1
|
Lian J, Xu L, Song T, Peng Z, Zhang Z, An X, Chen S, Zhong X, Shao Y. Reduced Resting-State EEG Power Spectra and Functional Connectivity after 24 and 36 Hours of Sleep Deprivation. Brain Sci 2023; 13:949. [PMID: 37371427 DOI: 10.3390/brainsci13060949] [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: 05/12/2023] [Revised: 06/06/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
Total sleep deprivation (TSD) leads to cognitive decline; however, the neurophysiological mechanisms underlying resting-state electroencephalogram (EEG) changes after TSD remain unclear. In this study, 42 healthy adult participants were subjected to 36 h of sleep deprivation (36 h TSD), and resting-state EEG data were recorded at baseline, after 24 h of sleep deprivation (24 h TSD), and after 36 h TSD. The analysis of resting-state EEG at baseline, after 24 h TSD, and after 36 h TSD using source localization analysis, power spectrum analysis, and functional connectivity analysis revealed a decrease in alpha-band power and a significant increase in delta-band power after TSD and impaired functional connectivity in the default mode network, precuneus, and inferior parietal lobule. The cortical activities of the precuneus, inferior parietal lobule, and superior parietal lobule were significantly reduced, but no difference was found between the 24 h and 36 h TSD groups. This may indicate that TSD caused some damage to the participants, but this damage temporarily slowed during the 24 h to 36 h TSD period.
Collapse
Affiliation(s)
- Jie Lian
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Lin Xu
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Tao Song
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Ziyi Peng
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Zheyuan Zhang
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Xin An
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Shufang Chen
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Xiao Zhong
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Yongcong Shao
- School of Psychology, Beijing Sport University, Beijing 100084, China
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Kayser KC, Puig VA, Estepp JR. Predicting and mitigating fatigue effects due to sleep deprivation: A review. Front Neurosci 2022; 16:930280. [PMID: 35992930 PMCID: PMC9389006 DOI: 10.3389/fnins.2022.930280] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 07/04/2022] [Indexed: 01/07/2023] Open
Abstract
The deleterious effects of insufficient sleep have been well-established in the literature and can lead to a wide range of adverse health outcomes. Some of the most replicated findings demonstrate significant declines in cognitive functions such as vigilance and executive attention, psychomotor and cognitive speed, and working memory. Consequently, these decrements often lead individuals who are in a fatigued state to engage in substandard performance on everyday tasks. In the interest of curtailing these effects, prior work has attempted to identify mechanisms that predict fatigue onset and develop techniques to mitigate its negative consequences. Nonetheless, these results are often confounded by variables such as an individual’s resistance to fatigue, sleep history, and unclear distinctions about whether certain performance decrements are present due to fatigue or due to other confounding factors. Similar areas of research have provided approaches to produce models for the prediction of cognitive performance decrements due to fatigue through the use of multi-modal recording and analysis of fatigue-related responses. Namely, gathering and combining response information from multiple sources (i.e., physiological and behavioral) at multiple timescales may provide a more comprehensive representation of what constitutes fatigue onset in the individual. Therefore, the purpose of this review is to discuss the relevant literature on the topic of fatigue-related performance effects with a special emphasis on a variety of physiological and behavioral response variables that have shown to be sensitive to changes in fatigue. Furthermore, an increasing reliance on sleep loss, meant to assist in meeting the demands of modern society, has led to an upsurge in the relevance of identifying dependable countermeasures for fatigued states. As such, we will also review methods for the mitigation of performance effects due to fatigue and discuss their usefulness in regulating these effects. In sum, this review aims to inspire future work that will create opportunities to detect fatigue and mitigate its effects prior to the onset of cognitive impairments.
Collapse
Affiliation(s)
- Kylie C. Kayser
- Air Force Research Laboratory, Oak Ridge Institute for Science and Education, Wright-Patterson AFB, OH, United States
| | - Vannia A. Puig
- Air Force Research Laboratory, Oak Ridge Institute for Science and Education, Wright-Patterson AFB, OH, United States
| | - Justin R. Estepp
- 711th Human Performance Wing, Air Force Research Laboratory, Wright-Patterson AFB, OH, United States
- *Correspondence: Justin R. Estepp,
| |
Collapse
|
4
|
EEG-based brain functional connectivity representation using amplitude locking value for fatigue-driving recognition. Cogn Neurodyn 2021; 16:325-336. [PMID: 35401867 PMCID: PMC8934897 DOI: 10.1007/s11571-021-09714-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 08/15/2021] [Accepted: 09/02/2021] [Indexed: 10/20/2022] Open
Abstract
It has been shown that brain functional networks constructed from electroencephalographic signals (EEG) continuously change topology as brain fatigue increases, and extracting the topological properties of the network can characterize the degree of brain fatigue. However, the traditional brain function network construction process often selects only the amplitude or phase components of the signal to measure the relationship between brain regions, and the use of a single component of the signal to construct a brain function network for analysis is rather one-sided. Therefore, we propose a method of functional synchronization analysis of brain regions. This method takes the EEG signal based on empirical modal decomposition (EMD) to obtain multiple intrinsic modal components (IMF) and inputs them into the Hilbert transform to obtain the instantaneous amplitude, and then calculates the amplitude locking value (ALV) to measure the synchronization relationship between all pairs of channels. The topological properties of the brain functional network are extracted to classify awake and fatigue states. The brain functional network is constructed based on the adjacency matrix of each waveform obtained from the ALV between all pairs of channels to realize the synchronization analysis between brain regions. Moreover, we achieved a satisfactory classification accuracy (82.84%) using the discriminative connection features in the Alpha band. In this study, we analyzed the functional network of ALV brain in fatigue and awake state, and the results showed that the connections between brain regions in fatigue state were significantly increased, and the connections between brain regions in the awake state were significantly decreased, and the information interaction between brain regions was more orderly and efficient.
Collapse
|
5
|
Driver fatigue detection based on prefrontal EEG using multi-entropy measures and hybrid model. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102857] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
6
|
Wang F, Xu Q, Fu R. Study on the Effect of Man-Machine Response Mode to Relieve Driving Fatigue Based on EEG and EOG. SENSORS (BASEL, SWITZERLAND) 2019; 19:E4883. [PMID: 31717422 PMCID: PMC6891316 DOI: 10.3390/s19224883] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 10/29/2019] [Accepted: 11/04/2019] [Indexed: 11/17/2022]
Abstract
Rapid and accurate detection of driver fatigue is of great significance to improve traffic safety. In the present work, we propose the man-machine response mode (MRM) to relieve driver fatigue caused by long-term driving. In this paper, the characteristics of the complex brain network, which can effectively reflect brain activity information, were used to detect the change of driving fatigue over time. Combined with the traditional eye movement characteristics and a subjective questionnaire (SQ), the changes in driving fatigue characteristics were comprehensively analyzed. The results show that driving fatigue can be effectively delayed using the MRM. Additionally, the response equipment is low in cost and practical, so it will be practical to use in actual driving situations in the future.
Collapse
Affiliation(s)
- Fuwang Wang
- School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China;
| | - Qing Xu
- School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China;
| | - Rongrong Fu
- College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China;
| |
Collapse
|
7
|
Han C, Sun X, Yang Y, Che Y, Qin Y. Brain Complex Network Characteristic Analysis of Fatigue during Simulated Driving Based on Electroencephalogram Signals. ENTROPY 2019; 21:e21040353. [PMID: 33267067 PMCID: PMC7514837 DOI: 10.3390/e21040353] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Revised: 03/28/2019] [Accepted: 03/28/2019] [Indexed: 12/12/2022]
Abstract
Fatigued driving is one of the major causes of traffic accidents. Frequent repetition of driving behavior for a long time may lead to driver fatigue, which is closely related to the central nervous system. In the present work, we designed a fatigue driving simulation experiment and collected the electroencephalogram (EEG) signals. Complex network theory was introduced to study the evolution of brain dynamics under different rhythms of EEG signals during several periods of the simulated driving. The results show that as the fatigue degree deepened, the functional connectivity and the clustering coefficients increased while the average shortest path length decreased for the delta rhythm. In addition, there was a significant increase of the degree centrality in partial channels on the right side of the brain for the delta rhythm. Therefore, it can be concluded that driving fatigue can cause brain complex network characteristics to change significantly for certain brain regions and certain rhythms. This exploration may provide a theoretical basis for further finding objective and effective indicators to evaluate the degree of driving fatigue and to help avoid fatigue driving.
Collapse
|
8
|
Wang F, Zhang X, Fu R, Sun G. EEG characteristic analysis of coach bus drivers based on brain connectivity as revealed via a graph theoretical network. RSC Adv 2018; 8:29745-29755. [PMID: 35547294 PMCID: PMC9085270 DOI: 10.1039/c8ra04846k] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 08/06/2018] [Indexed: 12/20/2022] Open
Abstract
This study describes the detection of driving fatigue using the characteristics of brain networks in a real driving environment. First, the θ, β and 36–44 Hz rhythm from the EEG signals of drivers were extracted using wavelet packet decomposition (WPD). The correlation between EEG channels was calculated using a Pearson correlation coefficient and subsequently, the brain networks were built. Furthermore, the clustering coefficient (C) and global efficiency (G) of the complex brain networks were calculated to analyze the functional differences in the brains of drivers over time. Combined with the relative power spectrum ratio (β/θ) of EEG signals and the mean value from questionnaires, the correlation of data characteristics between brain networks and subjective and objective data was analyzed. The results show that changes in the fatigue state of drivers can be effectively detected by calculating the data characteristics of brain networks in a real driving environment. This study describes the detection of driving fatigue using the characteristics of brain networks in a real driving environment.![]()
Collapse
Affiliation(s)
- Fuwang Wang
- School of Mechanic Engineering, Northeast Electric Power University Jilin 132012 China +86-432-64807382
| | - Xiaolei Zhang
- School of Mechanic Engineering, Northeast Electric Power University Jilin 132012 China +86-432-64807382
| | - Rongrong Fu
- College of Electrical Engineering, Yanshan University Qinhuangdao 066004 China
| | - Guangbin Sun
- Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences Beijing 100094 China
| |
Collapse
|
9
|
Gao Z, Zhang K, Dang W, Yang Y, Wang Z, Duan H, Chen G. An adaptive optimal-Kernel time-frequency representation-based complex network method for characterizing fatigued behavior using the SSVEP-based BCI system. Knowl Based Syst 2018. [DOI: 10.1016/j.knosys.2018.04.013] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
10
|
Taya F, Dimitriadis SI, Dragomir A, Lim J, Sun Y, Wong KF, Thakor NV, Bezerianos A. Fronto-Parietal Subnetworks Flexibility Compensates For Cognitive Decline Due To Mental Fatigue. Hum Brain Mapp 2018; 39:3528-3545. [PMID: 29691949 DOI: 10.1002/hbm.24192] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2017] [Revised: 03/29/2018] [Accepted: 04/05/2018] [Indexed: 12/22/2022] Open
Abstract
Fronto-parietal subnetworks were revealed to compensate for cognitive decline due to mental fatigue by community structure analysis. Here, we investigate changes in topology of subnetworks of resting-state fMRI networks due to mental fatigue induced by prolonged performance of a cognitively demanding task, and their associations with cognitive decline. As it is well established that brain networks have modular organization, community structure analyses can provide valuable information about mesoscale network organization and serve as a bridge between standard fMRI approaches and brain connectomics that quantify the topology of whole brain networks. We developed inter- and intramodule network metrics to quantify topological characteristics of subnetworks, based on our hypothesis that mental fatigue would impact on functional relationships of subnetworks. Functional networks were constructed with wavelet correlation and a data-driven thresholding scheme based on orthogonal minimum spanning trees, which allowed detection of communities with weak connections. A change from pre- to posttask runs was found for the intermodule density between the frontal and the temporal subnetworks. Seven inter- or intramodule network metrics, mostly at the frontal or the parietal subnetworks, showed significant predictive power of individual cognitive decline, while the network metrics for the whole network were less effective in the predictions. Our results suggest that the control-type fronto-parietal networks have a flexible topological architecture to compensate for declining cognitive ability due to mental fatigue. This community structure analysis provides valuable insight into connectivity dynamics under different cognitive states including mental fatigue.
Collapse
Affiliation(s)
- Fumihiko Taya
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore.,Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore
| | - Stavros I Dimitriadis
- Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, United Kingdom.,Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,Neuroinformatics Group, (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Andrei Dragomir
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore
| | - Julian Lim
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore.,Neuroscience and Behavioral Disorders Program, Duke-NUS Graduate Medical School, Singapore
| | - Yu Sun
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore
| | - Kian Foong Wong
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore
| | - Nitish V Thakor
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore.,Department of Electrical & Computer Engineering, National University of Singapore, Singapore.,Department of Biomedical Engineering, National University of Singapore, Singapore.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Anastasios Bezerianos
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore.,School of Medicine, University of Patras, Greece
| |
Collapse
|
11
|
Wang F, Wang H, Fu R. Real-Time ECG-Based Detection of Fatigue Driving Using Sample Entropy. ENTROPY (BASEL, SWITZERLAND) 2018; 20:E196. [PMID: 33265287 PMCID: PMC7512712 DOI: 10.3390/e20030196] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2018] [Revised: 03/03/2018] [Accepted: 03/13/2018] [Indexed: 11/25/2022]
Abstract
In present work, the heart rate variability (HRV) characteristics, calculated by sample entropy (SampEn), were used to analyze the driving fatigue state at successive driving stages. Combined with the relative power spectrum ratio β/(θ + α), subjective questionnaire, and brain network parameters of electroencephalogram (EEG) signals, the relationships between the different characteristics for driving fatigue were discussed. Thus, it can conclude that the HRV characteristics (RR SampEn and R peaks SampEn), as well as the relative power spectrum ratio β/(θ + α) of the channels (C3, C4, P3, P4), the subjective questionnaire, and the brain network parameters, can effectively detect driving fatigue at various driving stages. In addition, the method for collecting ECG signals from the palm part does not need patch electrodes, is convenient, and will be practical to use in actual driving situations in the future.
Collapse
Affiliation(s)
- Fuwang Wang
- School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China
| | - Hong Wang
- School of Mechanic Engineering, Northeastern University, Shenyang 110819, China
| | - Rongrong Fu
- College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
| |
Collapse
|
12
|
Chung SJ, Choi YH, Kwon H, Park YH, Yun HJ, Yoo HS, Moon SH, Ye BS, Sohn YH, Lee JM, Lee PH. Sleep Disturbance May Alter White Matter and Resting State Functional Connectivities in Parkinson's Disease. Sleep 2017; 40:2962411. [PMID: 28364425 DOI: 10.1093/sleep/zsx009] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Study Objectives To clarify whether sleep disturbance would alter the patterns of structural and functional networks underlying cognitive dysfunction in patients with Parkinson's disease (PD). Methods Among the 180 patients with nondemented PD in our cohort, 45 patients were classified as the group with sleep disturbance according to the 5-item scales for outcomes in Parkinson's disease nighttime scale. Based on propensity scores, another 45 PD patients without sleep disturbance were matched to this group. We performed a comparative analysis of cortical thickness, diffusion tensor imaging-based white matter integrity, resting-state functional connectivity, and cognitive performance between PD patients with and without sleep disturbance. Results PD patients with sleep disturbance showed poorer performance in attention and working memory and a tendency toward a lower score in frontal executive function relative to those without sleep disturbance. The PD with sleep disturbance group exhibited widespread white matter disintegration compared to the PD without sleep disturbance group, although there were no significant differences in cortical thickness between the PD subgroups. On functional network analysis, PD patients with sleep disturbance exhibited less severely decreased cortical functional connectivity within the default mode network, central executive network, and dorsal attention network when compared to those without sleep disturbance. Conclusions The present study suggests that sleep disturbance in PD patients could be associated with white matter and functional network alterations in conjunction with cognitive impairment.
Collapse
Affiliation(s)
- Seok Jong Chung
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea.,Jangseong Public Health Center, Jangseong, South Korea
| | - Yong-Ho Choi
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Hunki Kwon
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Yeong-Hun Park
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Hyuk Jin Yun
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Han Soo Yoo
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | | | - Byoung Seok Ye
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Young H Sohn
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Phil Hyu Lee
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea.,Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, South Korea
| |
Collapse
|
13
|
Sengupta A, Dasgupta A, Chaudhuri A, George A, Routray A, Guha R. A Multimodal System for Assessing Alertness Levels Due to Cognitive Loading. IEEE Trans Neural Syst Rehabil Eng 2017; 25:1037-1046. [DOI: 10.1109/tnsre.2017.2672080] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
14
|
Thakor N, Bezerianos A. Connectome pattern alterations with increment of mental fatigue in one-hour driving simulation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:4355-4358. [PMID: 29060861 DOI: 10.1109/embc.2017.8037820] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The importance of understanding mental fatigue can be seen from many studies that started back in past decades. It is only until recent years has mental fatigue been explored through connectivity network analysis using graph theory. Although previous studies have revealed certain properties of the mental fatigue network via graph theory, some of these findings seemingly conflict with one another. The differences in findings could be due to mental fatigue being caused by various factors or being analyzed using different methods. So, in this study, to further understand the functional connectivity of driving fatigue, a weighted and undirected connectivity matrix would be constructed before applying graph theory to identify the biomarker from the network property. To obtain data for analysis, a 64-channel EEG cap was used to record the brain signals of subjects undergoing a one-hour driving simulation. Using the recorded EEG signal, a connectivity matrix was constructed using a synchronous method known as phase lag index (PLI) for the graph theory analysis. Results from this graph theory analysis showed that the synchronous network had increased clustering coefficient and decreased path length with the accumulation of mental fatigue. Furthermore, by calculating clustering coefficient regionally, its results revealed that the significant increase occurred mainly in the parietal and occipital regions of the brain.
Collapse
|
15
|
Alonso JF, Romero S, Mañanas MA, Alcalá M, Antonijoan RM, Giménez S. Acute Sleep Deprivation Induces a Local Brain Transfer Information Increase in the Frontal Cortex in a Widespread Decrease Context. SENSORS (BASEL, SWITZERLAND) 2016; 16:E540. [PMID: 27089346 PMCID: PMC4851054 DOI: 10.3390/s16040540] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2016] [Revised: 04/05/2016] [Accepted: 04/12/2016] [Indexed: 01/24/2023]
Abstract
Sleep deprivation (SD) has adverse effects on mental and physical health, affecting the cognitive abilities and emotional states. Specifically, cognitive functions and alertness are known to decrease after SD. The aim of this work was to identify the directional information transfer after SD on scalp EEG signals using transfer entropy (TE). Using a robust methodology based on EEG recordings of 18 volunteers deprived from sleep for 36 h, TE and spectral analysis were performed to characterize EEG data acquired every 2 h. Correlation between connectivity measures and subjective somnolence was assessed. In general, TE showed medium- and long-range significant decreases originated at the occipital areas and directed towards different regions, which could be interpreted as the transfer of predictive information from parieto-occipital activity to the rest of the head. Simultaneously, short-range increases were obtained for the frontal areas, following a consistent and robust time course with significant maps after 20 h of sleep deprivation. Changes during sleep deprivation in brain network were measured effectively by TE, which showed increased local connectivity and diminished global integration. TE is an objective measure that could be used as a potential measure of sleep pressure and somnolence with the additional property of directed relationships.
Collapse
Affiliation(s)
- Joan F Alonso
- Biomedical Engineering Research Centre, Department of Automatic Control, Universitat Politècnica de Catalunya, Barcelona 08028, Spain.
- Barcelona College of Industrial Engineering, Universitat Politècnica de Catalunya, Barcelona 08037, Spain.
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza 50018, Spain.
| | - Sergio Romero
- Biomedical Engineering Research Centre, Department of Automatic Control, Universitat Politècnica de Catalunya, Barcelona 08028, Spain.
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza 50018, Spain.
| | - Miguel A Mañanas
- Biomedical Engineering Research Centre, Department of Automatic Control, Universitat Politècnica de Catalunya, Barcelona 08028, Spain.
- Barcelona College of Industrial Engineering, Universitat Politècnica de Catalunya, Barcelona 08037, Spain.
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza 50018, Spain.
| | - Marta Alcalá
- Biomedical Engineering Research Centre, Department of Automatic Control, Universitat Politècnica de Catalunya, Barcelona 08028, Spain.
- Barcelona College of Industrial Engineering, Universitat Politècnica de Catalunya, Barcelona 08037, Spain.
| | - Rosa M Antonijoan
- Drug Research Centre, Hospital de la Santa Creu i Sant Pau, Barcelona 08026, Spain.
- Department of Pharmacology and Therapeutics, Universitat Autònoma de Barcelona, Bellaterra 08193, Spain.
- CIBER de Salud Mental (CIBERSAM), Madrid 28029, Spain.
| | - Sandra Giménez
- Drug Research Centre, Hospital de la Santa Creu i Sant Pau, Barcelona 08026, Spain.
- Department of Pharmacology and Therapeutics, Universitat Autònoma de Barcelona, Bellaterra 08193, Spain.
- CIBER de Salud Mental (CIBERSAM), Madrid 28029, Spain.
- Sleep Unit, Respiratory Department, Hospital de la Santa Creu i Sant Pau, Barcelona 08028, Spain.
| |
Collapse
|
16
|
Singh R, Menon SN, Sinha S. Complex patterns arise through spontaneous symmetry breaking in dense homogeneous networks of neural oscillators. Sci Rep 2016; 6:22074. [PMID: 26916700 PMCID: PMC4768139 DOI: 10.1038/srep22074] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Accepted: 02/05/2016] [Indexed: 12/03/2022] Open
Abstract
There has been much interest in understanding collective dynamics in networks of brain regions due to their role in behavior and cognitive function. Here we show that a simple, homogeneous system of densely connected oscillators, representing the aggregate activity of local brain regions, can exhibit a rich variety of dynamical patterns emerging via spontaneous breaking of permutation or translational symmetries. Upon removing just a few connections, we observe a striking departure from the mean-field limit in terms of the collective dynamics, which implies that the sparsity of these networks may have very important consequences. Our results suggest that the origins of some of the complicated activity patterns seen in the brain may be understood even with simple connection topologies.
Collapse
Affiliation(s)
- Rajeev Singh
- The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai 600113, India
| | - Shakti N Menon
- The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai 600113, India
| | - Sitabhra Sinha
- The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai 600113, India.,National Institute of Advanced Studies, Indian Institute of Science Campus, Bangalore 560012, India
| |
Collapse
|
17
|
Alonso JF, Romero S, Ballester MR, Antonijoan RM, Mañanas MA. Stress assessment based on EEG univariate features and functional connectivity measures. Physiol Meas 2015; 36:1351-65. [PMID: 26015439 DOI: 10.1088/0967-3334/36/7/1351] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The biological response to stress originates in the brain but involves different biochemical and physiological effects. Many common clinical methods to assess stress are based on the presence of specific hormones and on features extracted from different signals, including electrocardiogram, blood pressure, skin temperature, or galvanic skin response. The aim of this paper was to assess stress using EEG-based variables obtained from univariate analysis and functional connectivity evaluation. Two different stressors, the Stroop test and sleep deprivation, were applied to 30 volunteers to find common EEG patterns related to stress effects. Results showed a decrease of the high alpha power (11 to 12 Hz), an increase in the high beta band (23 to 36 Hz, considered a busy brain indicator), and a decrease in the approximate entropy. Moreover, connectivity showed that the high beta coherence and the interhemispheric nonlinear couplings, measured by the cross mutual information function, increased significantly for both stressors, suggesting that useful stress indexes may be obtained from EEG-based features.
Collapse
Affiliation(s)
- J F Alonso
- Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain. Barcelona College of Industrial Engineering (EUETIB), UPC, Barcelona, Spain. Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain
| | | | | | | | | |
Collapse
|
18
|
Discriminative Analysis of Brain Functional Connectivity Patterns for Mental Fatigue Classification. Ann Biomed Eng 2014; 42:2084-94. [DOI: 10.1007/s10439-014-1059-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2014] [Accepted: 06/17/2014] [Indexed: 10/25/2022]
|
19
|
Piantoni G, Cheung BLP, Van Veen BD, Romeijn N, Riedner BA, Tononi G, Van Der Werf YD, Van Someren EJW. Disrupted directed connectivity along the cingulate cortex determines vigilance after sleep deprivation. Neuroimage 2013; 79:213-22. [PMID: 23643925 DOI: 10.1016/j.neuroimage.2013.04.103] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2012] [Revised: 04/25/2013] [Accepted: 04/26/2013] [Indexed: 11/19/2022] Open
Abstract
The cingulate cortex is regarded as the backbone of structural and functional connectivity of the brain. While its functional connectivity has been intensively studied, little is known about its effective connectivity, its modulation by behavioral states, and its involvement in cognitive performance. Given the previously reported effects on cingulate functional connectivity, we investigated how eye-closure and sleep deprivation changed cingulate effective connectivity, estimated from resting-state high-density electroencephalography (EEG) using a novel method to calculate Granger Causality directly in source space. Effective connectivity along the cingulate cortex was dominant in the forward direction. Eyes-open connectivity in the forward direction was greater compared to eyes-closed, in well-rested participants. The difference between eyes-open and eyes-closed connectivity was attenuated and no longer significant after sleep deprivation. Individual variability in the forward connectivity after sleep deprivation predicted subsequent task performance, such that those subjects who showed a greater increase in forward connectivity between the eyes-open and the eyes-closed periods also performed better on a sustained attention task. Effective connectivity in the opposite, backward, direction was not affected by whether the eyes were open or closed or by sleep deprivation. These findings indicate that the effective connectivity from posterior to anterior cingulate regions is enhanced when a well-rested subject has his eyes open compared to when they are closed. Sleep deprivation impairs this directed information flow, proportional to its deleterious effect on vigilance. Therefore, sleep may play a role in the maintenance of waking effective connectivity.
Collapse
Affiliation(s)
- Giovanni Piantoni
- Dept of Sleep and Cognition, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105BA Amsterdam, The Netherlands.
| | | | | | | | | | | | | | | |
Collapse
|
20
|
Li Y, Sato Y, Yamaguchi N. Potential biochemical pathways for the relationship between sleep duration and mortality. Sleep Med 2012; 14:98-104. [PMID: 23127582 DOI: 10.1016/j.sleep.2012.08.020] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2012] [Revised: 08/22/2012] [Accepted: 08/31/2012] [Indexed: 12/16/2022]
Abstract
OBJECTIVE The objective of this study was to elucidate new evidence on the presence of a relationship among sleep duration, cardiovascular mortality and total mortality, and to investigate sleep duration-related multiple biochemical changes. METHODS The longitudinal study is based on the SAKUCESS study of 12,489 residents of Japan aged 20-79 years at baseline. RESULTS In the study, compared to respondents who reported 7h of sleep, long sleep duration (≥9h) was associated with an increased risk of cardiovascular disease mortality and total mortality in men, hazard ratios (HRs) were 1.70 (95% confidence interval [CI]=1.07-2.70) and 2.73 (95% CI=1.22-6.11) and an increased risk of total mortality in women, HR was 1.85 (95% CI=1.09-3.13). Sleep duration was significantly associated with changes in blood biochemical levels. The results of the logistic regression analysis showed that the levels of multiple biochemical parameters are associated with increased risk of total mortality. CONCLUSIONS This is the first large longitudinal study to indicate that sleep duration was associated with changes in multiple biochemical levels in the blood and total mortality.
Collapse
Affiliation(s)
- Ying Li
- Department of Social Medicine, School of Public Health, Zhejiang University, China.
| | | | | |
Collapse
|
21
|
Abstract
Functional connectivity networks have become a central focus in neuroscience because they reveal key higher-dimensional features of normal and abnormal nervous system physiology. Functional networks reflect activity-based coupling between brain regions that may be constrained by relatively static anatomical connections, yet these networks appear to support tremendously dynamic behaviors. Within this growing field, the stability and temporal characteristics of functional connectivity brain networks have not been well characterized. We evaluated the temporal stability of spontaneous functional connectivity networks derived from multi-day scalp encephalogram (EEG) recordings in five healthy human subjects. Topological stability and graph characteristics of networks derived from averaged data epochs ranging from 1 s to multiple hours across different states of consciousness were compared. We show that, although functional networks are highly variable on the order of seconds, stable network templates emerge after as little as ∼100 s of recording and persist across different states and frequency bands (albeit with slightly different characteristics in different states and frequencies). Within these network templates, the most common edges are markedly consistent, constituting a network "core." Although average network topologies persist across time, measures of global network connectivity, density and clustering coefficient, are state and frequency specific, with sparsest but most highly clustered networks seen during sleep and in the gamma frequency band. These findings support the notion that a core functional organization underlies spontaneous cortical processing and may provide a reference template on which unstable, transient, and rapidly adaptive long-range assemblies are overlaid in a frequency-dependent manner.
Collapse
|
22
|
Effects of sleep deprivation and time-of-day on selected physical abilities in off-road motorcycle riders. Eur J Appl Physiol 2011; 112:59-67. [DOI: 10.1007/s00421-011-1948-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2011] [Accepted: 03/25/2011] [Indexed: 12/14/2022]
|