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Du X, Wang B, Shao L. Correlation analysis of 25(OH)D with cognitive function in epilepsy patients: A cross-sectional study. Epilepsy Behav 2024; 158:109935. [PMID: 39002277 DOI: 10.1016/j.yebeh.2024.109935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/31/2024] [Accepted: 06/30/2024] [Indexed: 07/15/2024]
Abstract
OBJECTIVE To analyze the correlation between the level of 25(OH)D in peripheral blood and cognitive function in patients with epilepsy, and to find the biomarkers of epilepsy complicated with cognitive dysfunction. METHODS 68 patients with epilepsy and 30 healthy subjects were included in this study. The 25(OH)D level in peripheral blood of all subjects was detected and the score of the Montreal Cognitive Assessment Scale was performed. The patients with epilepsy were divided into a cognitively normal group (36 cases) and a cognitively impaired group (32 cases) according to the scale score. The inter-group scale score and 25(OH)D level were compared, and the correlation was analyzed. RESULTS The levels of 25(OH)D and MOCA in epileptic group were significantly lower than those in healthy control group. The 25(OH)D and MOCA of the cognitively impaired group were significantly lower than those of the cognitively normal group. Logistic regression analysis indicated that serum 25(OH)D level was an independent risk factor for epilepsy combined with cognitive impairment (OR = 0.704, P = 0.014). The area under ROC curve of serum 25(OH)D for diagnosis of epilepsy combined with cognitive impairment was 0.924 (95 %CI 0.866,0.981), the critical value was 34.50 nmol/L, the sensitivity was 0.778, and the specificity was 0.906. CONCLUSION Decreased levels of vitamin D are associated with cognitive impairment associated with epilepsy, and it may be a biomarker for early screening of cognitive impairment.
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Affiliation(s)
- Xin Du
- Department of Neurology, The Municipal Hospital Affiliated to Xuzhou Medical University, Xuzhou City, China
| | - Bingbing Wang
- Department of Neurology, Suining County People's Hospital, Xuzhou City, China
| | - Li Shao
- Department of Neurology, The Municipal Hospital Affiliated to Xuzhou Medical University, Xuzhou City, China.
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Bröhl T, Rings T, Pukropski J, von Wrede R, Lehnertz K. The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 3:1338864. [PMID: 38293249 PMCID: PMC10825060 DOI: 10.3389/fnetp.2023.1338864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 12/19/2023] [Indexed: 02/01/2024]
Abstract
Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.
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Affiliation(s)
- Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Jan Pukropski
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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Sun Y, Shi Q, Ye M, Miao A. Topological properties and connectivity patterns in brain networks of patients with refractory epilepsy combined with intracranial electrical stimulation. Front Neurosci 2023; 17:1282232. [PMID: 38075280 PMCID: PMC10701286 DOI: 10.3389/fnins.2023.1282232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 11/07/2023] [Indexed: 02/12/2024] Open
Abstract
Objective Although intracranial electrical stimulation has emerged as a treatment option for various diseases, its impact on the properties of brain networks remains challenging due to its invasive nature. The combination of intracranial electrical stimulation and whole-brain functional magnetic resonance imaging (fMRI) in patients with refractory epilepsy (RE) makes it possible to study the network properties associated with electrical stimulation. Thus, our study aimed to investigate the brain network characteristics of RE patients with concurrent electrical stimulation and obtain possible clinical biomarkers. Methods Our study used the GRETNA toolbox, a graph theoretical network analysis toolbox for imaging connectomics, to calculate and analyze the network topological attributes including global measures (small-world parameters and network efficiency) and nodal characteristics. The resting-state fMRI (rs-fMRI) and the fMRI concurrent electrical stimulation (es-fMRI) of RE patients were utilized to make group comparisons with healthy controls to identify the differences in network topology properties. Network properties comparisons before and after electrode implantation in the same patient were used to further analyze stimulus-related changes in network properties. Modular analysis was used to examine connectivity and distribution characteristics in the brain networks of all participants in study. Results Compared to healthy controls, the rs-fMRI and the es-fMRI of RE patients exhibited impaired small-world property and reduced network efficiency. Nodal properties, such as nodal clustering coefficient (NCp), betweenness centrality (Bc), and degree centrality (Dc), exhibited differences between RE patients (including rs-fMRI and es-fMRI) and healthy controls. The network connectivity of RE patients (including rs-fMRI and es-fMRI) showed reduced intra-modular connections in subcortical areas and the occipital lobe, as well as decreased inter-modular connections between frontal and subcortical regions, and parieto-occipital regions compared to healthy controls. The brain networks of es-fMRI showed a relatively weaker small-world structure compared to rs-fMRI. Conclusion The brain networks of RE patients exhibited a reduced small-world property, with a tendency toward random networks. The network connectivity patterns in RE patients exhibited reduced connections between cortical and subcortical regions and enhanced connections among parieto-occipital regions. Electrical stimulation can modulate brain network activity, leading to changes in network connectivity patterns and properties.
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Affiliation(s)
- Yulei Sun
- Department of Neurology, Nanjing BenQ Medical Center, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Qi Shi
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, Jiangsu, China
| | - Min Ye
- Department of Neurology, Nanjing BenQ Medical Center, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Ailiang Miao
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
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Hatlestad-Hall C, Bruña R, Liljeström M, Renvall H, Heuser K, Taubøll E, Maestú F, Haraldsen IH. Reliable evaluation of functional connectivity and graph theory measures in source-level EEG: How many electrodes are enough? Clin Neurophysiol 2023; 150:1-16. [PMID: 36972647 DOI: 10.1016/j.clinph.2023.03.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 02/03/2023] [Accepted: 03/01/2023] [Indexed: 03/18/2023]
Abstract
OBJECTIVE Using EEG to characterise functional brain networks through graph theory has gained significant interest in clinical and basic research. However, the minimal requirements for reliable measures remain largely unaddressed. Here, we examined functional connectivity estimates and graph theory metrics obtained from EEG with varying electrode densities. METHODS EEG was recorded with 128 electrodes in 33 participants. The high-density EEG data were subsequently subsampled into three sparser montages (64, 32, and 19 electrodes). Four inverse solutions, four measures of functional connectivity, and five graph theory metrics were tested. RESULTS The correlation between the results obtained with 128-electrode and the subsampled montages decreased as a function of the number of electrodes. As a result of decreased electrode density, the network metrics became skewed: mean network strength and clustering coefficient were overestimated, while characteristic path length was underestimated. CONCLUSIONS Several graph theory metrics were altered when electrode density was reduced. Our results suggest that, for optimal balance between resource demand and result precision, a minimum of 64 electrodes should be utilised when graph theory metrics are used to characterise functional brain networks in source-reconstructed EEG data. SIGNIFICANCE Characterisation of functional brain networks derived from low-density EEG warrants careful consideration.
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Affiliation(s)
| | - Ricardo Bruña
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain; Department of Radiology, Universidad Complutense de Madrid, Madrid, Spain
| | - Mia Liljeström
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland; BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki, Finland
| | - Hanna Renvall
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland; BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki, Finland
| | - Kjell Heuser
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Erik Taubøll
- Department of Neurology, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Fernando Maestú
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain; Department of Experimental Psychology, Universidad Complutense de Madrid, Pozuelo de Alarcón, Spain
| | - Ira H Haraldsen
- Department of Neurology, Oslo University Hospital, Oslo, Norway; BrainSymph AS, Oslo, Norway
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Qi L, Zhao J, Zhao P, Zhang H, Zhong J, Pan P, Wang G, Yi Z, Xie L. Theory of mind and facial emotion recognition in adults with temporal lobe epilepsy: A meta-analysis. Front Psychiatry 2022; 13:976439. [PMID: 36276336 PMCID: PMC9582667 DOI: 10.3389/fpsyt.2022.976439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 09/16/2022] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND Mounting studies have investigated impairments in social cognitive domains (including theory of mind [ToM] and facial emotion recognition [FER] in adult patients with temporal lobe epilepsy (TLE). However, to date, inconsistent findings remain. METHODS A search of PubMed, Web of Science, and Embase databases was conducted until December 2021. Hedges g effect sizes were computed with a random-effects model. Meta-regressions were used to assess the potential confounding factors of between-study variability in effect sizes. RESULTS The meta-analysis included 41 studies, with a combined sample of 1,749 adult patients with TLE and 1,324 healthy controls (HCs). Relative to HCs, adult patients with TLE showed large impairments in ToM (g = -0.92) and cognitive ToM (g = -0.92), followed by medium impairments in affective ToM (g = -0.79) and FER (g = -0.77). Besides, no (statistically) significant differences were observed between the magnitude of social cognition impairment in adult with TLE who underwent and those who did not undergo epilepsy surgery. Meta-regressions exhibited that greater severity of executive functioning was associated with more severe ToM defects, and older age was associated with more severe FER defects. CONCLUSIONS Results of this meta-analysis suggest that adult patients with TLE show differential impairments in the core aspects of social cognitive domains (including ToM and FER), which may help in planning individualized treatment with appropriate cognitive and behavioral interventions.
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Affiliation(s)
- Liang Qi
- Department of Neurosurgery, The Affiliated Huai'an Hospital of Xuzhou Medical University, The Second People's Hospital of Huai'an, Huaian, China
| | - Jing Zhao
- Department of Central Laboratory, The Sixth Affiliated Hospital of Nantong University, Yancheng Third People's Hospital, Yancheng, China
| | - PanWen Zhao
- Department of Central Laboratory, The Sixth Affiliated Hospital of Nantong University, Yancheng Third People's Hospital, Yancheng, China
| | - Hui Zhang
- Department of Central Laboratory, The Sixth Affiliated Hospital of Nantong University, Yancheng Third People's Hospital, Yancheng, China
| | - JianGuo Zhong
- Department of Neurology, The Sixth Affiliated Hospital of Nantong University, Yancheng Third People's Hospital, Yancheng, China
| | - PingLei Pan
- Department of Central Laboratory, The Sixth Affiliated Hospital of Nantong University, Yancheng Third People's Hospital, Yancheng, China.,Department of Neurology, The Sixth Affiliated Hospital of Nantong University, Yancheng Third People's Hospital, Yancheng, China
| | - GenDi Wang
- Department of Neurology, The Sixth Affiliated Hospital of Nantong University, Yancheng Third People's Hospital, Yancheng, China
| | - ZhongQuan Yi
- Department of Central Laboratory, The Sixth Affiliated Hospital of Nantong University, Yancheng Third People's Hospital, Yancheng, China
| | - LiLi Xie
- Department of Neurology, The Sixth Affiliated Hospital of Nantong University, Yancheng Third People's Hospital, Yancheng, China
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