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Lee DA, Jang T, Kang J, Park S, Park KM. Functional Connectivity Alterations in Patients with Post-stroke Epilepsy Based on Source-level EEG and Graph Theory. Brain Topogr 2024:10.1007/s10548-024-01048-0. [PMID: 38625521 DOI: 10.1007/s10548-024-01048-0] [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: 01/03/2023] [Accepted: 03/28/2024] [Indexed: 04/17/2024]
Abstract
We investigated the differences in functional connectivity based on the source-level electroencephalography (EEG) analysis between stroke patients with and without post-stroke epilepsy (PSE). Thirty stroke patients with PSE and 35 stroke patients without PSE were enrolled. EEG was conducted during a resting state period. We used a Brainstorm program for source estimation and the connectivity matrix. Data were processed according to EEG frequency bands. We used a BRAPH program to apply a graph theoretical analysis. In the beta band, radius and diameter were increased in patients with PSE than in those without PSE (2.699 vs. 2.579, adjusted p = 0.03; 2.261 vs. 2.171, adjusted p = 0.03). In the low gamma band, radius was increased in patients with PSE than in those without PSE (2.808 vs. 2.617, adjusted p = 0.03). In the high gamma band, the radius, diameter, average eccentricity, and characteristic path length were increased (1.828 vs. 1.559, adjusted p < 0.01; 2.653 vs. 2.306, adjusted p = 0.01; 2.212 vs. 1.913, adjusted p < 0.01; 1.425 vs. 1.286, adjusted p = 0.01), whereas average strength, mean clustering coefficient, and transitivity were decreased in patients with PSE than in those without PSE (49.955 vs. 55.055, adjusted p < 0.01; 0.727 vs. 0.810, adjusted p < 0.01; 1.091 vs. 1.215, adjusted p < 0.01). However, in the delta, theta, and alpha bands, none of the functional connectivity measures were different between groups. We demonstrated significant alterations of functional connectivity in patients with PSE, who have decreased segregation and integration in brain network, compared to those without PSE.
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Affiliation(s)
- Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Korea
| | - Taeik Jang
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Korea
| | - Jaeho Kang
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Korea
| | - Seongho Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Korea
| | - Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Korea.
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2
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Luo Z, Yin E, Zeng LL, Shen H, Su J, Peng L, Yan Y, Hu D. Frequency-specific segregation and integration of human cerebral cortex: An intrinsic functional atlas. iScience 2024; 27:109206. [PMID: 38439977 PMCID: PMC10910261 DOI: 10.1016/j.isci.2024.109206] [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: 07/31/2023] [Revised: 11/24/2023] [Accepted: 02/07/2024] [Indexed: 03/06/2024] Open
Abstract
The cognitive and behavioral functions of the human brain are supported by its frequency multiplexing mechanism. However, there is limited understanding of the dynamics of the functional network topology. This study aims to investigate the frequency-specific topology of the functional human brain using 7T rs-fMRI data. Frequency-specific parcellations were first performed, revealing frequency-dependent dynamics within the frontoparietal control, parietal memory, and visual networks. An intrinsic functional atlas containing 456 parcels was proposed and validated using stereo-EEG. Graph theory analysis suggested that, in addition to the task-positive vs. task-negative organization observed in static networks, there was a cognitive control system additionally from a frequency perspective. The reproducibility and plausibility of the identified hub sets were confirmed through 3T fMRI analysis, and their artificial removal had distinct effects on network topology. These results indicate a more intricate and subtle dynamics of the functional human brain and emphasize the significance of accurate topography.
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Affiliation(s)
- Zhiguo Luo
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Erwei Yin
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Ling-Li Zeng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Jianpo Su
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Limin Peng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Ye Yan
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
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Novitskaya Y, Dümpelmann M, Schulze-Bonhage A. Physiological and pathological neuronal connectivity in the living human brain based on intracranial EEG signals: the current state of research. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1297345. [PMID: 38107334 PMCID: PMC10723837 DOI: 10.3389/fnetp.2023.1297345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/17/2023] [Indexed: 12/19/2023]
Abstract
Over the past decades, studies of human brain networks have received growing attention as the assessment and modelling of connectivity in the brain is a topic of high impact with potential application in the understanding of human brain organization under both physiological as well as various pathological conditions. Under specific diagnostic settings, human neuronal signal can be obtained from intracranial EEG (iEEG) recording in epilepsy patients that allows gaining insight into the functional organisation of living human brain. There are two approaches to assess brain connectivity in the iEEG-based signal: evaluation of spontaneous neuronal oscillations during ongoing physiological and pathological brain activity, and analysis of the electrophysiological cortico-cortical neuronal responses, evoked by single pulse electrical stimulation (SPES). Both methods have their own advantages and limitations. The paper outlines available methodological approaches and provides an overview of current findings in studies of physiological and pathological human brain networks, based on intracranial EEG recordings.
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Affiliation(s)
- Yulia Novitskaya
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Matthias Dümpelmann
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Microsystems Engineering (IMTEK), University of Freiburg, Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Center for Basics in NeuroModulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Ladisich B, Rampp S, Trinka E, Weisz N, Schwartz C, Kraus T, Sherif C, Marhold F, Demarchi G. Network topology in brain tumor patients with and without structural epilepsy: a prospective MEG study. Ther Adv Neurol Disord 2023; 16:17562864231190298. [PMID: 37655227 PMCID: PMC10467269 DOI: 10.1177/17562864231190298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 07/07/2023] [Indexed: 09/02/2023] Open
Abstract
Background It was proposed that network topology is altered in brain tumor patients. However, there is no consensus on the pattern of these changes and evidence on potential drivers is lacking. Objectives We aimed to characterize neurooncological patients' network topology by analyzing glial brain tumors (GBTs) and brain metastases (BMs) with respect to the presence of structural epilepsy. Methods Network topology derived from resting state magnetoencephalography was compared between (1) patients and controls, (2) GBTs and BMs, and (3) patients with (PSEs) and without structural epilepsy (PNSEs). Eligible patients were investigated from February 2019 to March 2021. We calculated whole brain (WB) connectivity in six frequency bands, network topological parameters (node degree, average shortest path length, local clustering coefficient) and performed a stratification, where differences in power were identified. For data analysis, we used Fieldtrip, Brain Connectivity MATLAB toolboxes, and in-house built scripts. Results We included 41 patients (21 men), with a mean age of 60.1 years (range 23-82), of those were: GBTs (n = 23), BMs (n = 14), and other histologies (n = 4). Statistical analysis revealed a significantly decreased WB node degree in patients versus controls in every frequency range at the corrected level (p1-30Hz = 0.002, pγ = 0.002, pβ = 0.002, pα = 0.002, pθ = 0.024, and pδ = 0.002). At the descriptive level, we found a significant augmentation for WB local clustering coefficient (p1-30Hz = 0.031, pδ = 0.013) in patients compared to controls, which did not persist the false discovery rate correction. No differences regarding networks of GBTs compared to BMs were identified. However, we found a significant increase in WB local clustering coefficient (pθ = 0.048) and decrease in WB node degree (pα = 0.039) in PSEs versus PNSEs at the uncorrected level. Conclusion Our data suggest that network topology is altered in brain tumor patients. Histology per se might not, however, tumor-related epilepsy seems to influence the brain's functional network. Longitudinal studies and analysis of possible confounders are required to substantiate these findings.
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Affiliation(s)
- Barbara Ladisich
- Department of Neurosurgery, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
- Department of Neurosurgery, University Hospital St. Poelten, Dunant-Platz 1, St Polten 3100 Austria
- Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
| | - Stefan Rampp
- Department of Neurosurgery, Department of Neuroradiology, University Hospital Erlangen, Germany
- Department of Neurosurgery, University Hospital Halle (Saale), Germany
| | - Eugen Trinka
- Department of Neurology, Center for Cognitive Neuroscience Salzburg, Member of the European Reference Network, EpiCARE, Neuroscience Institute, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
- Karl Landsteiner Institute of Neurorehabilitation and Space Neurology, Salzburg, Austria
| | - Nathan Weisz
- Neuroscience Institute, Christian Doppler University Hospital, Salzburg, Austria
- Center for Cognitive Neuroscience & Department of Psychology, Paris Lodron University, Salzburg, Austria
| | - Christoph Schwartz
- Department of Neurosurgery, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Theo Kraus
- Institute of Pathology, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Camillo Sherif
- Department of Neurosurgery, University Hospital St. Poelten, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
| | - Franz Marhold
- Department of Neurosurgery, University Hospital St. Poelten, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
| | - Gianpaolo Demarchi
- Neuroscience Institute, Christian Doppler University Hospital, Salzburg, Austria
- Center for Cognitive Neuroscience & Department of Psychology, Paris Lodron University, Salzburg, Austria
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5
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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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6
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Ismail L, Karwowski W, Farahani FV, Rahman M, Alhujailli A, Fernandez-Sumano R, Hancock PA. Modeling Brain Functional Connectivity Patterns during an Isometric Arm Force Exertion Task at Different Levels of Perceived Exertion: A Graph Theoretical Approach. Brain Sci 2022; 12:1575. [PMID: 36421899 PMCID: PMC9688629 DOI: 10.3390/brainsci12111575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/09/2022] [Accepted: 11/13/2022] [Indexed: 09/29/2023] Open
Abstract
The perception of physical exertion is the cognitive sensation of work demands associated with voluntary muscular actions. Measurements of exerted force are crucial for avoiding the risk of overexertion and understanding human physical capability. For this purpose, various physiological measures have been used; however, the state-of-the-art in-force exertion evaluation lacks assessments of underlying neurophysiological signals. The current study applied a graph theoretical approach to investigate the topological changes in the functional brain network induced by predefined force exertion levels for twelve female participants during an isometric arm task and rated their perceived physical comfort levels. The functional connectivity under predefined force exertion levels was assessed using the coherence method for 84 anatomical brain regions of interest at the electroencephalogram (EEG) source level. Then, graph measures were calculated to quantify the network topology for two frequency bands. The results showed that high-level force exertions are associated with brain networks characterized by more significant clustering coefficients (6%), greater modularity (5%), higher global efficiency (9%), and less distance synchronization (25%) under alpha coherence. This study on the neurophysiological basis of physical exertions with various force levels suggests that brain regions communicate and cooperate higher when muscle force exertions increase to meet the demands of physically challenging tasks.
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Affiliation(s)
- Lina Ismail
- Department of Industrial and Management Engineering, Arab Academy for Science Technology & Maritime Transport, Alexandria 2913, Egypt
| | - Waldemar Karwowski
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
| | - Farzad V. Farahani
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Mahjabeen Rahman
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
| | - Ashraf Alhujailli
- Department of Management Science, Yanbu Industrial College, Yanbu 46452, Saudi Arabia
| | - Raul Fernandez-Sumano
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
| | - P. A. Hancock
- Department of Psychology, University of Central Florida, Orlando, FL 32816, USA
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7
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Classification of Contrasting Discrete Emotional States Indicated by EEG Based Graph Theoretical Network Measures. Neuroinformatics 2022; 20:863-877. [PMID: 35286574 DOI: 10.1007/s12021-022-09579-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/22/2022] [Indexed: 12/31/2022]
Abstract
The present study shows new findings that reveal the high association between emotional arousal and neuro-functional brain connectivity measures. For this purpose, contrasting discrete emotional states (happiness vs sadness, amusement vs disgust, calmness vs excitement, calmness vs anger, fear vs anger) are classified by using Support Vector Machines (SVMs) driven by Graph Theoretical segregation (clustering coefficients, transitivity, modularity) and integration (global efficiency, local efficiency) measures of the brain network. Emotional EEG data mediated by short duration video film clips is downloaded from publicly available database called DREAMER. Pearson Correlation (PC) and Spearman Correlation have been examined to estimate statistical dependencies between relatively shorter (6 sec) and longer (12 sec) non-overlapped EEG segments across the cortex. Then the corresponding brain connectivity encoded as a graph is transformed into binary numbers with respect to two different thresholds (60%max and mean). Statistical differences between contrasting emotions are obtained by using both one-way Anova tests and step-wise logistic regression modelling in accordance with variables (dependency estimation, segment length, threshold, network measure). Combined integration measures provided the highest classification accuracies (CAs) (75.00% 80.65%) when PC is applied to longer segments in accordance with particular threshold as the mean. The segregation measures also provided useful CAs (74.13% 80.00%), while the combination of both measures did not. The results reveal that discrete emotional states are characterized by balanced network measures even if both segregation and integration measures vary depending on arousal scores of audio-visual stimuli due to neurotransmitter release during video watching.
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8
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Liu Y, Xi P, Li B, Zhang M, Liu H, Tang R, Xin S, Huang Q, He J, Liu Z, Yuan Z, Lang Y. Effect of neuromorphic transcutaneous electrical nerve stimulation (nTENS) of cortical functional networks on tactile perceptions: An event-related electroencephalogram study. J Neural Eng 2022; 19. [PMID: 35263714 DOI: 10.1088/1741-2552/ac5bf6] [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: 07/14/2021] [Accepted: 03/09/2022] [Indexed: 11/12/2022]
Abstract
BACKGROUND Transcutaneous electrical nerve stimulation (TENS) is generally applied for tactile feedback in the field of prosthetics. The distinct mechanisms of evoked tactile perception between stimulus patterns in conventional TENS (cTENS) and neuromorphic TENS (nTENS) are relatively unknown. This is the first study to investigate the neurobiological effect of nTENS for cortical functional mechanism in evoked tactile perception. METHODS Twenty-one healthy participants were recruited in this study. Electroencephalogram (EEG) was recorded while the participants underwent a tactile discrimination task. One cTENS pattern (square pattern) and two nTENS patterns (electromyography and single motor unit patterns) were applied to evoke tactile perception in four fingers, including the right and left index and little fingers. EEG was preprocessed and somatosensory-evoked potentials (SEPs) were determined. Then, source-level functional networks based on graph theory were evaluated, including clustering coefficient, path length, global efficiency, and local efficiency in six frequency bands. RESULTS Behavioral results suggested that the single motor units (SMU) pattern of nTENS was the most natural tactile perception. SEPs results revealed that SMU pattern exhibited significant shorter latency in P1 and N1 components than the other patterns, while nTENS patterns have significantly longer latency in P3 component than cTENS pattern. Cortical functional networks showed that the SMU pattern had the lowest short path and highest efficiency in beta and gamma bands. CONCLUSION This study highlighted that distinct TENS patterns could affect brain activities. The new characteristics in tactile manifestation of nTENS would provide insights for the application of tactile perception restoration.
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Affiliation(s)
- Yafei Liu
- School of Mechatronical Engineering, Beijing Institute of Technology, No.5 South Zhongguancun street, Haidian District, Beijing, 100081, CHINA
| | - Pengcheng Xi
- School of Mechatronical Engineering, Beijing Institute of Technology, No.5 South Zhongguancun street, Haidian District, Beijing, Beijing, 100081, CHINA
| | - Bo Li
- School of Mechatronical Engineering, Beijing Institute of Technology, No. 5, South Street, Zhongguancun, Haidian District, Beijing, Bei Jing, Bei Jing, 100081, CHINA
| | - Minjian Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, No.5 South Zhongguancun street, Haidian District, Beijing, Beijing, 100081, CHINA
| | - Honghao Liu
- School of Mechatronical Engineering, Beijing Institute of Technology, No.5 South Zhongguancun street, Haidian District, Beijing, Beijing, 100081, CHINA
| | - Rongyu Tang
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, No.1 Zhanlanguan Road, Xicheng District, Beijing, Beijing, 100044, CHINA
| | - Shan Xin
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, NO.1, Zhanlanguan Road, Xicheng District, Beijing, Beijing, 100044, CHINA
| | - Qiang Huang
- Beijing Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, No.5 South Zhongguancun street, Haidian District, Beijing, Beijing, 100081, CHINA
| | - Jiping He
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, No.5 South Zhongguancun street, Haidian District, Beijing, Beijing, 100081, CHINA
| | - Zhiqiang Liu
- Beijing institute of basic medical sciences, 27 Taiping Road, HaidianDistrict, Beijing, Beijing, 100850, CHINA
| | - Zengqiang Yuan
- Beijing institute of basic medical sciences, 27 Taiping Road, HaidianDistrict, Beijing, 100850, CHINA
| | - Yiran Lang
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Haidian Dist. Zhongguancun South Street No. 5, Beijing, 100081, CHINA
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9
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Hatlestad-Hall C, Bruña R, Erichsen A, Andersson V, Syvertsen MR, Skogan AH, Renvall H, Marra C, Maestú F, Heuser K, Taubøll E, Solbakk AK, Haraldsen IH. The organization of functional neurocognitive networks in focal epilepsy correlates with domain-specific cognitive performance. J Neurosci Res 2021; 99:2669-2687. [PMID: 34173259 DOI: 10.1002/jnr.24896] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 04/28/2021] [Accepted: 05/15/2021] [Indexed: 11/10/2022]
Abstract
Understanding and diagnosing cognitive impairment in epilepsy remains a prominent challenge. New etiological models suggest that cognitive difficulties might not be directly linked to seizure activity, but are rather a manifestation of a broader brain pathology. Consequently, treating seizures is not sufficient to alleviate cognitive symptoms, highlighting the need for novel diagnostic tools. Here, we investigated whether the organization of three intrinsic, resting-state functional connectivity networks was correlated with domain-specific cognitive test performance. Using individualized EEG source reconstruction and graph theory, we examined the association between network small worldness and cognitive test performance in 23 patients with focal epilepsy and 17 healthy controls, who underwent a series of standardized pencil-and-paper and digital cognitive tests. We observed that the specific networks robustly correlated with test performance in distinct cognitive domains. Specifically, correlations were evident between the default mode network and memory in patients, the central-executive network and executive functioning in controls, and the salience network and social cognition in both groups. Interestingly, the correlations were evident in both groups, but in different domains, suggesting an alteration in these functional neurocognitive networks in focal epilepsy. The present findings highlight the potential clinical relevance of functional brain network dysfunction in cognitive impairment.
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Affiliation(s)
| | - Ricardo Bruña
- Center for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcón, Spain.,Department of Experimental Psychology, Complutense University of Madrid, Pozuelo de Alarcón, Spain.,Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Aksel Erichsen
- Department of Neurology, Oslo University Hospital, Oslo, Norway.,Department of Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | | | - Marte Roa Syvertsen
- Department of Neurology, Drammen Hospital, Vestre Viken Health Care Trust, Drammen, Norway
| | - Annette Holth Skogan
- Division of Clinical Neuroscience, National Centre for Epilepsy, Oslo University Hospital, Oslo, Norway
| | - Hanna Renvall
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland.,BioMag Laboratory, HUS Diagnostic Center, Helsinki University Hospital, University of Helsinki and Aalto, Helsinki, Finland
| | - Camillo Marra
- Department of Neuroscience, Fondazione Policlinico Agostino Gemelli, Rome, Italy
| | - Fernando Maestú
- Center for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcón, Spain.,Department of Experimental Psychology, Complutense University of Madrid, Pozuelo de Alarcón, Spain.,Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Kjell Heuser
- Department of Neurology, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Erik Taubøll
- Department of Neurology, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Anne-Kristin Solbakk
- Department of Psychology, Faculty of Social Sciences, University of Oslo, Oslo, Norway.,RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway.,Department of Neurosurgery, Oslo University Hospital, Oslo, Norway.,Department of Neuropsychology, Helgeland Hospital, Mosjøen, Norway
| | - Ira H Haraldsen
- Department of Neurology, Oslo University Hospital, Oslo, Norway
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