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Chen S, Wang Y, Lin X, Sun X, Li W, Ma W. Cross-subject emotion recognition in brain-computer interface based on frequency band attention graph convolutional adversarial neural networks. J Neurosci Methods 2024; 411:110276. [PMID: 39237038 DOI: 10.1016/j.jneumeth.2024.110276] [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: 03/29/2024] [Revised: 08/19/2024] [Accepted: 09/01/2024] [Indexed: 09/07/2024]
Abstract
BACKGROUND Emotion is an important area in neuroscience. Cross-subject emotion recognition based on electroencephalogram (EEG) data is challenging due to physiological differences between subjects. Domain gap, which refers to the different distributions of EEG data at different subjects, has attracted great attention for cross-subject emotion recognition. COMPARISON WITH EXISTING METHODS This study focuses on narrowing the domain gap between subjects through the emotional frequency bands and the relationship information between EEG channels. Emotional frequency band features represent the energy distribution of EEG data in different frequency ranges, while relationship information between EEG channels provides spatial distribution information about EEG data. NEW METHOD To achieve this, this paper proposes a model called the Frequency Band Attention Graph convolutional Adversarial neural Network (FBAGAN). This model includes three components: a feature extractor, a classifier, and a discriminator. The feature extractor consists of a layer with a frequency band attention mechanism and a graph convolutional neural network. The mechanism effectively extracts frequency band information by assigning weights and Graph Convolutional Networks can extract relationship information between EEG channels by modeling the graph structure. The discriminator then helps minimize the gap in the frequency information and relationship information between the source and target domains, improving the model's ability to generalize. RESULTS The FBAGAN model is extensively tested on the SEED, SEED-IV, and DEAP datasets. The accuracy and standard deviation scores are 88.17% and 4.88, respectively, on the SEED dataset, and 77.35% and 3.72 on the SEED-IV dataset. On the DEAP dataset, the model achieves 69.64% for Arousal and 65.18% for Valence. These results outperform most existing models. CONCLUSIONS The experiments indicate that FBAGAN effectively addresses the challenges of transferring EEG channel domain and frequency band domain, leading to improved performance.
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Affiliation(s)
- Shinan Chen
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, PR China
| | - Yuchen Wang
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, PR China.
| | - Xuefen Lin
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, PR China
| | - Xiaoyong Sun
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, PR China
| | - Weihua Li
- School of Engineering, Computer & Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
| | - Weifeng Ma
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, PR China
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2
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Qiu L, Zhong L, Li J, Feng W, Zhou C, Pan J. SFT-SGAT: A semi-supervised fine-tuning self-supervised graph attention network for emotion recognition and consciousness detection. Neural Netw 2024; 180:106643. [PMID: 39186838 DOI: 10.1016/j.neunet.2024.106643] [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: 10/31/2023] [Revised: 04/11/2024] [Accepted: 08/14/2024] [Indexed: 08/28/2024]
Abstract
Emotional recognition is highly important in the field of brain-computer interfaces (BCIs). However, due to the individual variability in electroencephalogram (EEG) signals and the challenges in obtaining accurate emotional labels, traditional methods have shown poor performance in cross-subject emotion recognition. In this study, we propose a cross-subject EEG emotion recognition method based on a semi-supervised fine-tuning self-supervised graph attention network (SFT-SGAT). First, we model multi-channel EEG signals by constructing a graph structure that dynamically captures the spatiotemporal topological features of EEG signals. Second, we employ a self-supervised graph attention neural network to facilitate model training, mitigating the impact of signal noise on the model. Finally, a semi-supervised approach is used to fine-tune the model, enhancing its generalization ability in cross-subject classification. By combining supervised and unsupervised learning techniques, the SFT-SGAT maximizes the utility of limited labeled data in EEG emotion recognition tasks, thereby enhancing the model's performance. Experiments based on leave-one-subject-out cross-validation demonstrate that SFT-SGAT achieves state-of-the-art cross-subject emotion recognition performance on the SEED and SEED-IV datasets, with accuracies of 92.04% and 82.76%, respectively. Furthermore, experiments conducted on a self-collected dataset comprising ten healthy subjects and eight patients with disorders of consciousness (DOCs) revealed that the SFT-SGAT attains high classification performance in healthy subjects (maximum accuracy of 95.84%) and was successfully applied to DOC patients, with four patients achieving emotion recognition accuracies exceeding 60%. The experiments demonstrate the effectiveness of the proposed SFT-SGAT model in cross-subject EEG emotion recognition and its potential for assessing levels of consciousness in patients with DOC.
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Affiliation(s)
- Lina Qiu
- School of Artificial Intelligence, South China Normal University, Guangzhou, 510630, China; Research Station in Mathematics, South China Normal University, Guangzhou, 510630, China.
| | - Liangquan Zhong
- School of Artificial Intelligence, South China Normal University, Guangzhou, 510630, China.
| | - Jianping Li
- School of Artificial Intelligence, South China Normal University, Guangzhou, 510630, China.
| | - Weisen Feng
- School of Artificial Intelligence, South China Normal University, Guangzhou, 510630, China.
| | - Chengju Zhou
- School of Artificial Intelligence, South China Normal University, Guangzhou, 510630, China.
| | - Jiahui Pan
- School of Artificial Intelligence, South China Normal University, Guangzhou, 510630, China.
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3
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Nadalizadeh F, Rajabioun M, Feyzi A. Driving fatigue detection based on brain source activity and ARMA model. Med Biol Eng Comput 2024; 62:1017-1030. [PMID: 38117429 DOI: 10.1007/s11517-023-02983-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 11/28/2023] [Indexed: 12/21/2023]
Abstract
Fatigue among drivers is a significant issue in society, and according to organizational reports, it substantially contributes to accidents. So accurate fatigue detection in drivers plays a crucial role in reducing the number of people fatalities or injured resulting from accidents. Several methods are proposed for fatigue driver recognition among which electroencephalography (EEG) is one. This paper proposed a method for fatigue recognition by EEG signals with extracted features from source and sensor spaces. The proposed method starts with preprocessing by applying filtering and artifact rejection. Then source localization methods are applied to EEG signals for active source extraction. A multivariate autoregressive (MVAR) model is fitted to selected sources, and a dual Kalman filter is applied to estimate the source activity and their relationships. Then multivariate autoregressive moving average (ARMA) is fitted between EEG and source activity signals. Features are extracted from model parameters, source relationship matrix, and wavelet transform of EEG and source activity signals. The novelty of this approach is the use of ARMA model between source activities (as input) and EEG signals (as output) and feature extraction from source relations. Relevant features are selected using a combination of RelifF and neighborhood component analysis (NCA) methods. Three classifiers, namely k-nearest neighbor (KNN), support vector machine (SVM), and naive Bayesian (NB) classifiers, are employed to classify drivers. To improve performance, the final label for fatigue detection is calculated by combining these classifiers using the voting method. The results demonstrate that the proposed method accurately recognizes and classifies fatigued drivers with the ensemble classifiers in comparison with other methods.
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Affiliation(s)
- Fahimeh Nadalizadeh
- Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | - Mehdi Rajabioun
- Department of Engineering, Mamaghan Branch, Islamic Azad University, Mamaghan, Iran.
| | - Amirreza Feyzi
- Department of Electrical and Computer Engineering, Tabriz University, Tabriz, Iran
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4
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Zeng L, Wang C, Sun K, Pu Y, Gao Y, Wang H, Liu X, Wen Z. Upregulation of a Small-World Brain Network Improves Inhibitory Control: An fNIRS Neurofeedback Training Study. Brain Sci 2023; 13:1516. [PMID: 38002477 PMCID: PMC10670110 DOI: 10.3390/brainsci13111516] [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: 09/20/2023] [Revised: 10/22/2023] [Accepted: 10/24/2023] [Indexed: 11/26/2023] Open
Abstract
The aim of this study was to investigate the inner link between the small-world brain network and inhibitory control. Functional near-infrared spectroscopy (fNIRS) was used to construct a neurofeedback (NF) training system and regulate the frontal small-world brain network. The small-world network downregulation group (DOWN, n = 17) and the small-world network upregulation group (UP, n = 17) received five days of fNIRS-NF training and performed the color-word Stroop task before and after training. The behavioral and functional brain network topology results of both groups were analyzed by a repeated-measures analysis of variance (ANOVA), which showed that the upregulation training helped to improve inhibitory control. The upregulated small-world brain network exhibits an increase in the brain network regularization, links widely dispersed brain resources, and reduces the lateralization of brain functional networks between hemispheres. This suggests an inherent correlation between small-world functional brain networks and inhibitory control; moreover, dynamic optimization under cost efficiency trade-offs provides a neural basis for inhibitory control. Inhibitory control is not a simple function of a single brain region or connectivity but rather an emergent property of a broader network.
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Affiliation(s)
- Lingwei Zeng
- Department of Medical Psychology, Fourth Military Medical University, Xi’an 710032, China; (L.Z.); (K.S.); (Y.P.); (Y.G.); (H.W.)
| | - Chunchen Wang
- Department of Aerospace Medicine, Fourth Military Medical University, Xi’an 710032, China;
| | - Kewei Sun
- Department of Medical Psychology, Fourth Military Medical University, Xi’an 710032, China; (L.Z.); (K.S.); (Y.P.); (Y.G.); (H.W.)
| | - Yue Pu
- Department of Medical Psychology, Fourth Military Medical University, Xi’an 710032, China; (L.Z.); (K.S.); (Y.P.); (Y.G.); (H.W.)
| | - Yuntao Gao
- Department of Medical Psychology, Fourth Military Medical University, Xi’an 710032, China; (L.Z.); (K.S.); (Y.P.); (Y.G.); (H.W.)
| | - Hui Wang
- Department of Medical Psychology, Fourth Military Medical University, Xi’an 710032, China; (L.Z.); (K.S.); (Y.P.); (Y.G.); (H.W.)
| | - Xufeng Liu
- Department of Medical Psychology, Fourth Military Medical University, Xi’an 710032, China; (L.Z.); (K.S.); (Y.P.); (Y.G.); (H.W.)
| | - Zhihong Wen
- Department of Aerospace Medicine, Fourth Military Medical University, Xi’an 710032, China;
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5
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Alves CL, Toutain TGLDO, Porto JAM, Aguiar PMDC, de Sena EP, Rodrigues FA, Pineda AM, Thielemann C. Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia. J Neural Eng 2023; 20:056025. [PMID: 37673060 DOI: 10.1088/1741-2552/acf734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 09/06/2023] [Indexed: 09/08/2023]
Abstract
Objective. Schizophrenia(SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals.Approach.For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature.Main results.When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ.Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain.
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Affiliation(s)
- Caroline L Alves
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
- BioMEMS Lab, Aschaffenburg University of Applied Sciences, Aschaffenburg, Germany
| | | | | | - Patrícia Maria de Carvalho Aguiar
- Hospital Israelita Albert Einstein, São Paulo, Brazil
- Federal University of São Paulo, Department of Neurology and Neurosurgery, São Paulo, Brazil
| | | | - Francisco A Rodrigues
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
| | - Aruane M Pineda
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
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6
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Lan C, Chan RHM. Comparing the Graphical Features of Simple Artificial Neural Networks and Cortical Development. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082639 DOI: 10.1109/embc40787.2023.10340927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Brain development is characterized by changes in connections and information processing complexity. These changes inspire the training process of artificial neural network (ANN), which requires adjusting the neuron weights and biases to enhance efficiency in performing a specific task. In this work, we found affinities in the ratio of positive and negative weights in simple ANNs during training with that of excitatory and inhibitory synapses in the cortex. Additionally, we present a graphical representation of simple ANNs formed by pruning unimportant weights and aligning neurons and connections of different layers. Our findings suggest a strong relationship between the accuracy of simple neural network and graphical representation features, with graphical features at the inflection point resembling the graphical representation of the cortex.
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7
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Currey D, Craley J, Hsu D, Ahmed R, Venkataraman A. EPViz: A flexible and lightweight visualizer to facilitate predictive modeling for multi-channel EEG. PLoS One 2023; 18:e0282268. [PMID: 36848345 PMCID: PMC9970073 DOI: 10.1371/journal.pone.0282268] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 02/11/2023] [Indexed: 03/01/2023] Open
Abstract
Scalp Electroencephalography (EEG) is one of the most popular noninvasive modalities for studying real-time neural phenomena. While traditional EEG studies have focused on identifying group-level statistical effects, the rise of machine learning has prompted a shift in computational neuroscience towards spatio-temporal predictive analyses. We introduce a novel open-source viewer, the EEG Prediction Visualizer (EPViz), to aid researchers in developing, validating, and reporting their predictive modeling outputs. EPViz is a lightweight and standalone software package developed in Python. Beyond viewing and manipulating the EEG data, EPViz allows researchers to load a PyTorch deep learning model, apply it to EEG features, and overlay the output channel-wise or subject-level temporal predictions on top of the original time series. These results can be saved as high-resolution images for use in manuscripts and presentations. EPViz also provides valuable tools for clinician-scientists, including spectrum visualization, computation of basic data statistics, and annotation editing. Finally, we have included a built-in EDF anonymization module to facilitate sharing of clinical data. Taken together, EPViz fills a much needed gap in EEG visualization. Our user-friendly interface and rich collection of features may also help to promote collaboration between engineers and clinicians.
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Affiliation(s)
- Danielle Currey
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States of America
| | - Jeff Craley
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - David Hsu
- Department of Neurology, University of Wisconsin Madison, Madison, WI, United States of America
| | - Raheel Ahmed
- Department of Neurosurgery, University of Wisconsin Madison, Madison, WI, United States of America
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States of America
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, United States of America
- * E-mail:
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8
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Li J, Pan W, Huang H, Pan J, Wang F. STGATE: Spatial-temporal graph attention network with a transformer encoder for EEG-based emotion recognition. Front Hum Neurosci 2023; 17:1169949. [PMID: 37125349 PMCID: PMC10133470 DOI: 10.3389/fnhum.2023.1169949] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 03/27/2023] [Indexed: 05/02/2023] Open
Abstract
Electroencephalogram (EEG) is a crucial and widely utilized technique in neuroscience research. In this paper, we introduce a novel graph neural network called the spatial-temporal graph attention network with a transformer encoder (STGATE) to learn graph representations of emotion EEG signals and improve emotion recognition performance. In STGATE, a transformer-encoder is applied for capturing time-frequency features which are fed into a spatial-temporal graph attention for emotion classification. Using a dynamic adjacency matrix, the proposed STGATE adaptively learns intrinsic connections between different EEG channels. To evaluate the cross-subject emotion recognition performance, leave-one-subject-out experiments are carried out on three public emotion recognition datasets, i.e., SEED, SEED-IV, and DREAMER. The proposed STGATE model achieved a state-of-the-art EEG-based emotion recognition performance accuracy of 90.37% in SEED, 76.43% in SEED-IV, and 76.35% in DREAMER dataset, respectively. The experiments demonstrated the effectiveness of the proposed STGATE model for cross-subject EEG emotion recognition and its potential for graph-based neuroscience research.
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9
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Wang L, Sheng A, Chang L, Zhou R. Improving fluid intelligence of children through working memory training: The role of inhibition control. Front Psychol 2022; 13:1025036. [PMID: 36507034 PMCID: PMC9732572 DOI: 10.3389/fpsyg.2022.1025036] [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: 08/22/2022] [Accepted: 11/03/2022] [Indexed: 11/27/2022] Open
Abstract
Intelligence is strongly associated with working memory. Working memory training can improve fluid intelligence, but the underlying mechanism requires further investigation. Because inhibition control may play a key role in working memory training, this study investigated this process from an electrophysiological perspective. In total, 40 children aged 9 to 11 years were enrolled and randomly divided into a training group (n = 20) and a control group (n = 20). The training group received 20 days of working memory training, whereas the control group did not receive any training. Before and after the training period, all participants were tested using Raven's Standard Progressive Matrices (RSPM), and electrophysiological indicators were recorded while they performed go/no-go and Stroop tasks. The results revealed that relative to the control group, the training group had significantly improved RSPM scores in the test conducted after their training. For the go/no-go tasks, the training group exhibited a significant decrease in N2 amplitude, a significant increase in P3 amplitude, a significant decrease in theta band energy, and an improvement in response inhibition ability. No significant change was observed for the Stroop task. Correlation analysis revealed that an improvement in individual response inhibition can positively predict an improvement in fluid intelligence. These results suggest that working memory training enhances the fluid intelligence of children by enhancing their response inhibition ability.
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Affiliation(s)
- Lei Wang
- Department of Psychology, Nanjing University, Nanjing, Jiangsu, China
| | - Ang Sheng
- Department of Psychology, Nanjing University, Nanjing, Jiangsu, China
| | - Lei Chang
- Department of Psychology, Faculty of Social Sciences, University of Macau, Taipa, Macao SAR, China
| | - Renlai Zhou
- Department of Psychology, Nanjing University, Nanjing, Jiangsu, China,State Key Laboratory of Media Convergence Production Technology and Systems, Beijing, China,*Correspondence: Renlai Zhou,
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10
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Banea OC, Bandeira Dos Santos LG, Marcu S, Stefánnson SB, Wassermann EM, Ívarsson E, Jónasson VD, Aubonnet R, Jónasson AD, Magnúsdóttir BB, Haraldsson M, Gargiulo P. Network signatures of rTMS treatment in patients with schizophrenia and auditory verbal hallucination during an auditory-motor task using HD-EEG. Schizophr Res 2022; 243:310-314. [PMID: 34217547 DOI: 10.1016/j.schres.2021.06.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/11/2021] [Accepted: 06/13/2021] [Indexed: 11/15/2022]
Affiliation(s)
- Ovidiu C Banea
- Neurology Department, National University Hospital of Iceland, Reykjavik, Iceland; School of Science and Engineering, Reykjavik University, Reykjavik, Iceland.
| | | | - Sara Marcu
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland; University of Padua, Padua, Italy
| | | | - Eric M Wassermann
- National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States
| | - Eysteinn Ívarsson
- Neurology Department, National University Hospital of Iceland, Reykjavik, Iceland
| | - Viktor D Jónasson
- Department of Psychology, Reykjavik University, Reykjavik, Iceland; Department of Psychiatry, National University Hospital of Iceland, Reykjavik, Iceland
| | - Romain Aubonnet
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
| | - Aron D Jónasson
- Neurology Department, National University Hospital of Iceland, Reykjavik, Iceland
| | | | - Magnús Haraldsson
- Department of Psychiatry, National University Hospital of Iceland, Reykjavik, Iceland
| | - Paolo Gargiulo
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
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11
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Liu X, Han F, Fu R, Wang Q, Luan G. Epileptogenic Zone Location of Temporal Lobe Epilepsy by Cross-Frequency Coupling Analysis. Front Neurol 2021; 12:764821. [PMID: 34867749 PMCID: PMC8636749 DOI: 10.3389/fneur.2021.764821] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 10/12/2021] [Indexed: 12/02/2022] Open
Abstract
Epilepsy is a chronic brain disease with dysfunctional brain networks, and electroencephalography (EEG) is an important tool for epileptogenic zone (EZ) identification, with rich information about frequencies. Different frequency oscillations have different contributions to brain function, and cross-frequency coupling (CFC) has been found to exist within brain regions. Cross-channel and inter-channel analysis should be both focused because they help to analyze how epilepsy networks change and also localize the EZ. In this paper, we analyzed long-term stereo-electroencephalography (SEEG) data from 17 patients with temporal lobe epilepsy. Single-channel and cross-channel CFC features were combined to establish functional brain networks, and the network characteristics under different periods and the localization of EZ were analyzed. It was observed that theta–gamma phase amplitude coupling (PAC) within the electrodes in the seizure region increased during the ictal (p < 0.05). Theta–gamma and delta–gamma PAC of cross-channel were enhanced in the early and mid-late ictal, respectively. It was also found that there was a strong cross-frequency coupling state between channels of EZ in the functional network during the ictal, along with a more regular network than interictal. The accuracy rate of EZ localization was 82.4%. Overall, the combination of single-channel and multi-channel cross-band coupling analysis can help identify seizures and localize EZ for temporal lobe epilepsy. Rhythmic coupling reveals a relationship between the functional network and the seizure status of epilepsy.
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Affiliation(s)
- Xiaotong Liu
- Department of Dynamics and Control, Beihang University, Beijing, China
| | - Fang Han
- College of Information Science and Technology, Donghua University, Shanghai, China
| | - Rui Fu
- Department of Dynamics and Control, Beihang University, Beijing, China
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing, China
| | - Guoming Luan
- Beijing Key Laboratory of Epilepsy, Sanbo Brain Hospital, Capital Medical University, Beijing, China
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12
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Wang H, Liu X, Li J, Xu T, Bezerianos A, Sun Y, Wan F. Driving Fatigue Recognition With Functional Connectivity Based on Phase Synchronization. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2020.2985539] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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13
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Li J, Li S, Pan J, Wang F. Cross-Subject EEG Emotion Recognition With Self-Organized Graph Neural Network. Front Neurosci 2021; 15:611653. [PMID: 34177441 PMCID: PMC8221183 DOI: 10.3389/fnins.2021.611653] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 05/17/2021] [Indexed: 11/13/2022] Open
Abstract
As a physiological process and high-level cognitive behavior, emotion is an important subarea in neuroscience research. Emotion recognition across subjects based on brain signals has attracted much attention. Due to individual differences across subjects and the low signal-to-noise ratio of EEG signals, the performance of conventional emotion recognition methods is relatively poor. In this paper, we propose a self-organized graph neural network (SOGNN) for cross-subject EEG emotion recognition. Unlike the previous studies based on pre-constructed and fixed graph structure, the graph structure of SOGNN are dynamically constructed by self-organized module for each signal. To evaluate the cross-subject EEG emotion recognition performance of our model, leave-one-subject-out experiments are conducted on two public emotion recognition datasets, SEED and SEED-IV. The SOGNN is able to achieve state-of-the-art emotion recognition performance. Moreover, we investigated the performance variances of the models with different graph construction techniques or features in different frequency bands. Furthermore, we visualized the graph structure learned by the proposed model and found that part of the structure coincided with previous neuroscience research. The experiments demonstrated the effectiveness of the proposed model for cross-subject EEG emotion recognition.
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Affiliation(s)
- Jingcong Li
- School of Software, South China Normal University, Guangzhou, China.,Pazhou Lab, Guangzhou, China
| | - Shuqi Li
- School of Computer Science, South China Normal University, Guangzhou, China
| | - Jiahui Pan
- School of Software, South China Normal University, Guangzhou, China.,Pazhou Lab, Guangzhou, China
| | - Fei Wang
- School of Software, South China Normal University, Guangzhou, China.,Pazhou Lab, Guangzhou, China
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14
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Zhang M, Li B, Lv X, Liu S, Liu Y, Tang R, Lang Y, Huang Q, He J. Low-Intensity Focused Ultrasound-Mediated Attenuation of Acute Seizure Activity Based on EEG Brain Functional Connectivity. Brain Sci 2021; 11:brainsci11060711. [PMID: 34071964 PMCID: PMC8228165 DOI: 10.3390/brainsci11060711] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 05/18/2021] [Accepted: 05/24/2021] [Indexed: 11/30/2022] Open
Abstract
(1) Background: Ultrasound has been used for noninvasive stimulation and is a promising technique for treating neurological diseases. Epilepsy is a common neurological disorder, that is attributed to uncontrollable abnormal neuronal hyperexcitability. Abnormal synchronized activities can be observed across multiple brain regions during a seizure. (2) Methods: we used low-intensity focused ultrasound (LIFU) to sonicate the brains of epileptic rats, analyzed the EEG functional brain network to explore the effect of LIFU on the epileptic brain network, and continued to explore the mechanism of ultrasound neuromodulation. LIFU was used in the hippocampus of epileptic rats in which a seizure was induced by kainic acid. (3) Results: By comparing the brain network characteristics before and after sonication, we found that LIFU significantly impacted the functional brain network, especially in the low-frequency band. The brain network connection strength across multiple brain regions significantly decreased after sonication compared to the connection strength in the control group. The brain network indicators (the path length, clustering coefficient, small-worldness, local efficiency and global efficiency) all changed significantly in the low-frequency. (4) Conclusions: These results revealed that LIFU could reduce the network connections of epilepsy circuits and change the structure of the brain network at the whole-brain level.
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Affiliation(s)
- Minjian Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; (M.Z.); (B.L.); (S.L.); (Y.L.); (Q.H.); (J.H.)
| | - Bo Li
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; (M.Z.); (B.L.); (S.L.); (Y.L.); (Q.H.); (J.H.)
| | - Xiaodong Lv
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China; (X.L.); (Y.L.)
| | - Sican Liu
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; (M.Z.); (B.L.); (S.L.); (Y.L.); (Q.H.); (J.H.)
| | - Yafei Liu
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; (M.Z.); (B.L.); (S.L.); (Y.L.); (Q.H.); (J.H.)
| | - Rongyu Tang
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China; (X.L.); (Y.L.)
- Correspondence:
| | - Yiran Lang
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China; (X.L.); (Y.L.)
| | - Qiang Huang
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; (M.Z.); (B.L.); (S.L.); (Y.L.); (Q.H.); (J.H.)
| | - Jiping He
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; (M.Z.); (B.L.); (S.L.); (Y.L.); (Q.H.); (J.H.)
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15
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Puxeddu MG, Petti M, Astolfi L. A Comprehensive Analysis of Multilayer Community Detection Algorithms for Application to EEG-Based Brain Networks. Front Syst Neurosci 2021; 15:624183. [PMID: 33732115 PMCID: PMC7956967 DOI: 10.3389/fnsys.2021.624183] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 01/21/2021] [Indexed: 12/21/2022] Open
Abstract
Modular organization is an emergent property of brain networks, responsible for shaping communication processes and underpinning brain functioning. Moreover, brain networks are intrinsically multilayer since their attributes can vary across time, subjects, frequency, or other domains. Identifying the modular structure in multilayer brain networks represents a gateway toward a deeper understanding of neural processes underlying cognition. Electroencephalographic (EEG) signals, thanks to their high temporal resolution, can give rise to multilayer networks able to follow the dynamics of brain activity. Despite this potential, the community organization has not yet been thoroughly investigated in brain networks estimated from EEG. Furthermore, at the state of the art, there is still no agreement about which algorithm is the most suitable to detect communities in multilayer brain networks, and a way to test and compare them all under a variety of conditions is lacking. In this work, we perform a comprehensive analysis of three algorithms at the state of the art for multilayer community detection (namely, genLouvain, DynMoga, and FacetNet) as compared with an approach based on the application of a single-layer clustering algorithm to each slice of the multilayer network. We test their ability to identify both steady and dynamic modular structures. We statistically evaluate their performances by means of ad hoc benchmark graphs characterized by properties covering a broad range of conditions in terms of graph density, number of clusters, noise level, and number of layers. The results of this simulation study aim to provide guidelines about the choice of the more appropriate algorithm according to the different properties of the brain network under examination. Finally, as a proof of concept, we show an application of the algorithms to real functional brain networks derived from EEG signals collected at rest with closed and open eyes. The test on real data provided results in agreement with the conclusions of the simulation study and confirmed the feasibility of multilayer analysis of EEG-based brain networks in both steady and dynamic conditions.
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Affiliation(s)
- Maria Grazia Puxeddu
- Department of Computer, Control and Management Engineering "Antonio Ruberti", University of Rome Sapienza, Rome, Italy.,IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Manuela Petti
- Department of Computer, Control and Management Engineering "Antonio Ruberti", University of Rome Sapienza, Rome, Italy.,IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Laura Astolfi
- Department of Computer, Control and Management Engineering "Antonio Ruberti", University of Rome Sapienza, Rome, Italy.,IRCCS Fondazione Santa Lucia, Rome, Italy
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16
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Lin E, Freydefont L, Schmid PC. Psychological power alters cognitive efficiency. Psychophysiology 2021; 58:e13773. [PMID: 33496973 DOI: 10.1111/psyp.13773] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 12/10/2020] [Accepted: 01/04/2021] [Indexed: 12/01/2022]
Abstract
Power is known to promote cognitive processing in a goal-directed way. However, it is unknown whether powerful individuals invest more resources when pursuing their goals or whether they invest their resources more efficiently. We examined how experiencing high versus low power affects the efficient investment of cognitive resources using electroencephalography (EEG). Specifically, event-related desynchronization (ERD) in the upper alpha band (10.5-12.75 Hz) was used to quantify the use of cognitive resources during task completion. Results showed that high-power participants used fewer neural resources compared to low-power participants across the whole brain but task performance did not differ between groups. These findings demonstrate that, instead of investing more resources, high-power participants performed the task with greater cognitive efficiency compared to low-power participants. Performing tasks efficiently could help powerholders deal with their demanding jobs and responsibilities.
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Affiliation(s)
- Enru Lin
- Department of Management, Technology, and Economics, ETH Zürich, Zürich, Switzerland
| | - Laure Freydefont
- Department of Management, Technology, and Economics, ETH Zürich, Zürich, Switzerland
| | - Petra C Schmid
- Department of Management, Technology, and Economics, ETH Zürich, Zürich, Switzerland
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17
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Chang W, Wang H, Yan G, Lu Z, Liu C, Hua C. EEG based functional connectivity analysis of human pain empathy towards humans and robots. Neuropsychologia 2020; 151:107695. [PMID: 33245968 DOI: 10.1016/j.neuropsychologia.2020.107695] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 11/12/2020] [Accepted: 11/21/2020] [Indexed: 11/30/2022]
Abstract
Humans can show emotional reactions toward humanoid robots, such as empathy. Previous neuroimaging studies have indicated that neural responses of empathy for others' pain are modulated by an early automatic emotional sharing and a late controlled cognitive evaluation process. Recent studies about pain empathy for robots found humans present similar empathy process towards humanoid robots under painful stimuli as well as to humans. However, the whole-brain functional connectivity and the spatial dynamics of neural activities underlying empathic processes are still unknown. In the present study, the functional connectivity was investigated for ERPs recorded from 18 healthy adults who were presented with pictures of human hand and robot hand under painful and non-painful situations. Functional brain networks for both early and late empathy responses were constructed and a new parameter, empathy index (EI), was proposed to represent the empathy ability of humans quantitatively. We found that the mutual dependences between early ERP components was significantly decreased, but for the late components, there were no significant changes. The mutual dependences for human hand stimuli were larger than to robot hand stimuli for early components, but not for late components. The connectivity weights for early components were larger than late components. EI value shows significant difference between painful and non-painful stimuli, indicating it is a good indicator to represent the empathy of humans. This study enriches our understanding of the neurological mechanisms implicated in human empathy, and provides evidence of functional connectivity for both early and late responses of pain empathy towards humans and robots.
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Affiliation(s)
- Wenwen Chang
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, 730070, Lanzhou, China.
| | - Hong Wang
- School of Mechanical Engineering and Automation, Northeastern University, 110819, Shenyang, China.
| | - Guanghui Yan
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, 730070, Lanzhou, China.
| | - Zhiguo Lu
- School of Mechanical Engineering and Automation, Northeastern University, 110819, Shenyang, China.
| | - Chong Liu
- School of Mechanical Engineering and Automation, Northeastern University, 110819, Shenyang, China.
| | - Chengcheng Hua
- School of Automation, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
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18
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Automated detection of dynamical change in EEG signals based on a new rhythm measure. Artif Intell Med 2020; 107:101920. [DOI: 10.1016/j.artmed.2020.101920] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 05/25/2020] [Accepted: 06/29/2020] [Indexed: 12/27/2022]
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19
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Hu F, Wu Q, Li Y, Xu W, Zhao L, Sun Q. Love at First Glance but Not After Deep Consideration: The Impact of Sexually Appealing Advertising on Product Preferences. Front Neurosci 2020; 14:465. [PMID: 32547359 PMCID: PMC7273180 DOI: 10.3389/fnins.2020.00465] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Accepted: 04/15/2020] [Indexed: 11/23/2022] Open
Abstract
In advertising studies, the impact of sexually appealing advertisements (hereafter “ads”) on consumers’ product preferences is highly controversial. This paper explores (1) how such ads affect consumers’ product preferences at the gazing stage (initial stage of exposure to the ad) and evaluation stage (final product preference), and (2) which type of product (utilitarian vs. hedonic) is more suited to such ads. We used an electroencephalogram to record participants’ product preferences at the gazing stage and self-reported product preferences at the evaluation stage. The results indicated that participants preferred ads with high sex appeal at the gazing stage and ads with low sex appeal at the evaluation stage. Further, compared to utilitarian products, hedonic products were more suited to sexually appealing ads. The findings suggest that the effect of such ads on consumers’ product preferences varies depending on their cognitive stage and the type of product advertised.
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Affiliation(s)
- Fengpei Hu
- School of Management, Zhejiang University of Technology, Hangzhou, China
| | - Qingyuan Wu
- School of Management, Zhejiang University of Technology, Hangzhou, China
| | - Yiwei Li
- School of Management, Zhejiang University of Technology, Hangzhou, China
| | - Weijie Xu
- School of Management, Zhejiang University of Technology, Hangzhou, China
| | - Lei Zhao
- School of Management, Zhejiang University of Technology, Hangzhou, China
| | - Qingzhou Sun
- School of Management, Zhejiang University of Technology, Hangzhou, China
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20
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Li G, Jiang Y, Jiao W, Xu W, Huang S, Gao Z, Zhang J, Wang C. The Maximum Eigenvalue of the Brain Functional Network Adjacency Matrix: Meaning and Application in Mental Fatigue Evaluation. Brain Sci 2020; 10:brainsci10020092. [PMID: 32050462 PMCID: PMC7071607 DOI: 10.3390/brainsci10020092] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 02/06/2020] [Accepted: 02/08/2020] [Indexed: 02/04/2023] Open
Abstract
The maximum eigenvalue of the adjacency matrix (AM) has been supposed to contain rich information about the corresponding network. An experimental study focused on revealing the meaning and application of the maximum eigenvalue is missing. To this end, AM was constructed using mutual information (MI) to determine the functional connectivity with electroencephalogram (EEG) data recorded with a mental fatigue model, and then was converted into both binary and weighted brain functional network (BFN) and corresponding random networks (RNs). Both maximum eigenvalue and corresponding network characters in BFNs and RNs were considered to explore the changes during the formation of mental fatigue. The results indicated that large maximum eigenvalue means more edges in the corresponding network, along with a high degree and a short characteristic path length both in weighted and binary BFNs. Interestingly, the maximum eigenvalue of AM was always a little larger than that of the corresponding random matrix (RM), and had an obvious linearity with the sum of the AM elements, indicating that the maximum eigenvalue can be able to distinguish the network structures which have the same mean degree. What is more, the maximum eigenvalue, which increased with the deepening of mental fatigue, can become a good indicator for mental fatigue estimation.
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Affiliation(s)
- Gang Li
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (G.L.); (W.J.); (W.X.); (S.H.); (Z.G.); (C.W.)
| | - Yonghua Jiang
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (G.L.); (W.J.); (W.X.); (S.H.); (Z.G.); (C.W.)
- Correspondence: (Y.J.); (J.Z.)
| | - Weidong Jiao
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (G.L.); (W.J.); (W.X.); (S.H.); (Z.G.); (C.W.)
| | - Wanxiu Xu
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (G.L.); (W.J.); (W.X.); (S.H.); (Z.G.); (C.W.)
| | - Shan Huang
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (G.L.); (W.J.); (W.X.); (S.H.); (Z.G.); (C.W.)
| | - Zhao Gao
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (G.L.); (W.J.); (W.X.); (S.H.); (Z.G.); (C.W.)
| | - Jianhua Zhang
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education of China, School of Mechanical Engineering, Shandong University, Jinan 250061, China
- Correspondence: (Y.J.); (J.Z.)
| | - Chengwu Wang
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (G.L.); (W.J.); (W.X.); (S.H.); (Z.G.); (C.W.)
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21
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Diykh M, Li Y, Abdulla S. EEG sleep stages identification based on weighted undirected complex networks. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 184:105116. [PMID: 31629158 DOI: 10.1016/j.cmpb.2019.105116] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 09/14/2019] [Accepted: 10/02/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Sleep scoring is important in sleep research because any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings can cause serious problems such as incorrect diagnosis, medication errors, and misinterpretations of patient's EEG recordings. The aim of this research is to develop a new automatic method for EEG sleep stages classification based on a statistical model and weighted brain networks. METHODS Each EEG segment is partitioned into a number of blocks using a sliding window technique. A set of statistical features are extracted from each block. As a result, a vector of features is obtained to represent each EEG segment. Then, the vector of features is mapped into a weighted undirected network. Different structural and spectral attributes of the networks are extracted and forwarded to a least square support vector machine (LS-SVM) classifier. At the same time the network's attributes are also thoroughly investigated. It is found that the network's characteristics vary with their sleep stages. Each sleep stage is best represented using the key features of their networks. RESULTS In this paper, the proposed method is evaluated using two datasets acquired from different channels of EEG (Pz-Oz and C3-A2) according to the R&K and the AASM without pre-processing the original EEG data. The obtained results by the LS-SVM are compared with those by Naïve, k-nearest and a multi-class-SVM. The proposed method is also compared with other benchmark sleep stages classification methods. The comparison results demonstrate that the proposed method has an advantage in scoring sleep stages based on single channel EEG signals. CONCLUSIONS An average accuracy of 96.74% is obtained with the C3-A2 channel according to the AASM standard, and 96% with the Pz-Oz channel based on the R&K standard.
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Affiliation(s)
- Mohammed Diykh
- School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Australia; College of Education for Pure Science, University of Thi-Qar, Iraq.
| | - Yan Li
- School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Australia.
| | - Shahab Abdulla
- Open Access College, University of Southern Queensland, Australia.
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22
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Effects of Mental Fatigue on Small-World Brain Functional Network Organization. Neural Plast 2019; 2019:1716074. [PMID: 31885535 PMCID: PMC6918937 DOI: 10.1155/2019/1716074] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 10/22/2019] [Accepted: 11/09/2019] [Indexed: 12/31/2022] Open
Abstract
Brain functional network has been widely applied to investigate brain function changes among different conditions and proved to be a small-world-like network. But seldom researches explore the effects of mental fatigue on the small-world brain functional network organization. In the present study, 20 healthy individuals were included to do a consecutive mental arithmetic task to induce mental fatigue, and scalp electroencephalogram (EEG) signals were recorded before and after the task. Correlations between all pairs of EEG channels were determined by mutual information (MI). The resulting adjacency matrices were converted into brain functional networks by applying a threshold, and then, the clustering coefficient (C), characteristic path length (L), and corresponding small-world feature were calculated. Through performing analysis of variance (ANOVA) on the mean MI for every EEG rhythm, only the data of α1 rhythm during the task state were emerged for the further explorations of mental fatigue. For a wide range of thresholds, C increased and L and small-world feature decreased with the deepening mental fatigue. The pattern of the small-world characteristic still existed when computed with a constant degree. Our present findings indicated that more functional connectivities were activated at the mental fatigue stage for efficient information transmission and processing, and mental fatigue can be characterized by a reduced small-world network characteristic. Our results provide a new perspective to understand the neural mechanisms of mental fatigue based on complex network theories.
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23
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Machida K, Murias M, Johnson KA. Electrophysiological Correlates of Response Time Variability During a Sustained Attention Task. Front Hum Neurosci 2019; 13:363. [PMID: 31680915 PMCID: PMC6803451 DOI: 10.3389/fnhum.2019.00363] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 09/27/2019] [Indexed: 11/21/2022] Open
Abstract
Individuals with Attention Deficit Hyperactivity Disorder (ADHD) tend to perform cognitive tasks with greater Response Time Variability (RTV). Greater RTV in ADHD may be due to inefficient functional connectivity of the brain during information processing. This study aimed to investigate the relationship between brain connectivity, RTV, and levels of ADHD symptoms. Twenty-eight children aged 9–12 years and 49 adolescents aged 15–18 years performed the Sustained Attention to Response Task (SART) while EEG was recorded. The participants’ levels of ADHD symptoms were measured using self- and parent-rated questionnaires. The ex-Gaussian analysis and The Fast Fourier Transform were used to measure multiple aspects of RTV. Functional connectivity between 64 electrodes was computed during task performance, and global efficiency and modularity were calculated, reflecting integration and segregation of the brain, respectively. There was a positive association between multiple RTV measures and the level of ADHD symptoms, where participants with higher levels of ADHD symptoms showed greater RTV, except for sigma from the ex-Gaussian analysis. More efficient brain network activity, measured by global efficiency, was associated with reduced RTV. Children showed greater RTV and less efficient brain network activity compared with the adolescents. These findings support the view that stable responses are achieved with more integrated (and efficient) brain connectivity.
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Affiliation(s)
- Keitaro Machida
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Michael Murias
- Institute for Innovations in Developmental Sciences, Northwestern University, Chicago, IL, United States
| | - Katherine A Johnson
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, VIC, Australia
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24
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Small-world properties of the whole-brain functional networks in patients with obstructive sleep apnea‐hypopnea syndrome. Sleep Med 2019; 62:53-58. [DOI: 10.1016/j.sleep.2018.08.037] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Revised: 08/03/2018] [Accepted: 08/27/2018] [Indexed: 11/21/2022]
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25
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Shah P, Ashourvan A, Mikhail F, Pines A, Kini L, Oechsel K, Das SR, Stein JM, Shinohara RT, Bassett DS, Litt B, Davis KA. Characterizing the role of the structural connectome in seizure dynamics. Brain 2019; 142:1955-1972. [PMID: 31099821 PMCID: PMC6598625 DOI: 10.1093/brain/awz125] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 02/11/2019] [Accepted: 03/07/2019] [Indexed: 12/23/2022] Open
Abstract
How does the human brain's structural scaffold give rise to its intricate functional dynamics? This is a central question in translational neuroscience that is particularly relevant to epilepsy, a disorder affecting over 50 million subjects worldwide. Treatment for medication-resistant focal epilepsy is often structural-through surgery or laser ablation-but structural targets, particularly in patients without clear lesions, are largely based on functional mapping via intracranial EEG. Unfortunately, the relationship between structural and functional connectivity in the seizing brain is poorly understood. In this study, we quantify structure-function coupling, specifically between white matter connections and intracranial EEG, across pre-ictal and ictal periods in 45 seizures from nine patients with unilateral drug-resistant focal epilepsy. We use high angular resolution diffusion imaging (HARDI) tractography to construct structural connectivity networks and correlate these networks with time-varying broadband and frequency-specific functional networks derived from coregistered intracranial EEG. Across all frequency bands, we find significant increases in structure-function coupling from pre-ictal to ictal periods. We demonstrate that short-range structural connections are primarily responsible for this increase in coupling. Finally, we find that spatiotemporal patterns of structure-function coupling are highly stereotyped for each patient. These results suggest that seizures harness the underlying structural connectome as they propagate. Mapping the relationship between structural and functional connectivity in epilepsy may inform new therapies to halt seizure spread, and pave the way for targeted patient-specific interventions.
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Affiliation(s)
- Preya Shah
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Arian Ashourvan
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Fadi Mikhail
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Adam Pines
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lohith Kini
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Kelly Oechsel
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sandhitsu R Das
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joel M Stein
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Brian Litt
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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26
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Jawed S, Amin HU, Malik AS, Faye I. Classification of Visual and Non-visual Learners Using Electroencephalographic Alpha and Gamma Activities. Front Behav Neurosci 2019; 13:86. [PMID: 31133829 PMCID: PMC6513874 DOI: 10.3389/fnbeh.2019.00086] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 04/11/2019] [Indexed: 11/13/2022] Open
Abstract
This study analyzes the learning styles of subjects based on their electroencephalo-graphy (EEG) signals. The goal is to identify how the EEG features of a visual learner differ from those of a non-visual learner. The idea is to measure the students' EEGs during the resting states (eyes open and eyes closed conditions) and when performing learning tasks. For this purpose, 34 healthy subjects are recruited. The subjects have no background knowledge of the animated learning content. The subjects are shown the animated learning content in a video format. The experiment consists of two sessions and each session comprises two parts: (1) Learning task: the subjects are shown the animated learning content for an 8-10 min duration. (2) Memory retrieval task The EEG signals are measured during the leaning task and memory retrieval task in two sessions. The retention time for the first session was 30 min, and 2 months for the second session. The analysis is performed for the EEG measured during the memory retrieval tasks. The study characterizes and differentiates the visual learners from the non-visual learners considering the extracted EEG features, such as the power spectral density (PSD), power spectral entropy (PSE), and discrete wavelet transform (DWT). The PSD and DWT features are analyzed. The EEG PSD and DWT features are computed for the recorded EEG in the alpha and gamma frequency bands over 128 scalp sites. The alpha and gamma frequency band for frontal, occipital, and parietal regions are analyzed as these regions are activated during learning. The extracted PSD and DWT features are then reduced to 8 and 15 optimum features using principal component analysis (PCA). The optimum features are then used as an input to the k-nearest neighbor (k-NN) classifier using the Mahalanobis distance metric, with 10-fold cross validation and support vector machine (SVM) classifier using linear kernel, with 10-fold cross validation. The classification results showed 97% and 94% accuracies rate for the first session and 96% and 93% accuracies for the second session in the alpha and gamma bands for the visual learners and non-visual learners, respectively, for k-NN classifier for PSD features and 68% and 100% accuracies rate for first session and 100% accuracies rate for second session for DWT features using k-NN classifier for the second session in the alpha and gamma band. For PSD features 97% and 96% accuracies rate for the first session, 100% and 95% accuracies rate for second session using SVM classifier and 79% and 82% accuracy for first session and 56% and 74% accuracy for second session for DWT features using SVM classifier. The results showed that the PSDs in the alpha and gamma bands represent distinct and stable EEG signatures for visual learners and non-visual learners during the retrieval of the learned contents.
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Affiliation(s)
- Soyiba Jawed
- Centre of Intelligent Signal and Imaging Research, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia.,Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
| | - Hafeez Ullah Amin
- Centre of Intelligent Signal and Imaging Research, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia.,Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
| | | | - Ibrahima Faye
- Centre of Intelligent Signal and Imaging Research, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia.,Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
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Wang M, Yuan Z, Niu H. Reliability evaluation on weighted graph metrics of fNIRS brain networks. Quant Imaging Med Surg 2019; 9:832-841. [PMID: 31281779 DOI: 10.21037/qims.2019.05.08] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background Resting-state fNIRS (R-fNIRS) imaging data has proven to be a valuable technique to quantitatively characterize functional architectures of human brain network. However, whether the brain network metrics derived using weighted brain network model is test-retest (TRT) reliable remains largely unknown. Methods Here, we firstly constructed weighted brain networks on a group of 18 participants, and then applied graph-theory approach to quantify topological parameters of each weighted network. The intraclass correlation coefficient (ICC) was further applied to quantify the TRT reliability of network metrics. Results We found that the reliability of the weighted network metrics is threshold-sensitive, and most of these network metrics showed fair to excellent reliability. Specifically, the global network metrics, e.g., clustering coefficient, path length, local efficiency and global efficiency were of excellent level reliability (ICC >0.75) on both HbO and HbR signals. The nodal network metrics, e.g., degree and efficiency, generally also showed excellent level reliability on both HbO and HbR signals, and the reliability of these two metrics was better than that of nodal betweenness. Conclusions Overall, these findings demonstrated that most weighted network metrics derived from fNIRS are TRT reliable and can be used for brain network research.
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Affiliation(s)
- Mengjing Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Zhen Yuan
- Faculty of Health Sciences, University of Macau, Macao 999078, China
| | - Haijing Niu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
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28
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Li H, Yao R, Xia X, Yin G, Deng H, Yang P. Adjustment of Synchronization Stability of Dynamic Brain-Networks Based on Feature Fusion. Front Hum Neurosci 2019; 13:98. [PMID: 31001095 PMCID: PMC6455007 DOI: 10.3389/fnhum.2019.00098] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Accepted: 03/04/2019] [Indexed: 11/13/2022] Open
Abstract
When the brain is active, the neural activities of different regions are integrated on various spatial and temporal scales; this is termed the synchronization phenomenon in neurobiological theory. This synchronicity is also the main underlying mechanism for information integration and processing in the brain. Clinical medicine has found that some of the neurological diseases that are difficult to cure have deficiencies or abnormalities in the whole or local integration processes of the brain. By studying the synchronization capabilities of the brain-network, we can intensively describe and characterize both the state of the interactions between brain regions and their differences between people with a mental illness and a set of controls by measuring the rapid changes in brain activity in patients with psychiatric disorders and the strength and integrity of their entire brain network. This is significant for the study of mental illness. Because static brain network connection methods are unable to assess the dynamic interactions within the brain, we introduced the concepts of dynamics and variability in a constructed EEG brain functional network based on dynamic connections, and used it to analyze the variability in the time characteristics of the EEG functional network. We used the spectral features of the brain network to extract its synchronization features and used the synchronization features to describe the process of change and the differences in the brain network's synchronization ability between a group of patients and healthy controls during a working memory task. We propose a method based on the fusion of traditional features and spectral features to achieve an adjustment of the patient's brain network synchronization ability, so that its synchronization ability becomes consistent with that of healthy controls, theoretically achieving the purpose of the treatment of the diseases. Studying the stability of brain network synchronization can provide new insights into the pathogenic mechanism and cure of mental diseases and has a wide range of potential applications.
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Affiliation(s)
- Haifang Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Rong Yao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Xiaoluan Xia
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Guimei Yin
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Hongxia Deng
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Pengfei Yang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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29
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Graph theoretical modeling of baby brain networks. Neuroimage 2019; 185:711-727. [DOI: 10.1016/j.neuroimage.2018.06.038] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Revised: 05/22/2018] [Accepted: 06/11/2018] [Indexed: 11/20/2022] Open
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30
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Zhao L, Shi Z, Zheng Q, Chu H, Xu L, Hu F. Use of Electroencephalography for the Study of Gain-Loss Asymmetry in Intertemporal Decision-Making. Front Neurosci 2018; 12:984. [PMID: 30622455 PMCID: PMC6308187 DOI: 10.3389/fnins.2018.00984] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Accepted: 12/10/2018] [Indexed: 11/13/2022] Open
Abstract
Intertemporal decision-making refers to the process whereby an individual evaluates and selects among competing alternatives based on the cost and benefit over time. While most previous studies on temporal discounting focused their attention on the gain context, only a few explored the loss context. In the present study, both the event-related potentials (ERPs) and the graph theory analysis were employed to investigate the differences in intertemporal decision-making between the gain and loss frameworks. Our results suggested that participants preferred the short latency/small amount (SS) alternatives and exhibited a smaller discount rate in a loss context compared to a gain framework. Furthermore, our ERP data indicated that the P200 component could constitute a preliminary assessment of the decision-making, related to gain and loss. In contrast, the N2 component was associated with negative emotions and showed significantly bigger amplitudes in the loss context, when compared to the gain framework. Further analyses of brain networks suggested the loss decision-making brain network to have a larger small-worldness index given individuals' loss aversion. Taken together, intertemploral decision-making in a loss context was accompanied by a greater brain response due to the negative emotions linked to loss aversion.
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Affiliation(s)
- Lei Zhao
- College of Economics and Management, Zhejiang University of Technology, Hangzhou, China
| | - Zuoli Shi
- College of Economics and Management, Zhejiang University of Technology, Hangzhou, China
| | - Qian Zheng
- College of Economics and Management, Zhejiang University of Technology, Hangzhou, China
| | - Huadong Chu
- Zhijiang College of Zhejiang University of Technology, Shaoxin, China
| | - Lin Xu
- School of Humanities and Law, Hangzhou Dianzi University, Hangzhou, China
| | - Fengpei Hu
- College of Economics and Management, Zhejiang University of Technology, Hangzhou, China
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31
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Altered brain functional network in children with type 1 Gaucher disease: a longitudinal graph theory-based study. Neuroradiology 2018; 61:63-70. [DOI: 10.1007/s00234-018-2104-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2018] [Accepted: 09/18/2018] [Indexed: 01/02/2023]
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32
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Kaminski M, Blinowska KJ. Is Graph Theoretical Analysis a Useful Tool for Quantification of Connectivity Obtained by Means of EEG/MEG Techniques? Front Neural Circuits 2018; 12:76. [PMID: 30319364 PMCID: PMC6168619 DOI: 10.3389/fncir.2018.00076] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 09/04/2018] [Indexed: 11/13/2022] Open
Affiliation(s)
- Maciej Kaminski
- Department of Biomedical Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
| | - Katarzyna J Blinowska
- Department of Biomedical Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland.,Institute of Biocybernetics and Biomedical Engineering of Polish Academy of Sciences, Warsaw, Poland
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33
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34
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Tillem S, van Dongen J, Brazil IA, Baskin-Sommers A. Psychopathic traits are differentially associated with efficiency of neural communication. Psychophysiology 2018; 55:e13194. [DOI: 10.1111/psyp.13194] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Revised: 03/17/2018] [Accepted: 04/04/2018] [Indexed: 11/30/2022]
Affiliation(s)
- Scott Tillem
- Yale University, Department of Psychology; New Haven Connecticut USA
| | - Josanne van Dongen
- Erasmus University Rotterdam, Department of Psychology, Education and Child Studies; Rotterdam The Netherlands
| | - Inti A. Brazil
- Radboud University, Donders Institute for Brain, Cognition and Behaviour; Nijmegen The Netherlands
- Forensic Psychiatric Centre Pompestichting; Nijmegen The Netherlands
- Collaborative Antwerp Psychiatric Research Institute (CAPRI), University of Antwerp; Antwerp Belgium
- Centre for Advances in Behavioural Science, Coventry University; Coventry United Kingdom
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35
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Time estimation and beta segregation: An EEG study and graph theoretical approach. PLoS One 2018; 13:e0195380. [PMID: 29624619 PMCID: PMC5889177 DOI: 10.1371/journal.pone.0195380] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Accepted: 03/21/2018] [Indexed: 11/28/2022] Open
Abstract
Elucidation of the neural correlates of time perception constitutes an important research topic in cognitive neuroscience. The focus to date has been on durations in the millisecond to seconds range, but here we used electroencephalography (EEG) to examine brain functional connectivity during much longer durations (i.e., 15 min). For this purpose, we conducted an initial exploratory experiment followed by a confirmatory experiment. Our results showed that those participants who overestimated time exhibited lower activity of beta (18–30 Hz) at several electrode sites. Furthermore, graph theoretical analysis indicated significant differences in the beta range (15–30 Hz) between those that overestimated and underestimated time. Participants who underestimated time showed higher clustering coefficient compared to those that overestimated time. We discuss our results in terms of two aspects. FFT results, as a linear approach, are discussed within localized/dedicated models (i.e., scalar timing model). Second, non-localized properties of psychological interval timing (as emphasized by intrinsic models) are addressed and discussed based on results derived from graph theory. Results suggested that although beta amplitude in central regions (related to activity of BG-thalamocortical pathway as a dedicated module) is important in relation to timing mechanisms, the properties of functional activity of brain networks; such as the segregation of beta network, are also crucial for time perception. These results may suggest subjective time may be created by vector units instead of scalar ticks.
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36
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Gao C, Sun J, Yang X, Gong H. Gender differences in brain networks during verbal Sternberg tasks: A simultaneous near-infrared spectroscopy and electro-encephalography study. JOURNAL OF BIOPHOTONICS 2018; 11:e201700120. [PMID: 28921863 DOI: 10.1002/jbio.201700120] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2017] [Revised: 09/04/2017] [Accepted: 09/08/2017] [Indexed: 06/07/2023]
Abstract
Gender differences in psychological processes have been of great interest in a variety of fields including verbal fluency, emotion processing and working memory. Previous studies suggested that women outperform men in verbal working memory (VWM). However, the inherent mechanisms are still unclear. To obtain a deeper insight into the gender differences in brain networks in VWM, this study used near-infrared spectroscopy (NIRS) and electro-encephalography (EEG) simultaneously to investigate gender-related brain networks during verbal Sternberg tasks. NIRS results confirmed that women surpass men in VWM from the perspective of both brain activation and connectivity. Results of EEG (effective connectivity and event-related spectral power) showed that men tend to use a more visuospatial strategy to encode memory. In addition, novel analysis methods of brain networks can provide useful information about the gender specifics of brain functions. Gender-related pseudo-color maps constructed from all channels of average HbO2 activity during low- and high-load tasks (from 0 to 6 seconds after beginning).
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Affiliation(s)
- Chenyang Gao
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, P. R. China
- Key Laboratory of Biomedical Photonics of Ministry of Education, Huazhong University of Science and technology, Wuhan, P. R. China
| | - Jinyan Sun
- Department of Biomedical Engineering, Guangdong Medical University, Dongguan, P. R. China
| | - Xiaoquan Yang
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, P. R. China
- Key Laboratory of Biomedical Photonics of Ministry of Education, Huazhong University of Science and technology, Wuhan, P. R. China
| | - Hui Gong
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, P. R. China
- Key Laboratory of Biomedical Photonics of Ministry of Education, Huazhong University of Science and technology, Wuhan, P. R. China
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37
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Mandke K, Meier J, Brookes MJ, O'Dea RD, Van Mieghem P, Stam CJ, Hillebrand A, Tewarie P. Comparing multilayer brain networks between groups: Introducing graph metrics and recommendations. Neuroimage 2018; 166:371-384. [DOI: 10.1016/j.neuroimage.2017.11.016] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Revised: 09/27/2017] [Accepted: 11/08/2017] [Indexed: 12/29/2022] Open
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38
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Duru AD, Assem M. Investigating neural efficiency of elite karate athletes during a mental arithmetic task using EEG. Cogn Neurodyn 2017; 12:95-102. [PMID: 29435090 DOI: 10.1007/s11571-017-9464-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Revised: 11/05/2017] [Accepted: 11/28/2017] [Indexed: 12/15/2022] Open
Abstract
Neural efficiency is proposed as one of the neural mechanisms underlying elite athletic performances. Previous sports studies examined neural efficiency using tasks that involve motor functions. In this study we investigate the extent of neural efficiency beyond motor tasks by using a mental subtraction task. A group of elite karate athletes are compared to a matched group of non-athletes. Electroencephalogram is used to measure cognitive dynamics during resting and increased mental workload periods. Mainly posterior alpha band power of the karate players was found to be higher than control subjects under both tasks. Moreover, event related synchronization/desynchronization has been computed to investigate the neural efficiency hypothesis among subjects. Finally, this study is the first study to examine neural efficiency related to a cognitive task, not a motor task, in elite karate players using ERD/ERS analysis. The results suggest that the effect of neural efficiency in the brain is global rather than local and thus might be contributing to the elite athletic performances. Also the results are in line with the neural efficiency hypothesis tested for motor performance studies.
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Affiliation(s)
- Adil Deniz Duru
- 1Neuroscience in Sports Laboratory, Faculty of Sport Science, Marmara University, Anadolu Hisarı Campus, Beykoz, Istanbul, Turkey
| | - Moataz Assem
- 2Neurosignal Analysis Laboratory, Institute of Biomedical Engineering, Bogazici University, Kandilli Kampusu, Cengelkoy, 34684 Istanbul, Turkey
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39
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Wig GS. Segregated Systems of Human Brain Networks. Trends Cogn Sci 2017; 21:981-996. [DOI: 10.1016/j.tics.2017.09.006] [Citation(s) in RCA: 128] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 09/06/2017] [Accepted: 09/11/2017] [Indexed: 12/17/2022]
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40
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Koirala N, Fleischer V, Glaser M, Zeuner KE, Deuschl G, Volkmann J, Muthuraman M, Groppa S. Frontal Lobe Connectivity and Network Community Characteristics are Associated with the Outcome of Subthalamic Nucleus Deep Brain Stimulation in Patients with Parkinson's Disease. Brain Topogr 2017; 31:311-321. [PMID: 28986718 DOI: 10.1007/s10548-017-0597-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Accepted: 09/26/2017] [Indexed: 12/23/2022]
Abstract
Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is nowadays an evidence-based state of the art therapy option for motor and non-motor symptoms in patients with Parkinson's disease (PD). However, the exact anatomical regions of the cerebral network that are targeted by STN-DBS have not been precisely described and no definitive pre-intervention predictors of the clinical response exist. In this study, we test the hypothesis that the clinical effectiveness of STN-DBS depends on the connectivity profile of the targeted brain networks. Therefore, we used diffusion-weighted imaging (DWI) and probabilistic tractography to reconstruct the anatomical networks and the graph theoretical framework to quantify the connectivity profile. DWI was obtained pre-operatively from 15 PD patients who underwent DBS (mean age = 67.87 ± 7.88, 11 males, H&Y score = 3.5 ± 0.8) using a 3T MRI scanner (Philips Achieva). The pre-operative connectivity properties of a network encompassing frontal, prefrontal cortex and cingulate gyrus were directly linked to the postoperative clinical outcome. Eccentricity as a topological-characteristic of the network defining how cerebral regions are embedded in relation to distant sites correlated inversely with the applied voltage at the active electrode for optimal clinical response. We found that network topology and pre-operative connectivity patterns have direct influence on the clinical response to DBS and may serve as important and independent predictors of the postoperative clinical outcome.
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Affiliation(s)
- Nabin Koirala
- Department of Neurology, Johannes Gutenberg University, 55131, Mainz, Germany
| | - Vinzenz Fleischer
- Department of Neurology, Johannes Gutenberg University, 55131, Mainz, Germany
| | - Martin Glaser
- Department of Neurosurgery, Johannes Gutenberg University, 55131, Mainz, Germany
| | - Kirsten E Zeuner
- Department of Neurology, University of Kiel, 24105, Kiel, Germany
| | - Günther Deuschl
- Department of Neurology, University of Kiel, 24105, Kiel, Germany
| | - Jens Volkmann
- Department of Neurology, University of Würzburg, 97080, Würzburg, Germany
| | | | - Sergiu Groppa
- Department of Neurology, Johannes Gutenberg University, 55131, Mainz, Germany.
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41
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Tóth B, Urbán G, Háden GP, Márk M, Török M, Stam CJ, Winkler I. Large-scale network organization of EEG functional connectivity in newborn infants. Hum Brain Mapp 2017; 38:4019-4033. [PMID: 28488308 PMCID: PMC6867159 DOI: 10.1002/hbm.23645] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Revised: 04/26/2017] [Accepted: 04/28/2017] [Indexed: 12/12/2022] Open
Abstract
The organization of functional brain networks changes across human lifespan. The present study analyzed functional brain networks in healthy full-term infants (N = 139) within 1-6 days from birth by measuring neural synchrony in EEG recordings during quiet sleep. Large-scale phase synchronization was measured in six frequency bands with the Phase Lag Index. Macroscopic network organization characteristics were quantified by constructing unweighted minimum spanning tree graphs. The cortical networks in early infancy were found to be significantly more hierarchical and had a more cost-efficient organization compared with MST of random control networks, more so in the theta and alpha than in other frequency bands. Frontal and parietal sites acted as the main hubs of these networks, the topological characteristics of which were associated with gestation age (GA). This suggests that individual differences in network topology are related to cortical maturation during the prenatal period, when functional networks shift from strictly centralized toward segregated configurations. Hum Brain Mapp 38:4019-4033, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Brigitta Tóth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of SciencesBudapestHungary
| | - Gábor Urbán
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of SciencesBudapestHungary
- Department of Cognitive ScienceFaculty of Natural Sciences, Budapest University of Technology and EconomicsBudapestHungary
| | - Gábor P. Háden
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of SciencesBudapestHungary
| | - Molnár Márk
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of SciencesBudapestHungary
| | - Miklós Török
- Department of Obstetrics‐Gynaecology and Perinatal Intensive Care UnitMilitary HospitalBudapestHungary
| | - Cornelis Jan Stam
- Department of Clinical NeurophysiologyVU University Medical CenterAmsterdamNetherlands
| | - István Winkler
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of SciencesBudapestHungary
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42
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Moore RA, Mills M, Marshman P, Corr P. Group and individual analyses of pre-, peri-, and post-movement related alpha and beta oscillations during a single continuous monitoring task. Int J Psychophysiol 2017; 120:108-117. [PMID: 28739481 DOI: 10.1016/j.ijpsycho.2017.07.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Revised: 04/26/2017] [Accepted: 07/19/2017] [Indexed: 10/19/2022]
Abstract
Band power linked to lower and upper alpha (i.e. 8-10Hz; 10-12Hz) and lower and upper beta (i.e. 12-20Hz; 20-30Hz) were examined during response related stages, including anticipation, response execution (RE), response inhibition (RI) and post response recovery (PRR). Group and individual data from 34 participants were considered. The participant's objective was to press a response key immediately following 4 non-repeating, single integer odd digits. These were presented amongst a continuous stream of digits and Xs. Electroencephalogram (EEG) signals were recorded from 32 electrodes (pooled to 12 regions). In the group analyses, participant EEG response was compared to baseline revealing that upper alpha desynchronised during anticipation, RE and RI; lower beta during anticipation and RE; and upper beta just RE. Upper alpha desynchronisation during rapid, unplanned RI is novel. Also, upper alpha and lower/upper beta synchronised during PRR. For upper alpha, we speculate this indexes brief cortical deactivation; for beta we propose this indexes response set maintenance. Lastly, lower alpha fluctuations correlated negatively with RT, indexing neural efficiency. Individual analyses involved calculation of the proportion of individuals displaying the typical RE and PRR trends; these were not reflected by all participants. The former was displayed individually by the largest proportion in upper alpha recorded left fronto-centrally; the latter was most reliably displayed individually in lower beta recorded mid centro-parietally. Therefore, group analyses identified typical alpha and beta synchronisation/desynchronisation trends, whilst individual analyses identified their degree of representation in single participants. Attention is drawn to the clinical relevance of this issue.
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Affiliation(s)
- Roger A Moore
- Department of Psychology, University of Portsmouth, King Henry I Street, Portsmouth PO1 2DY, United Kingdom.
| | - Matthew Mills
- Tom Rudd Unit, Moorgreen Hospital, West End, Southampton SO30 3JB, United Kingdom
| | - Paul Marshman
- Department of Psychology, University of Portsmouth, King Henry I Street, Portsmouth PO1 2DY, United Kingdom
| | - Philip Corr
- City, University of London, Northampton Square, London EC1V 0HB, United Kingdom
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43
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Philips GR, Daly JJ, Príncipe JC. Topographical measures of functional connectivity as biomarkers for post-stroke motor recovery. J Neuroeng Rehabil 2017; 14:67. [PMID: 28683745 PMCID: PMC5501348 DOI: 10.1186/s12984-017-0277-3] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Accepted: 06/20/2017] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Biomarkers derived from neural activity of the brain present a vital tool for the prediction and evaluation of post-stroke motor recovery, as well as for real-time biofeedback opportunities. METHODS In order to encapsulate recovery-related reorganization of brain networks into such biomarkers, we have utilized the generalized measure of association (GMA) and graph analyses, which include global and local efficiency, as well as hemispheric interdensity and intradensity. These methods were applied to electroencephalogram (EEG) data recorded during a study of 30 stroke survivors (21 male, mean age 57.9 years, mean stroke duration 22.4 months) undergoing 12 weeks of intensive therapeutic intervention. RESULTS We observed that decreases of the intradensity of the unaffected hemisphere are correlated (r s =-0.46;p<0.05) with functional recovery, as measured by the upper-extremity portion of the Fugl-Meyer Assessment (FMUE). In addition, high initial values of local efficiency predict greater improvement in FMUE (R 2=0.16;p<0.05). In a subset of 17 subjects possessing lesions of the cerebral cortex, reductions of global and local efficiency, as well as the intradensity of the unaffected hemisphere are found to be associated with functional improvement (r s =-0.60,-0.66,-0.75;p<0.05). Within the same subgroup, high initial values of global and local efficiency, are predictive of improved recovery (R 2=0.24,0.25;p<0.05). All significant findings were specific to the 12.5-25 Hz band. CONCLUSIONS These topological measures show promise for prognosis and evaluation of therapeutic outcomes, as well as potential application to BCI-enabled biofeedback.
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Affiliation(s)
- Gavin R. Philips
- Computational NeuroEngineering Laboratory, Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
| | - Janis J. Daly
- Department of Neurology, University of Florida, Gainesville, Florida, USA
- Malcolm Randall VA Medical Center, Gainesville, Florida, USA
| | - José C. Príncipe
- Computational NeuroEngineering Laboratory, Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
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Dai Z, de Souza J, Lim J, Ho PM, Chen Y, Li J, Thakor N, Bezerianos A, Sun Y. EEG Cortical Connectivity Analysis of Working Memory Reveals Topological Reorganization in Theta and Alpha Bands. Front Hum Neurosci 2017; 11:237. [PMID: 28553215 PMCID: PMC5427143 DOI: 10.3389/fnhum.2017.00237] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Accepted: 04/24/2017] [Indexed: 01/21/2023] Open
Abstract
Numerous studies have revealed various working memory (WM)-related brain activities that originate from various cortical regions and oscillate at different frequencies. However, multi-frequency band analysis of the brain network in WM in the cortical space remains largely unexplored. In this study, we employed a graph theoretical framework to characterize the topological properties of the brain functional network in the theta and alpha frequency bands during WM tasks. Twenty-eight subjects performed visual n-back tasks at two difficulty levels, i.e., 0-back (control task) and 2-back (WM task). After preprocessing, Electroencephalogram (EEG) signals were projected into the source space and 80 cortical brain regions were selected for further analysis. Subsequently, the theta- and alpha-band networks were constructed by calculating the Pearson correlation coefficients between the power series (obtained by concatenating the power values of all epochs in each session) of all pairs of brain regions. Graph theoretical approaches were then employed to estimate the topological properties of the brain networks at different WM tasks. We found higher functional integration in the theta band and lower functional segregation in the alpha band in the WM task compared with the control task. Moreover, compared to the 0-back task, altered regional centrality was revealed in the 2-back task in various brain regions that mainly resided in the frontal, temporal and occipital lobes, with distinct presentations in the theta and alpha bands. In addition, significant negative correlations were found between the reaction time with the average path length of the theta-band network and the local clustering of the alpha-band network, which demonstrates the potential for using the brain network metrics as biomarkers for predicting the task performance during WM tasks.
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Affiliation(s)
- Zhongxiang Dai
- Centre for Life Science, Singapore Institute for Neurotechnology, National University of SingaporeSingapore, Singapore
| | - Joshua de Souza
- Centre for Life Science, Singapore Institute for Neurotechnology, National University of SingaporeSingapore, Singapore
| | - Julian Lim
- Neuroscience and Behavioral Disorders Program, Centre of Cognitive Neuroscience, Duke-NUS Graduate Medical SchoolSingapore, Singapore
| | - Paul M Ho
- Centre for Life Science, Singapore Institute for Neurotechnology, National University of SingaporeSingapore, Singapore
| | - Yu Chen
- Computational Intelligence Lab, School of Computer Engineering, Nanyang Technological UniversitySingapore, Singapore
| | - Junhua Li
- Centre for Life Science, Singapore Institute for Neurotechnology, National University of SingaporeSingapore, Singapore
| | - Nitish Thakor
- Centre for Life Science, Singapore Institute for Neurotechnology, National University of SingaporeSingapore, Singapore
| | - Anastasios Bezerianos
- Centre for Life Science, Singapore Institute for Neurotechnology, National University of SingaporeSingapore, Singapore
| | - Yu Sun
- Centre for Life Science, Singapore Institute for Neurotechnology, National University of SingaporeSingapore, Singapore
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45
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EEG Microstate Correlates of Fluid Intelligence and Response to Cognitive Training. Brain Topogr 2017; 30:502-520. [DOI: 10.1007/s10548-017-0565-z] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 04/24/2017] [Indexed: 01/12/2023]
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46
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Ahmadlou M, Adeli H. Complexity of weighted graph: A new technique to investigate structural complexity of brain activities with applications to aging and autism. Neurosci Lett 2017; 650:103-108. [DOI: 10.1016/j.neulet.2017.04.009] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 04/03/2017] [Accepted: 04/04/2017] [Indexed: 01/21/2023]
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47
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The Reorganization of Human Brain Networks Modulated by Driving Mental Fatigue. IEEE J Biomed Health Inform 2017; 21:743-755. [PMID: 28113875 DOI: 10.1109/jbhi.2016.2544061] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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48
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Pezoulas VC, Zervakis M, Michelogiannis S, Klados MA. Resting-State Functional Connectivity and Network Analysis of Cerebellum with Respect to [corrected] IQ and Gender. Front Hum Neurosci 2017; 11:189. [PMID: 28491028 PMCID: PMC5405083 DOI: 10.3389/fnhum.2017.00189] [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: 09/08/2016] [Accepted: 03/31/2017] [Indexed: 11/17/2022] Open
Abstract
During the last years, it has been established that the prefrontal and posterior parietal brain lobes, which are mostly related to intelligence, have many connections to cerebellum. However, there is a limited research investigating cerebellum's relationship with cognitive processes. In this study, the network of cerebellum was analyzed in order to investigate its overall organization in individuals with low and high fluid Intelligence Quotient (IQ). Functional magnetic resonance imaging (fMRI) data were selected from 136 subjects in resting-state from the Human Connectome Project (HCP) database and were further separated into two IQ groups composed of 69 low-IQ and 67 high-IQ subjects. Cerebellum was parcellated into 28 lobules/ROIs (per subject) using a standard cerebellum anatomical atlas. Thereafter, correlation matrices were constructed by computing Pearson's correlation coefficients between the average BOLD time-series for each pair of ROIs inside the cerebellum. By computing conventional graph metrics, small-world network properties were verified using the weighted clustering coefficient and the characteristic path length for estimating the trade-off between segregation and integration. In addition, a connectivity metric was computed for extracting the average cost per network. The concept of the Minimum Spanning Tree (MST) was adopted and implemented in order to avoid methodological biases in graph comparisons and retain only the strongest connections per network. Subsequently, six global and three local metrics were calculated in order to retrieve useful features concerning the characteristics of each MST. Moreover, the local metrics of degree and betweenness centrality were used to detect hubs, i.e., nodes with high importance. The computed set of metrics gave rise to extensive statistical analysis in order to examine differences between low and high-IQ groups, as well as between all possible gender-based group combinations. Our results reveal that both male and female networks have small-world properties with differences in females (especially in higher IQ females) indicative of higher neural efficiency in cerebellum. There is a trend toward the same direction in men, but without significant differences. Finally, three lobules showed maximum correlation with the median response time in low-IQ individuals, implying that there is an increased effort dedicated locally by this population in cognitive tasks.
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Affiliation(s)
- Vasileios C Pezoulas
- School of Electrical and Computer Engineering, Technical University of CreteChania, Greece
| | - Michalis Zervakis
- School of Electrical and Computer Engineering, Technical University of CreteChania, Greece
| | - Sifis Michelogiannis
- Neurophysiological Research Laboratory (L. Widén), School of Medicine, University of CreteHeraklion, Greece
| | - Manousos A Klados
- Max Planck Research Group for Neuroanatomy and Connectivity, Max Planck Institute for Human Cognitive and Brain SciencesLeipzig, Germany
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49
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Li W, Yang C, Shi F, Wu S, Wang Q, Nie Y, Zhang X. Construction of Individual Morphological Brain Networks with Multiple Morphometric Features. Front Neuroanat 2017; 11:34. [PMID: 28487638 PMCID: PMC5403938 DOI: 10.3389/fnana.2017.00034] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Accepted: 03/29/2017] [Indexed: 12/11/2022] Open
Abstract
In recent years, researchers have increased attentions to the morphological brain network, which is generally constructed by measuring the mathematical correlation across regions using a certain morphometric feature, such as regional cortical thickness and voxel intensity. However, cerebral structure can be characterized by various factors, such as regional volume, surface area, and curvature. Moreover, most of the morphological brain networks are population-based, which has limitations in the investigations of individual difference and clinical applications. Hence, we have extended previous studies by proposing a novel method for realizing the construction of an individual-based morphological brain network through a combination of multiple morphometric features. In particular, interregional connections are estimated using our newly introduced feature vectors, namely, the Pearson correlation coefficient of the concatenation of seven morphometric features. Experiments were performed on a healthy cohort of 55 subjects (24 males aged from 20 to 29 and 31 females aged from 20 to 28) each scanned twice, and reproducibility was evaluated through test-retest reliability. The robustness of morphometric features was measured firstly to select the more reproducible features to form the connectomes. Then the topological properties were analyzed and compared with previous reports of different modalities. Small-worldness was observed in all the subjects at the range of the entire network sparsity (20-40%), and configurations were comparable with previous findings at the sparsity of 23%. The spatial distributions of the hub were found to be significantly influenced by the individual variances, and the hubs obtained by averaging across subjects and sparsities showed correspondence with previous reports. The intraclass coefficient of graphic properties (clustering coefficient = 0.83, characteristic path length = 0.81, betweenness centrality = 0.78) indicates the robustness of the present method. Results demonstrate that the multiple morphometric features can be applied to form a rational reproducible individual-based morphological brain network.
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Affiliation(s)
- Wan Li
- College of Life Science and Bioengineering, Beijing University of TechnologyBeijing, China
| | - Chunlan Yang
- College of Life Science and Bioengineering, Beijing University of TechnologyBeijing, China
| | - Feng Shi
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Biomedical Imaging Research InstituteLos Angeles, CA, USA
| | - Shuicai Wu
- College of Life Science and Bioengineering, Beijing University of TechnologyBeijing, China
| | - Qun Wang
- Department of Internal Neurology, Tiantan HospitalBeijing, China
| | - Yingnan Nie
- College of Life Science and Bioengineering, Beijing University of TechnologyBeijing, China
| | - Xin Zhang
- College of Life Science and Bioengineering, Beijing University of TechnologyBeijing, China
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50
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Liao X, Vasilakos AV, He Y. Small-world human brain networks: Perspectives and challenges. Neurosci Biobehav Rev 2017; 77:286-300. [PMID: 28389343 DOI: 10.1016/j.neubiorev.2017.03.018] [Citation(s) in RCA: 221] [Impact Index Per Article: 31.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Revised: 01/19/2017] [Accepted: 03/31/2017] [Indexed: 12/15/2022]
Abstract
Modelling the human brain as a complex network has provided a powerful mathematical framework to characterize the structural and functional architectures of the brain. In the past decade, the combination of non-invasive neuroimaging techniques and graph theoretical approaches enable us to map human structural and functional connectivity patterns (i.e., connectome) at the macroscopic level. One of the most influential findings is that human brain networks exhibit prominent small-world organization. Such a network architecture in the human brain facilitates efficient information segregation and integration at low wiring and energy costs, which presumably results from natural selection under the pressure of a cost-efficiency balance. Moreover, the small-world organization undergoes continuous changes during normal development and ageing and exhibits dramatic alterations in neurological and psychiatric disorders. In this review, we survey recent advances regarding the small-world architecture in human brain networks and highlight the potential implications and applications in multidisciplinary fields, including cognitive neuroscience, medicine and engineering. Finally, we highlight several challenging issues and areas for future research in this rapidly growing field.
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Affiliation(s)
- Xuhong Liao
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
| | - Athanasios V Vasilakos
- Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, 97187 Lulea, Sweden
| | - Yong He
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.
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