients with high global clustering may have a preferred localization, namely regions with lower clustering in controls, suggesting that tumor localization relates to the extent of network disruption. Impact statement This work uses the innovative framework of network neuroscience to investigate functional connectivity patterns associated with brain tumors. Glioma (primary brain tumor) patients experience cognitive deficits and epileptic seizures, which have been related to brain network alterations. This study shows that glioma patients have a spatially widespread increase in global network clustering, which cannot be attributed to local effects of the tumor. Moreover, tumors occur more often in brain regions with higher network clustering in controls. This study emphasizes the global character of network alterations in glioma patients and suggests that preferred tumor locations are characterized by particular network profiles.
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Affiliation(s)
- Jolanda Derks
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Brain Tumor Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Shanna D Kulik
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Brain Tumor Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Tianne Numan
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Brain Tumor Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Philip C de Witt Hamer
- Brain Tumor Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Neurosurgery, Overarching Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - David P Noske
- Brain Tumor Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Neurosurgery, Overarching Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Martin Klein
- Brain Tumor Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Medical Psychology, and Overarching Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jaap C Reijneveld
- Brain Tumor Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Neurology, Overarching Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Brain Tumor Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging/Massachusetts General Hospital, Charlestown, Massachusetts, USA
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11
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Solis I, Janowich J, Candelaria-Cook F, Collishaw W, Wang YP, Wilson TW, Calhoun VD, Ciesielski KRT, Stephen JM. Frontoparietal network and neuropsychological measures in typically developing children. Neuropsychologia 2021; 159:107914. [PMID: 34119500 DOI: 10.1016/j.neuropsychologia.2021.107914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 05/30/2021] [Accepted: 06/07/2021] [Indexed: 10/21/2022]
Abstract
Resting-state activity has been used to gain a broader understanding of typical and aberrant developmental changes. However, the developmental trajectory of resting-state activity in relation to cognitive performance has not been studied in detail. The present study assessed spectral characteristics of theta (5-8 Hz) and alpha (9-13 Hz) frequency bands during resting-state in a priori selected regions of the frontoparietal network (FPN). We also examined the relationship between resting-state activity and cognitive performance in typically developing children. We hypothesized that older children and children with high attentional scores would have higher parietal alpha activity and frontal theta activity while at rest compared to young children and those with lower attentional scores. MEG data were collected in 65 typically developing children, ages 9-14 years, as part of the Developmental Chronnecto-Genomics study. Resting-state data were collected during eyes open and eyes closed for 5 min. Participants completed the NIH Toolbox Flanker Inhibitory Control (FICA) and Attention Test and Dimensional Change Card Sort Test (DCCS) to assess top-down attentional control. Spectral power density was used to characterize the FPN. We found during eyes open and eyes closed, all participants had higher theta and alpha power in parietal regions relative to frontal regions. The group with high attentional scores had higher alpha power during resting-state eyes closed compared to those with low attentional scores. However, there were no significant differences between age groups, suggesting changes in the maturation of neural oscillations in theta and alpha are not evident among children in the 9-14-year age range.
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Affiliation(s)
- Isabel Solis
- Department of Psychology, University of New Mexico, 2001 Redondo S Dr, Albuquerque, NM, 87106, USA; Mind Research Network and Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd N.E., Albuquerque, NM, 87106, USA.
| | - Jacki Janowich
- Department of Psychology, University of New Mexico, 2001 Redondo S Dr, Albuquerque, NM, 87106, USA; Mind Research Network and Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd N.E., Albuquerque, NM, 87106, USA.
| | - Felicha Candelaria-Cook
- Mind Research Network and Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd N.E., Albuquerque, NM, 87106, USA.
| | - William Collishaw
- Mind Research Network and Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd N.E., Albuquerque, NM, 87106, USA.
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, 6823 St. Charles Ave, New Orleans, LA, 70118, USA.
| | - Tony W Wilson
- Department of Neurological Sciences, University of Nebraska Medical Center, 988440 Nebraska Medical Center, Omaha, NE, 68198, USA.
| | - Vince D Calhoun
- Mind Research Network and Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd N.E., Albuquerque, NM, 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, 498 Terrace St NE, Albuquerque, NM, 87106, USA; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, 33 Glimer St SE, Atlanta, GA, 30303, USA.
| | - Kristina R T Ciesielski
- Department of Psychology, University of New Mexico, 2001 Redondo S Dr, Albuquerque, NM, 87106, USA; MGH/MIT A. A. Martinos Center for Biomed. Imaging, Dept of Radiology, Harvard Medical School, 149 Thirteenth St, Suite 2301, Charleston, MA, 02129, USA.
| | - Julia M Stephen
- Mind Research Network and Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd N.E., Albuquerque, NM, 87106, USA.
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12
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Hao J, Luo W, Xie Y, Feng Y, Sun W, Peng W, Zhao J, Zhang P, Ding J, Wang X. Functional Network Alterations as Markers for Predicting the Treatment Outcome of Cathodal Transcranial Direct Current Stimulation in Focal Epilepsy. Front Hum Neurosci 2021; 15:637071. [PMID: 33815082 PMCID: PMC8009991 DOI: 10.3389/fnhum.2021.637071] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 02/23/2021] [Indexed: 11/13/2022] Open
Abstract
Background and Purpose Transcranial direct current stimulation (tDCS) is an emerging non-invasive neuromodulation technique for focal epilepsy. Because epilepsy is a disease affecting the brain network, our study was aimed to evaluate and predict the treatment outcome of cathodal tDCS (ctDCS) by analyzing the ctDCS-induced functional network alterations. Methods Either the active 5-day, -1.0 mA, 20-min ctDCS or sham ctDCS targeting at the most active interictal epileptiform discharge regions was applied to 27 subjects suffering from focal epilepsy. The functional networks before and after ctDCS were compared employing graph theoretical analysis based on the functional magnetic resonance imaging (fMRI) data. A support vector machine (SVM) prediction model was built to predict the treatment outcome of ctDCS using the graph theoretical measures as markers. Results Our results revealed that the mean clustering coefficient and the global efficiency decreased significantly, as well as the characteristic path length and the mean shortest path length at the stimulation sites in the fMRI functional networks increased significantly after ctDCS only for the patients with response to the active ctDCS (at least 20% reduction rate of seizure frequency). Our prediction model achieved the mean prediction accuracy of 68.3% (mean sensitivity: 70.0%; mean specificity: 67.5%) after the nested cross validation. The mean area under the receiver operating curve was 0.75, which showed good prediction performance. Conclusion The study demonstrated that the response to ctDCS was related to the topological alterations in the functional networks of epilepsy patients detected by fMRI. The graph theoretical measures were promising for clinical prediction of ctDCS treatment outcome.
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Affiliation(s)
- Jiaxin Hao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Wenyi Luo
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yuhai Xie
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yu Feng
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Wei Sun
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Weifeng Peng
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jun Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Puming Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jing Ding
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China
| | - Xin Wang
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology, the Institutes of Brain Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
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13
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Postmortem Dissections of the Papez Circuit and Nonmotor Targets for Functional Neurosurgery. World Neurosurg 2020; 144:e866-e875. [DOI: 10.1016/j.wneu.2020.09.088] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 09/16/2020] [Accepted: 09/16/2020] [Indexed: 12/11/2022]
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14
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Sangare A, Marchi A, Pruvost-Robieux E, Soufflet C, Crepon B, Ramdani C, Chassoux F, Turak B, Landre E, Gavaret M. The Effectiveness of Vagus Nerve Stimulation in Drug-Resistant Epilepsy Correlates with Vagus Nerve Stimulation-Induced Electroencephalography Desynchronization. Brain Connect 2020; 10:566-577. [PMID: 33073582 PMCID: PMC7757623 DOI: 10.1089/brain.2020.0798] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Introduction: VNS is an adjunctive neuromodulation therapy for patients with drug-refractory epilepsy. The antiseizure effect of VNS is thought to be related to a diffuse modulation of functional connectivity but remains to be confirmed. Aim: To investigate electroencephalographic (EEG) metrics of functional connectivity in patients with drug-refractory epilepsy treated by vagus nerve stimulation (VNS), between VNS-stimulated “ON” and nonstimulated “OFF” periods and between responder (R) and nonresponder (NR) patients. Methods: Scalp-EEG was performed for 35 patients treated by VNS, using 21 channels and 2 additional electrodes on the neck to detect the VNS stimulation. Patients were defined as VNS responders if a reduction of seizure frequency of ∼50% was documented. We analyzed the synchronization in EEG time series during “ON” and “OFF” periods of stimulation, using average phase lag index (PLI) in signal space and phase-locking value (PLV) between 10 sources. Based on graph theory, we computed brain network models and analyzed minimum spanning tree (MST) for responder and nonresponder patients. Results: Among 35 patients treated by VNS for a median time of 7 years (range 4 months to 22 years), 20 were R and 15 were NR. For responder patients, PLI during ON periods was significantly lower than that during OFF periods in delta (p = 0.009), theta (p = 0.02), and beta (p = 0.04) frequency bands. For nonresponder patients, there were no significant differences between ON and OFF periods. Moreover, variations of seizure frequency with VNS correlated with the PLI OFF/ON ratio in delta (p = 0.02), theta (p = 0.04), and beta (p = 0.03) frequency bands. Our results were confirmed using PLV in theta band (p < 0.05). No significant differences in MST were observed between R and NR patients. Conclusion: The correlation between VNS-induced interictal EEG time-series desynchronization and decrease in seizure frequency suggested that VNS therapeutic impact might be related to changes in interictal functional connectivity.
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Affiliation(s)
- Aude Sangare
- Neurophysiology Department, GHU Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Paris, France
| | - Angela Marchi
- Neurophysiology Department, GHU Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Paris, France
| | - Estelle Pruvost-Robieux
- Neurophysiology Department, GHU Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Paris, France.,Université de Paris, Paris, France
| | - Christine Soufflet
- Neurophysiology Department, GHU Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Paris, France
| | - Benoit Crepon
- Neurophysiology Department, GHU Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Paris, France
| | - Céline Ramdani
- Institut de Recherche Biomédicale des Armées (IRBA), Paris, France
| | - Francine Chassoux
- Neurosurgery and Epileptology Department, GHU Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Paris, France
| | - Baris Turak
- Neurosurgery and Epileptology Department, GHU Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Paris, France
| | - Elisabeth Landre
- Neurosurgery and Epileptology Department, GHU Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Paris, France
| | - Martine Gavaret
- Neurophysiology Department, GHU Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Paris, France.,Université de Paris, Paris, France.,INSERM UMR 1266, IPNP, Paris, France
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15
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Ramaraju S, Wang Y, Sinha N, McEvoy AW, Miserocchi A, de Tisi J, Duncan JS, Rugg-Gunn F, Taylor PN. Removal of Interictal MEG-Derived Network Hubs Is Associated With Postoperative Seizure Freedom. Front Neurol 2020; 11:563847. [PMID: 33071948 PMCID: PMC7543719 DOI: 10.3389/fneur.2020.563847] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 08/20/2020] [Indexed: 01/21/2023] Open
Abstract
Objective: To investigate whether MEG network connectivity was associated with epilepsy duration, to identify functional brain network hubs in patients with refractory focal epilepsy, and assess if their surgical removal was associated with post-operative seizure freedom. Methods: We studied 31 patients with drug refractory focal epilepsy who underwent resting state magnetoencephalography (MEG), and structural magnetic resonance imaging (MRI) as part of pre-surgical evaluation. Using the structural MRI, we generated 114 cortical regions of interest, performed surface reconstruction and MEG source localization. Representative source localized signals for each region were correlated with each other to generate a functional brain network. We repeated this procedure across three randomly chosen one-minute epochs. Network hubs were defined as those with the highest intra-hemispheric mean correlations. Post-operative MRI identified regions that were surgically removed. Results: Greater mean MEG network connectivity was associated with a longer duration of epilepsy. Patients who were seizure free after surgery had more hubs surgically removed than patients who were not seizure free (AUC = 0.76, p = 0.01) consistently across three randomly chosen time segments. Conclusion: Our results support a growing literature implicating network hub involvement in focal epilepsy, the removal of which by surgery is associated with greater chance of post-operative seizure freedom.
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Affiliation(s)
- Sriharsha Ramaraju
- Interdisciplinary Computing and Complex BioSystems Group, CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems Group, CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.,Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom.,Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Nishant Sinha
- Interdisciplinary Computing and Complex BioSystems Group, CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.,Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Andrew W McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Fergus Rugg-Gunn
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Peter N Taylor
- Interdisciplinary Computing and Complex BioSystems Group, CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.,Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom.,Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, United Kingdom
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16
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Yu H, Zhu L, Cai L, Wang J, Liu C, Shi N, Liu J. Variation of functional brain connectivity in epileptic seizures: an EEG analysis with cross-frequency phase synchronization. Cogn Neurodyn 2020; 14:35-49. [PMID: 32015766 PMCID: PMC6973936 DOI: 10.1007/s11571-019-09551-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 07/22/2019] [Accepted: 08/02/2019] [Indexed: 11/26/2022] Open
Abstract
Frequency coupling in nervous system is believed to be associated with normal and impaired brain functions. However, most of the existing experiments have been concentrated on the coupling strength within frequency bands, while the coupling strength between different bands is ignored. In this work, we apply phase synchronization index (PSI) to investigate the cross-frequency coupling (CFC) of Electroencephalogram (EEG) signals. The PSI matrixes for the multi-channel EEG signals are calculated from interictal to ictal period in each sliding time window. The results show that CFC changes obviously once seizure occurs between the different bands, and such alteration is earlier than the appearance of clinical symptoms in seizure. Considering the similar role of the within-frequency coupling (WFC), we further reconstruct multi-layered brain networks, including CFC networks and WFC networks. The graph metrics are applied to investigate the variation of network structure of the epileptic brain. Significant decreases/increases of the local/global efficiency are found in δ-β, δ-α, θ-α and δ-θ bands from the CFC network, while WFC network shows a significant decline in the local efficiency in θ and α bands. These findings suggest that CFC may provide a new perspective to observe the alteration of brain structure when seizure occurs, and the investigation of functional connectivity across the full frequency spectrum can give us a deeper understanding of epileptic brains.
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Affiliation(s)
- Haitao Yu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Lin Zhu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Lihui Cai
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Chen Liu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Nan Shi
- Department of Neurology, Tangshan Gongren Hospital, Tangshan, 063000 Hebei China
| | - Jing Liu
- Department of Neurology, Tangshan Gongren Hospital, Tangshan, 063000 Hebei China
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17
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Exploring the fatigue affecting electroencephalography based functional brain networks during real driving in young males. Neuropsychologia 2019; 129:200-211. [PMID: 30995455 DOI: 10.1016/j.neuropsychologia.2019.04.004] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 04/11/2019] [Accepted: 04/12/2019] [Indexed: 11/24/2022]
Abstract
In recent years, a large proportion of traffic accidents are caused by driver fatigue. The brain has been conceived as a complex network, whose function can be assessed with EEG. Hence, in this research, fourteen subjects participated in the real driving experiments, and a comprehensive EEG-based expert system was designed for detecting driver fatigue. Collected EEG signals were first decomposed into delta-range, theta-range, alpha-range and beta-range by wavelet packet transform (WPT). Unlike other approaches, a multi-channel network construction method based on Phase Lag Index (PLI) was then proposed in this paper. Finally, the functional connectivity between alert state (at the beginning of the drive) and fatigue state (at the end of the drive) in multiple frequency bands were analyzed. The results indicate that functional connectivity of the brain area was significantly different between alert and fatigue states, especially in alpha-range and beta-range. Particularly, the frontal-to-parietal functional connectivity was weakened. Meanwhile, lower clustering coefficient (C) values and higher characteristic path length (L) values were observed in fatigue state in comparison with alert state. Based on this, two new EEG feature selection approaches, C and L in the corresponding sub-frequency range were applied to feature recognition and classification system. Using a support vector machine (SVM) machine learning algorithm, these features were combined to distinguish between alert and fatigue states, achieving an accuracy of 94.4%, precision of 94.3%, sensitivity of 94.6% and false alarm rate of 5.7%. The results suggest that brain network analysis approaches combined with SVM are helpful to alert drivers while being sleepy or even fatigue.
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18
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Meng L, Xiang J. Brain Network Analysis and Classification Based on Convolutional Neural Network. Front Comput Neurosci 2018; 12:95. [PMID: 30618690 PMCID: PMC6295646 DOI: 10.3389/fncom.2018.00095] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Accepted: 11/19/2018] [Indexed: 11/13/2022] Open
Abstract
Background: Convolution neural networks (CNN) is increasingly used in computer science and finds more and more applications in different fields. However, analyzing brain network with CNN is not trivial, due to the non-Euclidean characteristics of brain network built by graph theory. Method: To address this problem, we used a famous algorithm "word2vec" from the field of natural language processing (NLP), to represent the vertexes of graph in the node embedding space, and transform the brain network into images, which can bridge the gap between brain network and CNN. Using this model, we analyze and classify the brain network from Magnetoencephalography (MEG) data into two categories: normal controls and patients with migraine. Results: In the experiments, we applied our method on the clinical MEG dataset, and got the mean classification accuracy rate 81.25%. Conclusions: These results indicate that our method can feasibly analyze and classify the brain network, and all the abundant resources of CNN can be used on the analysis of brain network.
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Affiliation(s)
- Lu Meng
- College of Information Science and Engineering, Northeastern University, Shenyang, China
| | - Jing Xiang
- Department of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
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19
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Resting state connectivity in neocortical epilepsy: The epilepsy network as a patient-specific biomarker. Clin Neurophysiol 2018; 130:280-288. [PMID: 30605890 DOI: 10.1016/j.clinph.2018.11.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 09/04/2018] [Accepted: 11/03/2018] [Indexed: 02/01/2023]
Abstract
OBJECTIVE Localization related epilepsy (LRE) is increasingly accepted as a network disorder. To better understand the network specific characteristics of LRE, we defined individual epilepsy networks and compared them across patients. METHODS The epilepsy network was defined in the slow cortical potential frequency band in 10 patients using intracranial EEG data obtained during interictal periods. Cortical regions were included in the epilepsy network if their connectivity pattern was similar to the connectivity pattern of the seizure onset electrode contact. Patients were subdivided into frontal, temporal, and posterior quadrant cohorts according to the anatomic location of seizure onset. Jaccard similarity was calculated within each cohort to assess for similarity of the epilepsy network between patients within each cohort. RESULTS All patients exhibited an epilepsy network in the slow cortical potential frequency band. The topographic distribution of this correlated network activity was found to be unique at the single subject level. CONCLUSIONS The epilepsy network was unique at the single patient level, even between patients with similar seizure onset locations. SIGNIFICANCE We demonstrated that the epilepsy network is patient-specific. This is in keeping with our current understanding of brain networks and identifies the patient-specific epilepsy network as a possible biomarker in LRE.
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20
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Kitchigina VF. Alterations of Coherent Theta and Gamma Network Oscillations as an Early Biomarker of Temporal Lobe Epilepsy and Alzheimer's Disease. Front Integr Neurosci 2018; 12:36. [PMID: 30210311 PMCID: PMC6119809 DOI: 10.3389/fnint.2018.00036] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2018] [Accepted: 07/30/2018] [Indexed: 12/11/2022] Open
Abstract
Alzheimer's disease (AD) and temporal lobe epilepsy (TLE) are the most common forms of neurodegenerative disorders characterized by the loss of cells and progressive irreversible alteration of cognitive functions, such as attention and memory. AD may be an important cause of epilepsy in the elderly. Early diagnosis of diseases is very important for their successful treatment. Many efforts have been done for defining new biomarkers of these diseases. Significant advances have been made in the searching of some AD and TLE reliable biomarkers, including cerebrospinal fluid and plasma measurements and glucose positron emission tomography. However, there is a great need for the biomarkers that would reflect changes of brain activity within few milliseconds to obtain information about cognitive disturbances. Successful early detection of AD and TLE requires specific biomarkers capable of distinguishing individuals with the progressing disease from ones with other pathologies that affect cognition. In this article, we review recent evidence suggesting that magnetoencephalographic recordings and coherent analysis coupled with behavioral evaluation can be a promising approach to an early detection of AD and TLE. Highlights -Data reviewed include the results of clinical and experimental studies.-Theta and gamma rhythms are disturbed in epilepsy and AD.-Common and different behavioral and oscillatory features of pathologies are compared.-Coherent analysis can be useful for an early diagnostics of diseases.
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Affiliation(s)
- Valentina F Kitchigina
- Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences (RAS), Pushchino, Russia
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21
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Bio-Signal Complexity Analysis in Epileptic Seizure Monitoring: A Topic Review. SENSORS 2018; 18:s18061720. [PMID: 29861451 PMCID: PMC6022076 DOI: 10.3390/s18061720] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 05/22/2018] [Accepted: 05/23/2018] [Indexed: 01/03/2023]
Abstract
Complexity science has provided new perspectives and opportunities for understanding a variety of complex natural or social phenomena, including brain dysfunctions like epilepsy. By delving into the complexity in electrophysiological signals and neuroimaging, new insights have emerged. These discoveries have revealed that complexity is a fundamental aspect of physiological processes. The inherent nonlinearity and non-stationarity of physiological processes limits the methods based on simpler underlying assumptions to point out the pathway to a more comprehensive understanding of their behavior and relation with certain diseases. The perspective of complexity may benefit both the research and clinical practice through providing novel data analytics tools devoted for the understanding of and the intervention about epilepsies. This review aims to provide a sketchy overview of the methods derived from different disciplines lucubrating to the complexity of bio-signals in the field of epilepsy monitoring. Although the complexity of bio-signals is still not fully understood, bundles of new insights have been already obtained. Despite the promising results about epileptic seizure detection and prediction through offline analysis, we are still lacking robust, tried-and-true real-time applications. Multidisciplinary collaborations and more high-quality data accessible to the whole community are needed for reproducible research and the development of such applications.
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22
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den Heijer JM, Otte WM, van Diessen E, van Campen JS, Lorraine Hompe E, Jansen FE, Joels M, Braun KPJ, Sander JW, Zijlmans M. The relation between cortisol and functional connectivity in people with and without stress-sensitive epilepsy. Epilepsia 2017; 59:179-189. [DOI: 10.1111/epi.13947] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/12/2017] [Indexed: 01/21/2023]
Affiliation(s)
| | - Willem M. Otte
- Department of Pediatric Neurology; Brain Center Rudolf Magnus; University Medical Center Utrecht; Utrecht The Netherlands
- Biomedical MR Imaging and Spectroscopy Group; Center for Image Sciences; University Medical Center Utrecht; Utrecht The Netherlands
- Stichting Epilepsie Instellingen Nederland (SEIN); Heemstede The Netherlands
| | - Eric van Diessen
- Department of Pediatric Neurology; Brain Center Rudolf Magnus; University Medical Center Utrecht; Utrecht The Netherlands
| | - Jolien S. van Campen
- Department of Pediatric Neurology; Brain Center Rudolf Magnus; University Medical Center Utrecht; Utrecht The Netherlands
- Department of Translational Neuroscience; Brain Center Rudolf Magnus; University Medical Center Utrecht; Utrecht The Netherlands
| | | | - Floor E. Jansen
- Department of Pediatric Neurology; Brain Center Rudolf Magnus; University Medical Center Utrecht; Utrecht The Netherlands
| | - Marian Joels
- Department of Translational Neuroscience; Brain Center Rudolf Magnus; University Medical Center Utrecht; Utrecht The Netherlands
- University Medical Center Groningen; Groningen The Netherlands
| | - Kees P. J. Braun
- Department of Pediatric Neurology; Brain Center Rudolf Magnus; University Medical Center Utrecht; Utrecht The Netherlands
| | - Josemir W. Sander
- Stichting Epilepsie Instellingen Nederland (SEIN); Heemstede The Netherlands
- NIHR University College London Hospitals Biomedical Research Centre; UCL Institute of Neurology; London United Kingdom
- Epilepsy Society; Chalfont St Peter United Kingdom
| | - Maeike Zijlmans
- Department of Pediatric Neurology; Brain Center Rudolf Magnus; University Medical Center Utrecht; Utrecht The Netherlands
- Stichting Epilepsie Instellingen Nederland (SEIN); Heemstede The Netherlands
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Level Set Based Hippocampus Segmentation in MR Images with Improved Initialization Using Region Growing. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017; 2017:5256346. [PMID: 28191031 PMCID: PMC5274694 DOI: 10.1155/2017/5256346] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Revised: 12/10/2016] [Accepted: 12/22/2016] [Indexed: 02/05/2023]
Abstract
The hippocampus has been known as one of the most important structures referred to as Alzheimer's disease and other neurological disorders. However, segmentation of the hippocampus from MR images is still a challenging task due to its small size, complex shape, low contrast, and discontinuous boundaries. For the accurate and efficient detection of the hippocampus, a new image segmentation method based on adaptive region growing and level set algorithm is proposed. Firstly, adaptive region growing and morphological operations are performed in the target regions and its output is used for the initial contour of level set evolution method. Then, an improved edge-based level set method utilizing global Gaussian distributions with different means and variances is developed to implement the accurate segmentation. Finally, gradient descent method is adopted to get the minimization of the energy equation. As proved by experiment results, the proposed method can ideally extract the contours of the hippocampus that are very close to manual segmentation drawn by specialists.
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