1
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Balconi M, Angioletti L. Inter-brain entrainment (IBE) during interoception. A multimodal EEG-fNIRS coherence-based hyperscanning approach. Neurosci Lett 2024; 831:137789. [PMID: 38670524 DOI: 10.1016/j.neulet.2024.137789] [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: 10/18/2023] [Revised: 01/12/2024] [Accepted: 04/23/2024] [Indexed: 04/28/2024]
Abstract
This work examined the impact of interoceptive manipulation and the presence of a shared goal on inter-brain entrainment (IBE) during a motor synchronization task. A multimodal functional Near Infrared Spectroscopy - Electroencephalogram (fNIRS-EEG) system-based hyperscanning approach was applied to 13 dyads performing the motor synchrony task during an interoceptive (focus on the breath) and control condition. Additionally, two version of the motor task-one with and one without a clearly defined common goal-were presented to participants to emphasize the task's collaborative purpose. The multimodal approach was exploited to record the electrophysiological (EEG) cortical oscillation and hemodynamic (oxy-Hb and deoxy-Hb) levels. Results revealed significant correlations between EEG delta, theta, and alpha band and hemodynamic oxy-Hb in the left compared to right hemisphere for the interoceptive confronted with the control condition. This significant EEG/fNIRS IBE correlation was also found for delta and theta band whereas the task was presented with an explicit shared goal confronted with the no-social version. In addition to separate functional connectivity EEG and fNIRS analysis, this study proposed a novel analysis pipeline including statistical tests for examining the coherence between functional connectivity EEG-fNIRS signals within couples. Besides proposing methodological advancements on EEG-fNIRS signals hyperscanning analysis, this research demonstrated that, in dyads undertaking a motor synchronization task, both the interoceptive attention to respiration and an explicit joint intention activate left anterior regions.
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Affiliation(s)
- Michela Balconi
- International research center for Cognitive Applied Neuroscience (IrcCAN), Università Cattolica del Sacro Cuore, 20123 Milan, Italy; Research Unit in Affective and Social Neuroscience, Department of Psychology, Università Cattolica del Sacro Cuore, 20123 Milan, Italy
| | - Laura Angioletti
- International research center for Cognitive Applied Neuroscience (IrcCAN), Università Cattolica del Sacro Cuore, 20123 Milan, Italy; Research Unit in Affective and Social Neuroscience, Department of Psychology, Università Cattolica del Sacro Cuore, 20123 Milan, Italy.
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2
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Zhang H, Hu Y, Li Y, Li D, Liu H, Li X, Song Y, Zhao C. Neurovascular coupling in the attention during visual working memory processes. iScience 2024; 27:109368. [PMID: 38510112 PMCID: PMC10951642 DOI: 10.1016/j.isci.2024.109368] [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: 11/06/2023] [Revised: 11/19/2023] [Accepted: 02/26/2024] [Indexed: 03/22/2024] Open
Abstract
Focusing attention in visual working memory (vWM) depends on the ability to filter distractors and expand the scope of targets. Although many properties of attention processes in vWM have been well documented, it remains unclear how the mechanisms of neurovascular coupling (NVC) function during attention processes in vWM. Here, we show simultaneous multimodal data that reveal the similar temporal and spatial features of attention processes during vWM. These similarities lead to common NVC outcomes across individuals. When filtering out distractors, the electroencephalography (EEG)-informed NVC displayed broader engagement across the frontoparietal network. A negative correlation may exist between behavioral metrics and EEG-informed NVC strength related to attention control. On a dynamic basis, NVC features exhibited higher discriminatory power in predicting behavior than other features alone. These results underscore how multimodal approaches can advance our understanding of the role of attention in vWM, and how NVC fluctuations are associated with actual behavior.
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Affiliation(s)
- Hao Zhang
- School of Systems Science, Beijing Normal University, Beijing 100875, China
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai, Zhuhai 519087, China
- International Academic Center of Complex Systems, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, China
| | - Yiqing Hu
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Yang Li
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Dongwei Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Department of Applied Psychology, School of Arts and Sciences, Beijing Normal University, Zhuhai, China
| | - Hanli Liu
- Department of Bioengineering, the University of Texas at Arlington, Arlington, TX, USA
| | - Xiaoli Li
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai, Zhuhai 519087, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yan Song
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Chenguang Zhao
- School of Systems Science, Beijing Normal University, Beijing 100875, China
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai, Zhuhai 519087, China
- Chinese Institute for Brain Research, Beijing 102206, China
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3
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McLinden J, Rahimi N, Kumar C, Krusienski DJ, Shao M, Spencer KM, Shahriari Y. Investigation of electro-vascular phase-amplitude coupling during an auditory task. Comput Biol Med 2024; 169:107902. [PMID: 38159399 DOI: 10.1016/j.compbiomed.2023.107902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/24/2023] [Accepted: 12/23/2023] [Indexed: 01/03/2024]
Abstract
Multimodal neuroimaging using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) provides complementary views of cortical processes, including those related to auditory processing. However, current multimodal approaches often overlook potential insights that can be gained from nonlinear interactions between electrical and hemodynamic signals. Here, we explore electro-vascular phase-amplitude coupling (PAC) between low-frequency hemodynamic and high-frequency electrical oscillations during an auditory task. We further apply a temporally embedded canonical correlation analysis (tCCA)-general linear model (GLM)-based correction approach to reduce the possible effect of systemic physiology on fNIRS recordings. Before correction, we observed significant PAC between fNIRS and broadband EEG in the frontal region (p ≪ 0.05), β (p ≪ 0.05) and γ (p = 0.010) in the left temporal/temporoparietal (left auditory; LA) region, and γ (p = 0.032) in the right temporal/temporoparietal (right auditory; RA) region across the entire dataset. Significant differences in PAC across conditions (task versus silence) were observed in LA (p = 0.023) and RA (p = 0.049) γ sub-bands and in lower frequency (5-20 Hz) frontal activity (p = 0.005). After correction, significant fNIRS-γ-band PAC was observed in the frontal (p = 0.021) and LA (p = 0.025) regions, while fNIRS-α (p = 0.003) and fNIRS-β (p = 0.041) PAC were observed in RA. Decreased frontal γ-band (p = 0.008) and increased β-band (p ≪ 0.05) PAC were observed during the task. These outcomes represent the first characterization of electro-vascular PAC between fNIRS and EEG signals during an auditory task, providing insights into electro-vascular coupling in auditory processing.
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Affiliation(s)
- J McLinden
- Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA
| | - N Rahimi
- Department of Computer and Information Science, University of Massachusetts Dartmouth, MA, USA
| | - C Kumar
- Department of Computer and Information Science, University of Massachusetts Dartmouth, MA, USA
| | - D J Krusienski
- Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA, USA
| | - M Shao
- Department of Computer and Information Science, University of Massachusetts Dartmouth, MA, USA
| | - K M Spencer
- Department of Psychiatry, VA Boston Healthcare System and Harvard Medical School, Boston, MA, USA
| | - Y Shahriari
- Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA.
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4
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Cao J, Grover P, Kainerstorfer JM. A model of neurovascular coupling and its application to cortical spreading depolarization. J Theor Biol 2023; 572:111580. [PMID: 37459953 DOI: 10.1016/j.jtbi.2023.111580] [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: 12/16/2022] [Revised: 06/09/2023] [Accepted: 07/10/2023] [Indexed: 08/01/2023]
Abstract
Cortical spreading depolarization (CSD) is a neuropathological condition involving propagating waves of neuronal silence, and is related to multiple diseases, such as migraine aura, traumatic brain injury (TBI), stroke, and cardiac arrest, as well as poor outcome of patients. While CSDs of different severity share similar roots on the ion exchange level, they can lead to different vascular responses (namely spreading hyperemia and spreading ischemia). In this paper, we propose a mathematical model relating neuronal activities to predict vascular changes as measured with near-infrared spectroscopy (NIRS) and fMRI recordings, and apply it to the extreme case of CSD, where sustained near-complete neuronal depolarization is seen. We utilize three serially connected models (namely, ion exchange, neurovascular coupling, and hemodynamic model) which are described by differential equations. Propagating waves of ion concentrations, as well as the associated vasodynamics and hemodynamics, are simulated by solving these equations. Our proposed model predicts vasodynamics and hemodynamics that agree both qualitatively and quantitatively with experimental literature. Mathematical modeling and simulation offer a powerful tool to help understand the underlying mechanisms of CSD and help interpret the data. In addition, it helps develop novel monitoring techniques prior to data collection. Our simulated results strongly suggest that fMRI is unable to reliably distinguish between spreading hyperemia and spreading ischemia, while NIRS signals are substantially distinct in the two cases.
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Affiliation(s)
- Jiaming Cao
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, PA, United States
| | - Pulkit Grover
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, PA, United States; Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, PA, United States; Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, 15213, PA, United States
| | - Jana M Kainerstorfer
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, PA, United States; Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, PA, United States; Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, 15213, PA, United States.
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5
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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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6
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Lin J, Lu J, Shu Z, Yu N, Han J. An EEG-fNIRS neurovascular coupling analysis method to investigate cognitive-motor interference. Comput Biol Med 2023; 160:106968. [PMID: 37196454 DOI: 10.1016/j.compbiomed.2023.106968] [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: 12/26/2022] [Revised: 03/27/2023] [Accepted: 04/19/2023] [Indexed: 05/19/2023]
Abstract
BACKGROUND AND OBJECTIVE The simultaneous execution of a motor and cognitive dual task may lead to the deterioration of task performance in one or both tasks due to cognitive-motor interference (CMI). Neuroimaging techniques are promising ways to reveal the underlying neural mechanism of CMI. However, existing studies have only explored CMI from a single neuroimaging modality, which lack built-in validation and comparison of analysis results. This work is aimed to establish an effective analysis framework to comprehensively investigate the CMI by exploring the electrophysiological and hemodynamic activities as well as their neurovascular coupling. METHODS Experiments including an upper limb single motor task, single cognitive task, and cognitive-motor dual task were designed and performed with 16 healthy young participants. Bimodal signals of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were recorded simultaneously during the experiments. A novel bimodal signal analysis framework was proposed to extract the task-related components for EEG and fNIRS signals respectively and analyze their correlation. Indicators including within-class similarity and between-class distance were utilized to validate the effectiveness of the proposed analysis framework compared to the canonical channel-averaged method. Statistical analysis was performed to investigate the difference in the behavior and neural correlates between the single and dual tasks. RESULTS Our results revealed that the extra cognitive interference caused divided attention in the dual task, which led to the decreased neurovascular coupling between fNIRS and EEG in all theta, alpha, and beta rhythms. The proposed framework was demonstrated to have a better ability in characterizing the neural patterns than the canonical channel-averaged method with significantly higher within-class similarity and between-class distance indicators. CONCLUSIONS This study proposed a method to investigate CMI by exploring the task-related electrophysiological and hemodynamic activities as well as their neurovascular coupling. Our concurrent EEG-fNIRS study provides new insight into the EEG-fNIRS correlation analysis and novel evidence for the mechanism of neurovascular coupling in the CMI.
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Affiliation(s)
- Jianeng Lin
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China; Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China
| | - Jiewei Lu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China; Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China
| | - Zhilin Shu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China; Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China
| | - Ningbo Yu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China; Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China; Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China.
| | - Jianda Han
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China; Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China; Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China.
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7
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Yu H, Liu D, Li S, Wang J, Liu J, Liu C. Probing the flexible internal state transition and low-dimensional manifold dynamics of human brain with acupuncture. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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8
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Walia P, Fu Y, Norfleet J, Schwaitzberg SD, Intes X, De S, Cavuoto L, Dutta A. Error-related brain state analysis using electroencephalography in conjunction with functional near-infrared spectroscopy during a complex surgical motor task. Brain Inform 2022; 9:29. [PMID: 36484977 PMCID: PMC9733771 DOI: 10.1186/s40708-022-00179-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 11/14/2022] [Indexed: 12/13/2022] Open
Abstract
Error-based learning is one of the basic skill acquisition mechanisms that can be modeled as a perception-action system and investigated based on brain-behavior analysis during skill training. Here, the error-related chain of mental processes is postulated to depend on the skill level leading to a difference in the contextual switching of the brain states on error commission. Therefore, the objective of this paper was to compare error-related brain states, measured with multi-modal portable brain imaging, between experts and novices during the Fundamentals of Laparoscopic Surgery (FLS) "suturing and intracorporeal knot-tying" task (FLS complex task)-the most difficult among the five psychomotor FLS tasks. The multi-modal portable brain imaging combined functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) for brain-behavior analysis in thirteen right-handed novice medical students and nine expert surgeons. The brain state changes were defined by quasi-stable EEG scalp topography (called microstates) changes using 32-channel EEG data acquired at 250 Hz. Six microstate prototypes were identified from the combined EEG data from experts and novices during the FLS complex task that explained 77.14% of the global variance. Analysis of variance (ANOVA) found that the proportion of the total time spent in different microstates during the 10-s error epoch was significantly affected by the skill level (p < 0.01), the microstate type (p < 0.01), and the interaction between the skill level and the microstate type (p < 0.01). Brain activation based on the slower oxyhemoglobin (HbO) changes corresponding to the EEG band power (1-40 Hz) changes were found using the regularized temporally embedded Canonical Correlation Analysis of the simultaneously acquired fNIRS-EEG signals. The HbO signal from the overlying the left inferior frontal gyrus-opercular part, left superior frontal gyrus-medial orbital, left postcentral gyrus, left superior temporal gyrus, right superior frontal gyrus-medial orbital cortical areas showed significant (p < 0.05) difference between experts and novices in the 10-s error epoch. We conclude that the difference in the error-related chain of mental processes was the activation of cognitive top-down attention-related brain areas, including left dorsolateral prefrontal/frontal eye field and left frontopolar brain regions, along with a 'focusing' effect of global suppression of hemodynamic activation in the experts, while the novices had a widespread stimulus(error)-driven hemodynamic activation without the 'focusing' effect.
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Affiliation(s)
- Pushpinder Walia
- grid.273335.30000 0004 1936 9887Neuroengineering and Informatics for Rehabilitation Laboratory, Department of Biomedical Engineering, University at Buffalo, Buffalo, USA
| | - Yaoyu Fu
- grid.273335.30000 0004 1936 9887Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, USA
| | - Jack Norfleet
- U.S. Army Futures Command, Combat Capabilities Development Command Soldier Center STTC, Orlando, USA
| | - Steven D. Schwaitzberg
- grid.273335.30000 0004 1936 9887University at Buffalo School of Medicine and Biomedical Sciences, Buffalo, USA
| | - Xavier Intes
- grid.33647.350000 0001 2160 9198Center for Modeling, Simulation and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY USA ,grid.33647.350000 0001 2160 9198Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, USA
| | - Suvranu De
- grid.33647.350000 0001 2160 9198Center for Modeling, Simulation and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY USA ,grid.33647.350000 0001 2160 9198Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, USA
| | - Lora Cavuoto
- grid.273335.30000 0004 1936 9887Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, USA
| | - Anirban Dutta
- grid.36511.300000 0004 0420 4262Neuroengineering and Informatics for Rehabilitation and Simulation-Based Learning, University of Lincoln, Lincoln, UK
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9
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Keles HO, Karakulak EZ, Hanoglu L, Omurtag A. Screening for Alzheimer's disease using prefrontal resting-state functional near-infrared spectroscopy. Front Hum Neurosci 2022; 16:1061668. [PMID: 36518566 PMCID: PMC9742284 DOI: 10.3389/fnhum.2022.1061668] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 11/01/2022] [Indexed: 08/10/2023] Open
Abstract
INTRODUCTION Alzheimer's disease (AD) is neurodegenerative dementia that causes neurovascular dysfunction and cognitive impairment. Currently, 50 million people live with dementia worldwide, and there are nearly 10 million new cases every year. There is a need for relatively less costly and more objective methods of screening and early diagnosis. METHODS Functional near-infrared spectroscopy (fNIRS) systems are a promising solution for the early Detection of AD. For a practical clinically relevant system, a smaller number of optimally placed channels are clearly preferable. In this study, we investigated the number and locations of the best-performing fNIRS channels measuring prefrontal cortex activations. Twenty-one subjects diagnosed with AD and eighteen healthy controls were recruited for the study. RESULTS We have shown that resting-state fNIRS recordings from a small number of prefrontal locations provide a promising methodology for detecting AD and monitoring its progression. A high-density continuous-wave fNIRS system was first used to verify the relatively lower hemodynamic activity in the prefrontal cortical areas observed in patients with AD. By using the episode averaged standard deviation of the oxyhemoglobin concentration changes as features that were fed into a Support Vector Machine; we then showed that the accuracy of subsets of optical channels in predicting the presence and severity of AD was significantly above chance. The results suggest that AD can be detected with a 0.76 sensitivity score and a 0.68 specificity score while the severity of AD could be detected with a 0.75 sensitivity score and a 0.72 specificity score with ≤5 channels. DISCUSSION These scores suggest that fNIRS is a viable technology for conveniently detecting and monitoring AD as well as investigating underlying mechanisms of disease progression.
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Affiliation(s)
- Hasan Onur Keles
- Department of Biomedical Engineering, Ankara University, Ankara, Turkey
| | | | - Lutfu Hanoglu
- Department of Neurology, School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Ahmet Omurtag
- Department of Engineering, Nottingham Trent University, Nottingham, United Kingdom
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Zhao C, Li D, Guo J, Li B, Kong Y, Hu Y, Du B, Ding Y, Li X, Liu H, Song Y. The neurovascular couplings between electrophysiological and hemodynamic activities in anticipatory selective attention. Cereb Cortex 2022; 32:4953-4968. [PMID: 35076708 DOI: 10.1093/cercor/bhab525] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 12/20/2021] [Accepted: 12/20/2021] [Indexed: 12/27/2022] Open
Abstract
Selective attention is thought to involve target enhancement and distractor inhibition processes. Here, we recorded simultaneous electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) data from human adults when they were pre-cued by the visual field of coming target, distractor, or both of them. From the EEG data, we found alpha power relatively decreased contralaterally to the to-be-attended target, as reflected by the positive-going alpha modulation index. Late alpha power relatively increased contralaterally to the to-be-suppressed distractor, as reflected by the negative-going alpha modulation index. From the fNIRS data, we found enhancements of hemodynamic activity over the contralateral hemisphere in response to both the target and the distractor anticipation but within nonoverlapping posterior brain regions. More importantly, we described the specific neurovascular modulation between alpha power and oxygenated hemoglobin signal, which showed a positive coupling effect during target anticipation and a negative coupling effect during distractor anticipation. Such flexible neurovascular couplings between EEG oscillation and hemodynamic activity seem to play an essential role in the final behavioral outcomes. These results provide unique neurovascular evidence for the dissociation of the mechanisms of target enhancement and distractor inhibition. Individual behavioral differences can be related to individual differences in neurovascular coupling.
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Affiliation(s)
- Chenguang Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning &IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.,Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai 519087, China.,School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Dongwei Li
- State Key Laboratory of Cognitive Neuroscience and Learning &IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Jialiang Guo
- State Key Laboratory of Cognitive Neuroscience and Learning &IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Bingkun Li
- State Key Laboratory of Cognitive Neuroscience and Learning &IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yuanjun Kong
- State Key Laboratory of Cognitive Neuroscience and Learning &IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yiqing Hu
- State Key Laboratory of Cognitive Neuroscience and Learning &IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Boqi Du
- State Key Laboratory of Cognitive Neuroscience and Learning &IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yulong Ding
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning &IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.,Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai 519087, China
| | - Hanli Liu
- Department of Bioengineering, the University of Texas at Arlington, Arlington, TX 76019, USA
| | - Yan Song
- State Key Laboratory of Cognitive Neuroscience and Learning &IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.,Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai 519087, China
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11
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Eng CM, Pocsai M, Fulton VE, Moron SP, Thiessen ED, Fisher AV. Longitudinal investigation of executive function development employing task-based, teacher reports, and fNIRS multimethodology in 4- to 5-year-old children. Dev Sci 2022; 25:e13328. [PMID: 36221252 PMCID: PMC10408588 DOI: 10.1111/desc.13328] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 09/07/2022] [Accepted: 09/12/2022] [Indexed: 01/13/2023]
Abstract
Increased focus on resting-state functional connectivity (rsFC) and the use and accessibility of functional near-infrared spectroscopy (fNIRS) have advanced knowledge on the interconnected nature of neural substrates underlying executive function (EF) development in adults and clinical populations. Less is known about the relationship between rsFC and developmental changes in EF during preschool years in typically developing children, a gap the present study addresses employing task-based assessment, teacher reports, and fNIRS multimethodology. This preregistered study contributes to our understanding of the neural basis of EF development longitudinally with 41 children ages 4-5. Changes in prefrontal cortex (PFC) rsFC utilizing fNIRS, EF measured with a common task-based assessment (Day-Night task), and teacher reports of behavior (BRIEF-P) were monitored over multiple timepoints: Initial Assessment, 72 h follow-up, 1 Month Follow-up, and 4 Month Follow-up. Measures of rsFC were strongly correlated 72 h apart, providing evidence of high rsFC measurement reliability using fNIRS with preschool-aged children. PFC rsFC was positively correlated with performance on task-based and report-based EF assessments. Children's PFC functional connectivity at rest uniquely predicted later EF, controlling for verbal IQ, age, and sex. Functional connectivity at rest using fNIRS may potentially show the rapid changes in EF development in young children, not only neurophysiologically, but also as a correlate of task-based EF performance and ecologically-relevant teacher reports of EF in a classroom context.
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Affiliation(s)
- Cassondra M Eng
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, USA
- Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Melissa Pocsai
- Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
- Department of Psychology, City University of New York, New York, New York, USA
| | - Virginia E Fulton
- Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Suanna P Moron
- Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
- Graduate School of Education, Stanford University, Stanford, California, USA
| | - Erik D Thiessen
- Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Anna V Fisher
- Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
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12
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Trambaiolli LR, Cassani R, Biazoli CE, Cravo AM, Sato JR, Falk TH. Multimodal resting-state connectivity predicts affective neurofeedback performance. Front Hum Neurosci 2022; 16:977776. [PMID: 36158618 PMCID: PMC9493361 DOI: 10.3389/fnhum.2022.977776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 08/03/2022] [Indexed: 11/13/2022] Open
Abstract
Neurofeedback has been suggested as a potential complementary therapy to different psychiatric disorders. Of interest for this approach is the prediction of individual performance and outcomes. In this study, we applied functional connectivity-based modeling using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) modalities to (i) investigate whether resting-state connectivity predicts performance during an affective neurofeedback task and (ii) evaluate the extent to which predictive connectivity profiles are correlated across EEG and fNIRS techniques. The fNIRS oxyhemoglobin and deoxyhemoglobin concentrations and the EEG beta and gamma bands modulated by the alpha frequency band (beta-m-alpha and gamma-m-alpha, respectively) recorded over the frontal cortex of healthy subjects were used to estimate functional connectivity from each neuroimaging modality. For each connectivity matrix, relevant edges were selected in a leave-one-subject-out procedure, summed into "connectivity summary scores" (CSS), and submitted as inputs to a support vector regressor (SVR). Then, the performance of the left-out-subject was predicted using the trained SVR model. Linear relationships between the CSS across both modalities were evaluated using Pearson's correlation. The predictive model showed a mean absolute error smaller than 20%, and the fNIRS oxyhemoglobin CSS was significantly correlated with the EEG gamma-m-alpha CSS (r = -0.456, p = 0.030). These results support that pre-task electrophysiological and hemodynamic resting-state connectivity are potential predictors of neurofeedback performance and are meaningfully coupled. This investigation motivates the use of joint EEG-fNIRS connectivity as outcome predictors, as well as a tool for functional connectivity coupling investigation.
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Affiliation(s)
- Lucas R. Trambaiolli
- Basic Neuroscience Division, McLean Hospital–Harvard Medical School, Belmont, MA, United States
| | - Raymundo Cassani
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Claudinei E. Biazoli
- Center for Mathematics, Computing and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, United Kingdom
| | - André M. Cravo
- Center for Mathematics, Computing and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
| | - João R. Sato
- Center for Mathematics, Computing and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
- Big Data, Hospital Israelita Albert Einstein, São Paulo, Brazil
| | - Tiago H. Falk
- Institut National de la Recherche Scientifique, University of Quebec, Montreal, QC, Canada
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13
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Zhou G, Chen Y, Wang X, Wei H, Huang Q, Li L. The correlations between kinematic profiles and cerebral hemodynamics suggest changes of motor coordination in single and bilateral finger movement. Front Hum Neurosci 2022; 16:957364. [PMID: 36061505 PMCID: PMC9433536 DOI: 10.3389/fnhum.2022.957364] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 07/08/2022] [Indexed: 11/23/2022] Open
Abstract
Objective The correlation between the performance of coordination movement and brain activity is still not fully understood. The current study aimed to identify activated brain regions and brain network connectivity changes for several coordinated finger movements with different difficulty levels and to correlate the brain hemodynamics and connectivity with kinematic performance. Methods Twenty-one right-dominant-handed subjects were recruited and asked to complete circular motions of single and bilateral fingers in the same direction (in-phase, IP) and in opposite directions (anti-phase, AP) on a plane. Kinematic data including radius and angular velocity at each task and synchronized blood oxygen concentration data using functional near-infrared spectroscopy (fNIRS) were recorded covering six brain regions including the prefrontal cortex, motor cortex, and occipital lobes. A general linear model was used to locate activated brain regions, and changes compared with baseline in blood oxygen concentration were used to evaluate the degree of brain region activation. Small-world properties, clustering coefficients, and efficiency were used to measure information interaction in brain activity during the movement. Result It was found that the radius error of the dominant hand was significantly lower than that of the non-dominant hand (p < 0.001) in both clockwise and counterclockwise movements. The fNIRS results confirmed that the contralateral brain region was activated during single finger movement and the dominant motor area was activated in IP movement, while both motor areas were activated simultaneously in AP movement. The Δhbo were weakly correlated with radius errors (p = 0.002). Brain information interaction in IP movement was significantly larger than that from AP movement in the brain network (p < 0.02) in the right prefrontal cortex. Brain activity in the right motor cortex reduces motor performance (p < 0.001), while the right prefrontal cortex region promotes it (p < 0.05). Conclusion Our results suggest there was a significant correlation between motion performance and brain activation level, as well as between motion deviation and brain functional connectivity. The findings may provide a basis for further exploration of the operation of complex brain networks.
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Affiliation(s)
- Guangquan Zhou
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Yuzhao Chen
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Xiaohan Wang
- Institute of Medical Research, Northwestern Polytechnical University, Xi’an, China
| | - Hao Wei
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Qinghua Huang
- School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an, China
| | - Le Li
- Institute of Medical Research, Northwestern Polytechnical University, Xi’an, China
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14
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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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15
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Yeung MK, Chu VW. Viewing neurovascular coupling through the lens of combined EEG-fNIRS: A systematic review of current methods. Psychophysiology 2022; 59:e14054. [PMID: 35357703 DOI: 10.1111/psyp.14054] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 02/01/2022] [Accepted: 03/08/2022] [Indexed: 12/25/2022]
Abstract
Neurovascular coupling is a key physiological mechanism that occurs in the healthy human brain, and understanding this process has implications for understanding the aging and neuropsychiatric populations. Combined electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) has emerged as a promising, noninvasive tool for probing neurovascular interactions in humans. However, the utility of this approach critically depends on the methodological quality used for multimodal integration. Despite a growing number of combined EEG-fNIRS applications reported in recent years, the methodological rigor of past studies remains unclear, limiting the accurate interpretation of reported findings and hindering the translational application of this multimodal approach. To fill this knowledge gap, we critically evaluated various methodological aspects of previous combined EEG-fNIRS studies performed in healthy individuals. A literature search was conducted using PubMed and PsycINFO on June 28, 2021. Studies involving concurrent EEG and fNIRS measurements in awake and healthy individuals were selected. After screening and eligibility assessment, 96 studies were included in the methodological evaluation. Specifically, we critically reviewed various aspects of participant sampling, experimental design, signal acquisition, data preprocessing, outcome selection, data analysis, and results presentation reported in these studies. Altogether, we identified several notable strengths and limitations of the existing EEG-fNIRS literature. In light of these limitations and the features of combined EEG-fNIRS, recommendations are made to improve and standardize research practices to facilitate the use of combined EEG-fNIRS when studying healthy neurovascular coupling processes and alterations in neurovascular coupling among various populations.
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Affiliation(s)
- Michael K Yeung
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
| | - Vivian W Chu
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
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16
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Wu GR, Colenbier N, Van Den Bossche S, Clauw K, Johri A, Tandon M, Marinazzo D. rsHRF: A toolbox for resting-state HRF estimation and deconvolution. Neuroimage 2021; 244:118591. [PMID: 34560269 DOI: 10.1016/j.neuroimage.2021.118591] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 06/25/2021] [Accepted: 09/16/2021] [Indexed: 10/20/2022] Open
Abstract
The hemodynamic response function (HRF) greatly influences the intra- and inter-subject variability of brain activation and connectivity, and might confound the estimation of temporal precedence in connectivity analyses, making its estimation necessary for a correct interpretation of neuroimaging studies. Additionally, the HRF shape itself is a useful local measure. However, most algorithms for HRF estimation are specific for task-related fMRI data, and only a few can be directly applied to resting-state protocols. Here we introduce rsHRF, a Matlab and Python toolbox that implements HRF estimation and deconvolution from the resting-state BOLD signal. We first provide an overview of the main algorithm, practical implementations, and then demonstrate the feasibility and usefulness of rsHRF by validation experiments with a publicly available resting-state fMRI dataset. We also provide tools for statistical analyses and visualization. We believe that this toolbox may significantly contribute to a better analysis and understanding of the components and variability of BOLD signals.
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Affiliation(s)
- Guo-Rong Wu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing 400715, China; Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium.
| | - Nigel Colenbier
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium; Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven 3001, Belgium; Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice 30126, Italy
| | - Sofie Van Den Bossche
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium
| | - Kenzo Clauw
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium
| | - Amogh Johri
- International Institute of Information Technology, Bangalore 560100, India
| | - Madhur Tandon
- Indraprastha Institute of Information Technology, Delhi 110020, India
| | - Daniele Marinazzo
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium; Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice 30126, Italy
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17
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Si X, Xiang S, Zhang L, Li S, Zhang K, Ming D. Acupuncture With deqi Modulates the Hemodynamic Response and Functional Connectivity of the Prefrontal-Motor Cortical Network. Front Neurosci 2021; 15:693623. [PMID: 34483822 PMCID: PMC8415569 DOI: 10.3389/fnins.2021.693623] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 06/07/2021] [Indexed: 11/25/2022] Open
Abstract
As a world intangible cultural heritage, acupuncture is considered an essential modality of complementary and alternative therapy to Western medicine. Despite acupuncture’s long history and public acceptance, how the cortical network is modulated by acupuncture remains largely unclear. Moreover, as the basic acupuncture unit for regulating the central nervous system, how the cortical network is modulated during acupuncture at the Hegu acupoint is mostly unclear. Here, multi-channel functional near-infrared spectroscopy (fNIRS) data were recorded from twenty healthy subjects for acupuncture manipulation, pre- and post-manipulation tactile controls, and pre- and post-acupuncture rest controls. Results showed that: (1) acupuncture manipulation caused significantly increased acupuncture behavioral deqi performance compared with tactile controls. (2) The bilateral prefrontal cortex (PFC) and motor cortex were significantly inhibited during acupuncture manipulation than controls, which was evidenced by the decreased power of oxygenated hemoglobin (HbO) concentration. (3) The bilateral PFC’s hemodynamic responses showed a positive correlation trend with acupuncture behavioral performance. (4) The network connections with bilateral PFC as nodes showed significantly increased functional connectivity during acupuncture manipulation compared with controls. (5) Meanwhile, the network’s efficiency was improved by acupuncture manipulation, evidenced by the increased global efficiency and decreased shortest path length. Taken together, these results reveal that a cooperative PFC-Motor functional network could be modulated by acupuncture manipulation at the Hegu acupoint. This study provides neuroimaging evidence that explains acupuncture’s neuromodulation effects on the cortical network.
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Affiliation(s)
- Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China.,Tianjin International Engineering Institute, Tianjin University, Tianjin, China.,Institute of Applied Psychology, Tianjin University, Tianjin, China
| | - Shaoxin Xiang
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China.,Tianjin International Engineering Institute, Tianjin University, Tianjin, China
| | - Ludan Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Sicheng Li
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Kuo Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
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18
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19
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Feasibility of combining functional near-infrared spectroscopy with electroencephalography to identify chronic stroke responders to cerebellar transcranial direct current stimulation-a computational modeling and portable neuroimaging methodological study. THE CEREBELLUM 2021; 20:853-871. [PMID: 33675516 DOI: 10.1007/s12311-021-01249-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/21/2021] [Indexed: 10/22/2022]
Abstract
Feasibility of portable neuroimaging of cerebellar transcranial direct current stimulation (ctDCS) effects on the cerebral cortex has not been investigated vis-à-vis cerebellar lobular electric field strength. We studied functional near-infrared spectroscopy (fNIRS) in conjunction with electroencephalography (EEG) to measure changes in the brain activation at the prefrontal cortex (PFC) and the sensorimotor cortex (SMC) following ctDCS as well as virtual reality-based balance training (VBaT) before and after ctDCS treatment in 12 hemiparetic chronic stroke survivors. We performed general linear modeling (GLM) that putatively associated the lobular electric field strength with the changes in the fNIRS-EEG measures at the ipsilesional and contra-lesional PFC and SMC. Here, fNIRS-EEG measures were found in the latent space from canonical correlation analysis (CCA) between the changes in total hemoglobin (tHb) concentrations (0.01-0.07Hz and 0.07-0.13Hz bands) and log10-transformed EEG bandpower within 1-45 Hz where significant (Wilks' lambda>0.95) canonical correlations were found only for the 0.07-0.13-Hz band. Also, the first principal component (97.5% variance accounted for) of the mean lobular electric field strength was a good predictor of the latent variables of oxy-hemoglobin (O2Hb) concentrations and log10-transformed EEG bandpower. GLM also provided insights into non-responders to ctDCS who also performed poorly in the VBaT due to ideomotor apraxia. Future studies should investigate fNIRS-EEG joint-imaging in a larger cohort to identify non-responders based on GLM fitting to the fNIRS-EEG data.
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20
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Kaposzta Z, Stylianou O, Mukli P, Eke A, Racz FS. Decreased connection density and modularity of functional brain networks during n-back working memory paradigm. Brain Behav 2021; 11:e01932. [PMID: 33185986 PMCID: PMC7821619 DOI: 10.1002/brb3.1932] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 10/05/2020] [Accepted: 10/18/2020] [Indexed: 12/17/2022] Open
Abstract
INTRODUCTION Investigating how the brain adapts to increased mental workload through large-scale functional reorganization appears as an important research question. Functional connectivity (FC) aims at capturing how disparate regions of the brain dynamically interact, while graph theory provides tools for the topological characterization of the reconstructed functional networks. Although numerous studies investigated how FC is altered in response to increased working memory (WM) demand, current results are still contradictory as few studies confirmed the robustness of these findings in a low-density setting. METHODS In this study, we utilized the n-back WM paradigm, in which subjects were presented stimuli (single digits) sequentially, and their task was to decide for each given stimulus if it matched the one presented n-times earlier. Electroencephalography recordings were performed under a control (0-back) and two task conditions of varying difficulty (2- and 3-back). We captured the characteristic connectivity patterns for each difficulty level by performing FC analysis and described the reconstructed functional networks with various graph theoretical measures. RESULTS We found a substantial decrease in FC when transitioning from the 0- to the 2- or 3-back conditions, however, no differences relating to task difficulty were identified. The observed changes in brain network topology could be attributed to the dissociation of two (frontal and occipitotemporal) functional modules that were only present during the control condition. Furthermore, behavioral and performance measures showed both positive and negative correlations to connectivity indices, although only in the higher frequency bands. CONCLUSION The marked decrease in FC may be due to temporarily abandoned connections that are redundant or irrelevant in solving the specific task. Our results indicate that FC analysis is a robust tool for investigating the response of the brain to increased cognitive workload.
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Affiliation(s)
- Zalan Kaposzta
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | | | - Peter Mukli
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Andras Eke
- Department of Physiology, Semmelweis University, Budapest, Hungary
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21
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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] [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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22
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Physiological correlates of cognitive load in laparoscopic surgery. Sci Rep 2020; 10:12927. [PMID: 32737352 PMCID: PMC7395129 DOI: 10.1038/s41598-020-69553-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 07/14/2020] [Indexed: 02/06/2023] Open
Abstract
Laparoscopic surgery can be exhausting and frustrating, and the cognitive load experienced by surgeons may have a major impact on patient safety as well as healthcare economics. As cognitive load decreases with increasing proficiency, its robust assessment through physiological data can help to develop more effective training and certification procedures in this area. We measured data from 31 novices during laparoscopic exercises to extract features based on cardiac and ocular variables. These were compared with traditional behavioural and subjective measures in a dual-task setting. We found significant correlations between the features and the traditional measures. The subjective task difficulty, reaction time, and completion time were well predicted by the physiology features. Reaction times to randomly timed auditory stimuli were correlated with the mean of the heart rate (\documentclass[12pt]{minimal}
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\begin{document}$$r = 0.4$$\end{document}r=0.4). Completion times were correlated with the physiologically predicted values with a correlation coefficient of 0.84. We found that the multi-modal set of physiology features was a better predictor than any individual feature and artificial neural networks performed better than linear regression. The physiological correlates studied in this paper, translated into technological products, could help develop standardised and more easily regulated frameworks for training and certification.
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23
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Li R, Zhao C, Wang C, Wang J, Zhang Y. Enhancing fNIRS Analysis Using EEG Rhythmic Signatures: An EEG-Informed fNIRS Analysis Study. IEEE Trans Biomed Eng 2020; 67:2789-2797. [PMID: 32031925 DOI: 10.1109/tbme.2020.2971679] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Neurovascular coupling represents the relationship between changes in neuronal activity and cerebral hemodynamics. Concurrent Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) recording and integration analysis has emerged as a promising multi-modal neuroimaging approach to study the neurovascular coupling as it provides complementary properties with regard to high temporal and moderate spatial resolution of brain activity. In this study we developed an EEG-informed-fNIRS analysis framework to investigate the neuro-correlate between neuronal activity and cerebral hemodynamics by identifying specific EEG rhythmic modulations which contribute to the improvement of the fNIRS-based general linear model (GLM) analysis. Specifically, frequency-specific regressors derived from EEG were used to construct design matrices to guide the GLM analysis of the fNIRS signals collected during a hand grasp task. Our results showed that the EEG-informed fNIRS GLM analysis, especially the alpha and beta band, revealed significantly higher sensitivity and specificity in localizing the task-evoked regions compared to the canonical boxcar model, demonstrating the strong correlations between hemodynamic response and EEG rhythmic modulations. Results also indicated that analysis based on the deoxygenated hemoglobin (HbR) signal slightly outperformed the oxygenated hemoglobin (HbO)-based analysis. The findings in our study not only validate the feasibility of enhancing fNIRS GLM analysis using simultaneously recorded EEG signals, but also provide a new perspective to study the neurovascular coupling of brain activity.
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Ge S, Wang P, Liu H, Lin P, Gao J, Wang R, Iramina K, Zhang Q, Zheng W. Neural Activity and Decoding of Action Observation Using Combined EEG and fNIRS Measurement. Front Hum Neurosci 2019; 13:357. [PMID: 31680910 PMCID: PMC6803538 DOI: 10.3389/fnhum.2019.00357] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Accepted: 09/24/2019] [Indexed: 12/17/2022] Open
Abstract
In a social world, observing the actions of others is fundamental to understanding what they are doing, as well as their intentions and feelings. Studies of the neural basis and decoding of action observation are important for understanding action-related processes and have implications for cognitive, social neuroscience, and human-machine interaction (HMI). In the current study, we first investigated temporal-spatial dynamics during action observation using a combined 64-channel electroencephalography (EEG) and 48-channel functional near-infrared spectroscopy (fNIRS) system. We measured brain activation while 16 healthy participants observed three action tasks: (1) grasping a cup with the intention of drinking; (2) grasping a cup with the intention of moving it; and (3) touching a cup with an unclear intention. The EEG and fNIRS source analysis results revealed the dynamic involvement of both the mirror neuron system (MNS) and the theory of mind (ToM)/mentalizing network during action observation. The source analysis results suggested that the extent to which these two systems were engaged was determined by the clarity of the intention of the observed action. Based on the difference in neural activity observed among different action-observation tasks in the first experiment, we conducted a second experiment to classify the neural processes underlying action observation using a feature classification method. We constructed complex brain networks based on the EEG and fNIRS data. Fusing features from both EEG and fNIRS complex brain networks resulted in a classification accuracy of 72.7% for the three action observation tasks. This study provides a theoretical and empirical basis for elucidating the neural mechanisms of action observation and intention understanding, and a feasible method for decoding the underlying neural processes.
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Affiliation(s)
- Sheng Ge
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Peng Wang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Hui Liu
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Pan Lin
- Department of Psychology and Cognition and Human Behavior Key Laboratory of Hunan Province, Hunan Normal University, Changsha, China
- College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, China
| | - Junfeng Gao
- College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, China
| | - Ruimin Wang
- Department of Graduate School of Systems Life Sciences, Kyushu University, Fukuoka, Japan
| | - Keiji Iramina
- Department of Graduate School of Systems Life Sciences, Kyushu University, Fukuoka, Japan
| | - Quan Zhang
- Neural Systems Group, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Wenming Zheng
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
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Dvorak D, Shang A, Abdel-Baki S, Suzuki W, Fenton AA. Cognitive Behavior Classification From Scalp EEG Signals. IEEE Trans Neural Syst Rehabil Eng 2019; 26:729-739. [PMID: 29641377 DOI: 10.1109/tnsre.2018.2797547] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Electroencephalography (EEG) has become increasingly valuable outside of its traditional use in neurology. EEG is now used for neuropsychiatric diagnosis, neurological evaluation of traumatic brain injury, neurotherapy, gaming, neurofeedback, mindfulness, and cognitive enhancement training. The trend to increase the number of EEG electrodes, the development of novel analytical methods, and the availability of large data sets has created a data analysis challenge to find the "signal of interest" that conveys the most information about ongoing cognitive effort. Accordingly, we compare three common types of neural synchrony measures that are applied to EEG-power analysis, phase locking, and phase-amplitude coupling to assess which analytical measure provides the best separation between EEG signals that were recorded, while healthy subjects performed eight cognitive tasks-Hopkins Verbal Learning Test and its delayed version, Stroop Test, Symbol Digit Modality Test, Controlled Oral Word Association Test, Trail Marking Test, Digit Span Test, and Benton Visual Retention Test. We find that of the three analytical methods, phase-amplitude coupling, specifically theta (4-7 Hz)-high gamma (70-90 Hz) obtained from frontal and parietal EEG electrodes provides both the largest separation between the EEG during cognitive tasks and also the highest classification accuracy between pairs of tasks. We also find that phase-locking analysis provides the most distinct clustering of tasks based on their utilization of long-term memory. Finally, we show that phase-amplitude coupling is the least sensitive to contamination by intense jaw-clenching muscle artifact.
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Khan MJ, Ghafoor U, Hong KS. Early Detection of Hemodynamic Responses Using EEG: A Hybrid EEG-fNIRS Study. Front Hum Neurosci 2018; 12:479. [PMID: 30555313 PMCID: PMC6281984 DOI: 10.3389/fnhum.2018.00479] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2018] [Accepted: 11/15/2018] [Indexed: 01/06/2023] Open
Abstract
Enhanced classification accuracy and a sufficient number of commands are highly demanding in brain computer interfaces (BCIs). For a successful BCI, early detection of brain commands in time is essential. In this paper, we propose a novel classifier using a modified vector phase diagram and the power of electroencephalography (EEG) signal for early prediction of hemodynamic responses. EEG and functional near-infrared spectroscopy (fNIRS) signals for a motor task (thumb tapping) were obtained concurrently. Upon the resting state threshold circle in the vector phase diagram that uses the maximum values of oxy- and deoxy-hemoglobin (ΔHbO and ΔHbR) during the resting state, we introduce a secondary (inner) threshold circle using the ΔHbO and ΔHbR magnitudes during the time window of 1 s where an EEG activity is noticeable. If the trajectory of ΔHbO and ΔHbR touches the resting state threshold circle after passing through the inner circle, this indicates that ΔHbO was increasing and ΔHbR was decreasing (i.e., the start of a hemodynamic response). It takes about 0.5 s for an fNIRS signal to cross the resting state threshold circle after crossing the EEG-based circle. Thus, an fNIRS-based BCI command can be generated in 1.5 s. We achieved an improved accuracy of 86.0% using the proposed method in comparison with the 63.8% accuracy obtained using linear discriminant analysis in a window of 0~1.5 s. Moreover, the active brain locations (identified using the proposed scheme) were spatially specific when a t-map was made after 10 s of stimulation. These results demonstrate the possibility of enhancing the classification accuracy for a brain-computer interface with a time window of 1.5 s using the proposed method.
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Affiliation(s)
- M Jawad Khan
- School of Mechanical Engineering, Pusan National University, Busan, South Korea.,School of Mechanical and Manufacturing Engineering, National University of Science and Technology, Islamabad, Pakistan
| | - Usman Ghafoor
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
| | - Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Busan, South Korea.,Department of Cogno-Mechatronics Engineering, Pusan National University, Busan, South Korea
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Omurtag A, Aghajani H, Keles HO. Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance. J Neural Eng 2018; 14:066003. [PMID: 28730995 DOI: 10.1088/1741-2552/aa814b] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
OBJECTIVE Concurrent scalp electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), which we refer to as EEG+fNIRS, promises greater accuracy than the individual modalities while remaining nearly as convenient as EEG. We sought to quantify the hybrid system's ability to decode mental states and compare it with its unimodal components. APPROACH We recorded from healthy volunteers taking the category fluency test and applied machine learning techniques to the data. MAIN RESULTS EEG+fNIRS's decoding accuracy was greater than that of its subsystems, partly due to the new type of neurovascular features made available by hybrid data. SIGNIFICANCE Availability of an accurate and practical decoding method has potential implications for medical diagnosis, brain-computer interface design, and neuroergonomics.
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Affiliation(s)
- Ahmet Omurtag
- Engineering Department, Nottingham Trent University, Nottingham, United Kingdom
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Keles HO, Radoman M, Pachas GN, Evins AE, Gilman JM. Using Functional Near-Infrared Spectroscopy to Measure Effects of Delta 9-Tetrahydrocannabinol on Prefrontal Activity and Working Memory in Cannabis Users. Front Hum Neurosci 2017; 11:488. [PMID: 29066964 PMCID: PMC5641318 DOI: 10.3389/fnhum.2017.00488] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Accepted: 09/22/2017] [Indexed: 11/13/2022] Open
Abstract
Intoxication from cannabis impairs cognitive performance, in part due to the effects of Δ9-tetrahydrocannabinol (THC, the primary psychoactive compound in cannabis) on prefrontal cortex (PFC) function. However, a relationship between impairment in cognitive functioning with THC administration and THC-induced change in hemodynamic response has not been demonstrated. We explored the feasibility of using functional near-infrared spectroscopy (fNIRS) to examine the functional changes of the human PFC associated with cannabis intoxication and cognitive impairment. Eighteen adult regular cannabis users (final sample, n = 13) performed a working memory task (n-back) during fNIRS recordings, before and after receiving a single dose of oral synthetic THC (dronabinol; 20–50 mg). Functional data were collected using a continuous-wave NIRS device, in which 8 Sources and 7 detectors were placed on the forehead, resulting in 20 channels covering PFC regions. Physiological changes and subjective intoxication measures were collected. We found a significant increase in the oxygenated hemoglobin (HbO) concentration after THC administration in several channels on the PFC during both the high working memory load (2-back) and the low working memory load (0-back) condition. The increased HbO response was accompanied by a trend toward an increased number of omission errors after THC administration. The current study suggests that cannabis intoxication is associated with increases in hemodynamic blood flow to the PFC, and that this increase can be detected with fNIRS.
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Affiliation(s)
- Hasan O Keles
- Center for Addiction Medicine, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States.,Department of Psychiatry, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Milena Radoman
- Center for Addiction Medicine, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
| | - Gladys N Pachas
- Center for Addiction Medicine, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States.,Department of Psychiatry, Harvard Medical School, Harvard University, Boston, MA, United States
| | - A Eden Evins
- Center for Addiction Medicine, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States.,Department of Psychiatry, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Jodi M Gilman
- Center for Addiction Medicine, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States.,Department of Psychiatry, Harvard Medical School, Harvard University, Boston, MA, United States
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Chiarelli AM, Zappasodi F, Di Pompeo F, Merla A. Simultaneous functional near-infrared spectroscopy and electroencephalography for monitoring of human brain activity and oxygenation: a review. NEUROPHOTONICS 2017; 4:041411. [PMID: 28840162 PMCID: PMC5566595 DOI: 10.1117/1.nph.4.4.041411] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Accepted: 07/24/2017] [Indexed: 05/24/2023]
Abstract
Multimodal monitoring has become particularly common in the study of human brain function. In this context, combined, synchronous measurements of functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) are getting increased interest. Because of the absence of electro-optical interference, it is quite simple to integrate these two noninvasive recording procedures of brain activity. fNIRS and EEG are both scalp-located procedures. fNIRS estimates brain hemodynamic fluctuations relying on spectroscopic measurements, whereas EEG captures the macroscopic temporal dynamics of brain electrical activity through passive voltages evaluations. The "orthogonal" neurophysiological information provided by the two technologies and the increasing interest in the neurovascular coupling phenomenon further encourage their integration. This review provides, together with an introduction regarding the principles and future directions of the two technologies, an evaluation of major clinical and nonclinical applications of this flexible, low-cost combination of neuroimaging modalities. fNIRS-EEG systems exploit the ability of the two technologies to be conducted in an environment or experimental setting and/or on subjects that are generally not suited for other neuroimaging modalities, such as functional magnetic resonance imaging, positron emission tomography, and magnetoencephalography. fNIRS-EEG brain monitoring settles itself as a useful multimodal tool for brain electrical and hemodynamic activity investigation.
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Affiliation(s)
- Antonio M. Chiarelli
- University of Illinois at Urbana Champaign, Beckman Institute, Urbana, Illinois, United States
| | - Filippo Zappasodi
- Università G. d’Annunzio, Department of Neuroscience, Imaging and Clinical Science, Chieti, Italy
- Università G. d’Annunzio, Institute for Advanced Biomedical Technologies, Chieti, Italy
| | - Francesco Di Pompeo
- Università G. d’Annunzio, Department of Neuroscience, Imaging and Clinical Science, Chieti, Italy
- Università G. d’Annunzio, Institute for Advanced Biomedical Technologies, Chieti, Italy
| | - Arcangelo Merla
- Università G. d’Annunzio, Department of Neuroscience, Imaging and Clinical Science, Chieti, Italy
- Università G. d’Annunzio, Institute for Advanced Biomedical Technologies, Chieti, Italy
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Hong KS, Khan MJ. Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review. Front Neurorobot 2017; 11:35. [PMID: 28790910 PMCID: PMC5522881 DOI: 10.3389/fnbot.2017.00035] [Citation(s) in RCA: 116] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Accepted: 07/03/2017] [Indexed: 12/11/2022] Open
Abstract
In this article, non-invasive hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain-computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.
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Affiliation(s)
- Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Busan, South Korea.,Department of Cogno-Mechatronics Engineering, Pusan National University, Busan, South Korea
| | - Muhammad Jawad Khan
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
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31
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Aghajani H, Garbey M, Omurtag A. Measuring Mental Workload with EEG+fNIRS. Front Hum Neurosci 2017; 11:359. [PMID: 28769775 PMCID: PMC5509792 DOI: 10.3389/fnhum.2017.00359] [Citation(s) in RCA: 94] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 06/23/2017] [Indexed: 01/21/2023] Open
Abstract
We studied the capability of a Hybrid functional neuroimaging technique to quantify human mental workload (MWL). We have used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as imaging modalities with 17 healthy subjects performing the letter n-back task, a standard experimental paradigm related to working memory (WM). The level of MWL was parametrically changed by variation of n from 0 to 3. Nineteen EEG channels were covering the whole-head and 19 fNIRS channels were located on the forehead to cover the most dominant brain region involved in WM. Grand block averaging of recorded signals revealed specific behaviors of oxygenated-hemoglobin level during changes in the level of MWL. A machine learning approach has been utilized for detection of the level of MWL. We extracted different features from EEG, fNIRS, and EEG+fNIRS signals as the biomarkers of MWL and fed them to a linear support vector machine (SVM) as train and test sets. These features were selected based on their sensitivity to the changes in the level of MWL according to the literature. We introduced a new category of features within fNIRS and EEG+fNIRS systems. In addition, the performance level of each feature category was systematically assessed. We also assessed the effect of number of features and window size in classification performance. SVM classifier used in order to discriminate between different combinations of cognitive states from binary- and multi-class states. In addition to the cross-validated performance level of the classifier other metrics such as sensitivity, specificity, and predictive values were calculated for a comprehensive assessment of the classification system. The Hybrid (EEG+fNIRS) system had an accuracy that was significantly higher than that of either EEG or fNIRS. Our results suggest that EEG+fNIRS features combined with a classifier are capable of robustly discriminating among various levels of MWL. Results suggest that EEG+fNIRS should be preferred to only EEG or fNIRS, in developing passive BCIs and other applications which need to monitor users' MWL.
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Affiliation(s)
- Haleh Aghajani
- Department of Biomedical Engineering, University of HoustonHouston, TX, United States
| | - Marc Garbey
- Center for Computational Surgery, Department of Surgery, Research Institute, Houston MethodistHouston, TX, United States
| | - Ahmet Omurtag
- Department of Biomedical Engineering, University of HoustonHouston, TX, United States
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Santosa H, Aarabi A, Perlman SB, Huppert TJ. Characterization and correction of the false-discovery rates in resting state connectivity using functional near-infrared spectroscopy. JOURNAL OF BIOMEDICAL OPTICS 2017; 22:55002. [PMID: 28492852 PMCID: PMC5424771 DOI: 10.1117/1.jbo.22.5.055002] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Accepted: 04/11/2017] [Indexed: 05/18/2023]
Abstract
Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low levels of red to near-infrared light to measure changes in cerebral blood oxygenation. Spontaneous (resting state) functional connectivity (sFC) has become a critical tool for cognitive neuroscience for understanding task-independent neural networks, revealing pertinent details differentiating healthy from disordered brain function, and discovering fluctuations in the synchronization of interacting individuals during hyperscanning paradigms. Two of the main challenges to sFC-NIRS analysis are (i) the slow temporal structure of both systemic physiology and the response of blood vessels, which introduces false spurious correlations, and (ii) motion-related artifacts that result from movement of the fNIRS sensors on the participants’ head and can introduce non-normal and heavy-tailed noise structures. In this work, we systematically examine the false-discovery rates of several time- and frequency-domain metrics of functional connectivity for characterizing sFC-NIRS. Specifically, we detail the modifications to the statistical models of these methods needed to avoid high levels of false-discovery related to these two sources of noise in fNIRS. We compare these analysis procedures using both simulated and experimental resting-state fNIRS data. Our proposed robust correlation method has better performance in terms of being more reliable to the noise outliers due to the motion artifacts.
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Affiliation(s)
- Hendrik Santosa
- University of Pittsburgh, Department of Radiology, Pittsburgh, Pennsylvania, United States
| | - Ardalan Aarabi
- Universite de Picardie Jules Verne, Department of Medicine, Amiens, France
| | - Susan B. Perlman
- University of Pittsburgh, Department of Psychiatry, Pittsburgh, Pennsylvania, United States
| | - Theodore J. Huppert
- University of Pittsburgh, Departments of Radiology and Bioengineering, Clinical Science Translational Institute, and Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States
- Address all correspondence to: Theodore J. Huppert, E-mail:
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