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Xu F, Zhao J, Liu M, Yu X, Wang C, Lou Y, Shi W, Liu Y, Gao L, Yang Q, Zhang B, Lu S, Tang J, Leng J. Exploration of sleep function connection and classification strategies based on sub-period sleep stages. Front Neurosci 2023; 16:1088116. [PMID: 36760796 PMCID: PMC9906994 DOI: 10.3389/fnins.2022.1088116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 12/30/2022] [Indexed: 01/26/2023] Open
Abstract
Background As a medium for developing brain-computer interface systems, EEG signals are complex and difficult to identify due to their complexity, weakness, and differences between subjects. At present, most of the current research on sleep EEG signals are single-channel and dual-channel, ignoring the research on the relationship between different brain regions. Brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. Methods Phase-locked value (PLV) is used to construct a functional connection network. The connection network is used to analyze the connection mechanism and brain interaction in different sleep stages. Firstly, the entire EEG signal is divided into multiple sub-periods. Secondly, Phase-locked value is used for feature extraction on the sub-periods. Thirdly, the PLV of multiple sub-periods is used for feature fusion. Fourthly, the classification performance optimization strategy is used to discuss the impact of different frequency bands on sleep stage classification performance and to find the optimal frequency band. Finally, the brain function network is constructed by using the average value of the fusion features to analyze the interaction of brain regions in different frequency bands during sleep stages. Results The experimental results have shown that when the number of sub-periods is 30, the α (8-13 Hz) frequency band has the best classification effect, The classification result after 10-fold cross-validation reaches 92.59%. Conclusion The proposed algorithm has good sleep staging performance, which can effectively promote the development and application of an EEG sleep staging system.
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Affiliation(s)
- Fangzhou Xu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China,*Correspondence: Fangzhou Xu,
| | - Jinzhao Zhao
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Ming Liu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Xin Yu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Chongfeng Wang
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Yitai Lou
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Weiyou Shi
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Yanbing Liu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Licai Gao
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Qingbo Yang
- School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Baokun Zhang
- Department of Neurology, Shandong Institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, The First Affliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Shanshan Lu
- Department of Neurology, Shandong Institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, The First Affliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China,Department of Neurology, Cheeloo College of Medicine, Shandong Qianfoshan Hospital, Shandong University, Jinan, Shandong, China,Shanshan Lu,
| | - Jiyou Tang
- Department of Neurology, Shandong Institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, The First Affliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China,Department of Neurology, Cheeloo College of Medicine, Shandong Qianfoshan Hospital, Shandong University, Jinan, Shandong, China,Jiyou Tang,
| | - Jiancai Leng
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China,Jiancai Leng,
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Abdalbari H, Durrani M, Pancholi S, Patel N, Nasuto SJ, Nicolaou N. Brain and brain-heart Granger causality during wakefulness and sleep. Front Neurosci 2022; 16:927111. [PMID: 36188466 PMCID: PMC9520578 DOI: 10.3389/fnins.2022.927111] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 08/19/2022] [Indexed: 11/13/2022] Open
Abstract
In this exploratory study we apply Granger Causality (GC) to investigate the brain-brain and brain-heart interactions during wakefulness and sleep. Our analysis includes electroencephalogram (EEG) and electrocardiogram (ECG) data during all-night polysomnographic recordings from volunteers with apnea, available from the Massachusetts General Hospital's Computational Clinical Neurophysiology Laboratory and the Clinical Data Animation Laboratory. The data is manually annotated by clinical staff at the MGH in 30 second contiguous intervals (wakefulness and sleep stages 1, 2, 3, and rapid eye movement (REM). We applied GC to 4-s non-overlapping segments of available EEG and ECG across all-night recordings of 50 randomly chosen patients. To identify differences in GC between the different sleep stages, the GC for each sleep stage was subtracted from the GC during wakefulness. Positive (negative) differences indicated that GC was greater (lower) during wakefulness compared to the specific sleep stage. The application of GC to study brain-brain and brain-heart bidirectional connections during wakefulness and sleep confirmed the importance of fronto-posterior connectivity during these two states, but has also revealed differences in ipsilateral and contralateral mechanisms of these connections. It has also confirmed the existence of bidirectional brain-heart connections that are more prominent in the direction from brain to heart. Our exploratory study has shown that GC can be successfully applied to sleep data analysis and captures the varying physiological mechanisms that are related to wakefulness and different sleep stages.
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Affiliation(s)
- Helmi Abdalbari
- Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus
| | - Mohammad Durrani
- Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus
| | - Shivam Pancholi
- Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus
| | - Nikhil Patel
- Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus
| | - Slawomir J. Nasuto
- Department of Biomedical Engineering, School of Biological Sciences, University of Reading, Reading, United Kingdom
| | - Nicoletta Nicolaou
- Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus
- Center for Neuroscience and Integrative Brain Research (CENIBRE), University of Nicosia Medical School, Nicosia, Cyprus
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Zhang Y, Wang K, Yu W, Guo X, Wen J, Luo Y. Minimal EEG channel selection for depression detection with connectivity features during sleep. Comput Biol Med 2022; 147:105690. [DOI: 10.1016/j.compbiomed.2022.105690] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/29/2022] [Accepted: 05/31/2022] [Indexed: 11/30/2022]
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Song Y, Wang K, Wei Y, Zhu Y, Wen J, Luo Y. Graph Theory Analysis of the Cortical Functional Network During Sleep in Patients With Depression. Front Physiol 2022; 13:858739. [PMID: 35721531 PMCID: PMC9199990 DOI: 10.3389/fphys.2022.858739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 04/19/2022] [Indexed: 11/24/2022] Open
Abstract
Depression, a common mental illness that seriously affects the psychological health of patients, is also thought to be associated with abnormal brain functional connectivity. This study aimed to explore the differences in the sleep-state functional network topology in depressed patients. A total of 25 healthy participants and 26 depressed patients underwent overnight 16-channel electroencephalography (EEG) examination. The cortical networks were constructed by using functional connectivity metrics of participants based on the weighted phase lag index (WPLI) between the EEG signals. The results indicated that depressed patients exhibited higher global efficiency and node strength than healthy participants. Furthermore, the depressed group indicated right-lateralization in the δ band. The top 30% of connectivity in both groups were shown in undirected connectivity graphs, revealing the distinct link patterns between the depressed and control groups. Links between the hemispheres were noted in the patient group, while the links in the control group were only observed within each hemisphere, and there were many long-range links inside the hemisphere. The altered sleep-state functional network topology in depressed patients may provide clues for a better understanding of the depression pathology. Overall, functional network topology may become a powerful tool for the diagnosis of depression.
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Affiliation(s)
- Yingjie Song
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Kejie Wang
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yu Wei
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yongpeng Zhu
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Jinfeng Wen
- Department of Psychology, Guangdong, 999 Brain Hospital, Guangzhou, China
| | - Yuxi Luo
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Sensing Technology and Biomedical Instruments, Sun Yat-sen University, Guangzhou, China
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Functional network connectivity imprint in febrile seizures. Sci Rep 2022; 12:3267. [PMID: 35228583 PMCID: PMC8885759 DOI: 10.1038/s41598-022-07173-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 01/10/2022] [Indexed: 11/11/2022] Open
Abstract
Complex febrile seizures (CFS), a subset of paediatric febrile seizures (FS), have been studied for their prognosis, epileptogenic potential and neurocognitive outcome. We evaluated their functional connectivity differences with simple febrile seizures (SFS) in children with recent-onset FS. Resting-state fMRI (rs-fMRI) datasets of 24 children with recently diagnosed FS (SFS-n = 11; CFS-n = 13) were analysed. Functional connectivity (FC) was estimated using time series correlation of seed region–to-whole-brain-voxels and network topology was assessed using graph theory measures. Regional connectivity differences were correlated with clinical characteristics (FDR corrected p < 0.05). CFS patients demonstrated increased FC of the bilateral middle temporal pole (MTP), and bilateral thalami when compared to SFS. Network topology study revealed increased clustering coefficient and decreased participation coefficient in basal ganglia and thalamus suggesting an inefficient-unbalanced network topology in patients with CFS. The number of seizure recurrences negatively correlated with the integration of Left Thalamus (r = − 0.58) and FC of Left MTP to 'Right Supplementary Motor and left Precentral' gyrus (r = − 0.53). The FC of Right MTP to Left Amygdala, Putamen, Parahippocampal, and Orbital Frontal Cortex (r = 0.61) and FC of Left Thalamus to left Putamen, Pallidum, Caudate, Thalamus Hippocampus and Insula (r 0.55) showed a positive correlation to the duration of the longest seizure. The findings of the current study report altered connectivity in children with CFS proportional to the seizure recurrence and duration. Regardless of the causal/consequential nature, such observations demonstrate the imprint of these disease-defining variables of febrile seizures on the developing brain.
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Chen Y, Zhang J. How Energy Supports Our Brain to Yield Consciousness: Insights From Neuroimaging Based on the Neuroenergetics Hypothesis. Front Syst Neurosci 2021; 15:648860. [PMID: 34295226 PMCID: PMC8291083 DOI: 10.3389/fnsys.2021.648860] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Accepted: 05/26/2021] [Indexed: 11/13/2022] Open
Abstract
Consciousness is considered a result of specific neuronal processes and mechanisms in the brain. Various suggested neuronal mechanisms, including the information integration theory (IIT), global neuronal workspace theory (GNWS), and neuronal construction of time and space as in the context of the temporospatial theory of consciousness (TTC), have been laid forth. However, despite their focus on different neuronal mechanisms, these theories neglect the energetic-metabolic basis of the neuronal mechanisms that are supposed to yield consciousness. Based on the findings of physiology-induced (sleep), pharmacology-induced (general anesthesia), and pathology-induced [vegetative state/unresponsive wakeful syndrome (VS/UWS)] loss of consciousness in both human subjects and animals, we, in this study, suggest that the energetic-metabolic processes focusing on ATP, glucose, and γ-aminobutyrate/glutamate are indispensable for functional connectivity (FC) of normal brain networks that renders consciousness possible. Therefore, we describe the energetic-metabolic predispositions of consciousness (EPC) that complement the current theories focused on the neural correlates of consciousness (NCC).
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Affiliation(s)
- Yali Chen
- Department of Anesthesiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jun Zhang
- Department of Anesthesiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical college, Fudan University, Shanghai, China
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Avelar-Pereira B, Tam GKY, Hosseini SMH. The effect of body posture on resting-state functional connectivity. Brain Connect 2021; 12:275-284. [PMID: 34114506 DOI: 10.1089/brain.2021.0013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
INTRODUCTION An important but under-investigated confound of functional MRI (fMRI) is body posture. Although it is well-established that body position changes cerebral blood flow, the amount of cerebrospinal fluid in the brain, intracranial pressure, and even the firing rate of certain cell types, there is currently no study that directly examines its effect on fMRI measurements. Moreover, fMRI is typically done in a supine position, which often does not correspond to how these processes are performed in everyday settings. METHODS In this study, 20 healthy adults underwent resting-state fMRI under three body positions: supine, right lateral decubitus (RLD), and left lateral decubitus (LLD). We first investigated whether there were differences in overall organization of whole-brain connectivity between conditions using graph theory. Second, we examined whether functional connectivity of two most studied default mode network (DMN) seeds to the rest of the brain was altered as a function of body position. RESULTS Nonparametric statistical analyses revealed that global network measures differed among conditions, with the supine and LLD showing identical results compared to the RLD. There was decreased connectivity for DMN seeds in the RLD condition compared to the supine and LLD, but there were no significant differences between the latter two conditions. DISCUSSION Potential mechanisms underlying these alterations include gravity, changes in physiology, and body anatomy. Our results suggest that, compared to supine and LLD, the RLD position leads to changes in whole-brain and DMN connectivity. Finally, depending on the research question, combining imaging modalities that allow for more naturalistic settings can provide a better understanding of certain phenomena.
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Affiliation(s)
- Bárbara Avelar-Pereira
- Stanford University, 6429, Department of Psychiatry & Behavioral Sciences, 401 Quarry Rd, Stanford, California, United States, 94305;
| | - Grace K-Y Tam
- Stanford University, 6429, Department of Psychiatry & Behavioral Sciences, Stanford, California, United States;
| | - S M Hadi Hosseini
- Stanford University, 6429, Department of Psychiatry & Behavioral Sciences, Stanford, California, United States;
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Huang H, Zhang J, Zhu L, Tang J, Lin G, Kong W, Lei X, Zhu L. EEG-Based Sleep Staging Analysis with Functional Connectivity. SENSORS (BASEL, SWITZERLAND) 2021; 21:1988. [PMID: 33799850 PMCID: PMC7999974 DOI: 10.3390/s21061988] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 02/26/2021] [Accepted: 03/08/2021] [Indexed: 12/20/2022]
Abstract
Sleep staging is important in sleep research since it is the basis for sleep evaluation and disease diagnosis. Related works have acquired many desirable outcomes. However, most of current studies focus on time-domain or frequency-domain measures as classification features using single or very few channels, which only obtain the local features but ignore the global information exchanging between different brain regions. Meanwhile, brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. To explore the electroencephalography (EEG)-based brain mechanisms of sleep stages through functional connectivity, especially from different frequency bands, we applied phase-locked value (PLV) to build the functional connectivity network and analyze the brain interaction during sleep stages for different frequency bands. Then, we performed the feature-level, decision-level and hybrid fusion methods to discuss the performance of different frequency bands for sleep stages. The results show that (1) PLV increases in the lower frequency band (delta and alpha bands) and vice versa during different stages of non-rapid eye movement (NREM); (2) alpha band shows a better discriminative ability for sleeping stages; (3) the classification accuracy of feature-level fusion (six frequency bands) reaches 96.91% and 96.14% for intra-subject and inter-subjects respectively, which outperforms decision-level and hybrid fusion methods.
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Affiliation(s)
- Hui Huang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Jianhai Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Li Zhu
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Jiajia Tang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Guang Lin
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Wanzeng Kong
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China;
- Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing 400715, China
| | - Lei Zhu
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China;
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Hein M, Lanquart JP, Loas G, Hubain P, Linkowski P. Alterations of neural network organization during REM sleep in women: implication for sex differences in vulnerability to mood disorders. Biol Sex Differ 2020; 11:22. [PMID: 32334638 PMCID: PMC7183628 DOI: 10.1186/s13293-020-00297-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 04/07/2020] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Sleep plays an important role in vulnerability to mood disorders. However, despite the existence of sex differences in vulnerability to mood disorders, no study has yet investigated the sex effect on sleep network organization and its potential involvement in vulnerability to mood disorders. The aim of our study was to empirically investigate the sex effect on network organization during REM and slow-wave sleep using the effective connectivity measured by Granger causality. METHODS Polysomnographic data from 44 healthy individuals (28 men and 16 women) recruited prospectively were analysed. To obtain the 19 × 19 connectivity matrix of all possible pairwise combinations of electrodes by Granger causality method from our EEG data, we used the Toolbox MVGC multivariate Granger causality. The computation of the network measures was realized by importing these connectivity matrices into EEGNET Toolbox. RESULTS In men and women, all small-world coefficients obtained are compatible with a small-world network organization during REM and slow-wave sleep. However, compared to men, women present greater small-world coefficients during REM sleep as well as for all EEG bands during this sleep stage, which indicates the presence of a small-world network organization less marked during REM sleep as well as for all EEG bands during this sleep stage in women. In addition, in women, these small-world coefficients during REM sleep as well as for all EEG bands during this sleep stage are positively correlated with the presence of subclinical symptoms of depression. CONCLUSIONS Thus, the highlighting of these sex differences in network organization during REM sleep indicates the presence of differences in the global and local processing of information during sleep between women and men. In addition, this small-world network organization less marked during REM sleep appears to be a marker of vulnerability to mood disorders specific to women, which opens up new perspectives in understanding sex differences in the occurrence of mood disorders.
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Affiliation(s)
- Matthieu Hein
- Erasme Hospital, Department of Psychiatry and Sleep Laboratory, Université libre de Bruxelles, ULB, Route de Lennik, 808, 1070 Anderlecht, Brussels, Belgium.
| | - Jean-Pol Lanquart
- Erasme Hospital, Department of Psychiatry and Sleep Laboratory, Université libre de Bruxelles, ULB, Route de Lennik, 808, 1070 Anderlecht, Brussels, Belgium
| | - Gwénolé Loas
- Erasme Hospital, Department of Psychiatry and Sleep Laboratory, Université libre de Bruxelles, ULB, Route de Lennik, 808, 1070 Anderlecht, Brussels, Belgium
| | - Philippe Hubain
- Erasme Hospital, Department of Psychiatry and Sleep Laboratory, Université libre de Bruxelles, ULB, Route de Lennik, 808, 1070 Anderlecht, Brussels, Belgium
| | - Paul Linkowski
- Erasme Hospital, Department of Psychiatry and Sleep Laboratory, Université libre de Bruxelles, ULB, Route de Lennik, 808, 1070 Anderlecht, Brussels, Belgium
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Fractal dimension undirected correlation graph-based support vector machine model for identification of focal and non-focal electroencephalography signals. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.101611] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Wang J, Han J, Nguyen VT, Guo L, Guo CC. Improving the Test-Retest Reliability of Resting State fMRI by Removing the Impact of Sleep. Front Neurosci 2017; 11:249. [PMID: 28533739 PMCID: PMC5420587 DOI: 10.3389/fnins.2017.00249] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 04/18/2017] [Indexed: 01/04/2023] Open
Abstract
Resting state functional magnetic resonance imaging (rs-fMRI) provides a powerful tool to examine large-scale neural networks in the human brain and their disturbances in neuropsychiatric disorders. Thanks to its low demand and high tolerance, resting state paradigms can be easily acquired from clinical population. However, due to the unconstrained nature, resting state paradigm is associated with excessive head movement and proneness to sleep. Consequently, the test-retest reliability of rs-fMRI measures is moderate at best, falling short of widespread use in the clinic. Here, we characterized the effect of sleep on the test-retest reliability of rs-fMRI. Using measures of heart rate variability (HRV) derived from simultaneous electrocardiogram (ECG) recording, we identified portions of fMRI data when subjects were more alert or sleepy, and examined their effects on the test-retest reliability of functional connectivity measures. When volumes of sleep were excluded, the reliability of rs-fMRI is significantly improved, and the improvement appears to be general across brain networks. The amount of improvement is robust with the removal of as much as 60% volumes of sleepiness. Therefore, test-retest reliability of rs-fMRI is affected by sleep and could be improved by excluding volumes of sleepiness as indexed by HRV. Our results suggest a novel and practical method to improve test-retest reliability of rs-fMRI measures.
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Affiliation(s)
- Jiahui Wang
- School of Automation, Northwestern Polytechnical UniversityXi'an, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical UniversityXi'an, China
| | - Vinh T Nguyen
- QIMR Berghofer Medical Research InstituteBrisbane, QLD, Australia
| | - Lei Guo
- School of Automation, Northwestern Polytechnical UniversityXi'an, China
| | - Christine C Guo
- QIMR Berghofer Medical Research InstituteBrisbane, QLD, Australia
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Gkigkitzis I, Haranas I, Kotsireas I. Biological Relevance of Network Architecture. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 988:1-29. [PMID: 28971385 DOI: 10.1007/978-3-319-56246-9_1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Mathematical representations of brain networks in neuroscience through the use of graph theory may be very useful for the understanding of neurological diseases and disorders and such an explanatory power is currently under intense investigation. Graph metrics are expected to vary across subjects and are likely to reflect behavioural and cognitive performances. The challenge is to set up a framework that can explain how behaviour, cognition, memory, and other brain properties can emerge through the combined interactions of neurons, ensembles of neurons, and larger-scale brain regions that make information transfer possible. "Hidden" graph theoretic properties in the construction of brain networks may limit or enhance brain functionality and may be representative of aspects of human psychology. As theorems emerge from simple mathematical properties of graphs, similarly, cognition and behaviour may emerge from the molecular, cellular and brain region substrate interactions. In this review report, we identify some studies in the current literature that have used graph theoretical metrics to extract neurobiological conclusions, we briefly discuss the link with the human connectome project as an effort to integrate human data that may aid the study of emergent patterns and we suggest a way to start categorizing diseases according to their brain network pathologies as these are measured by graph theory.
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Affiliation(s)
- Ioannis Gkigkitzis
- Department of Mathematics, East Carolina University, 124 Austin Building, East Fifth Street, Greenville, NC, 27858-4353, USA.
| | - Ioannis Haranas
- Department of Physics and Computer Science, Wilfrid Laurier University, Science Building, Room N2078, 75 University Ave. W., Waterloo, ON, Canada, N2L 3C5
| | - Ilias Kotsireas
- Department of Physics and Computer Science, Wilfrid Laurier University, Science Building, Room N2078, 75 University Ave. W., Waterloo, ON, Canada, N2L 3C5
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