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Moradi N, Goodyear BG, Sotero RC. Deep EEG source localization via EMD-based fMRI high spatial frequency. PLoS One 2024; 19:e0299284. [PMID: 38427616 PMCID: PMC10906834 DOI: 10.1371/journal.pone.0299284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 02/07/2024] [Indexed: 03/03/2024] Open
Abstract
Brain imaging with a high-spatiotemporal resolution is crucial for accurate brain-function mapping. Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are two popular neuroimaging modalities with complementary features that record brain function with high temporal and spatial resolution, respectively. One popular non-invasive way to obtain data with both high spatial and temporal resolutions is to combine the fMRI activation map and EEG data to improve the spatial resolution of the EEG source localization. However, using the whole fMRI map may cause spurious results for the EEG source localization, especially for deep brain regions. Considering the head's conductivity, deep regions' sources with low activity are unlikely to be detected by the EEG electrodes at the scalp. In this study, we use fMRI's high spatial-frequency component to identify the local high-intensity activations that are most likely to be captured by the EEG. The 3D Empirical Mode Decomposition (3D-EMD), a data-driven method, is used to decompose the fMRI map into its spatial-frequency components. Different validation measurements for EEG source localization show improved performance for the EEG inverse-modeling informed by the fMRI's high-frequency spatial component compared to the fMRI-informed EEG source-localization methods. The level of improvement varies depending on the voxels' intensity and their distribution. Our experimental results also support this conclusion.
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Affiliation(s)
- Narges Moradi
- Biomedical Engineering Department, University of Calgary, Calgary, AB, Canada
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Bradley G. Goodyear
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Roberto C. Sotero
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
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2
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Fleury M, Figueiredo P, Vourvopoulos A, Lécuyer A. Two is better? combining EEG and fMRI for BCI and neurofeedback: a systematic review. J Neural Eng 2023; 20:051003. [PMID: 37879343 DOI: 10.1088/1741-2552/ad06e1] [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: 03/22/2023] [Accepted: 10/25/2023] [Indexed: 10/27/2023]
Abstract
Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two commonly used non-invasive techniques for measuring brain activity in neuroscience and brain-computer interfaces (BCI).Objective. In this review, we focus on the use of EEG and fMRI in neurofeedback (NF) and discuss the challenges of combining the two modalities to improve understanding of brain activity and achieve more effective clinical outcomes. Advanced technologies have been developed to simultaneously record EEG and fMRI signals to provide a better understanding of the relationship between the two modalities. However, the complexity of brain processes and the heterogeneous nature of EEG and fMRI present challenges in extracting useful information from the combined data.Approach. We will survey existing EEG-fMRI combinations and recent studies that exploit EEG-fMRI in NF, highlighting the experimental and technical challenges.Main results. We made a classification of the different combination of EEG-fMRI for NF, we provide a review of multimodal analysis methods for EEG-fMRI features. We also survey the current state of research on EEG-fMRI in the different existing NF paradigms. Finally, we also identify some of the remaining challenges in this field.Significance. By exploring EEG-fMRI combinations in NF, we are advancing our knowledge of brain function and its applications in clinical settings. As such, this review serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering and rehabilitation, highlighting the promising future of EEG-fMRI-based NF.
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Affiliation(s)
- Mathis Fleury
- Univ Rennes, Inria, CNRS, Inserm, Empenn ERL U1228 Rennes, France
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Patrícia Figueiredo
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Athanasios Vourvopoulos
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Anatole Lécuyer
- Univ Rennes, Inria, CNRS, Inserm, Empenn ERL U1228 Rennes, France
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3
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Zhao S, Lin H, Chi A, Gao Y. Effects of acute exercise fatigue on the spatiotemporal dynamics of resting-state large-scale brain networks. Front Neurosci 2023; 17:986368. [PMID: 36743803 PMCID: PMC9895387 DOI: 10.3389/fnins.2023.986368] [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: 07/05/2022] [Accepted: 01/05/2023] [Indexed: 01/21/2023] Open
Abstract
Introduction Various approaches have been used to explore different aspects of the regulation of brain activity by acute exercise, but few studies have been conducted on the effects of acute exercise fatigue on large-scale brain functional networks. Therefore, the present study aimed to explore the effects of acute exercise fatigue on resting-state electroencephalogram (EEG) microstates and large-scale brain network rhythm energy. Methods The Bruce protocol was used as the experimental exercise model with a self-controlled experimental design. Thirty males performed incremental load exercise tests on treadmill until exhaustion. EEG signal acquisition was completed before and after exercise. EEG microstates and resting-state cortical rhythm techniques were used to analyze the EEG signal. Results The microstate results showed that the duration, occurrence, and contribution of Microstate C were significantly higher after exhaustive exercise (p's < 0.01). There was a significantly lower contribution of Microstate D (p < 0.05), a significant increase in transition probabilities between Microstate A and C (p < 0.05), and a significant decrease in transition probabilities between Microstate B and D (p < 0.05). The results of EEG rhythm energy on the large-scale brain network showed that the energy in the high-frequency β band was significantly higher in the visual network (p < 0.05). Discussion Our results suggest that frequently Microstate C associated with the convexity network are important for the organism to respond to internal and external information stimuli and thus regulate motor behavior in time to protect organism integrity. The decreases in Microstate D parameters, associated with the attentional network, are an important neural mechanism explaining the decrease in attention-related cognitive or behavioral performance due to acute exercise fatigue. The high energy in the high-frequency β band on the visual network can be explained in the sense of the neural efficiency hypothesis, which indicates a decrease in neural efficiency.
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Affiliation(s)
- Shanguang Zhao
- Institute of Physical Education, Shaanxi Normal University, Xi’an, China,Faculty of Sports and Exercise Science, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Hao Lin
- Institute of Physical Education, Shaanxi Normal University, Xi’an, China
| | - Aiping Chi
- Institute of Physical Education, Shaanxi Normal University, Xi’an, China,*Correspondence: Aiping Chi,
| | - Yuanyuan Gao
- Institute of Physical Education, Shaanxi Normal University, Xi’an, China,Yuanyuan Gao,
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Zhang Q, Luo C, Ngetich R, Zhang J, Jin Z, Li L. Visual Selective Attention P300 Source in Frontal-Parietal Lobe: ERP and fMRI Study. Brain Topogr 2022; 35:636-650. [PMID: 36178537 DOI: 10.1007/s10548-022-00916-x] [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: 12/31/2021] [Accepted: 09/03/2022] [Indexed: 11/28/2022]
Abstract
Visual selective attention can be achieved into bottom-up and top-down attention. Different selective attention tasks involve different attention control ways. The pop-out task requires more bottom-up attention, whereas the search task involves more top-down attention. P300, which is the positive potential generated by the brain in the latency of 300 ~ 600 ms after stimulus, reflects the processing of attention. There is no consensus on the P300 source. The aim of present study is to study the source of P300 elicited by different visual selective attention. We collected thirteen participants' P300 elicited by pop-out and search tasks with event-related potentials (ERP). We collected twenty-six participants' activation brain regions in pop-out and search tasks with functional magnetic resonance imaging (fMRI). And we analyzed the sources of P300 using the ERP and fMRI integration with high temporal resolution and high spatial resolution. ERP results indicated that the pop-out task induced larger P300 than the search task. P300 induced by the two tasks distributed at frontal and parietal lobes, with P300 induced by the pop-out task mainly at the parietal lobe and that induced by the search task mainly at the frontal lobe. Further ERP and fMRI integration analysis showed that neural difference sources of P300 were the right precentral gyrus, left superior frontal gyrus (medial orbital), left middle temporal gyrus, left rolandic operculum, right postcentral gyrus, and left angular gyrus. Our study suggests that the frontal and parietal lobes contribute to the P300 component of visual selective attention.
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Affiliation(s)
- Qiuzhu Zhang
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Cimei Luo
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Ronald Ngetich
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Junjun Zhang
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Zhenlan Jin
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Ling Li
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
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5
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Yang B, Ma J, Qiu W, Zhang J, Wang X. The unilateral upper limb classification from fMRI-weighted EEG signals using convolutional neural network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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6
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Zhang H, Xu M, Zhang C, He F, Song X, Chen S, Jian X, Ming D. Experimental and simulation studies of localization and decoding of single and double dipoles. J Neural Eng 2022; 19. [PMID: 35468593 DOI: 10.1088/1741-2552/ac6a12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 04/24/2022] [Indexed: 11/11/2022]
Abstract
Electroencephalography (EEG) is a technique for measuring normal or abnormal neuronal activity in the human brain, but its low spatial resolution makes it difficult to locate the precise locations of neurons due to the volume conduction effect of brain tissue. The acoustoelectric (AE) effect has the advantage of detecting electrical signals with high temporal resolution and focused ultrasound with high spatial resolution. In this paper, we use dipoles to simulate real single and double neurons, and further investigate the localization and decoding of single and double dipoles based on AE effects from numerical simulations, brain tissue phantom experiments, and fresh porcine brain tissue experiments. The results show that the localization error of a single dipole is less than 0.3 mm, the decoding signal is highly correlated with the source signal, and the decoding accuracy is greater than 0.94; the location of double dipoles with an interval of 0.4 mm or more can be localized, the localization error tends to increase as the interval of dipoles decreases, and the decoding accuracy tends to decrease as the frequency of dipoles decreases. This study localizes and decodes dipole signals with high accuracy, and provides a technical method for the development of EEG.
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Affiliation(s)
- Hao Zhang
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, No.92 Weijin Road, Nankai District, Tianjin, Tianjin, 300072, CHINA
| | - Minpeng Xu
- Biomedical Engineering, Tianjin University, No.92 Weijin Road, Nankai District, Tianjin, Tianjin, Tianjin, 300072, CHINA
| | - Chen Zhang
- Tianjin University, No.92 Weijin Road, Nankai District, Tianjin, Tianjin, 300072, CHINA
| | - Feng He
- Tianjin University, No.92 Weijin Road, Nankai District, Tianjin, Tianjin, Tianjin, 300072, CHINA
| | - Xizi Song
- Academy of Medical Engineering and Translation Medicine, Tianjin University, No.92 Weijin Road, Nankai District, Tianjin, Tianjin, Tianjin, 300072, CHINA
| | - Shanguang Chen
- Tianjin University, No.92 Weijin Road, Nankai District, Tianjin, Tianjin, Tianjin, 300072, CHINA
| | - Xiqi Jian
- School of biomedical and engineering, Tianjin Medical University, No.22, Qixiangtai Road, Heping District, Tianjin, 300070, CHINA
| | - Dong Ming
- Department of Biomedical Engineering, Tianjin University, No.92 Weijin Road, Nankai District, Tianjin, Tianjin, 300072, CHINA
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7
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Optimizing EEG Source Reconstruction with Concurrent fMRI-Derived Spatial Priors. Brain Topogr 2022; 35:282-301. [PMID: 35142957 PMCID: PMC9098592 DOI: 10.1007/s10548-022-00891-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 01/31/2022] [Indexed: 02/01/2023]
Abstract
Reconstructing EEG sources involves a complex pipeline, with the inverse problem being the most challenging. Multiple inversion algorithms are being continuously developed, aiming to tackle the non-uniqueness of this problem, which has been shown to be partially circumvented by including prior information in the inverse models. Despite a few efforts, there are still current and persistent controversies regarding the inversion algorithm of choice and the optimal set of spatial priors to be included in the inversion models. The use of simultaneous EEG-fMRI data is one approach to tackle this problem. The spatial resolution of fMRI makes fMRI derived spatial priors very convenient for EEG reconstruction, however, only task activation maps and resting-state networks (RSNs) have been explored so far, overlooking the recent, but already accepted, notion that brain networks exhibit dynamic functional connectivity fluctuations. The lack of a systematic comparison between different source reconstruction algorithms, considering potentially more brain-informative priors such as fMRI, motivates the search for better reconstruction models. Using simultaneous EEG-fMRI data, here we compared four different inversion algorithms (minimum norm, MN; low resolution electromagnetic tomography, LORETA; empirical Bayes beamformer, EBB; and multiple sparse priors, MSP) under a Bayesian framework (as implemented in SPM), each with three different sets of priors consisting of: (1) those specific to the algorithm; (2) those specific to the algorithm plus fMRI task activation maps and RSNs; and (3) those specific to the algorithm plus fMRI task activation maps and RSNs and network modules of task-related dFC states estimated from the dFC fluctuations. The quality of the reconstructed EEG sources was quantified in terms of model-based metrics, namely the expectation of the posterior probability P(model|data) and variance explained of the inversion models, and the overlap/proportion of brain regions known to be involved in the visual perception tasks that the participants were submitted to, and RSN templates, with/within EEG source components. Model-based metrics suggested that model parsimony is preferred, with the combination MSP and priors specific to this algorithm exhibiting the best performance. However, optimal overlap/proportion values were found using EBB and priors specific to this algorithm and fMRI task activation maps and RSNs or MSP and considering all the priors (algorithm priors, fMRI task activation maps and RSNs and dFC state modules), respectively, indicating that fMRI spatial priors, including dFC state modules, might contain useful information to recover EEG source components reflecting neuronal activity of interest. Our main results show that providing fMRI spatial derived priors that reflect the dynamics of the brain might be useful to map neuronal activity more accurately from EEG-fMRI. Furthermore, this work paves the way towards a more informative selection of the optimal EEG source reconstruction approach, which may be critical in future studies.
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8
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fMRI-SI-STBF: An fMRI-informed Bayesian electromagnetic spatio-temporal extended source imaging. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.06.066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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9
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Zhang J, Xu Z, Zhou Y, Wang P, Fu P, Xu X, Zhang D. An Empirical Comparative Study on the Two Methods of Eliciting Singers' Emotions in Singing: Self-Imagination and VR Training. Front Neurosci 2021; 15:693468. [PMID: 34456670 PMCID: PMC8387635 DOI: 10.3389/fnins.2021.693468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 07/27/2021] [Indexed: 12/04/2022] Open
Abstract
Emotional singing can affect vocal performance and the audience's engagement. Chinese universities use traditional training techniques for teaching theoretical and applied knowledge. Self-imagination is the predominant training method for emotional singing. Recently, virtual reality (VR) technologies have been applied in several fields for training purposes. In this empirical comparative study, a VR training task was implemented to elicit emotions from singers and further assist them with improving their emotional singing performance. The VR training method was compared against the traditional self-imagination method. By conducting a two-stage experiment, the two methods were compared in terms of emotions' elicitation and emotional singing performance. In the first stage, electroencephalographic (EEG) data were collected from the subjects. In the second stage, self-rating reports and third-party teachers' evaluations were collected. The EEG data were analyzed by adopting the max-relevance and min-redundancy algorithm for feature selection and the support vector machine (SVM) for emotion recognition. Based on the results of EEG emotion classification and subjective scale, VR can better elicit the positive, neutral, and negative emotional states from the singers than not using this technology (i.e., self-imagination). Furthermore, due to the improvement of emotional activation, VR brings the improvement of singing performance. The VR hence appears to be an effective approach that may improve and complement the available vocal music teaching methods.
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Affiliation(s)
- Jin Zhang
- College of Arts, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Ziming Xu
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Yueying Zhou
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Pengpai Wang
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Ping Fu
- Department of Library Services, Central Washington University, Ellensburg, WA, United States
| | - Xijia Xu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Daoqiang Zhang
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
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10
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Wu Y, Zhao W, Chen X, Wan X, Lei X. Aberrant Awake Spontaneous Brain Activity in Obstructive Sleep Apnea: A Review Focused on Resting-State EEG and Resting-State fMRI. Front Neurol 2020; 11:768. [PMID: 32849223 PMCID: PMC7431882 DOI: 10.3389/fneur.2020.00768] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 06/22/2020] [Indexed: 12/27/2022] Open
Abstract
As one of the most common sleep-related respiratory disorders, obstructive sleep apnea (OSA) is characterized by excessive snoring, repetitive apnea, arousal, sleep fragmentation, and intermittent nocturnal hypoxemia. Focused on the resting-state brain imaging techniques, we reviewed the OSA-related resting-state electroencephalogram and resting-state functional magnetic resonance imaging (rsfMRI) studies. Compared with the healthy control group, patients with OSA presented increased frontal and central δ/θ powers during resting-state wakefulness, and their slow-wave activity showed a positive correlation with apnea–hypopnea index. For rsfMRI, the prefrontal cortex and insula may be the vital regions for OSA and are strongly related to the severity of the disease. Meanwhile, some large-scale brain networks, such as the default-mode network, salience network, and central executive network, play pivotal roles in the pathology of OSA. We then discussed the contribution of resting-state brain imaging as an evaluation approach for disease interventions. Finally, we briefly introduced the effects of OSA-related physiological and mental diseases and discussed some future research directions from the perspective of resting-state brain imaging.
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Affiliation(s)
- Yue Wu
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China.,Key Laboratory of Cognition and Personality of Ministry of Education, Chongqing, China
| | - Wenrui Zhao
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China.,Key Laboratory of Cognition and Personality of Ministry of Education, Chongqing, China
| | - Xinyuan Chen
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China.,Key Laboratory of Cognition and Personality of Ministry of Education, Chongqing, China
| | - Xiaoyong Wan
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China.,Key Laboratory of Cognition and Personality of Ministry of Education, Chongqing, China
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China.,Key Laboratory of Cognition and Personality of Ministry of Education, Chongqing, China.,Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
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Asadzadeh S, Yousefi Rezaii T, Beheshti S, Delpak A, Meshgini S. A systematic review of EEG source localization techniques and their applications on diagnosis of brain abnormalities. J Neurosci Methods 2020; 339:108740. [DOI: 10.1016/j.jneumeth.2020.108740] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 04/13/2020] [Accepted: 04/13/2020] [Indexed: 12/12/2022]
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12
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Liu Q, Zeng XC, Jiang XM, Zhou ZH, Hu XF. Altered Brain Functional Hubs and Connectivity Underlie Persistent Somatoform Pain Disorder. Front Neurosci 2019; 13:415. [PMID: 31114477 PMCID: PMC6502961 DOI: 10.3389/fnins.2019.00415] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2018] [Accepted: 04/11/2019] [Indexed: 12/14/2022] Open
Abstract
This study investigated the degree of brain functional impairment in persistent somatoform pain disorder (PSPD) by examining changes in the patterns of brain functional hubs. Resting-state functional magnetic resonance imaging was performed in 21 PSPD patients with headache as the main symptom and 17 sex- and age-matched healthy controls. Degree centrality (DC) analysis as well as the connectivity among these hubs by functional connectivity (FC) analysis and Granger causality analysis (GCA) were performed to characterize abnormal brain networks in PSPD (Gaussian random field corrected: P < 0.001, Z > 3.09). The relationships between DC and connectivity and clinical parameters were also examined. DC values in the bilateral inferior occipital gyrus (IOG), bilateral calcarine fissure (CAL), and left paracentral lobule (PCL) and FC values of right IOG–left CAL, right IOG–right CAL, right IOG–left IOG, left CAL–right CAL, left CAL–left IOG, left CAL–left PCL, right CAL–left PCL, and left IOG–left PCL were lower in PSPD patients as compared to controls. A negative causal effect from the left CAL to the left paracentral lobule and a positive effect from the right CAL to the right IOG were observed in PSPD patients. Abnormal DC, FC, and signed-path coefficients in PSPD patients were negatively correlated with self-rating anxiety and depression scale scores. These results indicate that altered functional hubs and connectivity patterns in the somatosensory cortex may reflect emotional disturbance in PSPD patients.
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Affiliation(s)
- Qu Liu
- Department of Neurology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Xian-Chun Zeng
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Xiao-Mei Jiang
- Department of Centre for Disease Prevention and Control, Chengdu Military Region, Chengdu, China
| | - Zhen-Hua Zhou
- Department of Neurology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Xiao-Fei Hu
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
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13
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Wang H, Wu X, Wen X, Lei X, Gao Y, Yao L. Exploring directed functional connectivity based on electroencephalography source signals using a global cortex factor-based multivariate autoregressive model. J Neurosci Methods 2019; 318:6-16. [DOI: 10.1016/j.jneumeth.2019.02.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 02/19/2019] [Accepted: 02/24/2019] [Indexed: 10/27/2022]
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14
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Liu K, Yu ZL, Wu W, Gu Z, Zhang J, Cen L, Nagarajan S, Li Y. Bayesian Electromagnetic Spatio-Temporal Imaging of Extended Sources Based on Matrix Factorization. IEEE Trans Biomed Eng 2019; 66:2457-2469. [PMID: 30605088 DOI: 10.1109/tbme.2018.2890291] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Accurate estimation of the locations and extents of neural sources from electroencephalography and magnetoencephalography (E/MEG) is challenging, especially for deep and highly correlated neural activities. In this study, we proposed a new fully data-driven source imaging method, source imaging based on spatio-temporal basis function (SI-STBF), which is built upon a Bayesian framework, to address this issue. The SI-STBF is based on the factorization of a source matrix as a product of a sparse coding matrix and a temporal basis function (TBF) matrix, which includes a few TBFs. The prior of the TBF is set in the empirical Bayesian manner. Similarly, for the spatial constraint, the SI-STBF assumes the prior covariance of the coding matrix as a weighted sum of several spatial covariance components. Both the TBFs and the coding matrix are learned from E/MEG simultaneously through variational Bayesian inference. To enable inference on high-resolution source space, we derived a scalable algorithm using convex analysis. The performance of the SI-STBF was assessed using both simulated and experimental E/MEG recordings. Compared with L2-norm constrained methods, the SI-STBF is superior in reconstructing extended sources with less spatial diffusion and less localization error. By virtue of the spatio-temporal factorization of source matrix, the SI-STBF also produces more accurate estimations than spatial-only constraint method for high correlated and deep sources.
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Chen Y, Tang JH, Shou G, Gleghorn D, Doudican BC, Besio W, Cha YH, Ding L, Yuan H. Effect of Body Positions on EEG signals in Mal de Debarquement Syndrome. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:1931-1934. [PMID: 30440776 DOI: 10.1109/embc.2018.8512699] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Multimodal neuroimaging, such as combined electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), are being increasingly used to investigate the human brain in healthy and diseased conditions. However, certain neuroimaging data are typically acquired in different body positions, e.g., supine fMRI and upright EEG, overlooking the effect of body position on signal characteristics. In the current study we examined EEG signals in three different positions, i.e., supine, standing and sitting, in patients with a balance disorder called mal de debarquement syndrome (MdDS). Individuals with MdDS experience a chronic illusion of self-motion triggered by prolonged exposure to passive motion, such as from sea or air travel. The degree of perception of rocking dizziness is modulated by body position, suggesting a physiological effect related to body positions. In the present study, EEG features were quantified as peak frequency, peak amplitude, and average amplitude of the alpha band due to its strongest signal characteristics compared to other frequencies. The effect of body position was examined in EEG features from data acquired before and after the individuals received treatment with repetitive transcranial magnetic stimulation. Our results indicate a significant effect of body positions on the EEG signals in MdDS.
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16
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Dong L, Luo C, Liu X, Jiang S, Li F, Feng H, Li J, Gong D, Yao D. Neuroscience Information Toolbox: An Open Source Toolbox for EEG-fMRI Multimodal Fusion Analysis. Front Neuroinform 2018; 12:56. [PMID: 30197593 PMCID: PMC6117508 DOI: 10.3389/fninf.2018.00056] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Accepted: 08/10/2018] [Indexed: 11/30/2022] Open
Abstract
Recently, scalp electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) multimodal fusion has been pursued in an effort to study human brain function and dysfunction to obtain more comprehensive information on brain activity in which the spatial and temporal resolutions are both satisfactory. However, a more flexible and easy-to-use toolbox for EEG–fMRI multimodal fusion is still lacking. In this study, we therefore developed a freely available and open-source MATLAB graphical user interface toolbox, known as the Neuroscience Information Toolbox (NIT), for EEG–fMRI multimodal fusion analysis. The NIT consists of three modules: (1) the fMRI module, which has batch fMRI preprocessing, nuisance signal removal, bandpass filtering, and calculation of resting-state measures; (2) the EEG module, which includes artifact removal, extracting EEG features (event onset, power, and amplitude), and marking interesting events; and (3) the fusion module, in which fMRI-informed EEG analysis and EEG-informed fMRI analysis are included. The NIT was designed to provide a convenient and easy-to-use toolbox for researchers, especially for novice users. The NIT can be downloaded for free at http://www.neuro.uestc.edu.cn/NIT.html, and detailed information, including the introduction of NIT, user’s manual and example data sets, can also be observed on this website. We hope that the NIT is a promising toolbox for exploring brain information in various EEG and fMRI studies.
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Affiliation(s)
- Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaobo Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongshuo Feng
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jianfu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Diankun Gong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
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17
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Mash LE, Reiter MA, Linke AC, Townsend J, Müller RA. Multimodal approaches to functional connectivity in autism spectrum disorders: An integrative perspective. Dev Neurobiol 2018; 78:456-473. [PMID: 29266810 PMCID: PMC5897150 DOI: 10.1002/dneu.22570] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Revised: 12/18/2017] [Accepted: 12/18/2017] [Indexed: 12/22/2022]
Abstract
Atypical functional connectivity has been implicated in autism spectrum disorders (ASDs). However, the literature to date has been largely inconsistent, with mixed and conflicting reports of hypo- and hyper-connectivity. These discrepancies are partly due to differences between various neuroimaging modalities. Functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG) measure distinct indices of functional connectivity (e.g., blood-oxygenation level-dependent [BOLD] signal vs. electrical activity). Furthermore, each method has unique benefits and disadvantages with respect to spatial and temporal resolution, vulnerability to specific artifacts, and practical implementation. Thus far, functional connectivity research on ASDs has remained almost exclusively unimodal; therefore, interpreting findings across modalities remains a challenge. Multimodal integration of fMRI, EEG, and MEG data is critical in resolving discrepancies in the literature, and working toward a unifying framework for interpreting past and future findings. This review aims to provide a theoretical foundation for future multimodal research on ASDs. First, we will discuss the merits and shortcomings of several popular theories in ASD functional connectivity research, using examples from the literature to date. Next, the neurophysiological relationships between imaging modalities, including their relationship with invasive neural recordings, will be reviewed. Finally, methodological approaches to multimodal data integration will be presented, and their future application to ASDs will be discussed. Analyses relating transient patterns of neural activity ("states") are particularly promising. This strategy provides a comparable measure across modalities, captures complex spatiotemporal patterns, and is a natural extension of recent dynamic fMRI research in ASDs. © 2017 Wiley Periodicals, Inc. Develop Neurobiol 78: 456-473, 2018.
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Affiliation(s)
- Lisa E. Mash
- SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University
| | - Maya A. Reiter
- SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University
| | - Annika C. Linke
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University
| | - Jeanne Townsend
- University of California, San Diego, Department of Neurosciences
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University
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18
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Liu Q, Ganzetti M, Wenderoth N, Mantini D. Detecting Large-Scale Brain Networks Using EEG: Impact of Electrode Density, Head Modeling and Source Localization. Front Neuroinform 2018; 12:4. [PMID: 29551969 PMCID: PMC5841019 DOI: 10.3389/fninf.2018.00004] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Accepted: 01/22/2018] [Indexed: 11/13/2022] Open
Abstract
Resting state networks (RSNs) in the human brain were recently detected using high-density electroencephalography (hdEEG). This was done by using an advanced analysis workflow to estimate neural signals in the cortex and to assess functional connectivity (FC) between distant cortical regions. FC analyses were conducted either using temporal (tICA) or spatial independent component analysis (sICA). Notably, EEG-RSNs obtained with sICA were very similar to RSNs retrieved with sICA from functional magnetic resonance imaging data. It still remains to be clarified, however, what technological aspects of hdEEG acquisition and analysis primarily influence this correspondence. Here we examined to what extent the detection of EEG-RSN maps by sICA depends on the electrode density, the accuracy of the head model, and the source localization algorithm employed. Our analyses revealed that the collection of EEG data using a high-density montage is crucial for RSN detection by sICA, but also the use of appropriate methods for head modeling and source localization have a substantial effect on RSN reconstruction. Overall, our results confirm the potential of hdEEG for mapping the functional architecture of the human brain, and highlight at the same time the interplay between acquisition technology and innovative solutions in data analysis.
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Affiliation(s)
- Quanying Liu
- Neural Control of Movement Laboratory, Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland.,Laboratory of Movement Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,Department of Control and Dynamical Systems, California Institute of Technology, Pasadena, CA, United States
| | - Marco Ganzetti
- Laboratory of Movement Control and Neuroplasticity, KU Leuven, Leuven, Belgium
| | - Nicole Wenderoth
- Neural Control of Movement Laboratory, Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland.,Laboratory of Movement Control and Neuroplasticity, KU Leuven, Leuven, Belgium
| | - Dante Mantini
- Neural Control of Movement Laboratory, Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland.,Laboratory of Movement Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
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19
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Li P, Huang X, Li F, Wang X, Zhou W, Liu H, Ma T, Zhang T, Guo D, Yao D, Xu P. Robust Granger Analysis in Lp Norm Space for Directed EEG Network Analysis. IEEE Trans Neural Syst Rehabil Eng 2017; 25:1959-1969. [PMID: 28600253 DOI: 10.1109/tnsre.2017.2711264] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Granger analysis (GA) is widely used to construct directed brain networks based on various physiological recordings, such as functional magnetic resonance imaging, and electroencephalogram (EEG). However, in real applications, EEGs are inevitably contaminated by unexpected artifacts that may distort the networks because of the L2 norm structure utilized in GAs when estimating directed links. Compared with the L2 norm, the Lp ( ) norm can compress outlier effects. In this paper, an extended GA is constructed by applying the Lp ( ) norm strategy to estimate robust causalities under outlier conditions, and a feasible iteration procedure is utilized to solve the new GA model. A quantitative evaluation using a predefined simulation network demonstrates smaller bias errors and higher linkage consistence for the Lp ( , 0.8, 0.6, 0.4, 0.2) -GAs compared with both the Lasso- and L2-GAs under various simulated outlier conditions. Applications in resting-state EEGs that contain ocular artifacts also show that the proposed GA can effectively compress the ocular outlier influence and recover the reliable networks. The proposed Lp-GA may be helpful in capturing the reliable network structure when EEGs are contaminated with artifacts in related studies.
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20
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Lei X, Liao K. Understanding the Influences of EEG Reference: A Large-Scale Brain Network Perspective. Front Neurosci 2017; 11:205. [PMID: 28450827 PMCID: PMC5390022 DOI: 10.3389/fnins.2017.00205] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Accepted: 03/27/2017] [Indexed: 11/16/2022] Open
Abstract
The influence of reference is a critical issue for the electroencephalography (EEG) and event-related potentials (ERPs) studies. However, previous investigations concentrated less on the location of source at a systematic neuroscience level. Our goal was to examine the EEG signal associated with the locations from a common network parcellation of the human brain function, offering a system perspective of the influence of EEG reference. In our simulation, vertices uniformly distributed in eight large-scale brain networks were adopted to generate the scalp EEG. The brain networks contain the visual, somatomotor, dorsal attention, ventral attention, limbic, frontoparietal, default networks, and the deep brain structure. The distributions of the most sensitive and neutral electrodes were calculated for each network based on the lead-field matrix. While the most sensitive electrode had a network-specific symmetric pattern, the electrodes in scalp surface had approximately equal chance to be the most neutral electrode. Simulated data were referenced at the FCz, the Oz, the mean mastoids (MM), the average (AVE), and the infinity reference obtained by the reference electrode standardization technique (REST). Intriguingly, the relative error followed the pattern REST<AVE<MM<(FCz, Oz), regardless of the number of electrodes and signal-to-noise ratios. Our findings suggested that REST was a potentially preferable reference for all large-scale networks and AVE virtually performed as REST under several conditions. As EEG and ERPs experiments within the same behavioral domain always have activations in some specific brain networks, the comparisons revealed here may provide a valuable recommendation for reference selection in clinical and basic researches.
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Affiliation(s)
- Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest UniversityChongqing, China.,Key Laboratory of Cognition and Personality of Ministry of EducationChongqing, China
| | - Keren Liao
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest UniversityChongqing, China.,Key Laboratory of Cognition and Personality of Ministry of EducationChongqing, China
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21
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Lifshitz M, Thibault RT, Roth RR, Raz A. Source Localization of Brain States Associated with Canonical Neuroimaging Postures. J Cogn Neurosci 2017; 29:1292-1301. [PMID: 28195522 DOI: 10.1162/jocn_a_01107] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Cognitive neuroscientists rarely consider the influence that body position exerts on brain activity; yet, postural variation holds important implications for the acquisition and interpretation of neuroimaging data. Whereas participants in most behavioral and EEG experiments sit upright, many prominent brain imaging techniques (e.g., fMRI) require participants to lie supine. Here we demonstrate that physical comportment profoundly alters baseline brain activity as measured by magnetoencephalography (MEG)-an imaging modality that permits multipostural acquisition. We collected resting-state MEG data from 12 healthy participants in three postures (lying supine, reclining at 45°, and sitting upright). Source-modeling analysis revealed a broadly distributed influence of posture on resting brain function. Sitting upright versus lying supine was associated with greater high-frequency (i.e., beta and gamma) activity in widespread parieto-occipital cortex. Moreover, sitting upright and reclining postures correlated with dampened activity in prefrontal regions across a range of bandwidths (i.e., from alpha to low gamma). The observed effects were large, with a mean Cohen's d of 0.95 ( SD = 0.23). In addition to neural activity, physiological parameters such as muscle tension and eye blinks may have contributed to these posture-dependent changes in brain signal. Regardless of the underlying mechanisms, however, the present results have important implications for the acquisition and interpretation of multimodal imaging data (e.g., studies combining fMRI or PET with EEG or MEG). More broadly, our findings indicate that generalizing results-from supine neuroimaging measurements to erect positions typical of ecological human behavior-would call for considering the influence that posture wields on brain dynamics.
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Affiliation(s)
- Michael Lifshitz
- 1 McGill University, Montreal, Canada.,2 Chapman University, Irvine, CA
| | - Robert T Thibault
- 1 McGill University, Montreal, Canada.,2 Chapman University, Irvine, CA
| | | | - Amir Raz
- 1 McGill University, Montreal, Canada.,2 Chapman University, Irvine, CA.,3 Sir Mortimer B. Davis Jewish General Hospital, Montreal, Canada
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22
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Yu Q, Wu L, Bridwell DA, Erhardt EB, Du Y, He H, Chen J, Liu P, Sui J, Pearlson G, Calhoun VD. Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study. Front Hum Neurosci 2016; 10:476. [PMID: 27733821 PMCID: PMC5039193 DOI: 10.3389/fnhum.2016.00476] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Accepted: 09/08/2016] [Indexed: 12/21/2022] Open
Abstract
The topological architecture of brain connectivity has been well-characterized by graph theory based analysis. However, previous studies have primarily built brain graphs based on a single modality of brain imaging data. Here we develop a framework to construct multi-modal brain graphs using concurrent EEG-fMRI data which are simultaneously collected during eyes open (EO) and eyes closed (EC) resting states. FMRI data are decomposed into independent components with associated time courses by group independent component analysis (ICA). EEG time series are segmented, and then spectral power time courses are computed and averaged within 5 frequency bands (delta; theta; alpha; beta; low gamma). EEG-fMRI brain graphs, with EEG electrodes and fMRI brain components serving as nodes, are built by computing correlations within and between fMRI ICA time courses and EEG spectral power time courses. Dynamic EEG-fMRI graphs are built using a sliding window method, versus static ones treating the entire time course as stationary. In global level, static graph measures and properties of dynamic graph measures are different across frequency bands and are mainly showing higher values in eyes closed than eyes open. Nodal level graph measures of a few brain components are also showing higher values during eyes closed in specific frequency bands. Overall, these findings incorporate fMRI spatial localization and EEG frequency information which could not be obtained by examining only one modality. This work provides a new approach to examine EEG-fMRI associations within a graph theoretic framework with potential application to many topics.
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Affiliation(s)
- Qingbao Yu
- The Mind Research Network Albuquerque, NM, USA
| | - Lei Wu
- The Mind Research Network Albuquerque, NM, USA
| | | | - Erik B Erhardt
- Department of Mathematics and Statistics, University of New Mexico Albuquerque, NM, USA
| | - Yuhui Du
- The Mind Research NetworkAlbuquerque, NM, USA; School of Information and Communication Engineering, North University of ChinaTaiyuan, China
| | - Hao He
- Department of Electrical and Computer Engineering, University of New Mexico Albuquerque, NM, USA
| | - Jiayu Chen
- The Mind Research Network Albuquerque, NM, USA
| | - Peng Liu
- The Mind Research NetworkAlbuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New MexicoAlbuquerque, NM, USA; Life Science Research Center, School of Life Sciences and Technology, Xidian UniversityShanxi, China
| | - Jing Sui
- The Mind Research NetworkAlbuquerque, NM, USA; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of SciencesBeijing, China
| | - Godfrey Pearlson
- Olin Neuropsychiatry Research CenterHartford, CT, USA; Department of Psychiatry, Yale UniversityNew Haven, CT, USA; Department of Neurobiology, Yale UniversityNew Haven, CT, USA
| | - Vince D Calhoun
- The Mind Research NetworkAlbuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New MexicoAlbuquerque, NM, USA; Department of Psychiatry, Yale UniversityNew Haven, CT, USA
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23
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Liu K, Yu ZL, Wu W, Gu Z, Li Y, Nagarajan S. Bayesian electromagnetic spatio-temporal imaging of extended sources with Markov Random Field and temporal basis expansion. Neuroimage 2016; 139:385-404. [PMID: 27355437 DOI: 10.1016/j.neuroimage.2016.06.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2016] [Revised: 05/23/2016] [Accepted: 06/16/2016] [Indexed: 10/21/2022] Open
Abstract
Estimating the locations and spatial extents of brain sources poses a long-standing challenge for electroencephalography and magnetoencephalography (E/MEG) source imaging. In the present work, a novel source imaging method, Bayesian Electromagnetic Spatio-Temporal Imaging of Extended Sources (BESTIES), which is built upon a Bayesian framework that determines the spatio-temporal smoothness of source activities in a fully data-driven fashion, is proposed to address this challenge. In particular, a Markov Random Field (MRF), which can precisely capture local cortical interactions, is employed to characterize the spatial smoothness of source activities, the temporal dynamics of which are modeled by a set of temporal basis functions (TBFs). Crucially, all of the unknowns in the MRF and TBF models are learned from the data. To accomplish model inference efficiently on high-resolution source spaces, a scalable algorithm is developed to approximate the posterior distribution of the source activities, which is based on the variational Bayesian inference and convex analysis. The performance of BESTIES is assessed using both simulated and actual human E/MEG data. Compared with L2-norm constrained methods, BESTIES is superior in reconstructing extended sources with less spatial diffusion and less localization error. By virtue of the MRF, BESTIES also overcomes the drawback of over-focal estimates in sparse constrained methods.
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Affiliation(s)
- Ke Liu
- College of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
| | - Zhu Liang Yu
- College of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China.
| | - Wei Wu
- College of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China; Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, United States.
| | - Zhenghui Gu
- College of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
| | - Yuanqing Li
- College of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
| | - Srikantan Nagarajan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143, United States.
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24
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Peng W, Tang D. Pain Related Cortical Oscillations: Methodological Advances and Potential Applications. Front Comput Neurosci 2016; 10:9. [PMID: 26869915 PMCID: PMC4740361 DOI: 10.3389/fncom.2016.00009] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Accepted: 01/18/2016] [Indexed: 01/14/2023] Open
Abstract
Alongside the time-locked event-related potentials (ERPs), nociceptive somatosensory inputs can induce modulations of ongoing oscillations, appeared as event-related synchronization or desynchronization (ERS/ERD) in different frequency bands. These ERD/ERS activities are suggested to reflect various aspects of pain perception, including the representation, encoding, assessment, and integration of the nociceptive sensory inputs, as well as behavioral responses to pain, even the precise details of their roles remain unclear. Previous studies investigating the functional relevance of ERD/ERS activities in pain perception were normally done by assessing their latencies, frequencies, magnitudes, and scalp distributions, which would be then correlated with subjective pain perception or stimulus intensity. Nevertheless, these temporal, spectral, and spatial profiles of stimulus induced ERD/ERS could only partly reveal the dynamics of brain oscillatory activities. Indeed, additional parameters, including but not limited to, phase, neural generator, and cross frequency couplings, should be paid attention to comprehensively and systemically evaluate the dynamics of oscillatory activities associated with pain perception and behavior. This would be crucial in exploring the psychophysiological mechanisms of neural oscillation, and in understanding the neural functions of cortical oscillations involved in pain perception and behavior. Notably, some chronic pain (e.g., neurogenic pain and complex regional pain syndrome) patients are often associated with the occurrence of abnormal synchronized oscillatory brain activities, and selectively modulating cortical oscillatory activities has been showed to be a potential therapy strategy to relieve pain with the application of neurostimulation techniques, e.g., repeated transcranial magnetic stimulation (rTMS) and transcranial alternating current stimulation (tACS). Thus, the investigation of the oscillatory activities proceeding from phenomenology to function, opens new perspectives to address questions in human pain psychophysiology and pathophysiology, thereby promoting the establishment of rational therapeutic strategy.
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Affiliation(s)
- Weiwei Peng
- Key Laboratory of Cognition and Personality (Ministry of Education), Faculty of Psychology, Southwest University Chongqing, China
| | - Dandan Tang
- School of Education Science, Zunyi Normal College Guizhou, China
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25
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Amyot F, Arciniegas DB, Brazaitis MP, Curley KC, Diaz-Arrastia R, Gandjbakhche A, Herscovitch P, Hinds SR, Manley GT, Pacifico A, Razumovsky A, Riley J, Salzer W, Shih R, Smirniotopoulos JG, Stocker D. A Review of the Effectiveness of Neuroimaging Modalities for the Detection of Traumatic Brain Injury. J Neurotrauma 2015; 32:1693-721. [PMID: 26176603 PMCID: PMC4651019 DOI: 10.1089/neu.2013.3306] [Citation(s) in RCA: 114] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The incidence of traumatic brain injury (TBI) in the United States was 3.5 million cases in 2009, according to the Centers for Disease Control and Prevention. It is a contributing factor in 30.5% of injury-related deaths among civilians. Additionally, since 2000, more than 260,000 service members were diagnosed with TBI, with the vast majority classified as mild or concussive (76%). The objective assessment of TBI via imaging is a critical research gap, both in the military and civilian communities. In 2011, the Department of Defense (DoD) prepared a congressional report summarizing the effectiveness of seven neuroimaging modalities (computed tomography [CT], magnetic resonance imaging [MRI], transcranial Doppler [TCD], positron emission tomography, single photon emission computed tomography, electrophysiologic techniques [magnetoencephalography and electroencephalography], and functional near-infrared spectroscopy) to assess the spectrum of TBI from concussion to coma. For this report, neuroimaging experts identified the most relevant peer-reviewed publications and assessed the quality of the literature for each of these imaging technique in the clinical and research settings. Although CT, MRI, and TCD were determined to be the most useful modalities in the clinical setting, no single imaging modality proved sufficient for all patients due to the heterogeneity of TBI. All imaging modalities reviewed demonstrated the potential to emerge as part of future clinical care. This paper describes and updates the results of the DoD report and also expands on the use of angiography in patients with TBI.
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Affiliation(s)
- Franck Amyot
- The Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - David B. Arciniegas
- Beth K. and Stuart C. Yudofsky Division of Neuropsychiatry, Baylor College of Medicine, Houston, Texas
- Brain Injury Research, TIRR Memorial Hermann, Houston, Texas
| | | | - Kenneth C. Curley
- Combat Casualty Care Directorate (RAD2), U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland
| | - Ramon Diaz-Arrastia
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Amir Gandjbakhche
- The Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland
| | - Peter Herscovitch
- Positron Emission Tomography Department, National Institutes of Health Clinical Center, Bethesda, Maryland
| | - Sidney R. Hinds
- Defense and Veterans Brain Injury Center, Defense Centers of Excellence for Psychological Health and Traumatic Brain Injury Silver Spring, Maryland
| | - Geoffrey T. Manley
- Brain and Spinal Injury Center, Department of Neurological Surgery, University of California, San Francisco, San Francisco, California
| | - Anthony Pacifico
- Congressionally Directed Medical Research Programs, Fort Detrick, Maryland
| | | | - Jason Riley
- Queens University, Kingston, Ontario, Canada
- ArcheOptix Inc., Picton, Ontario, Canada
| | - Wanda Salzer
- Congressionally Directed Medical Research Programs, Fort Detrick, Maryland
| | - Robert Shih
- Walter Reed National Military Medical Center, Bethesda, Maryland
| | - James G. Smirniotopoulos
- Department of Radiology, Neurology, and Biomedical Informatics, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Derek Stocker
- Walter Reed National Military Medical Center, Bethesda, Maryland
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26
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Body position alters human resting-state: Insights from multi-postural magnetoencephalography. Brain Imaging Behav 2015; 10:772-80. [DOI: 10.1007/s11682-015-9447-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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27
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Lei X, Wu T, Valdes-Sosa PA. Incorporating priors for EEG source imaging and connectivity analysis. Front Neurosci 2015; 9:284. [PMID: 26347599 PMCID: PMC4539512 DOI: 10.3389/fnins.2015.00284] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2015] [Accepted: 07/29/2015] [Indexed: 01/21/2023] Open
Abstract
Electroencephalography source imaging (ESI) is a useful technique to localize the generators from a given scalp electric measurement and to investigate the temporal dynamics of the large-scale neural circuits. By introducing reasonable priors from other modalities, ESI reveals the most probable sources and communication structures at every moment in time. Here, we review the available priors from such techniques as magnetic resonance imaging (MRI), functional MRI (fMRI), and positron emission tomography (PET). The modality's specific contribution is analyzed from the perspective of source reconstruction. For spatial priors, EEG-correlated fMRI, temporally coherent networks (TCNs) and resting-state fMRI are systematically introduced in the ESI. Moreover, the fiber tracking (diffusion tensor imaging, DTI) and neuro-stimulation techniques (transcranial magnetic stimulation, TMS) are also introduced as the potential priors, which can help to draw inferences about the neuroelectric connectivity in the source space. We conclude that combining EEG source imaging with other complementary modalities is a promising approach toward the study of brain networks in cognitive and clinical neurosciences.
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Affiliation(s)
- Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University Chongqing, China ; Key Laboratory of Cognition and Personality, Ministry of Education Chongqing, China
| | - Taoyu Wu
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University Chongqing, China ; Key Laboratory of Cognition and Personality, Ministry of Education Chongqing, China
| | - Pedro A Valdes-Sosa
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China ; Cuban Neuroscience Center Cubanacan, Playa, Cuba
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Abstract
Aim. In recent years, connectivity studies using neuroimaging data have increased the understanding of the organization of large-scale structural and functional brain networks. However, data analysis is time consuming as rigorous procedures must be assured, from structuring data and pre-processing to modality specific data procedures. Until now, no single toolbox was able to perform such investigations on truly multimodal image data from beginning to end, including the combination of different connectivity analyses. Thus, we have developed the Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox with the goal of diminishing time waste in data processing and to allow an innovative and comprehensive approach to brain connectivity. Materials and Methods. The MIBCA toolbox is a fully automated all-in-one connectivity toolbox that offers pre-processing, connectivity and graph theoretical analyses of multimodal image data such as diffusion-weighted imaging, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). It was developed in MATLAB environment and pipelines well-known neuroimaging softwares such as Freesurfer, SPM, FSL, and Diffusion Toolkit. It further implements routines for the construction of structural, functional and effective or combined connectivity matrices, as well as, routines for the extraction and calculation of imaging and graph-theory metrics, the latter using also functions from the Brain Connectivity Toolbox. Finally, the toolbox performs group statistical analysis and enables data visualization in the form of matrices, 3D brain graphs and connectograms. In this paper the MIBCA toolbox is presented by illustrating its capabilities using multimodal image data from a group of 35 healthy subjects (19–73 years old) with volumetric T1-weighted, diffusion tensor imaging, and resting state fMRI data, and 10 subjets with 18F-Altanserin PET data also. Results. It was observed both a high inter-hemispheric symmetry and an intra-hemispheric modularity associated with structural data, whilst functional data presented lower inter-hemispheric symmetry and a high inter-hemispheric modularity. Furthermore, when testing for differences between two subgroups (<40 and >40 years old adults) we observed a significant reduction in the volume and thickness, and an increase in the mean diffusivity of most of the subcortical/cortical regions. Conclusion. While bridging the gap between the high numbers of packages and tools widely available for the neuroimaging community in one toolbox, MIBCA also offers different possibilities for combining, analysing and visualising data in novel ways, enabling a better understanding of the human brain.
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Affiliation(s)
- Andre Santos Ribeiro
- Division of Brain Sciences, Department of Medicine, Centre for Neuropsychopharmacology, Imperial College London , UK ; Faculty of Sciences, Institute of Biophysics and Biomedical Engineering, University of Lisbon , Portugal
| | - Luis Miguel Lacerda
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London , UK ; Faculty of Sciences, Institute of Biophysics and Biomedical Engineering, University of Lisbon , Portugal
| | - Hugo Alexandre Ferreira
- Faculty of Sciences, Institute of Biophysics and Biomedical Engineering, University of Lisbon , Portugal
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Dong L, Zhang Y, Zhang R, Zhang X, Gong D, Valdes-Sosa PA, Xu P, Luo C, Yao D. Characterizing nonlinear relationships in functional imaging data using eigenspace maximal information canonical correlation analysis (emiCCA). Neuroimage 2015; 109:388-401. [PMID: 25592998 DOI: 10.1016/j.neuroimage.2015.01.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Revised: 12/24/2014] [Accepted: 01/01/2015] [Indexed: 10/24/2022] Open
Abstract
Many important problems in the analysis of neuroimages can be formulated as discovering the relationship between two sets of variables, a task for which linear techniques such as canonical correlation analysis (CCA) have been commonly used. However, to further explore potential nonlinear processes that might co-exist with linear ones in brain function, a more flexible method is required. Here, we propose a new unsupervised and data-driven method, termed the eigenspace maximal information canonical correlation analysis (emiCCA), which is capable of automatically capturing the linear and/or nonlinear relationships between various data sets. A simulation confirmed the superior performance of emiCCA in comparison with linear CCA and kernel CCA (a nonlinear version of CCA). An emiCCA framework for functional magnetic resonance imaging (fMRI) data processing was designed and applied to data from a real motor execution fMRI experiment. This analysis uncovered one linear (in primary motor cortex) and a few nonlinear networks (e.g., in the supplementary motor area, bilateral insula, and cerebellum). This suggests that these various task-related brain areas are part of networks that also contribute to the execution of movements of the hand. These results suggest that emiCCA is a promising technique for exploring various data.
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Affiliation(s)
- Li Dong
- The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yangsong Zhang
- The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Rui Zhang
- The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xingxing Zhang
- The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Diankun Gong
- The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Pedro A Valdes-Sosa
- The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China; Cuban Neuroscience Center, Havana, Cuba
| | - Peng Xu
- The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Cheng Luo
- The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Dezhong Yao
- The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
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30
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Jiang T, Zhou Y. Brainnetome of schizophrenia: focus on impaired cognitive function. SHANGHAI ARCHIVES OF PSYCHIATRY 2014; 24:3-10. [PMID: 25324595 PMCID: PMC4198886 DOI: 10.3969/j.issn.1002-0829.2012.01.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Impaired cognitive function, along with positive and negative symptoms, is a core clinical feature of schizophrenia. Earlier studies suggest that impaired cognitive functioning should be assessed from the perspective of brain networks. The recently developed brainnetome approach to evaluating brain networks—an approach that was initially developed by Chinese scientists—provides a new methodology for studying this issue. In this paper we first introduce the concept of brainnetome. We then review recent progress in developing a brainnetome of impaired cognitive function in people with schizophrenia. The models of the relevant brain networks considered were created using data obtained from functional and anatomical brain imaging technologies at different levels of analysis: networks centered on regions of interest, networks related to specific cognitive functions, whole brain networks, and the attributes of brain networks. Finally, we discuss the current challenges and potential new directions for research about brainnetome.
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Affiliation(s)
- Tianzi Jiang
- LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, China
| | - Yuan Zhou
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
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31
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Brain Scale-free Properties in Awake Rest and NREM Sleep: A Simultaneous EEG/fMRI Study. Brain Topogr 2014; 28:292-304. [DOI: 10.1007/s10548-014-0399-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2014] [Accepted: 09/06/2014] [Indexed: 11/27/2022]
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32
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Dong L, Gong D, Valdes-Sosa PA, Xia Y, Luo C, Xu P, Yao D. Simultaneous EEG-fMRI: trial level spatio-temporal fusion for hierarchically reliable information discovery. Neuroimage 2014; 99:28-41. [PMID: 24852457 DOI: 10.1016/j.neuroimage.2014.05.029] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2013] [Revised: 04/15/2014] [Accepted: 05/07/2014] [Indexed: 11/16/2022] Open
Abstract
Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have been pursued in an effort to integrate complementary noninvasive information on brain activity. The primary goal involves better information discovery of the event-related neural activations at a spatial region of the BOLD fluctuation with the temporal resolution of the electrical signal. Many techniques and algorithms have been developed to integrate EEGs and fMRIs; however, the relative reliability of the integrated information is unclear. In this work, we propose a hierarchical framework to ensure the relative reliability of the integrated results and attempt to understand brain activation using this hierarchical ideal. First, spatial Independent Component Analysis (ICA) of fMRI and temporal ICA of EEG were performed to extract features at the trial level. Second, the maximal information coefficient (MIC) was adopted to temporally match them across the modalities for both linear and non-linear associations. Third, fMRI-constrained EEG source imaging was utilized to spatially match components across modalities. The simultaneously occurring events in the above two match steps provided EEG-fMRI spatial-temporal reliable integrated information, resulting in the most reliable components with high spatial and temporal resolution information. The other components discovered in the second or third steps provided second-level complementary information for flexible and cautious explanations. This paper contains two simulations and an example of real data, and the results indicate that the framework is a feasible approach to reveal cognitive processing in the human brain.
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Affiliation(s)
- Li Dong
- The Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Diankun Gong
- The Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Pedro A Valdes-Sosa
- The Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China; Cuban Neuroscience Center, School of Life Science and Technology, Havana, Cuba
| | - Yang Xia
- The Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Cheng Luo
- The Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Peng Xu
- The Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Dezhong Yao
- The Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
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33
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Lu Y, Wang J, Wang L, Wang J, Qin J. Neural responses to cartoon facial attractiveness: An event-related potential study. Neurosci Bull 2014; 30:441-50. [PMID: 24526658 DOI: 10.1007/s12264-013-1401-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2013] [Accepted: 08/22/2013] [Indexed: 11/25/2022] Open
Abstract
Animation creates a vivid, virtual world and expands the scope of human imagination. In this study, we investigated the time-courses of brain responses related to the evaluation of the attractiveness of cartoon faces using the event-related potential (ERP) technique. The results demonstrated that N170 amplitude was higher for attractive than for unattractive cartoon faces in males, while the opposite was found in females. Facial attractiveness notably modulated the late positive component (LPC), which might reflect the task-related process of aesthetic appraisal of beauty. The mean LPC amplitude in males was significantly higher for attractive cartoon faces than for unattractive faces, while the LPC amplitude in females did not significantly differ between attractive and unattractive cartoon faces. Moreover, the paint mode (computer graphics, gouache, and stick figure) modulated the early encoding of facial structures and the late evaluative process. The early modulation effect by paint mode may be related to the spatial frequency of the pictures. The processing speed and intensity in females were both higher than those in males. In conclusion, our study, for the first time, reported ERP modulation based on the assessment of cartoon facial attractiveness, suggesting the facilitated selection of attractiveness information at the early stage, and that the attentional enhancement of attractive faces at the late stage only exists in males. This suggests that men's brains are hard-wired to be sensitive to facial beauty, even in cartoons.
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Affiliation(s)
- Yingjun Lu
- Laboratory of Developmental Cognitive Neuroscience, Zhejiang Normal University, Hangzhou, 310012, China,
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34
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Lei X, Wang Y, Yuan H, Mantini D. Neuronal oscillations and functional interactions between resting state networks. Hum Brain Mapp 2013; 35:3517-28. [PMID: 25050432 DOI: 10.1002/hbm.22418] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Functional magnetic imaging (fMRI) studies showed that resting state activity in the healthy brain is organized into multiple large-scale networks encompassing distant regions. A key finding of resting state fMRI studies is the anti-correlation typically observed between the dorsal attention network (DAN) and the default mode network (DMN), which - during task performance - are activated and deactivated, respectively. Previous studies have suggested that alcohol administration modulates the balance of activation/deactivation in brain networks, as well as it induces significant changes in oscillatory activity measured by electroencephalography (EEG). However, our knowledge of alcohol-induced changes in band-limited EEG power and their potential link with the functional interactions between DAN and DMN is still very limited. Here we address this issue, examining the neuronal effects of alcohol administration during resting state by using simultaneous EEG-fMRI. Our findings show increased EEG power in the theta frequency band (4-8 Hz) after administration of alcohol compared to placebo, which was prominent over the frontal cortex. More interestingly, increased frontal tonic EEG activity in this band was associated with greater anti-correlation between the DAN and the frontal component of the DMN. Furthermore, EEG theta power and DAN-DMN anti-correlation were relatively greater in subjects who reported a feeling of euphoria after alcohol administration, which may result from a diminished inhibition exerted by the prefrontal cortex. Overall, our findings suggest that slow brain rhythms are responsible for dynamic functional interactions between brain networks. They also confirm the applicability and potential usefulness of EEG-fMRI for central nervous system drug research.
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35
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Zhang X, Lei X, Wu T, Jiang T. A review of EEG and MEG for brainnetome research. Cogn Neurodyn 2013; 8:87-98. [PMID: 24624229 DOI: 10.1007/s11571-013-9274-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2013] [Revised: 10/17/2013] [Accepted: 11/06/2013] [Indexed: 11/29/2022] Open
Abstract
The majority of brain activities are performed by functionally integrating separate regions of the brain. Therefore, the synchronous operation of the brain's multiple regions or neuronal assemblies can be represented as a network with nodes that are interconnected by links. Because of the complexity of brain interactions and their varying effects at different levels of complexity, one of the corresponding authors of this paper recently proposed the brainnetome as a new -ome to explore and integrate the brain network at different scales. Because electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive and have outstanding temporal resolution and because they are the primary clinical techniques used to capture the dynamics of neuronal connections, they lend themselves to the analysis of the neural networks comprising the brainnetome. Because of EEG/MEG's applicability to brainnetome analyses, the aim of this review is to identify the procedures that can be used to form a network using EEG/MEG data in sensor or source space and to promote EEG/MEG network analysis for either neuroscience or clinical applications. To accomplish this aim, we show the relationship of the brainnetome to brain networks at the macroscale and provide a systematic review of network construction using EEG and MEG. Some potential applications of the EEG/MEG brainnetome are to use newly developed methods to associate the properties of a brainnetome with indices of cognition or disease conditions. Associations based on EEG/MEG brainnetome analysis may improve the comprehension of the functioning of the brain in neuroscience research or the recognition of abnormal patterns in neurological disease.
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Affiliation(s)
- Xin Zhang
- Brainnetome Center, Institute of Automation, The Chinese Academy of Sciences, Beijing, 100190 China ; National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, 100190 China
| | - Xu Lei
- Key Laboratory of Cognition and Personality (Ministry of Education) and School of Psychology, Southwest University, Chongqing, China ; Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 China
| | - Ting Wu
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 China ; Department of Magnetoencephalography, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, 210029 China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, The Chinese Academy of Sciences, Beijing, 100190 China ; National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, 100190 China ; Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054 China ; The Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072 Australia
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36
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Extraversion is encoded by scale-free dynamics of default mode network. Neuroimage 2013; 74:52-7. [PMID: 23454049 DOI: 10.1016/j.neuroimage.2013.02.020] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2012] [Revised: 02/06/2013] [Accepted: 02/11/2013] [Indexed: 11/21/2022] Open
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Zhang J, Raij T, Hämäläinen M, Yao D. MEG source localization using invariance of noise space. PLoS One 2013; 8:e58408. [PMID: 23505502 PMCID: PMC3591341 DOI: 10.1371/journal.pone.0058408] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2012] [Accepted: 02/06/2013] [Indexed: 11/18/2022] Open
Abstract
We propose INvariance of Noise (INN) space as a novel method for source localization of magnetoencephalography (MEG) data. The method is based on the fact that modulations of source strengths across time change the energy in signal subspace but leave the noise subspace invariant. We compare INN with classical MUSIC, RAP-MUSIC, and beamformer approaches using simulated data while varying signal-to-noise ratios as well as distance and temporal correlation between two sources. We also demonstrate the utility of INN with actual auditory evoked MEG responses in eight subjects. In all cases, INN performed well, especially when the sources were closely spaced, highly correlated, or one source was considerably stronger than the other.
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Affiliation(s)
- Junpeng Zhang
- Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States of America
- Department of Biomedical Engineering, Chengdu Medical College, Chengdu, China
| | - Tommi Raij
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States of America
| | - Matti Hämäläinen
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States of America
| | - Dezhong Yao
- Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China
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Billeke P, Aboitiz F. Social cognition in schizophrenia: from social stimuli processing to social engagement. Front Psychiatry 2013; 4:4. [PMID: 23444313 PMCID: PMC3580762 DOI: 10.3389/fpsyt.2013.00004] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2012] [Accepted: 02/06/2013] [Indexed: 11/13/2022] Open
Abstract
Social cognition consists of several skills which allow us to interact with other humans. These skills include social stimuli processing, drawing inferences about others' mental states, and engaging in social interactions. In recent years, there has been growing evidence of social cognitive impairments in patients with schizophrenia. Apparently, these impairments are separable from general neurocognitive impairments, such as attention, memory, and executive functioning. Moreover, social cognition seems to be a main determinant of functional outcome and could be used as a guide to elaborate new pharmacological and psychological treatments. However, most of these studies focus on individual mechanisms and observational perspectives; only few of them study schizophrenic patients during interactive situations. We first review evidences of social cognitive impairments both in social stimuli processing and in mental state attribution. We focus on the relationship between these functions and both general cognitive impairments and functional outcome. We next review recent game theory approaches to the study of how social engagement occurs in schizophrenic patients. The advantage of using game theory is that game-oriented tasks can assess social decision making in an interactive everyday situation model. Finally, we review proposed theoretical models used to explain social alterations and their underlying biological mechanisms. Based on interactive studies, we propose a framework which takes into account the dynamic nature of social processes. Thus, understanding social skills as a result of dynamical systems could facilitate the development of both basic research and clinical applications oriented to psychiatric populations.
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Affiliation(s)
- Pablo Billeke
- Centro Interdisciplinario de Neurociencia, Pontificia Universidad Católica de ChileSantiago, Chile
- Departamento de Psiquiatría, Escuela de Medicina, Pontificia Universidad Católica de ChileSantiago, Chile
| | - Francisco Aboitiz
- Centro Interdisciplinario de Neurociencia, Pontificia Universidad Católica de ChileSantiago, Chile
- Departamento de Psiquiatría, Escuela de Medicina, Pontificia Universidad Católica de ChileSantiago, Chile
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39
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Chen Z, Lei X, Ding C, Li H, Chen A. The neural mechanisms of semantic and response conflicts: an fMRI study of practice-related effects in the Stroop task. Neuroimage 2012; 66:577-84. [PMID: 23103691 DOI: 10.1016/j.neuroimage.2012.10.028] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2012] [Revised: 10/01/2012] [Accepted: 10/19/2012] [Indexed: 11/19/2022] Open
Abstract
Previous studies have demonstrated that there are separate neural mechanisms underlying semantic and response conflicts in the Stroop task. However, the practice effects of these conflicts need to be elucidated and the possible involvements of common neural mechanisms are yet to be established. We employed functional magnetic resonance imaging (fMRI) in a 4-2 mapping practice-related Stroop task to determine the neural substrates under these conflicts. Results showed that different patterns of brain activations are associated with practice in the attentional networks (e.g., dorsolateral prefrontal cortex (DLPFC), anterior cingulate cortex (ACC), and posterior parietal cortex (PPC)) for both conflicts, response control regions (e.g., inferior frontal junction (IFJ), inferior frontal gyrus (IFG)/insula, and pre-supplementary motor areas (pre-SMA)) for semantic conflict, and posterior cortex for response conflict. We also found areas of common activation in the left hemisphere within the attentional networks, for the early practice stage in semantic conflict and the late stage in "pure" response conflict using conjunction analysis. The different practice effects indicate that there are distinct mechanisms underlying these two conflict types: semantic conflict practice effects are attributable to the automation of stimulus processing, conflict and response control; response conflict practice effects are attributable to the proportional increase of conflict-related cognitive resources. In addition, the areas of common activation suggest that the semantic conflict effect may contain a partial response conflict effect, particularly at the beginning of the task. These findings indicate that there are two kinds of response conflicts contained in the key-pressing Stroop task: the vocal-level (mainly in the early stage) and key-pressing (mainly in the late stage) response conflicts; thus, the use of the subtraction method for the exploration of semantic and response conflicts may need to be further examined.
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Affiliation(s)
- Zhencai Chen
- Key Laboratory of Cognition and Personality (Ministry of Education) and School of Psychology, Southwest University, Chongqing, China
| | - Xu Lei
- Key Laboratory of Cognition and Personality (Ministry of Education) and School of Psychology, Southwest University, Chongqing, China
| | - Cody Ding
- Key Laboratory of Cognition and Personality (Ministry of Education) and School of Psychology, Southwest University, Chongqing, China; Department of Educational Psychology, Research, and Evaluation, University of Missouri-St. Louis, St. Louis, MO, USA
| | - Hong Li
- Research Center of Psychological Development and Education, Liaoning Normal University, Dalian, Liaoning, China
| | - Antao Chen
- Key Laboratory of Cognition and Personality (Ministry of Education) and School of Psychology, Southwest University, Chongqing, China; Research Center of Psychological Development and Education, Liaoning Normal University, Dalian, Liaoning, China.
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40
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Lei X, Valdes-Sosa PA, Yao D. EEG/fMRI fusion based on independent component analysis: integration of data-driven and model-driven methods. J Integr Neurosci 2012; 11:313-37. [PMID: 22985350 DOI: 10.1142/s0219635212500203] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide complementary noninvasive information of brain activity, and EEG/fMRI fusion can achieve higher spatiotemporal resolution than each modality separately. This focuses on independent component analysis (ICA)-based EEG/fMRI fusion. In order to appreciate the issues, we first describe the potential and limitations of the developed fusion approaches: fMRI-constrained EEG imaging, EEG-informed fMRI analysis, and symmetric fusion. We then outline some newly developed hybrid fusion techniques using ICA and the combination of data-/model-driven methods, with special mention of the spatiotemporal EEG/fMRI fusion (STEFF). Finally, we discuss the current trend in methodological development and the existing limitations for extrapolating neural dynamics.
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Affiliation(s)
- Xu Lei
- Key Laboratory of Cognition and Personality (Ministry of Education) and School of Psychology, Southwest University, Chongqing, 400715, PR China.
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Integration of consonant and pitch processing as revealed by the absence of additivity in mismatch negativity. PLoS One 2012; 7:e38289. [PMID: 22693614 PMCID: PMC3365020 DOI: 10.1371/journal.pone.0038289] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2011] [Accepted: 05/03/2012] [Indexed: 11/25/2022] Open
Abstract
Consonants, unlike vowels, are thought to be speech specific and therefore no interactions would be expected between consonants and pitch, a basic element for musical tones. The present study used an electrophysiological approach to investigate whether, contrary to this view, there is integrative processing of consonants and pitch by measuring additivity of changes in the mismatch negativity (MMN) of evoked potentials. The MMN is elicited by discriminable variations occurring in a sequence of repetitive, homogeneous sounds. In the experiment, event-related potentials (ERPs) were recorded while participants heard frequently sung consonant-vowel syllables and rare stimuli deviating in either consonant identity only, pitch only, or in both dimensions. Every type of deviation elicited a reliable MMN. As expected, the two single-deviant MMNs had similar amplitudes, but that of the double-deviant MMN was also not significantly different from them. This absence of additivity in the double-deviant MMN suggests that consonant and pitch variations are processed, at least at a pre-attentive level, in an integrated rather than independent way. Domain-specificity of consonants may depend on higher-level processes in the hierarchy of speech perception.
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Mijović B, Vanderperren K, Novitskiy N, Vanrumste B, Stiers P, Van den Bergh B, Lagae L, Sunaert S, Wagemans J, Van Huffel S, De Vos M. The “why” and “how” of JointICA: Results from a visual detection task. Neuroimage 2012; 60:1171-85. [DOI: 10.1016/j.neuroimage.2012.01.063] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2011] [Revised: 12/14/2011] [Accepted: 01/08/2012] [Indexed: 11/30/2022] Open
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Fukushima M, Yamashita O, Kanemura A, Ishii S, Kawato M, Sato MA. A state-space modeling approach for localization of focal current sources from MEG. IEEE Trans Biomed Eng 2012; 59:1561-71. [PMID: 22394573 DOI: 10.1109/tbme.2012.2189713] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
State-space modeling is a promising approach for current source reconstruction from magnetoencephalography (MEG) because it constrains the spatiotemporal behavior of inverse solutions in a flexible manner. However, state-space model-based source localization research remains underdeveloped; extraction of spatially focal current sources and handling of the high dimensionality of the distributed source model remain problematic. In this study, we propose a novel state-space model-based method that resolves these problems, extending our previous source localization method to include a temporal constraint by state-space modeling. To enable focal current reconstruction, we account for spatially inhomogeneous temporal dynamics by introducing dynamics model parameters that differ for each cortical position. The model parameters and the intensity of the current sources are jointly estimated according to a bayesian framework. We circumvent the high dimensionality of the problem by assuming prior distributions of the model parameters to reduce the sensitivity to unmodeled components, and by adopting variational bayesian inference to reduce the computational cost. Through simulation experiments and application to real MEG data, we have confirmed that our proposed method successfully reconstructs focal current activities, which evolve with their temporal dynamics.
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Affiliation(s)
- Makoto Fukushima
- Graduate School of Information Science, Nara Institute of Science and Technology, Nara 630-0192, Japan.
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Luo C, Qiu C, Guo Z, Fang J, Li Q, Lei X, Xia Y, Lai Y, Gong Q, Zhou D, Yao D. Disrupted functional brain connectivity in partial epilepsy: a resting-state fMRI study. PLoS One 2012; 7:e28196. [PMID: 22242146 PMCID: PMC3252302 DOI: 10.1371/journal.pone.0028196] [Citation(s) in RCA: 87] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2011] [Accepted: 11/03/2011] [Indexed: 11/19/2022] Open
Abstract
Examining the spontaneous activity to understand the neural mechanism of brain disorder is a focus in recent resting-state fMRI. In the current study, to investigate the alteration of brain functional connectivity in partial epilepsy in a systematical way, two levels of analyses (functional connectivity analysis within resting state networks (RSNs) and functional network connectivity (FNC) analysis) were carried out on resting-state fMRI data acquired from the 30 participants including 14 healthy controls(HC) and 16 partial epilepsy patients. According to the etiology, all patients are subdivided into temporal lobe epilepsy group (TLE, included 7 patients) and mixed partial epilepsy group (MPE, 9 patients). Using group independent component analysis, eight RSNs were identified, and selected to evaluate functional connectivity and FNC between groups. Compared with the controls, decreased functional connectivity within all RSNs was found in both TLE and MPE. However, dissociating patterns were observed within the 8 RSNs between two patient groups, i.e, compared with TLE, we found decreased functional connectivity in 5 RSNs increased functional connectivity in 1 RSN, and no difference in the other 2 RSNs in MPE. Furthermore, the hierarchical disconnections of FNC was found in two patient groups, in which the intra-system connections were preserved for all three subsystems while the lost connections were confined to intersystem connections in patients with partial epilepsy. These findings may suggest that decreased resting state functional connectivity and disconnection of FNC are two remarkable characteristics of partial epilepsy. The selective impairment of FNC implicated that it is unsuitable to understand the partial epilepsy only from global or local perspective. We presumed that studying epilepsy in the multi-perspective based on RSNs may be a valuable means to assess the functional changes corresponding to specific RSN and may contribute to the understanding of the neuro-pathophysiological mechanism of epilepsy.
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Affiliation(s)
- Cheng Luo
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chuan Qiu
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhiwei Guo
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiajia Fang
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, China
| | - Qifu Li
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, China
- Department of Neurology, The Affiliated Hospital of Hainan Medical College, Haikou, China
| | - Xu Lei
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Key Laboratory of Cognition and Personality, Ministry of Education, School of Psychology, Southwest University, Chongqing, China
| | - Yang Xia
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yongxiu Lai
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Qiyong Gong
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Dong Zhou
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, China
- * E-mail: (DY); (DZ)
| | - Dezhong Yao
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- * E-mail: (DY); (DZ)
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Multimodal functional network connectivity: an EEG-fMRI fusion in network space. PLoS One 2011; 6:e24642. [PMID: 21961040 PMCID: PMC3178514 DOI: 10.1371/journal.pone.0024642] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2010] [Accepted: 08/17/2011] [Indexed: 11/20/2022] Open
Abstract
EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state.
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Incorporating FMRI functional networks in EEG source imaging: a Bayesian model comparison approach. Brain Topogr 2011; 25:27-38. [PMID: 21547481 DOI: 10.1007/s10548-011-0187-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2011] [Accepted: 04/21/2011] [Indexed: 10/18/2022]
Abstract
Brain functional networks extracted from fMRI can improve the accuracy of EEG source localization. However, the coupling between EEG and fMRI remains poorly understood, i.e., whether fMRI networks provide information about the magnitude of neural activity, and whether neural sources demonstrate temporal correlations within each network. In this paper, we present an improved version of the NEtwork-based SOurce Imaging method (iNESOI) through Bayesian model comparison. Different models correspond to various matching between EEG and fMRI, and the appropriate one is selected by data with the model evidence. Synthetic and real data tests show that iNESOI has potential to select the appropriate fMRI priors to reach a better source reconstruction than some other typical approaches.
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