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Cao J, Bulger E, Shinn-Cunningham B, Grover P, Kainerstorfer JM. Diffuse Optical Tomography Spatial Prior for EEG Source Localization in Human Visual Cortex. Neuroimage 2023:120210. [PMID: 37311535 DOI: 10.1016/j.neuroimage.2023.120210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 05/30/2023] [Indexed: 06/15/2023] Open
Abstract
Electroencephalography (EEG) and diffuse optical tomography (DOT) are imaging methods which are widely used for neuroimaging. While the temporal resolution of EEG is high, the spatial resolution is typically limited. DOT, on the other hand, has high spatial resolution, but the temporal resolution is inherently limited by the slow hemodynamics it measures. In our previous work, we showed using computer simulations that when using the results of DOT reconstruction as the spatial prior for EEG source reconstruction, high spatio-temporal resolution could be achieved. In this work, we experimentally validate the algorithm by alternatingly flashing two visual stimuli at a speed that is faster than the temporal resolution of DOT. We show that the joint reconstruction using both EEG and DOT clearly resolves the two stimuli temporally, and the spatial confinement is drastically improved in comparison to reconstruction using EEG alone.
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Affiliation(s)
- Jiaming Cao
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States
| | - Eli Bulger
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States
| | - Barbara Shinn-Cunningham
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, 15213, Pennsylvania, United States; Department of Psychology, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States
| | - Pulkit Grover
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, 15213, Pennsylvania, United States
| | - Jana M Kainerstorfer
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, 15213, Pennsylvania, United States.
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Li R, Hosseini H, Saggar M, Balters SC, Reiss AL. Current opinions on the present and future use of functional near-infrared spectroscopy in psychiatry. NEUROPHOTONICS 2023; 10:013505. [PMID: 36777700 PMCID: PMC9904322 DOI: 10.1117/1.nph.10.1.013505] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 01/13/2023] [Indexed: 05/19/2023]
Abstract
Functional near-infrared spectroscopy (fNIRS) is an optical imaging technique for assessing human brain activity by noninvasively measuring the fluctuation of cerebral oxygenated- and deoxygenated-hemoglobin concentrations associated with neuronal activity. Owing to its superior mobility, low cost, and good tolerance for motion, the past few decades have witnessed a rapid increase in the research and clinical use of fNIRS in a variety of psychiatric disorders. In this perspective article, we first briefly summarize the state-of-the-art concerning fNIRS research in psychiatry. In particular, we highlight the diverse applications of fNIRS in psychiatric research, the advanced development of fNIRS instruments, and novel fNIRS study designs for exploring brain activity associated with psychiatric disorders. We then discuss some of the open challenges and share our perspectives on the future of fNIRS in psychiatric research and clinical practice. We conclude that fNIRS holds promise for becoming a useful tool in clinical psychiatric settings with respect to developing closed-loop systems and improving individualized treatments and diagnostics.
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Affiliation(s)
- Rihui Li
- Stanford University, Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford, California, United States
| | - Hadi Hosseini
- Stanford University, Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford, California, United States
| | - Manish Saggar
- Stanford University, Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford, California, United States
| | - Stephanie Christina Balters
- Stanford University, Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford, California, United States
| | - Allan L. Reiss
- Stanford University, Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford, California, United States
- Stanford University, Department of Radiology and Pediatrics, Stanford, California, United States
- Stanford University, Department of Pediatrics, Stanford, California, United States
- Address all correspondence to Allan L. Reiss,
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Bourguignon NJ, Bue SL, Guerrero-Mosquera C, Borragán G. Bimodal EEG-fNIRS in Neuroergonomics. Current Evidence and Prospects for Future Research. FRONTIERS IN NEUROERGONOMICS 2022; 3:934234. [PMID: 38235461 PMCID: PMC10790898 DOI: 10.3389/fnrgo.2022.934234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 06/20/2022] [Indexed: 01/19/2024]
Abstract
Neuroergonomics focuses on the brain signatures and associated mental states underlying behavior to design human-machine interfaces enhancing performance in the cognitive and physical domains. Brain imaging techniques such as functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) have been considered key methods for achieving this goal. Recent research stresses the value of combining EEG and fNIRS in improving these interface systems' mental state decoding abilities, but little is known about whether these improvements generalize over different paradigms and methodologies, nor about the potentialities for using these systems in the real world. We review 33 studies comparing mental state decoding accuracy between bimodal EEG-fNIRS and unimodal EEG and fNIRS in several subdomains of neuroergonomics. In light of these studies, we also consider the challenges of exploiting wearable versions of these systems in real-world contexts. Overall the studies reviewed suggest that bimodal EEG-fNIRS outperforms unimodal EEG or fNIRS despite major differences in their conceptual and methodological aspects. Much work however remains to be done to reach practical applications of bimodal EEG-fNIRS in naturalistic conditions. We consider these points to identify aspects of bimodal EEG-fNIRS research in which progress is expected or desired.
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Affiliation(s)
| | - Salvatore Lo Bue
- Department of Life Sciences, Royal Military Academy of Belgium, Brussels, Belgium
| | | | - Guillermo Borragán
- Center for Research in Cognition and Neuroscience, Université Libre de Bruxelles, Brussels, Belgium
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Li R, Yang D, Fang F, Hong KS, Reiss AL, Zhang Y. Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22155865. [PMID: 35957421 PMCID: PMC9371171 DOI: 10.3390/s22155865] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/27/2022] [Accepted: 07/30/2022] [Indexed: 05/29/2023]
Abstract
Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG has poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better spatial resolution, though it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal fNIRS-EEG integration analysis approach. Despite a growing number of studies using concurrent fNIRS-EEG designs reported in recent years, the methodological reference of past studies remains unclear. To fill this knowledge gap, this review critically summarizes the status of analysis methods currently used in concurrent fNIRS-EEG studies, providing an up-to-date overview and guideline for future projects to conduct concurrent fNIRS-EEG studies. A literature search was conducted using PubMed and Web of Science through 31 August 2021. After screening and qualification assessment, 92 studies involving concurrent fNIRS-EEG data recordings and analyses were included in the final methodological review. Specifically, three methodological categories of concurrent fNIRS-EEG data analyses, including EEG-informed fNIRS analyses, fNIRS-informed EEG analyses, and parallel fNIRS-EEG analyses, were identified and explained with detailed description. Finally, we highlighted current challenges and potential directions in concurrent fNIRS-EEG data analyses in future research.
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Affiliation(s)
- Rihui Li
- Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Biomedical Engineering, University of Houston, Houston, TX 77004, USA
| | - Dalin Yang
- School of Mechanical Engineering, Pusan National University, Pusan 43241, Korea
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, 4515 McKinley Avenue, St. Louis, MO 63110, USA
| | - Feng Fang
- Department of Biomedical Engineering, University of Houston, Houston, TX 77004, USA
| | - Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Pusan 43241, Korea
| | - Allan L. Reiss
- Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, TX 77004, USA
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Benitez-Andonegui A, Lührs M, Nagels-Coune L, Ivanov D, Goebel R, Sorger B. Guiding functional near-infrared spectroscopy optode-layout design using individual (f)MRI data: effects on signal strength. NEUROPHOTONICS 2021; 8:025012. [PMID: 34155480 PMCID: PMC8211086 DOI: 10.1117/1.nph.8.2.025012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 05/11/2021] [Indexed: 05/20/2023]
Abstract
Significance: Designing optode layouts is an essential step for functional near-infrared spectroscopy (fNIRS) experiments as the quality of the measured signal and the sensitivity to cortical regions-of-interest depend on how optodes are arranged on the scalp. This becomes particularly relevant for fNIRS-based brain-computer interfaces (BCIs), where developing robust systems with few optodes is crucial for clinical applications. Aim: Available resources often dictate the approach researchers use for optode-layout design. We investigated whether guiding optode layout design using different amounts of subject-specific magnetic resonance imaging (MRI) data affects the fNIRS signal quality and sensitivity to brain activation when healthy participants perform mental-imagery tasks typically used in fNIRS-BCI experiments. Approach: We compared four approaches that incrementally incorporated subject-specific MRI information while participants performed mental-calculation, mental-rotation, and inner-speech tasks. The literature-based approach (LIT) used a literature review to guide the optode layout design. The probabilistic approach (PROB) employed individual anatomical data and probabilistic maps of functional MRI (fMRI)-activation from an independent dataset. The individual fMRI (iFMRI) approach used individual anatomical and fMRI data, and the fourth approach used individual anatomical, functional, and vascular information of the same subject (fVASC). Results: The four approaches resulted in different optode layouts and the more informed approaches outperformed the minimally informed approach (LIT) in terms of signal quality and sensitivity. Further, PROB, iFMRI, and fVASC approaches resulted in a similar outcome. Conclusions: We conclude that additional individual MRI data lead to a better outcome, but that not all the modalities tested here are required to achieve a robust setup. Finally, we give preliminary advice to efficiently using resources for developing robust optode layouts for BCI and neurofeedback applications.
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Affiliation(s)
- Amaia Benitez-Andonegui
- Maastricht University, Maastricht Brain Imaging Center, Department of Cognitive Neuroscience, Maastricht, The Netherlands
- Maastricht University, Laboratory for Cognitive Robotics and Complex Self-Organizing Systems, Department of Data Science and Knowledge Engineering, Maastricht, The Netherlands
- Address all correspondence to Amaia Benitez-Andonegui,
| | - Michael Lührs
- Maastricht University, Maastricht Brain Imaging Center, Department of Cognitive Neuroscience, Maastricht, The Netherlands
- Brain Innovation B.V., Research Department, Maastricht, The Netherlands
| | - Laurien Nagels-Coune
- Maastricht University, Maastricht Brain Imaging Center, Department of Cognitive Neuroscience, Maastricht, The Netherlands
| | - Dimo Ivanov
- Maastricht University, Maastricht Brain Imaging Center, Department of Cognitive Neuroscience, Maastricht, The Netherlands
| | - Rainer Goebel
- Maastricht University, Maastricht Brain Imaging Center, Department of Cognitive Neuroscience, Maastricht, The Netherlands
- Brain Innovation B.V., Research Department, Maastricht, The Netherlands
| | - Bettina Sorger
- Maastricht University, Maastricht Brain Imaging Center, Department of Cognitive Neuroscience, Maastricht, The Netherlands
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6
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Cao J, Huppert TJ, Grover P, Kainerstorfer JM. Enhanced spatiotemporal resolution imaging of neuronal activity using joint electroencephalography and diffuse optical tomography. NEUROPHOTONICS 2021; 8:015002. [PMID: 33437847 PMCID: PMC7778454 DOI: 10.1117/1.nph.8.1.015002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 12/10/2020] [Indexed: 06/12/2023]
Abstract
Significance: Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are both commonly used methodologies for neuronal source reconstruction. While EEG has high temporal resolution (millisecond-scale), its spatial resolution is on the order of centimeters. On the other hand, in comparison to EEG, fNIRS, or diffuse optical tomography (DOT), when used for source reconstruction, can achieve relatively high spatial resolution (millimeter-scale), but its temporal resolution is poor because the hemodynamics that it measures evolve on the order of several seconds. This has important neuroscientific implications: e.g., if two spatially close neuronal sources are activated sequentially with only a small temporal separation, single-modal measurements using either EEG or DOT alone would fail to resolve them correctly. Aim: We attempt to address this issue by performing joint EEG and DOT neuronal source reconstruction. Approach: We propose an algorithm that utilizes DOT reconstruction as the spatial prior of EEG reconstruction, and demonstrate the improvements using simulations based on the ICBM152 brain atlas. Results: We show that neuronal sources can be reconstructed with higher spatiotemporal resolution using our algorithm than using either modality individually. Further, we study how the performance of the proposed algorithm can be affected by the locations of the neuronal sources, and how the performance can be enhanced by improving the placement of EEG electrodes and DOT optodes. Conclusions: We demonstrate using simulations that two sources separated by 2.3-3.3 cm and 50 ms can be recovered accurately using the proposed algorithm by suitably combining EEG and DOT, but not by either in isolation. We also show that the performance can be enhanced by optimizing the electrode and optode placement according to the locations of the neuronal sources.
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Affiliation(s)
- Jiaming Cao
- Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States
| | - Theodore J. Huppert
- University of Pittsburgh, Department of Electrical and Computer Engineering Pittsburgh, Pennsylvania, United States
- University of Pittsburgh, Center for Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States
| | - Pulkit Grover
- Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States
- Carnegie Mellon University, Department of Electrical and Computer Engineering, Pittsburgh, Pennsylvania, United States
- Carnegie Mellon University, Neuroscience Institute, Pittsburgh, Pennsylvania, United States
| | - Jana M. Kainerstorfer
- Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States
- Carnegie Mellon University, Department of Electrical and Computer Engineering, Pittsburgh, Pennsylvania, United States
- Carnegie Mellon University, Neuroscience Institute, Pittsburgh, Pennsylvania, United States
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7
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Khan MU, Hasan MAH. Hybrid EEG-fNIRS BCI Fusion Using Multi-Resolution Singular Value Decomposition (MSVD). Front Hum Neurosci 2020; 14:599802. [PMID: 33363459 PMCID: PMC7753369 DOI: 10.3389/fnhum.2020.599802] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 11/12/2020] [Indexed: 12/16/2022] Open
Abstract
Brain-computer interface (BCI) multi-modal fusion has the potential to generate multiple commands in a highly reliable manner by alleviating the drawbacks associated with single modality. In the present work, a hybrid EEG-fNIRS BCI system—achieved through a fusion of concurrently recorded electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals—is used to overcome the limitations of uni-modality and to achieve higher tasks classification. Although the hybrid approach enhances the performance of the system, the improvements are still modest due to the lack of availability of computational approaches to fuse the two modalities. To overcome this, a novel approach is proposed using Multi-resolution singular value decomposition (MSVD) to achieve system- and feature-based fusion. The two approaches based up different features set are compared using the KNN and Tree classifiers. The results obtained through multiple datasets show that the proposed approach can effectively fuse both modalities with improvement in the classification accuracy.
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Affiliation(s)
- Muhammad Umer Khan
- Department of Mechatronics Engineering, Atilim University, Ankara, Turkey
| | - Mustafa A H Hasan
- Department of Mechatronics Engineering, Atilim University, Ankara, Turkey
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8
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Endo H, Hiroe N, Yamashita O. Evaluation of Resting Spatio-Temporal Dynamics of a Neural Mass Model Using Resting fMRI Connectivity and EEG Microstates. Front Comput Neurosci 2020; 13:91. [PMID: 32009922 PMCID: PMC6978716 DOI: 10.3389/fncom.2019.00091] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 12/20/2019] [Indexed: 12/11/2022] Open
Abstract
Resting-state brain activities have been extensively investigated to understand the macro-scale network architecture of the human brain using non-invasive imaging methods such as fMRI, EEG, and MEG. Previous studies revealed a mechanistic origin of resting-state networks (RSNs) using the connectome dynamics modeling approach, where the neural mass dynamics model constrained by the structural connectivity is simulated to replicate the resting-state networks measured with fMRI and/or fast synchronization transitions with EEG/MEG. However, there is still little understanding of the relationship between the slow fluctuations measured with fMRI and the fast synchronization transitions with EEG/MEG. In this study, as a first step toward evaluating experimental evidence of resting state activity at two different time scales but in a unified way, we investigate connectome dynamics models that simultaneously explain resting-state functional connectivity (rsFC) and EEG microstates. Here, we introduce empirical rsFC and microstates as evaluation criteria of simulated neuronal dynamics obtained by the Larter-Breakspear model in one cortical region connected with those in other cortical regions based on structural connectivity. We optimized the global coupling strength and the local gain parameter (variance of the excitatory and inhibitory threshold) of the simulated neuronal dynamics by fitting both rsFC and microstate spatial patterns to those of experimental ones. As a result, we found that simulated neuronal dynamics in a narrow optimal parameter range simultaneously reproduced empirical rsFC and microstates. Two parameter groups had different inter-regional interdependence. One type of dynamics was synchronized across the whole brain region, and the other type was synchronized between brain regions with strong structural connectivity. In other words, both fast synchronization transitions and slow BOLD fluctuation changed based on structural connectivity in the two parameter groups. Empirical microstates were similar to simulated microstates in the two parameter groups. Thus, fast synchronization transitions correlated with slow BOLD fluctuation based on structural connectivity yielded characteristics of microstates. Our results demonstrate that a bottom-up approach, which extends the single neuronal dynamics model based on empirical observations into a neural mass dynamics model and integrates structural connectivity, effectively reveals both macroscopic fast, and slow resting-state network dynamics.
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Affiliation(s)
- Hidenori Endo
- Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan.,ATR Neural Information Analysis Laboratories, Kyoto, Japan
| | - Nobuo Hiroe
- ATR Neural Information Analysis Laboratories, Kyoto, Japan
| | - Okito Yamashita
- ATR Neural Information Analysis Laboratories, Kyoto, Japan.,Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
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9
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Takeda Y, Suzuki K, Kawato M, Yamashita O. MEG Source Imaging and Group Analysis Using VBMEG. Front Neurosci 2019; 13:241. [PMID: 30967756 PMCID: PMC6438955 DOI: 10.3389/fnins.2019.00241] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 03/01/2019] [Indexed: 11/13/2022] Open
Abstract
Variational Bayesian Multimodal EncephaloGraphy (VBMEG) is a MATLAB toolbox that estimates distributed source currents from magnetoencephalography (MEG)/electroencephalography (EEG) data by integrating functional MRI (fMRI) (https://vbmeg.atr.jp/). VBMEG also estimates whole-brain connectome dynamics using anatomical connectivity derived from a diffusion MRI (dMRI). In this paper, we introduce the VBMEG toolbox and demonstrate its usefulness. By collaborating with VBMEG's tutorial page (https://vbmeg.atr.jp/docs/v2/static/vbmeg2_tutorial_neuromag.html), we show its full pipeline using an open dataset recorded by Wakeman and Henson (2015). We import the MEG data and preprocess them to estimate the source currents. From the estimated source currents, we perform a group analysis and examine the differences of current amplitudes between conditions by controlling the false discovery rate (FDR), which yields results consistent with previous studies. We highlight VBMEG's characteristics by comparing these results with those obtained by other source imaging methods: weighted minimum norm estimate (wMNE), dynamic statistical parametric mapping (dSPM), and linearly constrained minimum variance (LCMV) beamformer. We also estimate source currents from the EEG data and the whole-brain connectome dynamics from the MEG data and dMRI. The observed results indicate the reliability, characteristics, and usefulness of VBMEG.
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Affiliation(s)
- Yusuke Takeda
- ATR Neural Information Analysis Laboratories, Kyoto, Japan
| | - Keita Suzuki
- ATR Neural Information Analysis Laboratories, Kyoto, Japan
| | - Mitsuo Kawato
- ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan
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Li R, Nguyen T, Potter T, Zhang Y. Dynamic cortical connectivity alterations associated with Alzheimer's disease: An EEG and fNIRS integration study. NEUROIMAGE-CLINICAL 2018; 21:101622. [PMID: 30527906 PMCID: PMC6411655 DOI: 10.1016/j.nicl.2018.101622] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 11/08/2018] [Accepted: 12/01/2018] [Indexed: 12/18/2022]
Abstract
Emerging evidence indicates that cognitive deficits in Alzheimer's disease (AD) are associated with disruptions in brain network. Exploring alterations in the AD brain network is therefore of great importance for understanding and treating the disease. This study employs an integrative functional near-infrared spectroscopy (fNIRS) – electroencephalography (EEG) analysis approach to explore dynamic, regional alterations in the AD-linked brain network. FNIRS and EEG data were simultaneously recorded from 14 participants (8 healthy controls and 6 patients with mild AD) during a digit verbal span task (DVST). FNIRS-based spatial constraints were used as priors for EEG source localization. Graph-based indices were then calculated from the reconstructed EEG sources to assess regional differences between the groups. Results show that patients with mild AD revealed weaker and suppressed cortical connectivity in the high alpha band and in beta band to the orbitofrontal and parietal regions. AD-induced brain networks, compared to the networks of age-matched healthy controls, were mainly characterized by lower degree, clustering coefficient at the frontal pole and medial orbitofrontal across all frequency ranges. Additionally, the AD group also consistently showed higher index values for these graph-based indices at the superior temporal sulcus. These findings not only validate the feasibility of utilizing the proposed integrated EEG-fNIRS analysis to better understand the spatiotemporal dynamics of brain activity, but also contribute to the development of network-based approaches for understanding the mechanisms that underlie the progression of AD. Dynamic brain networks of healthy controls and patients with mild AD are documented via an integrative fNIRS-EEG approach. FNIRS-based constraints are employed as spatial priors for EEG source localization. Mild AD group reveals weaker connectivity to the orbitofrontal and parietal regions in high alpha band and beta band. AD-linked brain networks are characterized by lower degree and clustering coefficient at the frontal area. AD group also reveals higher index values for these graph-based indices at the superior temporal sulcus.
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Affiliation(s)
- Rihui Li
- Department of Biomedical Engineering, University of Houston, Houston, USA; Guangdong Provincial Work Injury Rehabilitation Hospital, Guangzhou, China
| | - Thinh Nguyen
- Department of Biomedical Engineering, University of Houston, Houston, USA
| | - Thomas Potter
- Department of Biomedical Engineering, University of Houston, Houston, USA
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, USA.
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11
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Filatova OG, Yang Y, Dewald JPA, Tian R, Maceira-Elvira P, Takeda Y, Kwakkel G, Yamashita O, van der Helm FCT. Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke: A Proof-of-Principle Study. Front Neural Circuits 2018; 12:79. [PMID: 30327592 PMCID: PMC6174251 DOI: 10.3389/fncir.2018.00079] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 09/10/2018] [Indexed: 01/07/2023] Open
Abstract
In hemiparetic stroke, functional recovery of paretic limb may occur with the reorganization of neural networks in the brain. Neuroimaging techniques, such as magnetic resonance imaging (MRI), have a high spatial resolution which can be used to reveal anatomical changes in the brain following a stroke. However, low temporal resolution of MRI provides less insight of dynamic changes of brain activity. In contrast, electro-neurophysiological techniques, such as electroencephalography (EEG), have an excellent temporal resolution to measure such transient events, however are hindered by its low spatial resolution. This proof-of-principle study assessed a novel multimodal brain imaging technique namely Variational Bayesian Multimodal Encephalography (VBMEG), which aims to improve the spatial resolution of EEG for tracking the information flow inside the brain and its changes following a stroke. The limitations of EEG are complemented by constraints derived from anatomical MRI and diffusion weighted imaging (DWI). EEG data were acquired from individuals suffering from a stroke as well as able-bodied participants while electrical stimuli were delivered sequentially at their index finger in the left and right hand, respectively. The locations of active sources related to this stimulus were precisely identified, resulting in high Variance Accounted For (VAF above 80%). An accurate estimation of dynamic information flow between sources was achieved in this study, showing a high VAF (above 90%) in the cross-validation test. The estimated dynamic information flow was compared between chronic hemiparetic stroke and able-bodied individuals. The results demonstrate the feasibility of VBMEG method in revealing the changes of information flow in the brain after stroke. This study verified the VBMEG method as an advanced computational approach to track the dynamic information flow in the brain following a stroke. This may lead to the development of a quantitative tool for monitoring functional changes of the cortical neural networks after a unilateral brain injury and therefore facilitate the research into, and the practice of stroke rehabilitation.
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Affiliation(s)
- Olena G. Filatova
- Department of Biomechanical Engineering, Delft University of Technology, Delft, Netherlands
| | - Yuan Yang
- Department of Biomechanical Engineering, Delft University of Technology, Delft, Netherlands
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Julius P. A. Dewald
- Department of Biomechanical Engineering, Delft University of Technology, Delft, Netherlands
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Runfeng Tian
- Department of Biomechanical Engineering, Delft University of Technology, Delft, Netherlands
| | - Pablo Maceira-Elvira
- Department of Biomechanical Engineering, Delft University of Technology, Delft, Netherlands
- Clinical Neuroengineering, Centre for Neuroprosthetics, Swiss Federal Institute of Technology (EPFL), Clinique Romande de Réadaptation, Sion, Switzerland
| | - Yusuke Takeda
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
- Neural Information Analysis Laboratories, ATR, Kyoto, Japan
| | - Gert Kwakkel
- Department of Rehabilitation Medicine, Amsterdam Neurosciences and Amsterdam Movement Sciences, University Medical Centre Amsterdam, Amsterdam, Netherlands
| | - Okito Yamashita
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
- Neural Information Analysis Laboratories, ATR, Kyoto, Japan
| | - Frans C. T. van der Helm
- Department of Biomechanical Engineering, Delft University of Technology, Delft, Netherlands
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
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12
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Cai C, Ogawa K, Kochiyama T, Tanaka H, Imamizu H. Temporal recalibration of motor and visual potentials in lag adaptation in voluntary movement. Neuroimage 2018; 172:654-662. [PMID: 29428581 DOI: 10.1016/j.neuroimage.2018.02.015] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2017] [Revised: 12/11/2017] [Accepted: 02/07/2018] [Indexed: 11/29/2022] Open
Abstract
Adaptively recalibrating motor-sensory asynchrony is critical for animals to perceive self-produced action consequences. It is controversial whether motor- or sensory-related neural circuits recalibrate this asynchrony. By combining magnetoencephalography (MEG) and functional MRI (fMRI), we investigate the temporal changes in brain activities caused by repeated exposure to a 150-ms delay inserted between a button-press action and a subsequent flash. We found that readiness potentials significantly shift later in the motor system, especially in parietal regions (average: 219.9 ms), while visually evoked potentials significantly shift earlier in occipital regions (average: 49.7 ms) in the delay condition compared to the no-delay condition. Moreover, the shift in readiness potentials, but not in visually evoked potentials, was significantly correlated with the psychophysical measure of motor-sensory adaptation. These results suggest that although both motor and sensory processes contribute to the recalibration, the motor process plays the major role, given the magnitudes of shift and the correlation with the psychophysical measure.
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Affiliation(s)
- Chang Cai
- Cognitive Mechanisms Laboratories, Advanced Telecommunications Research Institute International, Keihanna Science City, Kyoto 619-0288, Japan; Center for Information and Neural Networks, National Institute of Information and Communications Technology and Osaka University, Suita, Osaka 565-0871, Japan.
| | - Kenji Ogawa
- Cognitive Mechanisms Laboratories, Advanced Telecommunications Research Institute International, Keihanna Science City, Kyoto 619-0288, Japan; Department of Psychology, Graduate School of Letters, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
| | - Takanori Kochiyama
- Brain Activity Imaging Center, ATR-Promotions, Keihanna Science City, Kyoto 619-0288, Japan
| | - Hirokazu Tanaka
- School of Information Science, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa 923-1211, Japan
| | - Hiroshi Imamizu
- Cognitive Mechanisms Laboratories, Advanced Telecommunications Research Institute International, Keihanna Science City, Kyoto 619-0288, Japan; Center for Information and Neural Networks, National Institute of Information and Communications Technology and Osaka University, Suita, Osaka 565-0871, Japan; Department of Psychology, Graduate School of Humanities and Sociology, The University of Tokyo, Tokyo 113-0033, Japan.
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13
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Mejia Tobar A, Hyoudou R, Kita K, Nakamura T, Kambara H, Ogata Y, Hanakawa T, Koike Y, Yoshimura N. Decoding of Ankle Flexion and Extension from Cortical Current Sources Estimated from Non-invasive Brain Activity Recording Methods. Front Neurosci 2018; 11:733. [PMID: 29358903 PMCID: PMC5766671 DOI: 10.3389/fnins.2017.00733] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 12/15/2017] [Indexed: 11/27/2022] Open
Abstract
The classification of ankle movements from non-invasive brain recordings can be applied to a brain-computer interface (BCI) to control exoskeletons, prosthesis, and functional electrical stimulators for the benefit of patients with walking impairments. In this research, ankle flexion and extension tasks at two force levels in both legs, were classified from cortical current sources estimated by a hierarchical variational Bayesian method, using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. The hierarchical prior for the current source estimation from EEG was obtained from activated brain areas and their intensities from an fMRI group (second-level) analysis. The fMRI group analysis was performed on regions of interest defined over the primary motor cortex, the supplementary motor area, and the somatosensory area, which are well-known to contribute to movement control. A sparse logistic regression method was applied for a nine-class classification (eight active tasks and a resting control task) obtaining a mean accuracy of 65.64% for time series of current sources, estimated from the EEG and the fMRI signals using a variational Bayesian method, and a mean accuracy of 22.19% for the classification of the pre-processed of EEG sensor signals, with a chance level of 11.11%. The higher classification accuracy of current sources, when compared to EEG classification accuracy, was attributed to the high number of sources and the different signal patterns obtained in the same vertex for different motor tasks. Since the inverse filter estimation for current sources can be done offline with the present method, the present method is applicable to real-time BCIs. Finally, due to the highly enhanced spatial distribution of current sources over the brain cortex, this method has the potential to identify activation patterns to design BCIs for the control of an affected limb in patients with stroke, or BCIs from motor imagery in patients with spinal cord injury.
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Affiliation(s)
| | - Rikiya Hyoudou
- Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
| | - Kahori Kita
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan.,Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Tatsuhiro Nakamura
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Hiroyuki Kambara
- Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
| | - Yousuke Ogata
- Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan.,Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Takashi Hanakawa
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Yasuharu Koike
- Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan.,Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Natsue Yoshimura
- Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan.,Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
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14
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Ahn S, Jun SC. Multi-Modal Integration of EEG-fNIRS for Brain-Computer Interfaces - Current Limitations and Future Directions. Front Hum Neurosci 2017; 11:503. [PMID: 29093673 PMCID: PMC5651279 DOI: 10.3389/fnhum.2017.00503] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2017] [Accepted: 10/05/2017] [Indexed: 11/13/2022] Open
Abstract
Multi-modal integration, which combines multiple neurophysiological signals, is gaining more attention for its potential to supplement single modality's drawbacks and yield reliable results by extracting complementary features. In particular, integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) is cost-effective and portable, and therefore is a fascinating approach to brain-computer interface (BCI). However, outcomes from the integration of these two modalities have yielded only modest improvement in BCI performance because of the lack of approaches to integrate the two different features. In addition, mismatch of recording locations may hinder further improvement. In this literature review, we surveyed studies of the integration of EEG/fNIRS in BCI thoroughly and discussed its current limitations. We also suggested future directions for efficient and successful multi-modal integration of EEG/fNIRS in BCI systems.
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Affiliation(s)
- Sangtae Ahn
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Sung C Jun
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea
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15
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Fujimoto H, Mihara M, Hattori N, Hatakenaka M, Yagura H, Kawano T, Miyai I, Mochizuki H. Neurofeedback-induced facilitation of the supplementary motor area affects postural stability. NEUROPHOTONICS 2017; 4:045003. [PMID: 29152530 PMCID: PMC5680482 DOI: 10.1117/1.nph.4.4.045003] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Accepted: 10/16/2017] [Indexed: 05/29/2023]
Abstract
Near-infrared spectroscopy-mediated neurofeedback (NIRS-NFB) is a promising therapeutic intervention for patients with neurological diseases. Studies have shown that NIRS-NFB can facilitate task-related cortical activation and induce task-specific behavioral changes. These findings indicate that the effect of neuromodulation depends on local cortical function. However, when the target cortical region has multiple functions, our understanding of the effects is less clear. This is true in the supplementary motor area (SMA), which is involved both in postural control and upper-limb movement. To address this issue, we investigated the facilitatory effect of NIRS SMA neurofeedback on cortical activity and behavior, without any specific task. Twenty healthy individuals participated in real and sham neurofeedback. Balance and hand dexterity were assessed before and after each NIRS-NFB session. We found a significant interaction between assessment periods (pre/post) and condition (real/sham) with respect to balance as assessed by the center of the pressure path length but not for hand dexterity as assessed by the 9-hole peg test. SMA activity only increased during real neurofeedback. Our findings indicate that NIRS-NFB itself has the potential to modulate focal cortical activation, and we suggest that it be considered a therapy to facilitate the SMA for patients with postural impairment.
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Affiliation(s)
- Hiroaki Fujimoto
- Morinomiya Hospital, Neurorehabilitation Research Institute, Osaka, Osaka, Japan
- Osaka University Graduate School of Medicine, Department of Neurology, Suita, Osaka, Japan
| | - Masahito Mihara
- Morinomiya Hospital, Neurorehabilitation Research Institute, Osaka, Osaka, Japan
- Osaka University Graduate School of Medicine, Department of Neurology, Suita, Osaka, Japan
- Kawasaki Medical School, Department of Neurology, Kurashiki, Okayama, Japan
| | - Noriaki Hattori
- Morinomiya Hospital, Neurorehabilitation Research Institute, Osaka, Osaka, Japan
- Osaka University Graduate School of Medicine, Department of Neurology, Suita, Osaka, Japan
| | - Megumi Hatakenaka
- Morinomiya Hospital, Neurorehabilitation Research Institute, Osaka, Osaka, Japan
| | - Hajime Yagura
- Morinomiya Hospital, Neurorehabilitation Research Institute, Osaka, Osaka, Japan
| | - Teiji Kawano
- Morinomiya Hospital, Neurorehabilitation Research Institute, Osaka, Osaka, Japan
| | - Ichiro Miyai
- Morinomiya Hospital, Neurorehabilitation Research Institute, Osaka, Osaka, Japan
| | - Hideki Mochizuki
- Osaka University Graduate School of Medicine, Department of Neurology, Suita, Osaka, Japan
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16
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Decoding finger movement in humans using synergy of EEG cortical current signals. Sci Rep 2017; 7:11382. [PMID: 28900188 PMCID: PMC5595824 DOI: 10.1038/s41598-017-09770-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Accepted: 07/31/2017] [Indexed: 12/31/2022] Open
Abstract
The synchronized activity of neuronal populations across multiple distant brain areas may reflect coordinated interactions of large-scale brain networks. Currently, there is no established method to investigate the temporal transitions between these large-scale networks that would allow, for example, to decode finger movements. Here we applied a matrix factorization method employing principal component and temporal independent component analyses to identify brain activity synchronizations. In accordance with previous studies investigating “muscle synergies”, we refer to this activity as “brain activity synergy”. Using electroencephalography (EEG), we first estimated cortical current sources (CSs) and then identified brain activity synergies within the estimated CS signals. A decoding analysis for finger movement in eight directions showed that such CS synergies provided more information for dissociating between movements than EEG sensor signals, EEG synergy, or CS signals, suggesting that temporal activation patterns of the synchronizing CSs may contain information related to motor control. A quantitative analysis of features selected by the decoders further revealed temporal transitions among the primary motor area, dorsal and ventral premotor areas, pre-supplementary motor area, and supplementary motor area, which may reflect transitions in motor planning and execution. These results provide a proof of concept for brain activity synergy estimation using CSs.
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17
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Nambu I, Ozawa T, Sato T, Aihara T, Fujiwara Y, Otaka Y, Osu R, Izawa J, Wada Y. Transient increase in systemic interferences in the superficial layer and its influence on event-related motor tasks: a functional near-infrared spectroscopy study. JOURNAL OF BIOMEDICAL OPTICS 2017; 22:35008. [PMID: 28294282 DOI: 10.1117/1.jbo.22.3.035008] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 02/24/2017] [Indexed: 05/07/2023]
Abstract
Functional near-infrared spectroscopy (fNIRS) is a widely utilized neuroimaging tool in fundamental neuroscience research and clinical investigation. Previous research has revealed that task-evoked systemic artifacts mainly originating from the superficial-tissue may preclude the identification of cerebral activation during a given task. We examined the influence of such artifacts on event-related brain activity during a brisk squeezing movement. We estimated task-evoked superficial-tissue hemodynamics from short source–detector distance channels (15 mm) by applying principal component analysis. The estimated superficial-tissue hemodynamics exhibited temporal profiles similar to the canonical cerebral hemodynamic model. Importantly, this task-evoked profile was also observed in data from a block design motor experiment, suggesting a transient increase in superficial-tissue hemodynamics occurs following motor behavior, irrespective of task design. We also confirmed that estimation of event-related cerebral hemodynamics was improved by a simple superficial-tissue hemodynamic artifact removal process using 15-mm short distance channels, compared to the results when no artifact removal was applied. Thus, our results elucidate task design-independent characteristics of superficial-tissue hemodynamics and highlight the need for the application of superficial-tissue hemodynamic artifact removal methods when analyzing fNIRS data obtained during event-related motor tasks.
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Affiliation(s)
- Isao Nambu
- Nagaoka University of Technology, Graduate School of Engineering, Nagaoka, Japan
| | - Takuya Ozawa
- Nagaoka University of Technology, Graduate School of Engineering, Nagaoka, JapanbATR Brain Information Communication Research Lab Group, Keihanna-Science City, Kyoto, Japan
| | - Takanori Sato
- Nagaoka University of Technology, Graduate School of Engineering, Nagaoka, Japan
| | - Takatsugu Aihara
- ATR Brain Information Communication Research Lab Group, Keihanna-Science City, Kyoto, Japan
| | - Yusuke Fujiwara
- ATR Brain Information Communication Research Lab Group, Keihanna-Science City, Kyoto, Japan
| | - Yohei Otaka
- ATR Brain Information Communication Research Lab Group, Keihanna-Science City, Kyoto, JapancTokyo Bay Rehabilitation Hospital, Narashino, Chiba, JapandKeio University School of Medicine, Department of Rehabilitation Medicine, Shinjuku-ku, Tokyo, Japan
| | - Rieko Osu
- ATR Brain Information Communication Research Lab Group, Keihanna-Science City, Kyoto, Japan
| | - Jun Izawa
- ATR Brain Information Communication Research Lab Group, Keihanna-Science City, Kyoto, JapaneUniversity of Tsukuba, Faculty of Engineering, Information and System, Tsukuba, Ibaraki, Japan
| | - Yasuhiro Wada
- Nagaoka University of Technology, Graduate School of Engineering, Nagaoka, Japan
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18
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The Role of Phonological Processing in Semantic Access of Chinese Characters: A Near-Infrared Spectroscopy Study. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016. [PMID: 27526148 DOI: 10.1007/978-3-319-38810-6_31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register]
Abstract
The Stroop task was used to investigate the role of phonological processing in semantic access for written Chinese language. Fourteen children were recruited to perform the Stroop task, using color characters, their homophones and neutral characters as stimuli. Near-infrared spectroscopy (NIRS) was used to measure the brain activation in the prefrontal cortex (PFC) during the task. In view of better sensitivity, oxy-hemoglobin was chosen to indicate the task activation. In behavioral performance, there was a significant classical Stroop interference effect as indexed by longer response time and higher error rate for the color task than the neutral task, whereas there was no evident interference effect for the color homophones. The NIRS data agreed with the behavioral data, and showed a significant Stroop effect only for the color characters in the bilateral PFC. These results suggested that phonology may not play an important role in semantic activation of Chinese characters for children.
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19
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Wang W, Wang B, Bu L, Xu L, Li Z, Fan Y. Vigilance Task-Related Change in Brain Functional Connectivity as Revealed by Wavelet Phase Coherence Analysis of Near-Infrared Spectroscopy Signals. Front Hum Neurosci 2016; 10:400. [PMID: 27547182 PMCID: PMC4974280 DOI: 10.3389/fnhum.2016.00400] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Accepted: 07/26/2016] [Indexed: 11/30/2022] Open
Abstract
This study aims to assess the vigilance task-related change in connectivity in healthy adults using wavelet phase coherence (WPCO) analysis of near-infrared spectroscopy signals (NIRS). NIRS is a non-invasive neuroimaging technique for assessing brain activity. Continuous recordings of the NIRS signals were obtained from the prefrontal cortex (PFC) and sensorimotor cortical areas of 20 young healthy adults (24.9 ± 3.3 years) during a 10-min resting state and a 20-min vigilance task state. The vigilance task was used to simulate driving mental load by judging three random numbers (i.e., whether odd numbers). The task was divided into two sessions: the first 10 min (Task t1) and the second 10 min (Task t2). The WPCO of six channel pairs were calculated in five frequency intervals: 0.6–2 Hz (I), 0.145–0.6 Hz (II), 0.052–0.145 Hz (III), 0.021–0.052 Hz (IV), and 0.0095–0.021 Hz (V). The significant WPCO formed global connectivity (GC) maps in intervals I and II and functional connectivity (FC) maps in intervals III to V. Results show that the GC levels in interval I and FC levels in interval III were significantly lower in the Task t2 than in the resting state (p < 0.05), particularly between the left PFC and bilateral sensorimotor regions. Also, the reaction time (RT) shows an increase in Task t2 compared with that in Task t1. However, no significant difference in WPCO was found between Task t1 and resting state. The results showed that the change in FC at the range of 0.6–2 Hz was not attributed to the vigilance task per se, but the interaction effect of vigilance task and time factors. The findings suggest that the decreased attention level might be partly attributed to the reduced GC levels between the left prefrontal region and sensorimotor area. The present results provide a new insight into the vigilance task-related brain activity.
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Affiliation(s)
- Wei Wang
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University Jinan, China
| | - Bitian Wang
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University Jinan, China
| | - Lingguo Bu
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University Jinan, China
| | - Liwei Xu
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University Jinan, China
| | - Zengyong Li
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong UniversityJinan, China; National Research Center for Rehabilitation Technical AidsBeijing, China
| | - Yubo Fan
- National Research Center for Rehabilitation Technical Aids Beijing, China
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20
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Murphy MD, Guggenmos DJ, Bundy DT, Nudo RJ. Current Challenges Facing the Translation of Brain Computer Interfaces from Preclinical Trials to Use in Human Patients. Front Cell Neurosci 2016; 9:497. [PMID: 26778962 PMCID: PMC4702293 DOI: 10.3389/fncel.2015.00497] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2015] [Accepted: 12/10/2015] [Indexed: 12/13/2022] Open
Abstract
Current research in brain computer interface (BCI) technology is advancing beyond preclinical studies, with trials beginning in human patients. To date, these trials have been carried out with several different types of recording interfaces. The success of these devices has varied widely, but different factors such as the level of invasiveness, timescale of recorded information, and ability to maintain stable functionality of the device over a long period of time all must be considered in addition to accuracy in decoding intent when assessing the most practical type of device moving forward. Here, we discuss various approaches to BCIs, distinguishing between devices focusing on control of operations extrinsic to the subject (e.g., prosthetic limbs, computer cursors) and those focusing on control of operations intrinsic to the brain (e.g., using stimulation or external feedback), including closed-loop or adaptive devices. In this discussion, we consider the current challenges facing the translation of various types of BCI technology to eventual human application.
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Affiliation(s)
- Maxwell D Murphy
- Bioengineering Graduate Program, University of KansasLawrence, KS, USA; Department of Rehabilitation Medicine, University of Kansas Medical CenterKansas City, KS, USA
| | - David J Guggenmos
- Department of Rehabilitation Medicine, University of Kansas Medical Center Kansas City, KS, USA
| | - David T Bundy
- Department of Rehabilitation Medicine, University of Kansas Medical Center Kansas City, KS, USA
| | - Randolph J Nudo
- Department of Rehabilitation Medicine, University of Kansas Medical CenterKansas City, KS, USA; Landon Center on Aging, University of Kansas Medical CenterKansas City, KS, USA
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21
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Simultaneous measurement of electroencephalography and near-infrared spectroscopy during voluntary motor preparation. Sci Rep 2015; 5:16438. [PMID: 26574186 PMCID: PMC4648105 DOI: 10.1038/srep16438] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 10/14/2015] [Indexed: 11/13/2022] Open
Abstract
We investigated the relationship between electrophysiological activity and haemodynamic response during motor preparation by simultaneous recording of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). It is still unknown how exactly EEG signals correlate with the haemodynamic response, although the activation in the premotor area during motor preparation has been captured by EEG and haemodynamic approaches separately. We conducted EEG-NIRS simultaneous recordings over the sensorimotor area with a self-paced button press task. Participants were instructed to press a button at their own pace after a cue was shown. The result showed that the readiness potential (RP), a negative slow potential shift occurring during motor preparation, on C3 in the extended 10–20 system occurred about 1000 ms before the movement onset. An increase in concentration of oxyhaemoglobin (oxyHb) in the premotor cortex during motor preparation was also confirmed by NIRS, which resulted in a significant correlation between the amplitude of the RP and the change in oxyHb concentration (Pearson’s correlation r2 = 0.235, p = 0.03). We show that EEG-NIRS simultaneous recording can demonstrate the correlation between the RP and haemodynamic response in the premotor cortex contralateral to the performing hand.
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22
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Morioka H, Kanemura A, Hirayama JI, Shikauchi M, Ogawa T, Ikeda S, Kawanabe M, Ishii S. Learning a common dictionary for subject-transfer decoding with resting calibration. Neuroimage 2015; 111:167-78. [DOI: 10.1016/j.neuroimage.2015.02.015] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2014] [Revised: 01/22/2015] [Accepted: 02/08/2015] [Indexed: 11/26/2022] Open
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23
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Giacometti P, Diamond SG. Correspondence of electroencephalography and near-infrared spectroscopy sensitivities to the cerebral cortex using a high-density layout. NEUROPHOTONICS 2014; 1:025001. [PMID: 25558462 PMCID: PMC4280681 DOI: 10.1117/1.nph.1.2.025001] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
This study investigates the correspondence of the cortical sensitivity of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). EEG forward model sensitivity to the cerebral cortex was calculated for 329 EEG electrodes following the 10-5 EEG positioning system using a segmented structural magnetic resonance imaging scan of a human subject. NIRS forward model sensitivity was calculated for the same subject using 156 NIRS source-detector pairs selected from 32 source and 32 detector optodes positioned on the scalp using a subset of the 10-5 EEG positioning system. Sensitivity correlations between colocalized NIRS source-detector pair groups and EEG channels yielded R = 0.46 ± 0.08. Groups of NIRS source-detector pairs with maximum correlations to EEG electrode sensitivities are tabulated. The mean correlation between the point spread functions for EEG and NIRS regions of interest (ROI) was R = 0.43 ± 0.07. Spherical ROIs with radii of 26 mm yielded the maximum correlation between EEG and NIRS averaged across all cortical mesh nodes. These sensitivity correlations between EEG and NIRS should be taken into account when designing multimodal studies of neurovascular coupling and when using NIRS as a statistical prior for EEG source localization.
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Affiliation(s)
- Paolo Giacometti
- Thayer School of Engineering at Dartmouth, 14 Engineering Drive, Hanover, New Hampshire 03755, United States
- Address all correspondence to: Paolo Giacometti, E-mail:
| | - Solomon G. Diamond
- Thayer School of Engineering at Dartmouth, 14 Engineering Drive, Hanover, New Hampshire 03755, United States
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24
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Kopton IM, Kenning P. Near-infrared spectroscopy (NIRS) as a new tool for neuroeconomic research. Front Hum Neurosci 2014; 8:549. [PMID: 25147517 PMCID: PMC4124877 DOI: 10.3389/fnhum.2014.00549] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2014] [Accepted: 07/07/2014] [Indexed: 11/23/2022] Open
Abstract
Over the last decade, the application of neuroscience to economic research has gained in importance and the number of neuroeconomic studies has grown extensively. The most common method for these investigations is fMRI. However, fMRI has limitations (particularly concerning situational factors) that should be countered with other methods. This review elaborates on the use of functional Near-Infrared Spectroscopy (fNIRS) as a new and promising tool for investigating economic decision making both in field experiments and outside the laboratory. We describe results of studies investigating the reliability of prototype NIRS studies, as well as detailing experiments using conventional and stationary fNIRS devices to analyze this potential. This review article shows that further research using mobile fNIRS for studies on economic decision making outside the laboratory could be a fruitful avenue helping to develop the potential of a new method for field experiments outside the laboratory.
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Affiliation(s)
- Isabella M Kopton
- Department of Corporate Management and Economics, Zeppelin Universität Friedrichshafen, Germany
| | - Peter Kenning
- Department of Corporate Management and Economics, Zeppelin Universität Friedrichshafen, Germany ; Faculty of Business Administration and Economics, Heinrich-Heine-Universität Düsseldorf, Germany
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25
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Morishige KI, Yoshioka T, Kawawaki D, Hiroe N, Sato MA, Kawato M. Estimation of hyper-parameters for a hierarchical model of combined cortical and extra-brain current sources in the MEG inverse problem. Neuroimage 2014; 101:320-36. [PMID: 25034620 DOI: 10.1016/j.neuroimage.2014.07.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2013] [Revised: 06/22/2014] [Accepted: 07/06/2014] [Indexed: 11/19/2022] Open
Abstract
One of the major obstacles in estimating cortical currents from MEG signals is the disturbance caused by magnetic artifacts derived from extra-cortical current sources such as heartbeats and eye movements. To remove the effect of such extra-brain sources, we improved the hybrid hierarchical variational Bayesian method (hyVBED) proposed by Fujiwara et al. (NeuroImage, 2009). hyVBED simultaneously estimates cortical and extra-brain source currents by placing dipoles on cortical surfaces as well as extra-brain sources. This method requires EOG data for an EOG forward model that describes the relationship between eye dipoles and electric potentials. In contrast, our improved approach requires no EOG and less a priori knowledge about the current variance of extra-brain sources. We propose a new method, "extra-dipole," that optimally selects hyper-parameter values regarding current variances of the cortical surface and extra-brain source dipoles. With the selected parameter values, the cortical and extra-brain dipole currents were accurately estimated from the simulated MEG data. The performance of this method was demonstrated to be better than conventional approaches, such as principal component analysis and independent component analysis, which use only statistical properties of MEG signals. Furthermore, we applied our proposed method to measured MEG data during covert pursuit of a smoothly moving target and confirmed its effectiveness.
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Affiliation(s)
- Ken-ichi Morishige
- Department of Intelligent Systems Design Engineering, Toyama Prefectural University, 5180 Kurokawa, Imizu-shi, Toyama 939-0398, Japan; Cognitive Mechanisms Laboratories, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Keihanna Science City, Kyoto 619-0288, Japan.
| | - Taku Yoshioka
- Chinou Jouhou Shisutemu Inc., 134 Chudoji-Minami-cho, Shimogyo-ku, Kyoto-shi, Kyoto 600-8813, Japan
| | - Dai Kawawaki
- Neural Information Analysis Laboratories, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Keihanna Science City, Kyoto 619-0288, Japan
| | - Nobuo Hiroe
- Neural Information Analysis Laboratories, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Keihanna Science City, Kyoto 619-0288, Japan
| | - Masa-aki Sato
- Neural Information Analysis Laboratories, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Keihanna Science City, Kyoto 619-0288, Japan
| | - Mitsuo Kawato
- Department of Intelligent Systems Design Engineering, Toyama Prefectural University, 5180 Kurokawa, Imizu-shi, Toyama 939-0398, Japan; Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Keihanna Science City, Kyoto 619-0288, Japan
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26
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Takeda Y, Yamanaka K, Yamagishi N, Sato MA. Revealing time-unlocked brain activity from MEG measurements by common waveform estimation. PLoS One 2014; 9:e98014. [PMID: 24879410 PMCID: PMC4039443 DOI: 10.1371/journal.pone.0098014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2013] [Accepted: 04/28/2014] [Indexed: 11/19/2022] Open
Abstract
Brain activities related to cognitive functions, such as attention, occur with unknown and variable delays after stimulus onsets. Recently, we proposed a method (Common Waveform Estimation, CWE) that could extract such brain activities from magnetoencephalography (MEG) or electroencephalography (EEG) measurements. CWE estimates spatiotemporal MEG/EEG patterns occurring with unknown and variable delays, referred to here as unlocked waveforms, without hypotheses about their shapes. The purpose of this study is to demonstrate the usefulness of CWE for cognitive neuroscience. For this purpose, we show procedures to estimate unlocked waveforms using CWE and to examine their role. We applied CWE to the MEG epochs during Go trials of a visual Go/NoGo task. This revealed unlocked waveforms with interesting properties, specifically large alpha oscillations around the temporal areas. To examine the role of the unlocked waveform, we attempted to estimate the strength of the brain activity of the unlocked waveform in various conditions. We made a spatial filter to extract the component reflecting the brain activity of the unlocked waveform, applied this spatial filter to MEG data under different conditions (a passive viewing, a simple reaction time, and Go/NoGo tasks), and calculated the powers of the extracted components. Comparing the powers across these conditions suggests that the unlocked waveforms may reflect the inhibition of the task-irrelevant activities in the temporal regions while the subject attends to the visual stimulus. Our results demonstrate that CWE is a potential tool for revealing new findings of cognitive brain functions without any hypothesis in advance.
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Affiliation(s)
- Yusuke Takeda
- Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, Kyoto, Japan
- * E-mail:
| | - Kentaro Yamanaka
- Graduate School of Human Life Sciences, Showa Women’s University, Tokyo, Japan
| | - Noriko Yamagishi
- Department of Cognitive Neuroscience, ATR Cognitive Mechanisms Laboratories, Kyoto, Japan
- Brain Networks and Communication Laboratory, Center for Information and Neural Networks, National Institute of Information and Communications Technology, Osaka, Japan
- Japan Science and Technology Agency, PRESTO, Saitama, Japan
| | - Masa-aki Sato
- Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, Kyoto, Japan
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27
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Machado A, Marcotte O, Lina JM, Kobayashi E, Grova C. Optimal optode montage on electroencephalography/functional near-infrared spectroscopy caps dedicated to study epileptic discharges. JOURNAL OF BIOMEDICAL OPTICS 2014; 19:026010. [PMID: 24525860 DOI: 10.1117/1.jbo.19.2.026010] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2013] [Accepted: 01/13/2014] [Indexed: 05/23/2023]
Abstract
Functional near-infrared spectroscopy (fNIRS), acquired simultaneously with electroencephalography (EEG), allows the investigation of hemodynamic brain responses to epileptic activity. Because the presumed epileptogenic focus is patient-specific, an appropriate source/detector (SD) montage has to be reconfigured for each patient. The combination of EEG and fNIRS, however, entails several constraints on montages, and finding an optimal arrangement of optodes on the cap is an important issue. We present a method for computing an optimal SD montage on an EEG/fNIRS cap that focuses on one or several specific brain regions; the montage maximizes the spatial sensitivity. We formulate this optimization problem as a linear integer programming problem. The method was evaluated on two EEG/fNIRS caps. We simulated absorbers at different locations on a head model and generated realistic optical density maps on the scalp. We found that the maps of optimal SD montages had spatial resolution properties comparable to those of regular SD arrangements for the whole head with significantly fewer sensors than regular SD arrangements. In addition, we observed that optimal montages yielded improved spatial density of fNIRS measurements over the targeted regions together with an increase in signal-to-noise ratio.
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Affiliation(s)
- Alexis Machado
- McGill University, Multimodal Functional Imaging Laboratory, Biomedical Engineering Department, H3A 2B4, Québec, Canada
| | - Odile Marcotte
- GERAD, École des HEC, Montréal, H3T 2A7, Québec, CanadaeUniversité du Québec à Montréal, Département d'informatique, H3C 3P8 Québec Canada
| | - Jean Marc Lina
- École de Technologie Supérieure de l'Université du Québec, H3C 1K3, Québec, Canada
| | - Eliane Kobayashi
- McGill University, Montreal Neurological Institute, Department of Neurology and Neurosurgery, H3A 2B4, Québec, Canada
| | - Christophe Grova
- McGill University, Multimodal Functional Imaging Laboratory, Biomedical Engineering Department, H3A 2B4, Québec, CanadabMcGill University, Montreal Neurological Institute, Department of Neurology and Neurosurgery, H3A 2B4, Québec, Canada
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28
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Arbib MA, Bonaiuto JJ, Bornkessel-Schlesewsky I, Kemmerer D, MacWhinney B, Nielsen FÅ, Oztop E. Action and language mechanisms in the brain: data, models and neuroinformatics. Neuroinformatics 2014; 12:209-25. [PMID: 24234916 PMCID: PMC4101894 DOI: 10.1007/s12021-013-9210-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
We assess the challenges of studying action and language mechanisms in the brain, both singly and in relation to each other to provide a novel perspective on neuroinformatics, integrating the development of databases for encoding – separately or together – neurocomputational models and empirical data that serve systems and cognitive neuroscience.
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Affiliation(s)
- Michael A. Arbib
- Computer Science and Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
| | - James J. Bonaiuto
- Division of Biology, California Institute of Technology, Pasadena, CA, USA
| | | | - David Kemmerer
- Speech, Language, & Hearing Sciences and Psychological Sciences, Purdue University, West Lafayette, IN, USA
| | - Brian MacWhinney
- Psychology, Computational Linguistics, and Modern Languages, Carnegie Mellon University, Pittsburgh, PA, USA
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29
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Morioka H, Kanemura A, Morimoto S, Yoshioka T, Oba S, Kawanabe M, Ishii S. Decoding spatial attention by using cortical currents estimated from electroencephalography with near-infrared spectroscopy prior information. Neuroimage 2013; 90:128-39. [PMID: 24374077 DOI: 10.1016/j.neuroimage.2013.12.035] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2013] [Accepted: 12/18/2013] [Indexed: 11/19/2022] Open
Abstract
For practical brain-machine interfaces (BMIs), electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are the only current methods that are non-invasive and available in non-laboratory environments. However, the use of EEG and NIRS involves certain inherent problems. EEG signals are generally a mixture of neural activity from broad areas, some of which may not be related to the task targeted by BMI, hence impairing BMI performance. NIRS has an inherent time delay as it measures blood flow, which therefore detracts from practical real-time BMI utility. To try to improve real environment EEG-NIRS-based BMIs, we propose here a novel methodology in which the subjects' mental states are decoded from cortical currents estimated from EEG, with the help of information from NIRS. Using a Variational Bayesian Multimodal EncephaloGraphy (VBMEG) methodology, we incorporated a novel form of NIRS-based prior to capture event related desynchronization from isolated current sources on the cortical surface. Then, we applied a Bayesian logistic regression technique to decode subjects' mental states from further sparsified current sources. Applying our methodology to a spatial attention task, we found our EEG-NIRS-based decoder exhibited significant performance improvement over decoding methods based on EEG sensor signals alone. The advancement of our methodology, decoding from current sources sparsely isolated on the cortex, was also supported by neuroscientific considerations; intraparietal sulcus, a region known to be involved in spatial attention, was a key responsible region in our task. These results suggest that our methodology is not only a practical option for EEG-NIRS-based BMI applications, but also a potential tool to investigate brain activity in non-laboratory and naturalistic environments.
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Affiliation(s)
- Hiroshi Morioka
- ATR Neural Information Analysis Laboratories, Kyoto 619-0288, Japan; Graduate School of Informatics, Kyoto University, Kyoto 611-0011, Japan
| | | | - Satoshi Morimoto
- ATR Neural Information Analysis Laboratories, Kyoto 619-0288, Japan
| | - Taku Yoshioka
- ATR Neural Information Analysis Laboratories, Kyoto 619-0288, Japan
| | - Shigeyuki Oba
- Graduate School of Informatics, Kyoto University, Kyoto 611-0011, Japan
| | - Motoaki Kawanabe
- ATR Neural Information Analysis Laboratories, Kyoto 619-0288, Japan
| | - Shin Ishii
- ATR Neural Information Analysis Laboratories, Kyoto 619-0288, Japan; Graduate School of Informatics, Kyoto University, Kyoto 611-0011, Japan.
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30
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Thanh Hai N, Cuong NQ, Dang Khoa TQ, Van Toi V. Temporal hemodynamic classification of two hands tapping using functional near-infrared spectroscopy. Front Hum Neurosci 2013; 7:516. [PMID: 24032008 PMCID: PMC3759001 DOI: 10.3389/fnhum.2013.00516] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2013] [Accepted: 08/12/2013] [Indexed: 11/13/2022] Open
Abstract
In recent decades, a lot of achievements have been obtained in imaging and cognitive neuroscience of human brain. Brain's activities can be shown by a number of different kinds of non-invasive technologies, such as: Near-Infrared Spectroscopy (NIRS), Magnetic Resonance Imaging (MRI), and ElectroEncephaloGraphy (EEG; Wolpaw et al., 2002; Weiskopf et al., 2004; Blankertz et al., 2006). NIRS has become the convenient technology for experimental brain purposes. The change of oxygenation changes (oxy-Hb) along task period depending on location of channel on the cortex has been studied: sustained activation in the motor cortex, transient activation during the initial segments in the somatosensory cortex, and accumulating activation in the frontal lobe (Gentili et al., 2010). Oxy-Hb concentration at the aforementioned sites in the brain can also be used as a predictive factor allows prediction of subject's investigation behavior with a considerable degree of precision (Shimokawa et al., 2009). In this paper, a study of recognition algorithm will be described for recognition whether one taps the left hand (LH) or the right hand (RH). Data with noises and artifacts collected from a multi-channel system will be pre-processed using a Savitzky-Golay filter for getting more smoothly data. Characteristics of the filtered signals during LH and RH tapping process will be extracted using a polynomial regression (PR) algorithm. Coefficients of the polynomial, which correspond to Oxygen-Hemoglobin (Oxy-Hb) concentration, will be applied for the recognition models of hand tapping. Support Vector Machines (SVM) will be applied to validate the obtained coefficient data for hand tapping recognition. In addition, for the objective of comparison, Artificial Neural Networks (ANNs) was also applied to recognize hand tapping side with the same principle. Experimental results have been done many trials on three subjects to illustrate the effectiveness of the proposed method.
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Affiliation(s)
- Nguyen Thanh Hai
- Biomedical Engineering Department, International University of Vietnam National Universities in Ho Chi Minh CityHo Chi Minh City, Vietnam
| | - Ngo Q. Cuong
- Department of Electronics and Telecommunications, Faculty of Electrical and Electronics Engineering, University of Technical Education HCMCHo Chi Minh City, Vietnam
| | - Truong Q. Dang Khoa
- Biomedical Engineering Department, International University of Vietnam National Universities in Ho Chi Minh CityHo Chi Minh City, Vietnam
| | - Vo Van Toi
- Biomedical Engineering Department, International University of Vietnam National Universities in Ho Chi Minh CityHo Chi Minh City, Vietnam
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31
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de Souza ACS, Yehia HC, Sato MA, Callan D. Brain activity underlying auditory perceptual learning during short period training: simultaneous fMRI and EEG recording. BMC Neurosci 2013; 14:8. [PMID: 23316957 PMCID: PMC3557158 DOI: 10.1186/1471-2202-14-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2012] [Accepted: 12/26/2012] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND There is an accumulating body of evidence indicating that neuronal functional specificity to basic sensory stimulation is mutable and subject to experience. Although fMRI experiments have investigated changes in brain activity after relative to before perceptual learning, brain activity during perceptual learning has not been explored. This work investigated brain activity related to auditory frequency discrimination learning using a variational Bayesian approach for source localization, during simultaneous EEG and fMRI recording. We investigated whether the practice effects are determined solely by activity in stimulus-driven mechanisms or whether high-level attentional mechanisms, which are linked to the perceptual task, control the learning process. RESULTS The results of fMRI analyses revealed significant attention and learning related activity in left and right superior temporal gyrus STG as well as the left inferior frontal gyrus IFG. Current source localization of simultaneously recorded EEG data was estimated using a variational Bayesian method. Analysis of current localized to the left inferior frontal gyrus and the right superior temporal gyrus revealed gamma band activity correlated with behavioral performance. CONCLUSIONS Rapid improvement in task performance is accompanied by plastic changes in the sensory cortex as well as superior areas gated by selective attention. Together the fMRI and EEG results suggest that gamma band activity in the right STG and left IFG plays an important role during perceptual learning.
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Affiliation(s)
| | | | - Masa-aki Sato
- ATR Neural Information Analysis Laboratories, Kyoto, Japan
| | - Daniel Callan
- ATR Neural Information Analysis Laboratories, Kyoto, Japan
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32
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Shimokawa T, Kosaka T, Yamashita O, Hiroe N, Amita T, Inoue Y, Sato MA. Hierarchical Bayesian estimation improves depth accuracy and spatial resolution of diffuse optical tomography. OPTICS EXPRESS 2012; 20:20427-46. [PMID: 23037092 DOI: 10.1364/oe.20.020427] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
High-density diffuse optical tomography (HD-DOT) is an emerging technique for visualizing the internal state of biological tissues. The large number of overlapping measurement channels due to the use of high-density probe arrays permits the reconstruction of the internal optical properties, even with a reflectance-only measurement. However, accurate three-dimensional reconstruction is still a challenging problem. First, the exponentially decaying sensitivity causes a systematic depth-localization error. Second, the nature of diffusive light makes the image blurred. In this paper, we propose a three-dimensional reconstruction method that overcomes these two problems by introducing sensitivity-normalized regularization and sparsity into the hierarchical Bayesian method. Phantom experiments were performed to validate the proposed method under three conditions of probe interval: 26 mm, 18.4 mm, and 13 mm. We found that two absorbers with distances shorter than the probe interval could be discriminated under the high-density conditions of 18.4-mm and 13-mm intervals. This discrimination ability was possible even if the depths of the two absorbers were different from each other. These results show the high spatial resolution of the proposed method in both depth and horizontal directions.
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Affiliation(s)
- Takeaki Shimokawa
- ATR Neural Information Analysis Laboratories, Kyoto 619-0288, Japan.
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