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Zhao J, Zhang Y, Qin Y, Liu Y, Chen Q, Zhao K, Long Z. Electroencephalographic oscillations of alpha and beta rhythms during phrase-guessing procedure. Cogn Neurodyn 2023; 17:1345-1355. [PMID: 37786656 PMCID: PMC10542055 DOI: 10.1007/s11571-022-09896-x] [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: 07/07/2022] [Revised: 09/15/2022] [Accepted: 09/29/2022] [Indexed: 11/30/2022] Open
Abstract
Phrases-guessing is one of the essential reasoning abilities in problem solving for human beings. However, it is still an open question about why individuals perform differently during the same reasoning task. In this study, we utilized a bilingual phrase-guessing task to explore the neural activities under the individually different performances with electroencephalography. Participants who had no knowledge of Greek were required to guess the meaning of a Greek phrase (long or short in length) by making an either-or selection as to which translation-equivalent Chinese word corresponds to Greek word. Names of color were used as experimental stimuli for which two Chinese words denoted the same color with one as a conventional color name and the other as a novel color name. The experiment yielded length of phrases (long vs. short) and novelty of phrases (novel vs. conventional) as variables. The behavioral results revealed significant length-by-novelty interaction on the number of selections. However, neither main effects nor interactive effects were found on response time. Further, the amplitude spectrums of high alpha rhythm, low alpha rhythm, and low beta rhythm during the task were positively associated with the participants' number of selections for a long Greek phrase with a novel and complex Chinese phrase (LNc) and a short Greek phrase with a conventional Chinese phrase (SCo), while negatively correlated with the response time of selections for LNc and SCo. Our findings suggested that the consistency between participants' behavior and electrophysiological oscillations (alpha and beta bands) could be employed as biomarkers for decoding the phrase-guessing procedure.
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Affiliation(s)
- Jia Zhao
- Faculty of Psychology, Southwest University, Chongqing, 400715 China
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, 400715 China
- Chongqing Collaborative Innovation Center for Brain Science, Chongqing, 400715 China
| | - Yong Zhang
- College of Foreign Languages, Chongqing Medical University, Chongqing, 400016 China
| | - Yingmei Qin
- Tianjin Key Laboratory of Information Sensing and Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, 300222 China
| | - Yong Liu
- Faculty of Psychology, Southwest University, Chongqing, 400715 China
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, 400715 China
| | - Qunlin Chen
- Faculty of Psychology, Southwest University, Chongqing, 400715 China
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, 400715 China
| | - Ke Zhao
- State Key Laboratory of Brain and Cognition Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101 China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Zhiliang Long
- Faculty of Psychology, Southwest University, Chongqing, 400715 China
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, 400715 China
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2
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Guo J, Li L, Zheng Y, Quratul A, Liu T, Wang J. Effect of Visual Feedback on Behavioral Control and Functional Activity During Bilateral Hand Movement. Brain Topogr 2023:10.1007/s10548-023-00969-6. [PMID: 37198376 DOI: 10.1007/s10548-023-00969-6] [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: 04/07/2022] [Accepted: 04/29/2023] [Indexed: 05/19/2023]
Abstract
Previous researches state vision as a vital source of information for movement control and more precisely for accurate hand movement. Further, fine bimanual motor activity may be associated with various oscillatory activities within distinct brain areas and inter-hemispheric interactions. However, neural coordination among the distinct brain areas responsible to enhance motor accuracy is still not adequate. In the current study, we investigated task-dependent modulation by simultaneously measuring high time resolution electroencephalogram (EEG), electromyogram (EMG) and force along with bi-manual and unimanual motor tasks. The errors were controlled using visual feedback. To complete the unimanual tasks, the participant was asked to grip the strain gauge using the index finger and thumb of the right hand thereby exerting force on the connected visual feedback system. Whereas the bi-manual task involved finger abduction of the left index finger in two contractions along with visual feedback system and at the same time the right hand gripped using definite force on two conditions that whether visual feedback existed or not for the right hand. Primarily, the existence of visual feedback for the right hand significantly decreased brain network global and local efficiency in theta and alpha bands when compared with the elimination of visual feedback using twenty participants. Brain network activity in theta and alpha bands coordinates to facilitate fine hand movement. The findings may provide new neurological insight on virtual reality auxiliary equipment and participants with neurological disorders that cause movement errors requiring accurate motor training. The current study investigates task-dependent modulation by simultaneously measuring high time resolution electroencephalogram, electromyogram and force along with bi-manual and unimanual motor tasks. The findings show that visual feedback for right hand decreases the force root mean square error of right hand. Visual feedback for right hand decreases local and global efficiency of brain network in theta and alpha bands.
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Affiliation(s)
- Jing Guo
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Sciences, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, People's Republic of China
- National Engineering Research Center for Healthcare Devices, Guangzhou, 510500, Guangdong, People's Republic of China
- The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, 710049, Shaanxi, People's Republic of China
| | - Long Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Sciences, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, People's Republic of China
- National Engineering Research Center for Healthcare Devices, Guangzhou, 510500, Guangdong, People's Republic of China
- The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, 710049, Shaanxi, People's Republic of China
| | - Yang Zheng
- State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, People's Republic of China
| | - Ain Quratul
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Sciences, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, People's Republic of China
- National Engineering Research Center for Healthcare Devices, Guangzhou, 510500, Guangdong, People's Republic of China
- The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, 710049, Shaanxi, People's Republic of China
| | - Tian Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Sciences, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, People's Republic of China.
- National Engineering Research Center for Healthcare Devices, Guangzhou, 510500, Guangdong, People's Republic of China.
- The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, 710049, Shaanxi, People's Republic of China.
| | - Jue Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Sciences, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, People's Republic of China.
- National Engineering Research Center for Healthcare Devices, Guangzhou, 510500, Guangdong, People's Republic of China.
- The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, 710049, Shaanxi, People's Republic of China.
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3
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Ghaderi A, Niemeier M, Crawford JD. Saccades and presaccadic stimulus repetition alter cortical network topology and dynamics: evidence from EEG and graph theoretical analysis. Cereb Cortex 2023; 33:2075-2100. [PMID: 35639544 DOI: 10.1093/cercor/bhac194] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 04/27/2022] [Accepted: 04/29/2022] [Indexed: 11/13/2022] Open
Abstract
Parietal and frontal cortex are involved in saccade generation, and their output signals modify visual signals throughout cortex. Local signals associated with these interactions are well described, but their large-scale progression and network dynamics are unknown. Here, we combined source localized electroencephalography (EEG) and graph theory analysis (GTA) to understand how saccades and presaccadic visual stimuli interactively alter cortical network dynamics in humans. Twenty-one participants viewed 1-3 vertical/horizontal grids, followed by grid with the opposite orientation just before a horizontal saccade or continued fixation. EEG signals from the presaccadic interval (or equivalent fixation period) were used for analysis. Source localization-through-time revealed a rapid frontoparietal progression of presaccadic motor signals and stimulus-motor interactions, with additional band-specific modulations in several frontoparietal regions. GTA analysis revealed a saccade-specific functional network with major hubs in inferior parietal cortex (alpha) and the frontal eye fields (beta), and major saccade-repetition interactions in left prefrontal (theta) and supramarginal gyrus (gamma). This network showed enhanced segregation, integration, synchronization, and complexity (compared with fixation), whereas stimulus repetition interactions reduced synchronization and complexity. These cortical results demonstrate a widespread influence of saccades on both regional and network dynamics, likely responsible for both the motor and perceptual aspects of saccades.
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Affiliation(s)
- Amirhossein Ghaderi
- Centre for Vision Research, York University, 4700 Keele St, Toronto, ON M3J 1P3, Canada.,Vision Science to Applications (VISTA) Program York University, 4700 Keele St, Toronto, ON M3J 1P3, Canada
| | - Matthias Niemeier
- Centre for Vision Research, York University, 4700 Keele St, Toronto, ON M3J 1P3, Canada.,Vision Science to Applications (VISTA) Program York University, 4700 Keele St, Toronto, ON M3J 1P3, Canada.,Department of Psychology, University of Toronto Scarborough, 1265 Military Trail, Scarborough, ON M1C 1A4, Canada
| | - John Douglas Crawford
- Centre for Vision Research, York University, 4700 Keele St, Toronto, ON M3J 1P3, Canada.,Vision Science to Applications (VISTA) Program York University, 4700 Keele St, Toronto, ON M3J 1P3, Canada.,Department of Biology, York University, 4700 Keele St,, Toronto, ON M3J 1P3, Canada.,Department of Psychology, York University, 4700 Keele St,, Toronto, ON M3J 1P3, Canada.,Department of Kinesiology and Health Sciences, York University, 4700 Keele St., Toronto, ON M3J 1P3, Canada
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Zhu S, Hosni SI, Huang X, Borgheai SB, McLinden J, Shahriari Y, Ostadabbas S. A Graph-Based Feature Extraction Algorithm Towards a Robust Data Fusion Framework for Brain-Computer Interfaces. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:878-881. [PMID: 34891430 DOI: 10.1109/embc46164.2021.9630804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
OBJECTIVE The topological information hidden in the EEG spectral dynamics is often ignored in the majority of the existing brain-computer interface (BCI) systems. Moreover, a systematic multimodal fusion of EEG with other informative brain signals such as functional near-infrared spectroscopy (fNIRS) towards enhancing the performance of the BCI systems is not fully investigated. In this study, we present a robust EEG-fNIRS data fusion framework utilizing a series of graph-based EEG features to investigate their performance on a motor imaginary (MI) classification task. METHOD We first extract the amplitude and phase sequences of users' multi-channel EEG signals based on the complex Morlet wavelet time-frequency maps, and then convert them into an undirected graph to extract EEG topological features. The graph-based features from EEG are then selected by a thresholding method and fused with the temporal features from fNIRS signals after each being selected by the least absolute shrinkage and selection operator (LASSO) algorithm. The fused features were then classified as MI task vs. baseline by a linear support vector machine (SVM) classifier. RESULTS The time-frequency graphs of EEG signals improved the MI classification accuracy by ∼5% compared to the graphs built on the band-pass filtered temporal EEG signals. Our proposed graph-based method also showed comparable performance to the classical EEG features based on power spectral density (PSD), however with a much smaller standard deviation, showing its robustness for potential use in a practical BCI system. Our fusion analysis revealed a considerable improvement of ∼17% as opposed to the highest average accuracy of EEG only and ∼3% compared with the highest fNIRS only accuracy demonstrating an enhanced performance when modality fusion is used relative to single modal outcomes. SIGNIFICANCE Our findings indicate the potential use of the proposed data fusion framework utilizing the graph-based features in the hybrid BCI systems by making the motor imaginary inference more accurate and more robust.
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Ghaderi AH, Jahan A, Akrami F, Moghadam Salimi M. Transcranial photobiomodulation changes topology, synchronizability, and complexity of resting state brain networks. J Neural Eng 2021; 18. [PMID: 33873167 DOI: 10.1088/1741-2552/abf97c] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 04/19/2021] [Indexed: 02/06/2023]
Abstract
Objective. Transcranial photobiomodulation (tPBM) is a recently proposed non-invasive brain stimulation approach with various effects on the nervous system from the cells to the whole brain networks. Specially in the neural network level, tPBM can alter the topology and synchronizability of functional brain networks. However, the functional properties of the neural networks after tPBM are still poorly clarified.Approach. Here, we employed electroencephalography and different methods (conventional and spectral) in the graph theory analysis to track the significant effects of tPBM on the resting state brain networks. The non-parametric statistical analysis showed that just one short-term tPBM session over right medial frontal pole can significantly change both topological (i.e. clustering coefficient, global efficiency, local efficiency, eigenvector centrality) and dynamical (i.e. energy, largest eigenvalue, and entropy) features of resting state brain networks.Main results. The topological results revealed that tPBM can reduce local processing, centrality, and laterality. Furthermore, the increased centrality of central electrode was observed.Significance. These results suggested that tPBM can alter topology of resting state brain network to facilitate the neural information processing. On the other hand, the dynamical results showed that tPBM reduced stability of synchronizability and increased complexity in the resting state brain networks. These effects can be considered in association with the increased complexity of connectivity patterns among brain regions and the enhanced information propagation in the resting state brain networks. Overall, both topological and dynamical features of brain networks suggest that although tPBM decreases local processing (especially in the right hemisphere) and disrupts synchronizability of network, but it can increase the level of information transferring and processing in the brain network.
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Affiliation(s)
- Amir Hossein Ghaderi
- Centre for Vision Research, York University, Toronto, Canada.,Department of psychology, University of Calgary, Calgary, Canada.,Iranian Neurowave Lab, Isfahan, Iran
| | - Ali Jahan
- Department of Speech Therapy, Faculty of Rehabilitation Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Fatemeh Akrami
- Iranian Neurowave Lab, Isfahan, Iran.,Faculty of Health Management and Information, Iran University of Medical Science, Tehran, Iran
| | - Maryam Moghadam Salimi
- Department of Physical Therapy, Faculty of Rehabilitation Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
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6
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Jouzizadeh M, Ghaderi AH, Cheraghmakani H, Baghbanian SM, Khanbabaie R. Resting-State Brain Network Deficits in Multiple Sclerosis Participants: Evidence from Electroencephalography and Graph Theoretical Analysis. Brain Connect 2021; 11:359-367. [PMID: 33780635 DOI: 10.1089/brain.2020.0857] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Multiple sclerosis (MS) is a chronic inflammatory disease leading to demyelination and axonal loss in the central nervous system that causes focal lesions of gray and white matter. However, the functional impairments of brain networks in this disease are still unspecified and need to be clearer. Materials and Methods: In the present study, we investigate the resting-state brain network impairments for MS participants in comparison to a normal group using electroencephalography (EEG) and graph theoretical analysis with a source localization method. Thirty-four age- and gender-matched participants from each MS group and normal group participated in this study. We recorded 5 min of EEG in the resting-state eyes open condition for each participant. One min (15 equal 4-sec artifact-free segments) of the EEG signals were selected for each participant, and the Low-Resolution Electromagnetic Tomography software was employed to calculate the functional connectivity among whole cortical regions in six frequency bands (delta, theta, alpha, beta1, beta2, and beta3). Graph theoretical analysis was used to calculate the clustering coefficient (CL), betweenness centrality (BC), shortest path length (SPL), and small-world propensity (SWP) for weighted connectivity matrices. Nonparametric permutation tests were utilized to compare these measures between groups. Results: Significant differences between the MS group and the normal group in the average of BC and SWP were found in the alpha band. The significant differences in the BC were spread over all lobes. Conclusion: These results suggest that the resting-state brain network for the MS group is disrupted in local and global scales, and EEG has the capability of revealing these impairments.
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Affiliation(s)
- Mojtaba Jouzizadeh
- Department of Physics, Babol Noshirvani University of Technology, Babol, Iran
| | - Amir Hossein Ghaderi
- Department of Psychology and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Hamed Cheraghmakani
- Department of Neurology, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | | | - Reza Khanbabaie
- Department of Physics, Babol Noshirvani University of Technology, Babol, Iran.,Department of Physics, I.K. Barber School of Arts and Sciences, University of British Columbia, Kelowna, British Columbia, Canada
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7
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Arciniega H, Shires J, Furlong S, Kilgore-Gomez A, Cerreta A, Murray NG, Berryhill ME. Impaired visual working memory and reduced connectivity in undergraduates with a history of mild traumatic brain injury. Sci Rep 2021; 11:2789. [PMID: 33531546 PMCID: PMC7854733 DOI: 10.1038/s41598-021-80995-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 01/01/2021] [Indexed: 12/30/2022] Open
Abstract
Mild traumatic brain injury (mTBI), or concussion, accounts for 85% of all TBIs. Yet survivors anticipate full cognitive recovery within several months of injury, if not sooner, dependent upon the specific outcome/measure. Recovery is variable and deficits in executive function, e.g., working memory (WM) can persist years post-mTBI. We tested whether cognitive deficits persist in otherwise healthy undergraduates, as a conservative indicator for mTBI survivors at large. We collected WM performance (change detection, n-back tasks) using various stimuli (shapes, locations, letters; aurally presented numbers and letters), and wide-ranging cognitive assessments (e.g., RBANS). We replicated the observation of a general visual WM deficit, with preserved auditory WM. Surprisingly, visual WM deficits were equivalent in participants with a history of mTBI (mean 4.3 years post-injury) and in undergraduates with recent sports-related mTBI (mean 17 days post-injury). In seeking the underlying mechanism of these behavioral deficits, we collected resting state fMRI (rsfMRI) and EEG (rsEEG). RsfMRI revealed significantly reduced connectivity within WM-relevant networks (default mode, central executive, dorsal attention, salience), whereas rsEEG identified no differences (modularity, global efficiency, local efficiency). In summary, otherwise healthy current undergraduates with a history of mTBI present behavioral deficits with evidence of persistent disconnection long after full recovery is expected.
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Affiliation(s)
- Hector Arciniega
- Department of Psychology, Programs in Cognitive and Brain Sciences, and Integrative Neuroscience, University of Nevada, 1664 N. Virginia St., MS 296, Reno, NV, 89557, USA.
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02215, USA.
| | - Jorja Shires
- Department of Psychology, Programs in Cognitive and Brain Sciences, and Integrative Neuroscience, University of Nevada, 1664 N. Virginia St., MS 296, Reno, NV, 89557, USA
| | - Sarah Furlong
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Alexandrea Kilgore-Gomez
- Department of Psychology, Programs in Cognitive and Brain Sciences, and Integrative Neuroscience, University of Nevada, 1664 N. Virginia St., MS 296, Reno, NV, 89557, USA
| | - Adelle Cerreta
- Department of Psychology, Programs in Cognitive and Brain Sciences, and Integrative Neuroscience, University of Nevada, 1664 N. Virginia St., MS 296, Reno, NV, 89557, USA
| | - Nicholas G Murray
- Department of Psychology, Programs in Cognitive and Brain Sciences, and Integrative Neuroscience, University of Nevada, 1664 N. Virginia St., MS 296, Reno, NV, 89557, USA
- School of Community Health Sciences, University of Nevada, Reno, 89557, USA
| | - Marian E Berryhill
- Department of Psychology, Programs in Cognitive and Brain Sciences, and Integrative Neuroscience, University of Nevada, 1664 N. Virginia St., MS 296, Reno, NV, 89557, USA
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8
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Ghaderi AH, Baltaretu BR, Andevari MN, Bharmauria V, Balci F. Synchrony and Complexity in State-Related EEG Networks: An Application of Spectral Graph Theory. Neural Comput 2020; 32:2422-2454. [DOI: 10.1162/neco_a_01327] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
The brain may be considered as a synchronized dynamic network with several coherent dynamical units. However, concerns remain whether synchronizability is a stable state in the brain networks. If so, which index can best reveal the synchronizability in brain networks? To answer these questions, we tested the application of the spectral graph theory and the Shannon entropy as alternative approaches in neuroimaging. We specifically tested the alpha rhythm in the resting-state eye closed (rsEC) and the resting-state eye open (rsEO) conditions, a well-studied classical example of synchrony in neuroimaging EEG. Since the synchronizability of alpha rhythm is more stable during the rsEC than the rsEO, we hypothesized that our suggested spectral graph theory indices (as reliable measures to interpret the synchronizability of brain signals) should exhibit higher values in the rsEC than the rsEO condition. We performed two separate analyses of two different datasets (as elementary and confirmatory studies). Based on the results of both studies and in agreement with our hypothesis, the spectral graph indices revealed higher stability of synchronizability in the rsEC condition. The k-mean analysis indicated that the spectral graph indices can distinguish the rsEC and rsEO conditions by considering the synchronizability of brain networks. We also computed correlations among the spectral indices, the Shannon entropy, and the topological indices of brain networks, as well as random networks. Correlation analysis indicated that although the spectral and the topological properties of random networks are completely independent, these features are significantly correlated with each other in brain networks. Furthermore, we found that complexity in the investigated brain networks is inversely related to the stability of synchronizability. In conclusion, we revealed that the spectral graph theory approach can be reliably applied to study the stability of synchronizability of state-related brain networks.
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Affiliation(s)
- Amir Hossein Ghaderi
- Centre for Vision Research and Canada Vision: Science to Applications Program, York University, Toronto M3J 1P3, Canada, and Iranian Neuro-Wave Lab., No. 32, Vilashahr, Isfahan, Iran
| | | | | | - Vishal Bharmauria
- Centre for Vision Research, York University, Toronto M3J 1P3, Canada
| | - Fuat Balci
- Department of Psychology and Research Center for Translational Medicine, Koç University, Istanbul, Turkey
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9
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Liu M, Backer RA, Amey RC, Splan EE, Magerman A, Forbes CE. Context Matters: Situational Stress Impedes Functional Reorganization of Intrinsic Brain Connectivity during Problem-Solving. Cereb Cortex 2020; 31:2111-2124. [PMID: 33251535 DOI: 10.1093/cercor/bhaa349] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 10/14/2020] [Accepted: 10/22/2020] [Indexed: 12/22/2022] Open
Abstract
Extensive research has established a relationship between individual differences in brain activity in a resting state and individual differences in behavior. Conversely, when individuals are engaged in various tasks, certain task-evoked reorganization occurs in brain functional connectivity, which can consequently influence individuals' performance as well. Here, we show that resting state and task-dependent state brain patterns interact as a function of contexts engendering stress. Findings revealed that when the resting state connectome was examined during performance, the relationship between connectome strength and performance only remained for participants under stress (who also performed worse than all other groups on the math task), suggesting that stress preserved brain patterns indicative of underperformance whereas non-stressed individuals spontaneously transitioned out of these patterns. Results imply that stress may impede the reorganization of a functional network in task-evoked brain states. This hypothesis was subsequently verified using graph theory measurements on a functional network, independent of behavior. For participants under stress, the functional network showed less topological alterations compared to non-stressed individuals during the transition from resting state to task-evoked state. Implications are discussed for network dynamics as a function of context.
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Affiliation(s)
- Mengting Liu
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA.,USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90033, USA
| | - Robert A Backer
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
| | - Rachel C Amey
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
| | - Eric E Splan
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
| | - Adam Magerman
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
| | - Chad E Forbes
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
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FREQUENCY AND POWER ANALYSIS OF EEG THETA ACTIVITY DURING DIFFERENT MATHEMATICAL AND VERBAL TASK IN MALES. INTERNATIONAL JOURNAL OF HEALTH SERVICES RESEARCH AND POLICY 2020. [DOI: 10.33457/ijhsrp.726453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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