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Berger A, Cerra M, Joris V, Danthine V, Macq B, Dricot L, Vandewalle G, Delinte N, El Tahry R. Identifying responders to vagus nerve stimulation based on microstructural features of thalamocortical tracts in drug-resistant epilepsy. Neurotherapeutics 2024:e00422. [PMID: 38964949 DOI: 10.1016/j.neurot.2024.e00422] [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: 03/07/2024] [Revised: 06/20/2024] [Accepted: 06/24/2024] [Indexed: 07/06/2024] Open
Abstract
The mechanisms of action of Vagus Nerve Stimulation (VNS) and the biological prerequisites to respond to the treatment are currently under investigation. It is hypothesized that thalamocortical tracts play a central role in the antiseizure effects of VNS by disrupting the genesis of pathological activity in the brain. This pilot study explored whether in vivo microstructural features of thalamocortical tracts may differentiate Drug-Resistant Epilepsy (DRE) patients responding and not responding to VNS treatment. Eighteen patients with DRE (37.11 ± 10.13 years, 10 females), including 11 responders or partial responders and 7 non-responders to VNS, were recruited for this high-gradient multi-shell diffusion Magnetic Resonance Imaging (MRI) study. Using Diffusion Tensor Imaging (DTI) and multi-compartment models - Neurite Orientation Dispersion and Density Imaging (NODDI) and Microstructure Fingerprinting (MF), we extracted microstructural features in 12 subsegments of thalamocortical tracts. These characteristics were compared between responders/partial responders and non-responders. Subsequently, a Support Vector Machine (SVM) classifier was built, incorporating microstructural features and 12 clinical covariates (including age, sex, duration of VNS therapy, number of antiseizure medications, benzodiazepine intake, epilepsy duration, epilepsy onset age, epilepsy type - focal or generalized, presence of an epileptic syndrome - no syndrome or Lennox-Gastaut syndrome, etiology of epilepsy - structural, genetic, viral, or unknown, history of brain surgery, and presence of a brain lesion detected on structural MRI images). Multiple diffusion metrics consistently demonstrated significantly higher white matter fiber integrity in patients with a better response to VNS (pFDR < 0.05) in different subsegments of thalamocortical tracts. The SVM model achieved a classification accuracy of 94.12%. The inclusion of clinical covariates did not improve the classification performance. The results suggest that the structural integrity of thalamocortical tracts may be linked to therapeutic effectiveness of VNS. This study reveals the great potential of diffusion MRI in improving our understanding of the biological factors associated with the response to VNS therapy.
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Affiliation(s)
- Alexandre Berger
- Epilepsy and Neurostimulation Lab, Institute of Neuroscience (IoNS), Department of Clinical Neuroscience, Catholic University of Louvain, 1200, Brussels, Belgium; Synergia Medical SA, 1435, Mont-Saint-Guibert, Belgium; Sleep and Chronobiology Lab, GIGA-Cyclotron Research Center-In Vivo Imaging, University of Liège, 4000, Liège, Belgium.
| | - Michele Cerra
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Catholic University of Louvain, 1348, Louvain-la-Neuve, Belgium; Politecnico di Torino, Department of Control and Computer Engineering, Corso Duca Degli Abruzzi 24, 10129, Torino, Italy
| | - Vincent Joris
- Epilepsy and Neurostimulation Lab, Institute of Neuroscience (IoNS), Department of Clinical Neuroscience, Catholic University of Louvain, 1200, Brussels, Belgium; Cliniques Universitaires Saint-Luc (CUSL), Department of Neurosurgery, 1200, Brussels, Belgium
| | - Venethia Danthine
- Epilepsy and Neurostimulation Lab, Institute of Neuroscience (IoNS), Department of Clinical Neuroscience, Catholic University of Louvain, 1200, Brussels, Belgium
| | - Benoit Macq
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Catholic University of Louvain, 1348, Louvain-la-Neuve, Belgium
| | - Laurence Dricot
- Epilepsy and Neurostimulation Lab, Institute of Neuroscience (IoNS), Department of Clinical Neuroscience, Catholic University of Louvain, 1200, Brussels, Belgium
| | - Gilles Vandewalle
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Center-In Vivo Imaging, University of Liège, 4000, Liège, Belgium
| | - Nicolas Delinte
- Epilepsy and Neurostimulation Lab, Institute of Neuroscience (IoNS), Department of Clinical Neuroscience, Catholic University of Louvain, 1200, Brussels, Belgium; Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Catholic University of Louvain, 1348, Louvain-la-Neuve, Belgium
| | - Riëm El Tahry
- Epilepsy and Neurostimulation Lab, Institute of Neuroscience (IoNS), Department of Clinical Neuroscience, Catholic University of Louvain, 1200, Brussels, Belgium; Center for Refractory Epilepsy, Cliniques Universitaires Saint-Luc (CUSL), Department of Neurology, 1200, Brussels, Belgium
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Cheng T, Hu Y, Qin X, Ma J, Zha D, Xie H, Ji T, Liu Q, Wang Z, Hao H, Wu Y, Li L. A predictive model combining connectomics and entropy biomarkers to discriminate long-term vagus nerve stimulation efficacy for pediatric patients with drug-resistant epilepsy. CNS Neurosci Ther 2024; 30:e14751. [PMID: 39015946 PMCID: PMC11252558 DOI: 10.1111/cns.14751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 07/18/2024] Open
Abstract
AIMS To predict the vagus nerve stimulation (VNS) efficacy for pediatric drug-resistant epilepsy (DRE) patients, we aim to identify preimplantation biomarkers through clinical features and electroencephalogram (EEG) signals and thus establish a predictive model from a multi-modal feature set with high prediction accuracy. METHODS Sixty-five pediatric DRE patients implanted with VNS were included and followed up. We explored the topological network and entropy features of preimplantation EEG signals to identify the biomarkers for VNS efficacy. A Support Vector Machine (SVM) integrated these biomarkers to distinguish the efficacy groups. RESULTS The proportion of VNS responders was 58.5% (38/65) at the last follow-up. In the analysis of parieto-occipital α band activity, higher synchronization level and nodal efficiency were found in responders. The central-frontal θ band activity showed significantly lower entropy in responders. The prediction model reached an accuracy of 81.5%, a precision of 80.1%, and an AUC (area under the receiver operating characteristic curve) of 0.838. CONCLUSION Our results revealed that, compared to nonresponders, VNS responders had a more efficient α band brain network, especially in the parieto-occipital region, and less spectral complexity of θ brain activities in the central-frontal region. We established a predictive model integrating both preimplantation clinical and EEG features and exhibited great potential for discriminating the VNS responders. This study contributed to the understanding of the VNS mechanism and improved the performance of the current predictive model.
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Affiliation(s)
- Tung‐yang Cheng
- National Engineering Research Center of Neuromodulation, School of Aerospace EngineeringTsinghua UniversityBeijingChina
| | - Yingbing Hu
- National Engineering Research Center of Neuromodulation, School of Aerospace EngineeringTsinghua UniversityBeijingChina
- Tsinghua‐Berkeley Shenzhen InstituteTsinghua UniversityShenzhenChina
| | - Xiaoya Qin
- National Engineering Research Center of Neuromodulation, School of Aerospace EngineeringTsinghua UniversityBeijingChina
- Tsinghua‐Berkeley Shenzhen InstituteTsinghua UniversityShenzhenChina
| | - Jiayi Ma
- Department of PediatricsPeking University First HospitalBeijingChina
| | - Daqi Zha
- National Engineering Research Center of Neuromodulation, School of Aerospace EngineeringTsinghua UniversityBeijingChina
| | - Han Xie
- Department of PediatricsPeking University First HospitalBeijingChina
| | - Taoyun Ji
- Department of PediatricsPeking University First HospitalBeijingChina
- Pediatric Epilepsy CenterPeking University First HospitalBeijingChina
| | - Qingzhu Liu
- Pediatric Epilepsy CenterPeking University First HospitalBeijingChina
| | - Zhiyan Wang
- CAS Key Laboratory of Mental Health, Institute of PsychologyChinese Academy of SciencesBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
| | - Hongwei Hao
- National Engineering Research Center of Neuromodulation, School of Aerospace EngineeringTsinghua UniversityBeijingChina
| | - Ye Wu
- Department of PediatricsPeking University First HospitalBeijingChina
- Pediatric Epilepsy CenterPeking University First HospitalBeijingChina
| | - Luming Li
- National Engineering Research Center of Neuromodulation, School of Aerospace EngineeringTsinghua UniversityBeijingChina
- IDG/McGovern Institute for Brain Research at Tsinghua UniversityBeijingChina
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Wang Q, Ren Z, Yue M, Zhao Y, Wang B, Zhao Z, Wen B, Hong Y, Chen Y, Zhao T, Wang N, Zhao P, Hong Y, Han X. A model for the diagnosis of anxiety in patients with epilepsy based on phase locking value and Lempel-Ziv complexity features of the electroencephalogram. Brain Res 2024; 1824:148662. [PMID: 37924926 DOI: 10.1016/j.brainres.2023.148662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 09/09/2023] [Accepted: 10/30/2023] [Indexed: 11/06/2023]
Abstract
OBJECTIVE Anxiety disorders (AD) are critical factors that significantly (about one-fifth) impact the quality of life (QoL) in patients with epilepsy (PWE). Objective diagnostic methods have contributed to the identification of PWE susceptible to AD. This study aimed to identify AD in PWE by constructing a diagnostic model based on the phase locking value (PLV) and Lempel-Ziv Complexity (LZC) features of the electroencephalogram (EEG). METHODS EEG data from 131 patients with epilepsy (PWE) were enrolled in this study. Patients were divided into two groups, anxiety disorder (AD, n = 61) and non-anxiety disorder (NAD, n = 70), according to the Hamilton Rating Scale for Anxiety (HAM-A). Support vector machine (SVM) and K-Nearest-Neighbor(KNN) algorithms were used to construct three models - the PLVEEG, LZCEEG, and PLVEEG + LZCEEG feature models. Finally, the area under the receiver operating characteristic curve (AUC) and statistical analyses were performed to evaluate the model performance. RESULTS The efficiency of the KNN-based PLCEEG + LZCEEG feature model was the best, and the accuracy, precision, recall, F1-score, and AUC of the model after five-fold cross-validations scores were 87.89 %, 82.27 %, 98.33 %, 88.95 %, and 0.89, respectively. When the model efficiency was optimal, 29 EEG features were suggested. Further analysis of these features indicated 22 EEG features that were significantly different between the two groups, including 50 % features of the alpha (α)-band. CONCLUSIONS The PLVEEG + LZCEEG model features can identify AD in PWE. The PLVEEG and LZCEEG characteristics of the α-band may further be explored as potential biomarkers for AD in PWE.
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Affiliation(s)
- Qi Wang
- Department of Neurology, Zhengzhou University People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Zhe Ren
- Department of Neurology, Zhengzhou University People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Mengyan Yue
- Department of Rehabilitation, The First Hospital of Shanxi Medical University, Shanxi Province, Taiyuan 030000, China
| | - Yibo Zhao
- Department of Neurology, Zhengzhou University People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Bin Wang
- Department of Neurology, Henan Provincial People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Zongya Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453000, Henan Province, China
| | - Bin Wen
- School of Life Sciences and Technology, Xi'an Jiaotong University, Xi'an 710000, Shaanxi Province, China
| | - Yang Hong
- Department of Neurology, People's Hospital of Henan University, Zhengzhou 450003, Henan Province, China
| | - Yanan Chen
- Department of Neurology, Henan Provincial People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Ting Zhao
- Department of Neurology, Henan Provincial People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Na Wang
- Department of Neurology, Henan Provincial People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Pan Zhao
- Department of Neurology, Henan Provincial People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Yingxing Hong
- Department of Neurology, People's Hospital of Henan University, Zhengzhou 450003, Henan Province, China
| | - Xiong Han
- Department of Neurology, Henan Provincial People's Hospital, Henan Province, Zhengzhou 450003, China.
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Jia S, Meng Y, Gao Y, Ao L, Yang L, Wang H, Liu Y. Romantic relationships attenuated competition between lovers: evidence from brain synchronization. Cereb Cortex 2024; 34:bhae028. [PMID: 38300221 DOI: 10.1093/cercor/bhae028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 01/12/2024] [Accepted: 01/15/2024] [Indexed: 02/02/2024] Open
Abstract
Competition is an essential component of social interaction and is influenced by interpersonal relationships. This study is based on social exchange theory and explores the relationship between brain synchronization and competition in the binary system of romantic relationships through electroencephalogram hyperscanning technology. The results found that females had a greater win rate in the romantic and friend groups. During the early stage (0-200 ms), when the competitive target appeared, the stranger group exhibited greater interbrain synchronicity in the Alpha frequency band. However, during the later stage (600-800 ms), the romantic group showed higher Alpha band interbrain synchrony when the competitive target appeared. Significant interbrain synchronizations were observed in the Theta frequency band of the stranger and friend groups at 400-600 ms and 800-1000 ms. Moreover, these interbrain synchronizations were significantly positively correlated with the winning rates of females in the competition. These findings suggest a close relationship between interpersonal coordination and interbrain synchronization. Furthermore, romantic relationships reduce participants' willingness to compete, affecting their attention regulation, emotional processing, and goal orientation, thus influencing competition. This study investigated the impact of romantic relationships on competition, providing a theoretical foundation for promoting the positive and healthy development of romantic relationships.
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Affiliation(s)
- Shuyu Jia
- School of Psychology and Mental Health, North China University of Science and Technology, 21 Bohai avenue, Caofeidian district, Tangshan, Hebei province, China
| | - Yujia Meng
- School of Psychology and Mental Health, North China University of Science and Technology, 21 Bohai avenue, Caofeidian district, Tangshan, Hebei province, China
| | - Yuan Gao
- School of Psychology and Mental Health, North China University of Science and Technology, 21 Bohai avenue, Caofeidian district, Tangshan, Hebei province, China
| | - Lihong Ao
- School of Psychology and Mental Health, North China University of Science and Technology, 21 Bohai avenue, Caofeidian district, Tangshan, Hebei province, China
| | - Lei Yang
- School of Psychology and Mental Health, North China University of Science and Technology, 21 Bohai avenue, Caofeidian district, Tangshan, Hebei province, China
| | - He Wang
- School of Psychology and Mental Health, North China University of Science and Technology, 21 Bohai avenue, Caofeidian district, Tangshan, Hebei province, China
- School of Public Health, School of Psychology and Mental Health, North China University of Science and Technology, 21 Bohai avenue, Caofeidian district, Tangshan, Hebei province, China
| | - Yingjie Liu
- School of Psychology and Mental Health, North China University of Science and Technology, 21 Bohai avenue, Caofeidian district, Tangshan, Hebei province, China
- School of Public Health, School of Psychology and Mental Health, North China University of Science and Technology, 21 Bohai avenue, Caofeidian district, Tangshan, Hebei province, China
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Jia S, Meng Y, Gao Y, Ao L, Yang L, Wang H, Liu Y. The absence of one's intimate partner promotes dyadic competition through enhanced interbrain synchronization between opponents. Front Psychol 2024; 15:1298175. [PMID: 38328380 PMCID: PMC10847280 DOI: 10.3389/fpsyg.2024.1298175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 01/09/2024] [Indexed: 02/09/2024] Open
Abstract
Competition is a common occurrence in life, but the influence of intimate relationships on people's competitiveness remains unknown. Grounded in Darwin's theory of sexual selection, this study utilized EEG hyperscanning technology to investigate the influence of the presence of romantic partners and the gender of competitors on the interbrain synchronization of female individuals during competitive contexts. The research results showed that in competitive interactions, there was a significant increase in Theta and Alpha frequency band activity between females and their competitors. Interbrain synchronization was strongest when their partners were not nearby and females competed with same gender competitors. The research results indicate that intimate companionship has an impact on the early alertness and late cognitive execution mechanisms of female individuals in competition, and due to intimate relationships, females pay more attention to same-gender competitors. This study demonstrates that the presence of intimate partners can affect a female's competitive state and brain synchronization with opponents of different genders, improving the theoretical explanation of intimate relationships and competitive interactions.
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Affiliation(s)
- Shuyu Jia
- School of Psychology and Mental Health, North China University of Science and Technology, Tangshan, Hebei, China
| | - Yujia Meng
- School of Psychology and Mental Health, North China University of Science and Technology, Tangshan, Hebei, China
| | - Yuan Gao
- School of Psychology and Mental Health, North China University of Science and Technology, Tangshan, Hebei, China
| | - Lihong Ao
- School of Psychology and Mental Health, North China University of Science and Technology, Tangshan, Hebei, China
| | - Lei Yang
- School of Psychology and Mental Health, North China University of Science and Technology, Tangshan, Hebei, China
| | - He Wang
- School of Public Health, School of Psychology and Mental Health, North China University of Science and Technology, Tangshan, Hebei, China
| | - Yingjie Liu
- School of Public Health, School of Psychology and Mental Health, North China University of Science and Technology, Tangshan, Hebei, China
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Chen H, Wang Y, Ji T, Jiang Y, Zhou X. Brain functional connectivity-based prediction of vagus nerve stimulation efficacy in pediatric pharmacoresistant epilepsy. CNS Neurosci Ther 2023; 29:3259-3268. [PMID: 37170486 PMCID: PMC10580342 DOI: 10.1111/cns.14257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 04/13/2023] [Accepted: 04/24/2023] [Indexed: 05/13/2023] Open
Abstract
OBJECTIVE Although vagus nerve stimulation (VNS) is a common and widely used therapy for pharmacoresistant epilepsy, the reported efficacy of VNS in pediatric patients varies, so it is unclear which children will respond to VNS therapy. This study aimed to identify functional brain network features associated with VNS action to distinguish VNS responders from nonresponders using scalp electroencephalogram (EEG) data. METHODS Twenty-three children were included in this study, 16 in the discovery cohort and 7 in the test cohort. Using partial correlation value as a measure of whole-brain functional connectivity, we identified the differential edges between responders and nonresponders. Results derived from this were used as input to generate a support vector machine-learning classifier to predict VNS outcomes. RESULTS The postcentral gyrus in the left and right parietal lobe regions was identified as the most significant differential brain region between VNS responders and nonresponders (p < 0.001). The resultant classifier demonstrated a mean AUC value of 0.88, a mean sensitivity rate of 91.4%, and a mean specificity rate of 84.3% on fivefold cross-validation in the discovery cohort. In the testing cohort, our study demonstrated an AUC value of 0.91, a sensitivity rate of 86.6%, and a specificity rate of 79.3%. Furthermore, for prediction accuracy, our model can achieve 81.4% accuracy at the epoch level and 100% accuracy at the patient level. SIGNIFICANCE This study provides the first treatment response prediction model for VNS using scalp EEG data with ictal recordings and offers new insights into its mechanism of action. Our results suggest that brain functional connectivity features can help predict therapeutic response to VNS therapy. With further validation, our model could facilitate the selection of targeted pediatric patients and help avoid risky and costly procedures for patients who are unlikely to benefit from VNS therapy.
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Affiliation(s)
- Hao Chen
- Beijing International Center for Mathematical ResearchPeking UniversityBeijingChina
| | - Yi Wang
- Beijing International Center for Mathematical ResearchPeking UniversityBeijingChina
| | - Taoyun Ji
- Department of Pediatrics and Pediatric Epilepsy CenterPeking University First HospitalBeijingChina
| | - Yuwu Jiang
- Department of Pediatrics and Pediatric Epilepsy CenterPeking University First HospitalBeijingChina
| | - Xiao‐Hua Zhou
- Beijing International Center for Mathematical ResearchPeking UniversityBeijingChina
- Department of Biostatistics, School of Public HealthPeking UniversityBeijingChina
- Pazhou LabGuangzhouChina
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Germany E, Teixeira I, Danthine V, Santalucia R, Cakiroglu I, Torres A, Verleysen M, Delbeke J, Nonclercq A, Tahry RE. Functional brain connectivity indexes derived from low-density EEG of pre-implanted patients as VNS outcome predictors. J Neural Eng 2023; 20:046039. [PMID: 37595607 DOI: 10.1088/1741-2552/acf1cd] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 08/18/2023] [Indexed: 08/20/2023]
Abstract
Objective. In 1/3 of patients, anti-seizure medications may be insufficient, and resective surgery may be offered whenever the seizure onset is localized and situated in a non-eloquent brain region. When surgery is not feasible or fails, vagus nerve stimulation (VNS) therapy can be used as an add-on treatment to reduce seizure frequency and/or severity. However, screening tools or methods for predicting patient response to VNS and avoiding unnecessary implantation are unavailable, and confident biomarkers of clinical efficacy are unclear.Approach. To predict the response of patients to VNS, functional brain connectivity measures in combination with graph measures have been primarily used with respect to imaging techniques such as functional magnetic resonance imaging, but connectivity graph-based analysis based on electrophysiological signals such as electroencephalogram, have been barely explored. Although the study of the influence of VNS on functional connectivity is not new, this work is distinguished by using preimplantation low-density EEG data to analyze discriminative measures between responders and non-responder patients using functional connectivity and graph theory metrics.Main results. By calculating five functional brain connectivity indexes per frequency band upon partial directed coherence and direct transform function connectivity matrices in a population of 37 refractory epilepsy patients, we found significant differences (p< 0.05) between the global efficiency, average clustering coefficient, and modularity of responders and non-responders using the Mann-Whitney U test with Benjamini-Hochberg correction procedure and use of a false discovery rate of 5%.Significance. Our results indicate that these measures may potentially be used as biomarkers to predict responsiveness to VNS therapy.
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Affiliation(s)
- Enrique Germany
- IoNS, Universite Catholique de Louvain, Brussels, Belgium
- WELBIO Department, WEL Research Institute, Wavre, Belgium
| | - Igor Teixeira
- IoNS, Universite Catholique de Louvain, Brussels, Belgium
| | | | | | - Inci Cakiroglu
- IoNS, Universite Catholique de Louvain, Brussels, Belgium
| | - Andres Torres
- IoNS, Universite Catholique de Louvain, Brussels, Belgium
| | | | - Jean Delbeke
- IoNS, Universite Catholique de Louvain, Brussels, Belgium
| | - Antoine Nonclercq
- Bio-Electro-and Mechanical Systems (BEAMS), Université Libre de Bruxelles, Brussels, Belgium
| | - Riëm El Tahry
- IoNS, Universite Catholique de Louvain, Brussels, Belgium
- WELBIO Department, WEL Research Institute, Wavre, Belgium
- Cliniques Universitaires Saint-Luc, Brussels, Belgium
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Ren Z, Zhao Y, Han X, Yue M, Wang B, Zhao Z, Wen B, Hong Y, Wang Q, Hong Y, Zhao T, Wang N, Zhao P. An objective model for diagnosing comorbid cognitive impairment in patients with epilepsy based on the clinical-EEG functional connectivity features. Front Neurosci 2023; 16:1060814. [PMID: 36711136 PMCID: PMC9878185 DOI: 10.3389/fnins.2022.1060814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 12/28/2022] [Indexed: 01/15/2023] Open
Abstract
Objective Cognitive impairment (CI) is a common disorder in patients with epilepsy (PWEs). Objective assessment method for diagnosing CI in PWEs would be beneficial in reality. This study proposed to construct a diagnostic model for CI in PWEs using the clinical and the phase locking value (PLV) functional connectivity features of the electroencephalogram (EEG). Methods PWEs who met the inclusion and exclusion criteria were divided into a cognitively normal (CON) group (n = 55) and a CI group (n = 76). The 23 clinical features and 684 PLV EEG features at the time of patient visit were screened and ranked using the Fisher score. Adaptive Boosting (AdaBoost) and Gradient Boosting Decision Tree (GBDT) were used as algorithms to construct diagnostic models of CI in PWEs either with pure clinical features, pure PLV EEG features, or combined clinical and PLV EEG features. The performance of these models was assessed using a five-fold cross-validation method. Results GBDT-built model with combined clinical and PLV EEG features performed the best with accuracy, precision, recall, F1-score, and an area under the curve (AUC) of 90.11, 93.40, 89.50, 91.39, and 0.95%. The top 5 features found to influence the model performance based on the Fisher scores were the magnetic resonance imaging (MRI) findings of the head for abnormalities, educational attainment, PLV EEG in the beta (β)-band C3-F4, seizure frequency, and PLV EEG in theta (θ)-band Fp1-Fz. A total of 12 of the top 5% of features exhibited statistically different PLV EEG features, while eight of which were PLV EEG features in the θ band. Conclusion The model constructed from the combined clinical and PLV EEG features could effectively identify CI in PWEs and possess the potential as a useful objective evaluation method. The PLV EEG in the θ band could be a potential biomarker for the complementary diagnosis of CI comorbid with epilepsy.
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Affiliation(s)
- Zhe Ren
- Department of Neurology, Zhengzhou University People’s Hospital, Zhengzhou, Henan, China
| | - Yibo Zhao
- Department of Neurology, Zhengzhou University People’s Hospital, Zhengzhou, Henan, China
| | - Xiong Han
- Department of Neurology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China,*Correspondence: Xiong Han,
| | - Mengyan Yue
- Department of Rehabilitation, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Bin Wang
- Department of Neurology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Zongya Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan, China
| | - Bin Wen
- School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Yang Hong
- Department of Neurology, People’s Hospital of Henan University, Zhengzhou, Henan, China
| | - Qi Wang
- Department of Neurology, Zhengzhou University People’s Hospital, Zhengzhou, Henan, China
| | - Yingxing Hong
- Department of Neurology, People’s Hospital of Henan University, Zhengzhou, Henan, China
| | - Ting Zhao
- Department of Neurology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Na Wang
- Department of Neurology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Pan Zhao
- Department of Neurology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
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