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Ye S, Tao L, Gong S, Ma Y, Wu J, Li W, Kang J, Tang M, Zuo G, Shi C. Upper limb motor assessment for stroke with force, muscle activation and interhemispheric balance indices based on sEMG and fNIRS. Front Neurol 2024; 15:1337230. [PMID: 38694770 PMCID: PMC11061400 DOI: 10.3389/fneur.2024.1337230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 04/08/2024] [Indexed: 05/04/2024] Open
Abstract
Introduction Upper limb rehabilitation assessment plays a pivotal role in the recovery process of stroke patients. The current clinical assessment tools often rely on subjective judgments of healthcare professionals. Some existing research studies have utilized physiological signals for quantitative assessments. However, most studies used single index to assess the motor functions of upper limb. The fusion of surface electromyography (sEMG) and functional near-infrared spectroscopy (fNIRS) presents an innovative approach, offering simultaneous insights into the central and peripheral nervous systems. Methods We concurrently collected sEMG signals and brain hemodynamic signals during bilateral elbow flexion in 15 stroke patients with subacute and chronic stages and 15 healthy control subjects. The sEMG signals were analyzed to obtain muscle synergy based indexes including synergy stability index (SSI), closeness of individual vector (CV) and closeness of time profile (CT). The fNIRS signals were calculated to extract laterality index (LI). Results The primary findings were that CV, SSI and LI in posterior motor cortex (PMC) and primary motor cortex (M1) on the affected hemisphere of stroke patients were significantly lower than those in the control group (p < 0.05). Moreover, CV, SSI and LI in PMC were also significantly different between affected and unaffected upper limb movements (p < 0.05). Furthermore, a linear regression model was used to predict the value of the Fugl-Meyer score of upper limb (FMul) (R2 = 0.860, p < 0.001). Discussion This study established a linear regression model using force, CV, and LI features to predict FMul scale values, which suggests that the combination of force, sEMG and fNIRS hold promise as a novel method for assessing stroke rehabilitation.
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Affiliation(s)
- Sijia Ye
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- University of Chinese Academy of Sciences, Beijing, China
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo, China
| | - Liang Tao
- Department of Neurological Rehabilitation, Ningbo Rehabilitation Hospital, Ningbo, China
| | - Shuang Gong
- Department of Neurological Rehabilitation, Ningbo Rehabilitation Hospital, Ningbo, China
| | - Yehao Ma
- Robotics Institute, Ningbo University of Technology, Ningbo, China
| | - Jiajia Wu
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo, China
| | - Wanyi Li
- Department of Neurological Rehabilitation, Ningbo Rehabilitation Hospital, Ningbo, China
| | - Jiliang Kang
- Department of Neurological Rehabilitation, Ningbo Rehabilitation Hospital, Ningbo, China
| | - Min Tang
- Department of Neurological Rehabilitation, Ningbo Rehabilitation Hospital, Ningbo, China
| | - Guokun Zuo
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- University of Chinese Academy of Sciences, Beijing, China
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo, China
| | - Changcheng Shi
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- University of Chinese Academy of Sciences, Beijing, China
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo, China
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Ma Y, Luo K, Zhou Z, Li X, Yin S, Li Y. Passive leg raising modulates low-frequency oscillation propagation in peripheral atherosclerosis: A pilot study. Vascular 2024:17085381241240686. [PMID: 38513670 DOI: 10.1177/17085381241240686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Abstract
OBJECTIVE Low-frequency oscillations (LFOs) observed in the periphery may reflect physiological processes. The aim of this study was to investigate these processes' effects on LFOs and the differences between healthy subjects and those with peripheral arteriosclerosis disease (PAD). METHODS 14 PAD patients and 25 healthy controls were studied in resting (RS) and passive leg raising (PLR) states. We simultaneously measured LFOs at the peripheral left earlobes (LE), right earlobes (RE), left fingertips (LF), right fingertips (RF), left toes (LT), and right toes (RT), along with coherence and phase shift analysis processing. RESULTS The coherence coefficients in the PAD group were lower than those in the healthy group (p < .01), and the phase shifts in the PAD group were higher than those in the healthy group (p < .01) in a resting state. Mild to moderate PAD patients had greater coherence coefficients and smaller phase shifts than severe PAD patients. 0.05 Hz PLR LFOs originating in the LT can be observed in other peripheral positions. The proportion of occurrence times for 0.05 Hz PLR LFOs peaks observed at different peripheral positions was different in healthy subjects, patients with bilateral multiple lower limb arteriosclerosis, and those with left or right lower limb arteriosclerosis. CONCLUSION The coherence coefficient and phase shift characteristics of LFOs were different between healthy subjects and PAD patients. LFOs have the potential to provide valuable physiological process information associated with atherosclerosis in the periphery.
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Affiliation(s)
- Yunfei Ma
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- College of Artificial Intelligence, North China University of Science and Technology, Tangshan, China
| | - Kexin Luo
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Zhengxuan Zhou
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Xiaoli Li
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China
| | - Shimin Yin
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Department of Neurology, PLA Rocket Force Characteristic Medical Center, Beijing, China
| | - Yingwei Li
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
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Bowie DC, Low KA, Rubenstein SL, Islam SS, Zimmerman B, Camacho PB, Sutton BP, Gratton G, Fabiani M. Neurovascular Mechanisms of Cognitive Aging: Sex-Related Differences in the Average Progression of Arteriosclerosis, White Matter Atrophy, and Cognitive Decline. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.06.556562. [PMID: 38562861 PMCID: PMC10983862 DOI: 10.1101/2023.09.06.556562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Arterial stiffness (arteriosclerosis) has been linked to heightened risks for cognitive decline, and ultimately for Alzheimer's disease and other forms of dementia. Importantly, neurovascular outcomes generally vary according to one's biological sex. Here, capitalizing on a large sample of participants with neuroimaging and behavioral data ( N = 203, age range = 18-87 years), we aimed to provide support for a hierarchical model of neurocognitive aging, which links age-related declines in cerebrovascular health to the rate of cognitive decline via a series of intervening variables, such as white matter integrity. By applying a novel piecewise regression approach to our cross-sectional sample to support Granger-like causality inferences, we show that, on average, a precipitous decline in cerebral arterial elasticity (measured with diffuse optical imaging of the cerebral arterial pulse; pulse-DOT) temporally precedes an acceleration in the development of white matter lesions by nearly a decade, with women protected from these deleterious effects until approximately age 50, the average onset of menopause. By employing multiple-mediator path analyses while controlling for sex, we show that age may impair cognition via the sequential indirect effects of arteriosclerosis and white matter atrophy on fluid, but not crystallized, abilities. Importantly, we replicate these results using pulse pressure, an independent index of arterial health, thereby providing converging evidence for the central role of arteriosclerosis as an accelerating factor in normal and pathological aging and identifying robust sex-related differences in the progression of cerebral arteriosclerosis and white matter degradation.
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Tyagi O, Rana Mukherjee T, Mehta RK. Neurophysiological, muscular, and perceptual adaptations of exoskeleton use over days during overhead work with competing cognitive demands. APPLIED ERGONOMICS 2023; 113:104097. [PMID: 37506618 DOI: 10.1016/j.apergo.2023.104097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 07/09/2023] [Accepted: 07/12/2023] [Indexed: 07/30/2023]
Abstract
This study captured neurophysiological, muscular, and perceptual adaptations to shoulder exoskeleton use during overhead work with competing physical-cognitive demands. Twenty-four males and females, randomly divided into control and exoskeleton groups, performed an overhead reaching and pointing task over three days without (single task) and with (dual task) a working memory task. Task performance, electromyography (EMG), neural activity, heart rate, and subjective responses were collected. While task completion time reduced for both groups at the same rate over days, EMG activity of shoulder muscles was lower for the exoskeleton group for both tasks, specifically for females during the dual task. Dual task reduced the physiological benefits of exoskeletons and neuromotor strategies to adapt to the dual task demands differed between the groups. Neuromuscular benefits of exoskeleton use were immediately realized irrespective of cognitive demand, however the perceptual, physiological, and neural adaptations with exoskeleton use were task- and sex-specific.
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Affiliation(s)
- Oshin Tyagi
- Wm. Michael Barnes '64 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Tiash Rana Mukherjee
- J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Ranjana K Mehta
- Wm. Michael Barnes '64 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, 77843, USA; J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, College Station, TX, 77843, USA.
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Xie E, Li K, Gu R, Zhang D, Li X. Verbal information exchange enhances collective performance through increasing group identification. Neuroimage 2023; 279:120339. [PMID: 37611814 DOI: 10.1016/j.neuroimage.2023.120339] [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: 06/22/2023] [Revised: 08/05/2023] [Accepted: 08/21/2023] [Indexed: 08/25/2023] Open
Abstract
Information exchange is a key factor in the attainment of collective outcomes and the navigation of social life. In the current study, we investigated whether and how information exchange enhanced collective performance by combining behavioral and neuroimaging approaches from the perspective of multiparticipant neuroscience. To evaluate collective performance, we measured the collaborative problem-solving abilities of triads working on a murder mystery case. We first found that verbal information exchange significantly enhanced collective performance compared to nonverbal exchange. Moreover, both group sharing and group discussion positively contributed to this effect, with group discussion being more essential. Importantly, group identification mediated the positive effect of verbal information exchange on collective performance. This mediation was supported by higher interactive frequency and enhanced within-group neural synchronization (GNS) in the dorsolateral prefrontal cortex (DLPFC). Taken together, we provided a multiparticipant theoretical model to explain how verbal information exchange enhanced collective performance. Our findings deepen the insight into the workings of group decision-making.
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Affiliation(s)
- Enhui Xie
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China
| | - Keshuang Li
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China
| | - Ruolei Gu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China
| | - Dandan Zhang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Xianchun Li
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China; Shanghai Changning Mental Health Center, Shanghai 200335, China; Institute of Wisdom in China, East China Normal University, Shanghai 200062, China.
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Ma D, Izzetoglu M, Holtzer R, Jiao X. Deep Learning Based Walking Tasks Classification in Older Adults Using fNIRS. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3437-3447. [PMID: 37594868 PMCID: PMC11044905 DOI: 10.1109/tnsre.2023.3306365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/20/2023]
Abstract
Decline in gait features is common in older adults and an indicator of increased risk of disability, morbidity, and mortality. Under dual task walking (DTW) conditions, further degradation in the performance of both the gait and the secondary cognitive task were found in older adults which were significantly correlated to falls history. Cortical control of gait, specifically in the pre-frontal cortex (PFC) as measured by functional near infrared spectroscopy (fNIRS), during DTW in older adults has recently been studied. However, the automatic classification of differences in cognitive activations under single and dual task gait conditions has not been extensively studied yet. In this paper, by considering single task walking (STW) as a lower attentional walking state and DTW as a higher attentional walking state, we aimed to formulate this as an automatic detection of low and high attentional walking states and leverage deep learning methods to perform their classification. We conduct analysis on the data samples which reveals the characteristics on the difference between HbO2 and Hb values that are subsequently used as additional features. We perform feature engineering to formulate the fNIRS features as a 3-channel image and apply various image processing techniques for data augmentation to enhance the performance of deep learning models. Experimental results show that pre-trained deep learning models that are fine-tuned using the collected fNIRS dataset together with gender and cognitive status information can achieve around 81% classification accuracy which is about 10% higher than the traditional machine learning algorithms. We present additional sensitivity metrics such as confusion matrix, precision and F1 score, as well as accuracy on two-way classification between condition pairings. We further performed an extensive ablation study to evaluate factors such as the voxel locations, channels of input images, zero-paddings and pre-training of deep learning model on their contribution or impact to the classification task. Results showed that using pre-trained model, all the voxel locations, and HbO2 - Hb as the third channel of the input image can achieve the best classification accuracy.
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Liu J, Zhang R, Xie E, Lin Y, Chen D, Liu Y, Li K, Chen M, Li Y, Wang G, Li X. Shared intentionality modulates interpersonal neural synchronization at the establishment of communication system. Commun Biol 2023; 6:832. [PMID: 37563301 PMCID: PMC10415255 DOI: 10.1038/s42003-023-05197-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 07/31/2023] [Indexed: 08/12/2023] Open
Abstract
Whether and how shared intentionality (SI) influences the establishment of a novel interpersonal communication system is poorly understood. To investigate this issue, we designed a coordinating symbolic communication game (CSCG) and applied behavioral, functional near-infrared spectroscopy (fNIRS)-based hyperscanning, and hyper-transcranial alternating current stimulation (hyper-tACS) methods. Here we show that SI is a strong contributor to communicative accuracy. Moreover, SI, communicative accuracy, and interpersonal neural synchronization (INS) in the right superior temporal gyrus (rSTG) are higher when dyads successfully establish a novel communication system. Furthermore, the SI influences communicative accuracy by increasing INS. Additionally, using time series and long short-term memory neural network analyses, we find that the INS can predict communicative accuracy at the early formation stage of the communication system. Importantly, the INS partially mediates the relationship between the SI and the communicative accuracy only at the formation stage of the communication system. In contrast, when the communication system is established, SI and INS no longer contribute to communicative accuracy. Finally, the hyper-tACS experiment confirms that INS has a causal effect on communicative accuracy. These findings suggest a behavioral and neural mechanism, subserved by the SI and INS, that underlies the establishment of a novel interpersonal communication system.
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Affiliation(s)
- Jieqiong Liu
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
- Paediatric Translational Medicine Institute, Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ruqian Zhang
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Enhui Xie
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Yixuan Lin
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Danni Chen
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Yang Liu
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Keshuang Li
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Mei Chen
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Yangzhuo Li
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Guanghai Wang
- Paediatric Translational Medicine Institute, Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Xianchun Li
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China.
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Ma Y, Luo K, Ding P, Yin S, Li X, Li Y. Differences in symmetrical low-frequency oscillations among healthy subjects, and those with stroke or peripheral arterial disease. Heliyon 2023; 9:e17015. [PMID: 37484434 PMCID: PMC10361110 DOI: 10.1016/j.heliyon.2023.e17015] [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: 03/06/2023] [Revised: 05/30/2023] [Accepted: 06/04/2023] [Indexed: 07/25/2023] Open
Abstract
Low-frequency oscillations (LFOs) observed in near-infrared spectroscopy (NIRS) reflect autonomic physiological processes, and may serve as useful indicators for detecting and monitoring circulatory dysfunction. The aim of this study was to reveal whether LFOs can be used as vascular perfusion biomarkers to differentiate different types and degrees of vascular lesions based on clinical patient data. Materials and Methods: In this study, healthy controls, ischemic stroke patients and peripheral atherosclerosis patients completed a resting-state LFO detection experiment. LFOs were collected simultaneously at peripheral right and left earlobes, fingertips and toes, along with coherence and phase shift analyses processing. Results: The results showed that the coherence coefficients of symmetric peripheral positions and the absolute value-phase shifts of fingers and toes can be used to distinguish healthy individuals, ischemic stroke patients and peripheral atherosclerosis patients. The symmetric earlobes' absolute value-phase shifts could be used to differentiate mild and severe ischemic stroke patients; the coherence coefficients and absolute value-phase shifts of the symmetric toes could be used to differentiate mild and severe peripheral arteriosclerosis patients. The accuracy of differentiating between types of patients was 70%; those with different degrees of peripheral atherosclerosis was 85%, and those with different degrees of ischemic stroke was 72%. Conclusions: LFOs can serve as vascular perfusion biomarkers to differentiate types and degrees of vascular lesions. Therefore, LFOs have the potential to provide valuable patient information to assist researchers and clinicians in identifying specific peripheral circulatory damage subgroups.
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Affiliation(s)
- Yunfei Ma
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, 066004, China
| | - Kexin Luo
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, 066004, China
| | - Peng Ding
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, 066004, China
| | - Shimin Yin
- Department of Neurology, PLA Rocket Force Characteristic Medical Center, Beijing, 100088, China
| | - Xiaoli Li
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, 100875, China
| | - Yingwei Li
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, 066004, China
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Liang Z, Wang X, Yu Z, Tong Y, Li X, Ma Y, Guo H. Age-dependent neurovascular coupling characteristics in children and adults during general anesthesia. BIOMEDICAL OPTICS EXPRESS 2023; 14:2240-2259. [PMID: 37206124 PMCID: PMC10191645 DOI: 10.1364/boe.482127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 04/06/2023] [Accepted: 04/11/2023] [Indexed: 05/21/2023]
Abstract
General anesthesia is an indispensable procedure in clinical practice. Anesthetic drugs induce dramatic changes in neuronal activity and cerebral metabolism. However, the age-related changes in neurophysiology and hemodynamics during general anesthesia remain unclear. Therefore, the objective of this study was to explore the neurovascular coupling between neurophysiology and hemodynamics in children and adults during general anesthesia. We analyzed frontal electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals recorded from children (6-12 years old, n = 17) and adults (18-60 years old, n = 25) during propofol-induced and sevoflurane-maintained general anesthesia. The neurovascular coupling was evaluated in wakefulness, maintenance of a surgical state of anesthesia (MOSSA), and recovery by using correlation, coherence and Granger-causality (GC) between the EEG indices [EEG power in different bands and permutation entropy (PE)], and hemodynamic responses the oxyhemoglobin (Δ[HbO]) and deoxy-hemoglobin (Δ[Hb]) from fNIRS in the frequency band in 0.01-0.1 Hz. The PE and Δ[Hb] performed well in distinguishing the anesthesia state (p > 0.001). The correlation between PE and Δ[Hb] was higher than those of other indices in the two age groups. The coherence significantly increased during MOSSA (p < 0.05) compared with wakefulness, and the coherences between theta, alpha and gamma, and hemodynamic activities of children are significantly stronger than that of adults' bands. The GC from neuronal activities to hemodynamic responses decreased during MOSSA, and can better distinguish anesthesia state in adults. Propofol-induced and sevoflurane-maintained combination exhibited age-dependent neuronal activities, hemodynamics, and neurovascular coupling, which suggests the need for separate rules for children's and adults' brain states monitoring during general anesthesia.
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Affiliation(s)
- Zhenhu Liang
- School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, China
| | - Xin Wang
- School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, China
| | - Zhenyang Yu
- School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, China
| | - Yunjie Tong
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Xiaoli Li
- Center for Cognition and Neuroergonomics, Beijing Normal University (Zhuhai), Zhuhai, Guangdong, 519087, China
| | - Yaqun Ma
- Department of Anesthesiology, the Seventh Medical Center to Chinese PLA General Hospital, Beijing, 100700, China
| | - Hang Guo
- Department of Anesthesiology, the Seventh Medical Center to Chinese PLA General Hospital, Beijing, 100700, China
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Xie E, Liu M, Li K, Nastase SA, Gao X, Li X. The single- and dual-brain mechanisms underlying the adviser's confidence expression strategy switching during influence management. Neuroimage 2023; 270:119957. [PMID: 36822251 DOI: 10.1016/j.neuroimage.2023.119957] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 02/11/2023] [Accepted: 02/18/2023] [Indexed: 02/24/2023] Open
Abstract
Effective influence management during advice-giving requires individuals to express confidence in the advice properly and switch timely between the 'competitive' strategy and the 'defensive' strategy. However, how advisers switch between these two strategies, and whether and why there exist individual differences during this process remain elusive. We used an advice-giving game that manipulated incentive contexts (Incentivized/Non-Incentivized) to induce the adviser's confidence expression strategy switching and measured the brain activities of adviser and advisee concurrently using functional near-infrared spectroscopy (fNIRS). Behaviorally, we observed individual differences in strategy switching. Some advisers applied the 'defensive' strategy when incentivized and the 'competitive' strategy when not incentivized, while others applied the 'competitive' strategy when incentivized and the 'defensive' strategy when not incentivized. This effect was mediated by the adviser's perceived stress in each condition and was reflected by the frequencies of advice-taking in the advisees. Neurally, brain activation in the dorsolateral prefrontal cortex (DLPFC) supported strategy switching, as well as interpersonal neural synchronization (INS) in the temporoparietal junction (TPJ) that supported influence management. This two-in-one process, i.e., confidence expression strategy switching and the corresponding influence management, was linked and modulated by the strength of DLPFC-TPJ functional connectivity in the adviser. We further developed a descriptive model that contributed to understanding the adviser's strategy switching during influence management.
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Affiliation(s)
- Enhui Xie
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062, China
| | - Mengdie Liu
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062, China
| | - Keshuang Li
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062, China
| | - Samuel A Nastase
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 08544, United States
| | - Xiaoxue Gao
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062, China.
| | - Xianchun Li
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062, China; Shanghai Changning Mental Health Center, Shanghai, China, 200335; Institute of Wisdom in China, East China Normal University, Shanghai, China, 200062.
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11
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Shahbakhti M, Hakimi N, Horschig JM, Floor-Westerdijk M, Claassen J, Colier WNJM. Estimation of Respiratory Rate during Biking with a Single Sensor Functional Near-Infrared Spectroscopy (fNIRS) System. SENSORS (BASEL, SWITZERLAND) 2023; 23:3632. [PMID: 37050692 PMCID: PMC10099192 DOI: 10.3390/s23073632] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 03/28/2023] [Accepted: 03/29/2023] [Indexed: 06/19/2023]
Abstract
OBJECTIVE The employment of wearable systems for continuous monitoring of vital signs is increasing. However, due to substantial susceptibility of conventional bio-signals recorded by wearable systems to motion artifacts, estimation of the respiratory rate (RR) during physical activities is a challenging task. Alternatively, functional Near-Infrared Spectroscopy (fNIRS) can be used, which has been proven less vulnerable to the subject's movements. This paper proposes a fusion-based method for estimating RR during bicycling from fNIRS signals recorded by a wearable system. METHODS Firstly, five respiratory modulations are extracted, based on amplitude, frequency, and intensity of the oxygenated hemoglobin concentration (O2Hb) signal. Secondly, the dominant frequency of each modulation is computed using the fast Fourier transform. Finally, dominant frequencies of all modulations are fused, based on averaging, to estimate RR. The performance of the proposed method was validated on 22 young healthy subjects, whose respiratory and fNIRS signals were simultaneously recorded during a bicycling task, and compared against a zero delay Fourier domain band-pass filter. RESULTS The comparison between results obtained by the proposed method and band-pass filtering indicated the superiority of the former, with a lower mean absolute error (3.66 vs. 11.06 breaths per minute, p<0.05). The proposed fusion strategy also outperformed RR estimations based on the analysis of individual modulation. SIGNIFICANCE This study orients towards the practical limitations of traditional bio-signals for RR estimation during physical activities.
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Affiliation(s)
- Mohammad Shahbakhti
- Artinis Medical Systems, B.V., Einsteinweg 17, 6662 PW Elst, The Netherlands
- Biomedical Engineering Institute, Kaunas University of Technology, K. Barsausko 59, LT-51423 Kaunas, Lithuania
| | - Naser Hakimi
- Artinis Medical Systems, B.V., Einsteinweg 17, 6662 PW Elst, The Netherlands
- Department of Neonatology, Wilhelmina Children’s Hospital, University Medical Center Utrecht, Lundlaan 6, 3584 EA Utrecht, The Netherlands
| | - Jörn M. Horschig
- Artinis Medical Systems, B.V., Einsteinweg 17, 6662 PW Elst, The Netherlands
| | | | - Jurgen Claassen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Houtlaan 4, 6525 XZ Nijmegen, The Netherlands
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12
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Patashov D, Menahem Y, Gurevitch G, Kameda Y, Goldstein D, Balberg M. fNIRS: Non-stationary preprocessing methods. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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13
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Huang R, Hong KS, Yang D, Huang G. Motion artifacts removal and evaluation techniques for functional near-infrared spectroscopy signals: A review. Front Neurosci 2022; 16:878750. [PMID: 36263362 PMCID: PMC9576156 DOI: 10.3389/fnins.2022.878750] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 07/06/2022] [Indexed: 12/04/2022] Open
Abstract
With the emergence of an increasing number of functional near-infrared spectroscopy (fNIRS) devices, the significant deterioration in measurement caused by motion artifacts has become an essential research topic for fNIRS applications. However, a high requirement for mathematics and programming limits the number of related researches. Therefore, here we provide the first comprehensive review for motion artifact removal in fNIRS aiming to (i) summarize the latest achievements, (ii) present the significant solutions and evaluation metrics from the perspective of application and reproduction, and (iii) predict future topics in the field. The present review synthesizes information from fifty-one journal articles (screened according to three criteria). Three hardware-based solutions and nine algorithmic solutions are summarized, and their application requirements (compatible signal types, the availability for online applications, and limitations) and extensions are discussed. Five metrics for noise suppression and two metrics for signal distortion were synthesized to evaluate the motion artifact removal methods. Moreover, we highlight three deficiencies in the existing research: (i) The balance between the use of auxiliary hardware and that of an algorithmic solution is not clarified; (ii) few studies mention the filtering delay of the solutions, and (iii) the robustness and stability of the solution under extreme application conditions are not discussed.
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Affiliation(s)
- Ruisen Huang
- 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
- *Correspondence: Keum-Shik Hong,
| | - Dalin Yang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Guanghao Huang
- Institute for Future, School of Automation, Qingdao University, Qingdao, China
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14
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Steinmetzger K, Meinhardt B, Praetorius M, Andermann M, Rupp A. A direct comparison of voice pitch processing in acoustic and electric hearing. Neuroimage Clin 2022; 36:103188. [PMID: 36113196 PMCID: PMC9483634 DOI: 10.1016/j.nicl.2022.103188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/24/2022] [Accepted: 09/06/2022] [Indexed: 12/14/2022]
Abstract
In single-sided deafness patients fitted with a cochlear implant (CI) in the affected ear and preserved normal hearing in the other ear, acoustic and electric hearing can be directly compared without the need for an external control group. Although poor pitch perception is a crucial limitation when listening through CIs, it remains unclear how exactly the cortical processing of pitch information differs between acoustic and electric hearing. Hence, we separately presented both ears of 20 of these patients with vowel sequences in which the pitch contours were either repetitive or variable, while simultaneously recording functional near-infrared spectroscopy (fNIRS) and EEG data. Overall, the results showed smaller and delayed auditory cortex activity in electric hearing, particularly for the P2 event-related potential component, which appears to reflect the processing of voice pitch information. Both the fNIRS data and EEG source reconstructions furthermore showed that vowel sequences with variable pitch contours evoked additional activity in posterior right auditory cortex in electric but not acoustic hearing. This surprising discrepancy demonstrates, firstly, that the acoustic detail transmitted by CIs is sufficient to distinguish between speech sounds that only vary regarding their pitch information. Secondly, the absence of a condition difference when stimulating the normal-hearing ears suggests a saturation of cortical activity levels following unilateral deafness. Taken together, these results provide strong evidence in favour of using CIs in this patient group.
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Affiliation(s)
- Kurt Steinmetzger
- Section of Biomagnetism, Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany,Corresponding author.
| | - Bastian Meinhardt
- Section of Biomagnetism, Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Mark Praetorius
- Section of Otology and Neurootology, ENT Clinic, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Martin Andermann
- Section of Biomagnetism, Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - André Rupp
- Section of Biomagnetism, Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
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15
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Yi C, Li Y, Huo X, Tsui KL. A promising new tool for fault diagnosis of railway wheelset bearings: SSO-based Kurtogram. ISA TRANSACTIONS 2022; 128:498-512. [PMID: 34593241 DOI: 10.1016/j.isatra.2021.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 09/14/2021] [Accepted: 09/15/2021] [Indexed: 06/13/2023]
Abstract
A promising method is proposed systematically to select an accurate resonance frequency band and separate refined resonance response from periodic excitation in this study. This work expanded the short-time Fourier transform (STFT)- and wavelet transform (WT)-based Kurtograms and developed a hybrid signal separation operator (SSO)-spectral kurtosis computational scheme to implement Kurtogram by introducing the SSO method-SSO-based Kurtogram. The ability to accurately extract the refined resonance frequency band of SSO greatly improves its adaptivity for engineering applications. The effectiveness of the SSO-based Kurtogram is studied by using a bearing fault simulation signal, and the influence of window function on the detection effect of the proposed method is explored. Furthermore the validity of the SSO-based Kurtogram for bearing fault detection is verified by a set of railway wheelset-bearing experiments on the wheelset running-in testbed bench. Experimental results show that the SSO-based Kurtogram performs highly in detecting various kinds of single and compound faults of bearings. Compared with the WT- and STFT-based Kurtogram, the proposed method has obvious advantages in terms of effectiveness and visual inspection ability. In engineering practice, a railway wheelset-bearing-fault experiment on an in-service high-speed train in the real world is taken as a case study, which makes the verification of SSO-based Kurtogram more convincing and demonstrates the practical engineering value of the proposed method. The results show that in case of equal effectiveness, SSO-based Kurtogram has an absolute advantage in the visual inspection ability, embodied in eliminating other vibrations unrelated to the target fault and making the fault feature frequency and its harmonics remarkable.
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Affiliation(s)
- Cai Yi
- State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China.
| | - Yiqun Li
- City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China.
| | - Xiaoming Huo
- Georgia Institute of Technology, North Avenue, Atlanta, USA.
| | - Kwok-Leung Tsui
- City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China.
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16
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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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17
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Ayaz H, Baker WB, Blaney G, Boas DA, Bortfeld H, Brady K, Brake J, Brigadoi S, Buckley EM, Carp SA, Cooper RJ, Cowdrick KR, Culver JP, Dan I, Dehghani H, Devor A, Durduran T, Eggebrecht AT, Emberson LL, Fang Q, Fantini S, Franceschini MA, Fischer JB, Gervain J, Hirsch J, Hong KS, Horstmeyer R, Kainerstorfer JM, Ko TS, Licht DJ, Liebert A, Luke R, Lynch JM, Mesquida J, Mesquita RC, Naseer N, Novi SL, Orihuela-Espina F, O’Sullivan TD, Peterka DS, Pifferi A, Pollonini L, Sassaroli A, Sato JR, Scholkmann F, Spinelli L, Srinivasan VJ, St. Lawrence K, Tachtsidis I, Tong Y, Torricelli A, Urner T, Wabnitz H, Wolf M, Wolf U, Xu S, Yang C, Yodh AG, Yücel MA, Zhou W. Optical imaging and spectroscopy for the study of the human brain: status report. NEUROPHOTONICS 2022; 9:S24001. [PMID: 36052058 PMCID: PMC9424749 DOI: 10.1117/1.nph.9.s2.s24001] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.
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Affiliation(s)
- Hasan Ayaz
- Drexel University, School of Biomedical Engineering, Science, and Health Systems, Philadelphia, Pennsylvania, United States
- Drexel University, College of Arts and Sciences, Department of Psychological and Brain Sciences, Philadelphia, Pennsylvania, United States
| | - Wesley B. Baker
- Children’s Hospital of Philadelphia, Division of Neurology, Philadelphia, Pennsylvania, United States
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Giles Blaney
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - David A. Boas
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
- Boston University, College of Engineering, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Heather Bortfeld
- University of California, Merced, Departments of Psychological Sciences and Cognitive and Information Sciences, Merced, California, United States
| | - Kenneth Brady
- Lurie Children’s Hospital, Northwestern University Feinberg School of Medicine, Department of Anesthesiology, Chicago, Illinois, United States
| | - Joshua Brake
- Harvey Mudd College, Department of Engineering, Claremont, California, United States
| | - Sabrina Brigadoi
- University of Padua, Department of Developmental and Social Psychology, Padua, Italy
| | - Erin M. Buckley
- Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
- Emory University School of Medicine, Department of Pediatrics, Atlanta, Georgia, United States
| | - Stefan A. Carp
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Robert J. Cooper
- University College London, Department of Medical Physics and Bioengineering, DOT-HUB, London, United Kingdom
| | - Kyle R. Cowdrick
- Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Joseph P. Culver
- Washington University School of Medicine, Department of Radiology, St. Louis, Missouri, United States
| | - Ippeita Dan
- Chuo University, Faculty of Science and Engineering, Tokyo, Japan
| | - Hamid Dehghani
- University of Birmingham, School of Computer Science, Birmingham, United Kingdom
| | - Anna Devor
- Boston University, College of Engineering, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Turgut Durduran
- ICFO – The Institute of Photonic Sciences, The Barcelona Institute of Science and Technology, Castelldefels, Barcelona, Spain
- Institució Catalana de Recerca I Estudis Avançats (ICREA), Barcelona, Spain
| | - Adam T. Eggebrecht
- Washington University in St. Louis, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States
| | - Lauren L. Emberson
- University of British Columbia, Department of Psychology, Vancouver, British Columbia, Canada
| | - Qianqian Fang
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Sergio Fantini
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - Maria Angela Franceschini
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Jonas B. Fischer
- ICFO – The Institute of Photonic Sciences, The Barcelona Institute of Science and Technology, Castelldefels, Barcelona, Spain
| | - Judit Gervain
- University of Padua, Department of Developmental and Social Psychology, Padua, Italy
- Université Paris Cité, CNRS, Integrative Neuroscience and Cognition Center, Paris, France
| | - Joy Hirsch
- Yale School of Medicine, Department of Psychiatry, Neuroscience, and Comparative Medicine, New Haven, Connecticut, United States
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Keum-Shik Hong
- Pusan National University, School of Mechanical Engineering, Busan, Republic of Korea
- Qingdao University, School of Automation, Institute for Future, Qingdao, China
| | - Roarke Horstmeyer
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
- Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
- Duke University, Department of Physics, Durham, North Carolina, United States
| | - Jana M. Kainerstorfer
- Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States
- Carnegie Mellon University, Neuroscience Institute, Pittsburgh, Pennsylvania, United States
| | - Tiffany S. Ko
- Children’s Hospital of Philadelphia, Division of Cardiothoracic Anesthesiology, Philadelphia, Pennsylvania, United States
| | - Daniel J. Licht
- Children’s Hospital of Philadelphia, Division of Neurology, Philadelphia, Pennsylvania, United States
| | - Adam Liebert
- Polish Academy of Sciences, Nalecz Institute of Biocybernetics and Biomedical Engineering, Warsaw, Poland
| | - Robert Luke
- Macquarie University, Department of Linguistics, Sydney, New South Wales, Australia
- Macquarie University Hearing, Australia Hearing Hub, Sydney, New South Wales, Australia
| | - Jennifer M. Lynch
- Children’s Hospital of Philadelphia, Division of Cardiothoracic Anesthesiology, Philadelphia, Pennsylvania, United States
| | - Jaume Mesquida
- Parc Taulí Hospital Universitari, Critical Care Department, Sabadell, Spain
| | - Rickson C. Mesquita
- University of Campinas, Institute of Physics, Campinas, São Paulo, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, São Paulo, Brazil
| | - Noman Naseer
- Air University, Department of Mechatronics and Biomedical Engineering, Islamabad, Pakistan
| | - Sergio L. Novi
- University of Campinas, Institute of Physics, Campinas, São Paulo, Brazil
- Western University, Department of Physiology and Pharmacology, London, Ontario, Canada
| | | | - Thomas D. O’Sullivan
- University of Notre Dame, Department of Electrical Engineering, Notre Dame, Indiana, United States
| | - Darcy S. Peterka
- Columbia University, Zuckerman Mind Brain Behaviour Institute, New York, United States
| | | | - Luca Pollonini
- University of Houston, Department of Engineering Technology, Houston, Texas, United States
| | - Angelo Sassaroli
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - João Ricardo Sato
- Federal University of ABC, Center of Mathematics, Computing and Cognition, São Bernardo do Campo, São Paulo, Brazil
| | - Felix Scholkmann
- University of Bern, Institute of Complementary and Integrative Medicine, Bern, Switzerland
- University of Zurich, University Hospital Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Zürich, Switzerland
| | - Lorenzo Spinelli
- National Research Council (CNR), IFN – Institute for Photonics and Nanotechnologies, Milan, Italy
| | - Vivek J. Srinivasan
- University of California Davis, Department of Biomedical Engineering, Davis, California, United States
- NYU Langone Health, Department of Ophthalmology, New York, New York, United States
- NYU Langone Health, Department of Radiology, New York, New York, United States
| | - Keith St. Lawrence
- Lawson Health Research Institute, Imaging Program, London, Ontario, Canada
- Western University, Department of Medical Biophysics, London, Ontario, Canada
| | - Ilias Tachtsidis
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Yunjie Tong
- Purdue University, Weldon School of Biomedical Engineering, West Lafayette, Indiana, United States
| | - Alessandro Torricelli
- Politecnico di Milano, Dipartimento di Fisica, Milan, Italy
- National Research Council (CNR), IFN – Institute for Photonics and Nanotechnologies, Milan, Italy
| | - Tara Urner
- Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Heidrun Wabnitz
- Physikalisch-Technische Bundesanstalt (PTB), Berlin, Germany
| | - Martin Wolf
- University of Zurich, University Hospital Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Zürich, Switzerland
| | - Ursula Wolf
- University of Bern, Institute of Complementary and Integrative Medicine, Bern, Switzerland
| | - Shiqi Xu
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
| | - Changhuei Yang
- California Institute of Technology, Department of Electrical Engineering, Pasadena, California, United States
| | - Arjun G. Yodh
- University of Pennsylvania, Department of Physics and Astronomy, Philadelphia, Pennsylvania, United States
| | - Meryem A. Yücel
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
- Boston University, College of Engineering, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Wenjun Zhou
- University of California Davis, Department of Biomedical Engineering, Davis, California, United States
- China Jiliang University, College of Optical and Electronic Technology, Hangzhou, Zhejiang, China
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18
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Lanka P, Bortfeld H, Huppert TJ. Correction of global physiology in resting-state functional near-infrared spectroscopy. NEUROPHOTONICS 2022; 9:035003. [PMID: 35990173 PMCID: PMC9386281 DOI: 10.1117/1.nph.9.3.035003] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 07/08/2022] [Indexed: 05/30/2023]
Abstract
Significance: Resting-state functional connectivity (RSFC) analyses of functional near-infrared spectroscopy (fNIRS) data reveal cortical connections and networks across the brain. Motion artifacts and systemic physiology in evoked fNIRS signals present unique analytical challenges, and methods that control for systemic physiological noise have been explored. Whether these same methods require modification when applied to resting-state fNIRS (RS-fNIRS) data remains unclear. Aim: We systematically examined the sensitivity and specificity of several RSFC analysis pipelines to identify the best methods for correcting global systemic physiological signals in RS-fNIRS data. Approach: Using numerically simulated RS-fNIRS data, we compared the rates of true and false positives for several connectivity analysis pipelines. Their performance was scored using receiver operating characteristic analysis. Pipelines included partial correlation and multivariate Granger causality, with and without short-separation measurements, and a modified multivariate causality model that included a non-traditional zeroth-lag cross term. We also examined the effects of pre-whitening and robust statistical estimators on performance. Results: Consistent with previous work on bivariate correlation models, our results demonstrate that robust statistics and pre-whitening are effective methods to correct for motion artifacts and autocorrelation in the fNIRS time series. Moreover, we found that pre-filtering using principal components extracted from short-separation fNIRS channels as part of a partial correlation model was most effective in reducing spurious correlations due to shared systemic physiology when the two signals of interest fluctuated synchronously. However, when there was a temporal lag between the signals, a multivariate Granger causality test incorporating the short-separation channels was better. Since it is unknown if such a lag exists in experimental data, we propose a modified version of Granger causality that includes the non-traditional zeroth-lag term as a compromising solution. Conclusions: A combination of pre-whitening, robust statistical methods, and partial correlation in the processing pipeline to reduce autocorrelation, motion artifacts, and global physiology are suggested for obtaining statistically valid connectivity metrics with RS-fNIRS. Further studies should validate the effectiveness of these methods using human data.
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Affiliation(s)
- Pradyumna Lanka
- University of California, Merced, Department of Psychological Sciences, Merced, California, United States
| | - Heather Bortfeld
- University of California, Merced, Department of Psychological Sciences, Merced, California, United States
- University of California, Merced, Department of Cognitive and Information Sciences, Merced, California, United States
| | - Theodore J. Huppert
- University of Pittsburgh, Department of Electrical and Computer Engineering, Pittsburgh, Pennsylvania, United States
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19
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Tyagi O, Mehta RK. Mind over body: A neuroergonomic approach to assessing motor performance under stress in older adults. APPLIED ERGONOMICS 2022; 101:103691. [PMID: 35086006 DOI: 10.1016/j.apergo.2022.103691] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 12/20/2021] [Accepted: 01/14/2022] [Indexed: 06/14/2023]
Abstract
Stress impairs motor performance, which is exacerbated with age. Stress also impairs brain activity in the prefrontal cortex, which communicates with the motor areas of the brain to regulate exercise and motor performance. To develop ergogenic strategies for the aging workforce, mind (brain)-body mechanisms behind the effect of stress on neuromuscular performance need to be well understood. This study investigated the influence of social stress on motor performance and information flow between the frontal and motor regions of the brain during intermittent handgrip contractions among older adults. Thirty older adults, balanced by gender, performed intermittent handgrip contractions at 30% of maximum strength before and after being subjected to a social stressor. Force steadiness, strength loss, root mean square electromyogram (EMG) activity, activation of the brain regions, and functional and effective connectivity between the frontal and motor brain regions were computed for pre- and post-stressor handgrip contractions. Older men exhibited improved motor performance after the stressor and concomitant reduction in functional connectivity between the frontal-motor brain regions ipsilateral to the contracting hand. Additionally, while both sexes exhibited significant causal information flow, i.e., effective connectivity, from the frontal to the motor regions of the brain, irrespective of the stressor, older women exhibited a bidirectional effective connectivity between the frontal-motor brain regions after the stressor. Stress had a facilitative effect on the motor performance of older men through compensatory brain network reorganization. Older women exhibited comparable motor performance pre/post stress, despite showing an increase in bidirectional information flow between the frontal-motor areas. Employing brain hemodynamics can facilitate better understanding of the impact of stress on neuromuscular performance and its differential impacts on brain network reorganization between the sexes.
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Affiliation(s)
- Oshin Tyagi
- Wm. Michael Barnes '64 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Ranjana K Mehta
- Wm. Michael Barnes '64 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, 77843, USA; J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, College Station, TX, 77843, USA.
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20
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Hossain MS, Chowdhury MEH, Reaz MBI, Ali SHM, Bakar AAA, Kiranyaz S, Khandakar A, Alhatou M, Habib R, Hossain MM. Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22093169. [PMID: 35590859 DOI: 10.1109/access.2022.3159155] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/24/2022] [Accepted: 03/25/2022] [Indexed: 05/27/2023]
Abstract
The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. Since successful detection of various neurological and neuromuscular disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove/reduce motion artifacts from EEG and fNIRS signals using reliable and robust methods. In this regard, this paper proposes two robust methods: (i) Wavelet packet decomposition (WPD) and (ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: (i) difference in the signal to noise ratio ( ) and (ii) percentage reduction in motion artifacts ( ). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique, i.e., the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average and values of 30.76 dB and 59.51%, respectively, for all the EEG recordings. On the other hand, for the available 16 fNIRS recordings, the two-stage motion artifacts removal technique, i.e., WPD-CCA has produced the best average (16.55 dB, utilizing db1 wavelet packet) and largest average (41.40%, using fk8 wavelet packet). The highest average and using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively, for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed in comparison with the single-stage WPD method. In addition, the average also increases when WPD-CCA techniques are used instead of single-stage WPD for both EEG and fNIRS signals. The increment in both and values is a clear indication that two-stage WPD-CCA performs relatively better compared to single-stage WPD. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.
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Affiliation(s)
- Md Shafayet Hossain
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | | | - Mamun Bin Ibne Reaz
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Sawal Hamid Md Ali
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Ahmad Ashrif A Bakar
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Serkan Kiranyaz
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Mohammed Alhatou
- Neuromuscular Division, Department of Neurology, Al-Khor Branch, Hamad General Hospital, Doha 3050, Qatar
| | - Rumana Habib
- Department of Neurology, BIRDEM General Hospital, Dhaka 1000, Bangladesh
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21
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Hossain MS, Chowdhury MEH, Reaz MBI, Ali SHM, Bakar AAA, Kiranyaz S, Khandakar A, Alhatou M, Habib R, Hossain MM. Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22093169. [PMID: 35590859 PMCID: PMC9102309 DOI: 10.3390/s22093169] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/24/2022] [Accepted: 03/25/2022] [Indexed: 05/14/2023]
Abstract
The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. Since successful detection of various neurological and neuromuscular disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove/reduce motion artifacts from EEG and fNIRS signals using reliable and robust methods. In this regard, this paper proposes two robust methods: (i) Wavelet packet decomposition (WPD) and (ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: (i) difference in the signal to noise ratio ( ) and (ii) percentage reduction in motion artifacts ( ). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique, i.e., the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average and values of 30.76 dB and 59.51%, respectively, for all the EEG recordings. On the other hand, for the available 16 fNIRS recordings, the two-stage motion artifacts removal technique, i.e., WPD-CCA has produced the best average (16.55 dB, utilizing db1 wavelet packet) and largest average (41.40%, using fk8 wavelet packet). The highest average and using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively, for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed in comparison with the single-stage WPD method. In addition, the average also increases when WPD-CCA techniques are used instead of single-stage WPD for both EEG and fNIRS signals. The increment in both and values is a clear indication that two-stage WPD-CCA performs relatively better compared to single-stage WPD. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.
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Affiliation(s)
- Md Shafayet Hossain
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia; (M.S.H.); (S.H.M.A.); (A.A.A.B.)
| | - Muhammad E. H. Chowdhury
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; (S.K.); (A.K.)
- Correspondence: (M.E.H.C.); (M.B.I.R.)
| | - Mamun Bin Ibne Reaz
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia; (M.S.H.); (S.H.M.A.); (A.A.A.B.)
- Correspondence: (M.E.H.C.); (M.B.I.R.)
| | - Sawal Hamid Md Ali
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia; (M.S.H.); (S.H.M.A.); (A.A.A.B.)
| | - Ahmad Ashrif A. Bakar
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia; (M.S.H.); (S.H.M.A.); (A.A.A.B.)
| | - Serkan Kiranyaz
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; (S.K.); (A.K.)
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; (S.K.); (A.K.)
| | - Mohammed Alhatou
- Neuromuscular Division, Department of Neurology, Al-Khor Branch, Hamad General Hospital, Doha 3050, Qatar;
| | - Rumana Habib
- Department of Neurology, BIRDEM General Hospital, Dhaka 1000, Bangladesh;
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22
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Cortical activity evoked by voice pitch changes: a combined fNIRS and EEG study. Hear Res 2022; 420:108483. [DOI: 10.1016/j.heares.2022.108483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 03/02/2022] [Accepted: 03/10/2022] [Indexed: 11/22/2022]
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23
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Liang Z, Wang Y, Tian H, Gu Y, Arimitsu T, Takahashi T, Minagawa Y, Niu H, Tong Y. Spatial complexity method for tracking brain development and degeneration using functional near-infrared spectroscopy. BIOMEDICAL OPTICS EXPRESS 2022; 13:1718-1736. [PMID: 35414994 PMCID: PMC8973163 DOI: 10.1364/boe.449341] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 02/07/2022] [Accepted: 02/16/2022] [Indexed: 06/14/2023]
Abstract
Brain complexity analysis using functional near-infrared spectroscopy (fNIRS) has attracted attention as a biomarker for evaluating brain development and degeneration processes. However, most methods have focused on the temporal scale without capturing the spatial complexity. In this study, we propose a spatial time-delay entropy (STDE) method as the spatial complexity measure based on the time-delay measure between two oxy-hemoglobin (Δ[HbO]) or two deoxy-hemoglobin (Δ[Hb]) oscillations within the 0.01-0.1 Hz frequency band. To do this, we analyze fNIRS signals recorded from infants in their sleeping state, children, adults, and healthy seniors in their resting states. We also evaluate the effects of various noise to STDE calculations and STDE's performance in distinguishing various developmental age groups. Lastly, we compare the results with the normalized global spatial complexity (NGSC) and sample entropy (SampEn) measures. Among these measures, STDEHbO (STDE based on Δ[HbO] oscillations) performs best. The STDE value increases with age throughout childhood (p < 0.001), and then decreases in adults and healthy seniors in the 0.01-0.1 Hz frequency band. This trajectory correlates with cerebrovascular development and degeneration. These findings demonstrate that STDE can be used as a new tool for tracking cerebrovascular development and degeneration across a lifespan based on the fNIRS resting-state measurements.
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Affiliation(s)
- Zhenhu Liang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, China
| | - Yuxi Wang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, China
| | - Hao Tian
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, China
| | - Yue Gu
- Key Laboratory of Computer Vision and System (Ministry of Education), School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Takeshi Arimitsu
- Department of Pediatrics, Keio University School of Medicine, Tokyo, Japan
| | - Takao Takahashi
- Department of Pediatrics, Keio University School of Medicine, Tokyo, Japan
| | - Yasuyo Minagawa
- Department of Psychology, Faculty of Letters, Keio University, Tokyo, Japan
| | - Haijing Niu
- Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Yunjie Tong
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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Gao L, Wei Y, Wang Y, Wang G, Zhang Q, Zhang J, Chen X, Yan X. Hybrid motion artifact detection and correction approach for functional near-infrared spectroscopy measurements. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:025003. [PMID: 35212200 PMCID: PMC8871689 DOI: 10.1117/1.jbo.27.2.025003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 01/11/2022] [Indexed: 06/14/2023]
Abstract
SIGNIFICANCE Functional near-infrared spectroscopy (fNIRS) is a promising optical neuroimaging technique, measuring the hemodynamic signals from the cortex. However, improving signal quality and reducing artifacts arising from oscillation and baseline shift (BS) are still challenging up to now for fNIRS applications. AIM Considering the advantages and weaknesses of the different algorithms to reduce the artifact effect in fNIRS signals, we propose a hybrid artifact detection and correction approach. APPROACH First, distinct artifact detection was realized through an fNIRS detection strategy. Then the artifacts were divided into three categories: BS, slight oscillation, and severe oscillation. A comprehensive correction was applied through three main steps: severe artifact correction by cubic spline interpolation, BS removal by spline interpolation, and slight oscillation reduction by dual-threshold wavelet-based method. RESULTS Using fNIRS data acquired during whole night sleep monitoring, we compared the performance of our approach with existing algorithms in signal-to-noise ratio (SNR) and Pearson's correlation coefficient (R). We found that the proposed method showed improvements in performance in SNR and R with strong stability. CONCLUSIONS These results suggest that the new hybrid artifact detection and correction method enhances the viability of fNIRS as a functional neuroimaging modality.
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Affiliation(s)
- Lin Gao
- Xi’an Jiaotong University, State Key Laboratory of Manufacturing Systems Engineering, Xi’an, Shaanxi, China
- Xi’an Jiaotong University, School of Mechanical Engineering, Xi’an, Shaanxi, China
| | - Yuhui Wei
- Xi’an Jiaotong University, Key Laboratory of Biomedical Information Engineering of Education Ministry, School of Life Science and Technology, Xi’an, Shaanxi, China
- Xi’an Jiaotong University, National Engineering Research Center of Health Care and Medical Devices Xi’an Jiaotong University Branch, Xi’an, Shaanxi, China
| | - Yifei Wang
- Xi’an Jiaotong University, Key Laboratory of Biomedical Information Engineering of Education Ministry, School of Life Science and Technology, Xi’an, Shaanxi, China
- Xi’an Jiaotong University, National Engineering Research Center of Health Care and Medical Devices Xi’an Jiaotong University Branch, Xi’an, Shaanxi, China
| | - Gang Wang
- Xi’an Jiaotong University, Key Laboratory of Biomedical Information Engineering of Education Ministry, School of Life Science and Technology, Xi’an, Shaanxi, China
- Xi’an Jiaotong University, National Engineering Research Center of Health Care and Medical Devices Xi’an Jiaotong University Branch, Xi’an, Shaanxi, China
| | - Quan Zhang
- Massachusetts General Hospital, Harvard Medical School, Department of Psychiatry, Charlestown, Massachusetts, United States
| | - Jianbao Zhang
- Xi’an Jiaotong University, Key Laboratory of Biomedical Information Engineering of Education Ministry, School of Life Science and Technology, Xi’an, Shaanxi, China
- Xi’an Jiaotong University, National Engineering Research Center of Health Care and Medical Devices Xi’an Jiaotong University Branch, Xi’an, Shaanxi, China
| | - Xiang Chen
- Xi’an Jiaotong University, Key Laboratory of Biomedical Information Engineering of Education Ministry, School of Life Science and Technology, Xi’an, Shaanxi, China
- Xi’an Jiaotong University, National Engineering Research Center of Health Care and Medical Devices Xi’an Jiaotong University Branch, Xi’an, Shaanxi, China
| | - Xiangguo Yan
- Xi’an Jiaotong University, Key Laboratory of Biomedical Information Engineering of Education Ministry, School of Life Science and Technology, Xi’an, Shaanxi, China
- Xi’an Jiaotong University, National Engineering Research Center of Health Care and Medical Devices Xi’an Jiaotong University Branch, Xi’an, Shaanxi, China
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25
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Kim M, Lee S, Dan I, Tak S. A deep convolutional neural network for estimating hemodynamic response function with reduction of motion artifacts in fNIRS. J Neural Eng 2022; 19. [PMID: 35038682 DOI: 10.1088/1741-2552/ac4bfc] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 01/17/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique for monitoring hemoglobin concentration changes in a non-invasive manner. However, subject movements are often significant sources of artifacts. While several methods have been developed for suppressing this confounding noise, the conventional techniques have limitations on optimal selections of model parameters across participants or brain regions. To address this shortcoming, we aim to propose a method based on a deep convolutional neural network (CNN). APPROACH The U-net is employed as a CNN architecture. Specifically, large-scale training and testing data are generated by combining variants of hemodynamic response function (HRF) with experimental measurements of motion noises. The neural network is then trained to reconstruct hemodynamic response coupled to neuronal activity with a reduction of motion artifacts. MAIN RESULTS Using extensive analysis, we show that the proposed method estimates the task-related HRF more accurately than the existing methods of wavelet decomposition and autoregressive models. Specifically, the mean squared error and variance of HRF estimates, based on the CNN, are the smallest among all methods considered in this study. These results are more prominent when the semi-simulated data contains variants of shapes and amplitudes of HRF. SIGNIFICANCE The proposed CNN method allows for accurately estimating amplitude and shape of HRF with significant reduction of motion artifacts. This method may have a great potential for monitoring HRF changes in real-life settings that involve excessive motion artifacts.
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Affiliation(s)
- MinWoo Kim
- School of Biomedical Convergence Engineering, Pusan National University, 49 Busandaehak-ro, Mulgeum-eup, Yangsan-si, Gyeongsangnam-do, Yangsan, 50612, Korea (the Republic of)
| | - Seonjin Lee
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, 162 Yeongudanji-ro, Cheongwon-gu, Ochang-eup, Cheongju, 28119, Korea (the Republic of)
| | - Ippeita Dan
- Faculty of Science and Engineering, Chuo University, Tama Campus 742-1 Higashinakano Hachioji-shi, Tokyo, 192-0393, JAPAN
| | - Sungho Tak
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, 162 Yeongudanji-ro, Cheongwon-gu, Ochang-eup, Cheongju, 28119, Korea (the Republic of)
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26
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Fan W, Dehghani H, Eggebrecht AT. Investigation of effect of modulation frequency on high-density diffuse optical tomography image quality. NEUROPHOTONICS 2021; 8:045002. [PMID: 34849379 PMCID: PMC8612746 DOI: 10.1117/1.nph.8.4.045002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 11/04/2021] [Indexed: 05/16/2023]
Abstract
Significance: By incorporating multiple overlapping functional near-infrared spectroscopy (fNIRS) measurements, high-density diffuse optical tomography (HD-DOT) images human brain function with fidelity comparable to functional magnetic resonance imaging (fMRI). Previous work has shown that frequency domain high-density diffuse optical tomography (FD-HD-DOT) may further improve image quality over more traditional continuous wave (CW) HD-DOT. Aim: The effects of modulation frequency on image quality as obtainable with FD-HD-DOT is investigated through simulations with a realistic noise model of functional activations in human head models, arising from 11 source modulation frequencies between CW and 1000 MHz. Approach: Simulations were performed using five representative head models with an HD regular grid of 158 light sources and 166 detectors and an empirically derived noise model. Functional reconstructions were quantitatively assessed with multiple image quality metrics including the localization error (LE), success rate, full width at half maximum, and full volume at half maximum (FVHM). All metrics were evaluated against CW-based models. Results: Compared to CW, localization accuracy is improved by >40% throughout brain depths of 13 to 25 mm below the surface with 300 to 500 MHz modulation frequencies. Additionally, the reliable field of view in brain tissue is enlarged by 35% to 48% within an optimal frequency of 300 MHz after considering realistic noise, depending on the dynamic range of the system. Conclusions: These results point to the tremendous opportunities in further development of high bandwidth FD-HD-DOT system hardware for applications in human brain mapping.
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Affiliation(s)
- Weihao Fan
- Washington University, Department of Physics, St. Louis, Missouri, United States
| | - Hamid Dehghani
- University of Birmingham, School of Computer Science, Birmingham, United Kingdom
| | - Adam T. Eggebrecht
- Washington University School of Medicine, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States
- Washington University, Department of Biomedical Engineering, St. Louis, Missouri, United States
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27
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Zhu Y, Weston EB, Mehta RK, Marras WS. Neural and biomechanical tradeoffs associated with human-exoskeleton interactions. APPLIED ERGONOMICS 2021; 96:103494. [PMID: 34126572 DOI: 10.1016/j.apergo.2021.103494] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 05/30/2021] [Accepted: 06/01/2021] [Indexed: 06/12/2023]
Abstract
Industrial passive low-back exoskeletons have gained recent attention as ergonomic interventions to manual handling tasks. This research utilized a two-armed experimental approach (single vs dual-task paradigms) to quantify neural and biomechanical tradeoffs associated with short-term human-exoskeleton interaction (HEI) during asymmetrical lifting in twelve healthy adults balanced by gender. A dynamic, electromyography-assisted spine model was employed that indicated statistical, but marginal, biomechanical benefits of the tested exoskeleton, which diminished with the introduction of the cognitive dual-task. Using Near Infrared Spectroscopy (fNIRS)-based brain connectivity analyses, we found that the tested exoskeleton imposed greater neurocognitive and motor adaptation efforts by engaging action monitoring and error processing brain networks. Collectively, these findings indicate that a wearer's biomechanical response to increased cognitive demands in the workplace may offset the mechanical advantages of exoskeletons. We also demonstrate the utility of ambulatory fNIRS to capture the neural cost of HEI without the need for elaborate dual-task manipulations.
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Affiliation(s)
- Yibo Zhu
- Wm. Michael Barnes '64 Department of Industrial & Systems Engineering, Texas A&M University, College Station, TX, 77840, USA
| | - Eric B Weston
- Department of Integrated Systems Engineering, The Ohio State University, Columbus, OH, 43210, USA; Spine Research Institute, The Ohio State University, Columbus, OH, 43210, USA
| | - Ranjana K Mehta
- Wm. Michael Barnes '64 Department of Industrial & Systems Engineering, Texas A&M University, College Station, TX, 77840, USA.
| | - William S Marras
- Department of Integrated Systems Engineering, The Ohio State University, Columbus, OH, 43210, USA; Spine Research Institute, The Ohio State University, Columbus, OH, 43210, USA
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28
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Huo C, Xu G, Li W, Xie H, Zhang T, Liu Y, Li Z. A review on functional near-infrared spectroscopy and application in stroke rehabilitation. MEDICINE IN NOVEL TECHNOLOGY AND DEVICES 2021. [DOI: 10.1016/j.medntd.2021.100064] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
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29
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Mehta RK, Rhee J. Revealing Sex Differences During Upper and Lower Extremity Neuromuscular Fatigue in Older Adults Through a Neuroergonomics Approach. FRONTIERS IN NEUROERGONOMICS 2021; 2:663368. [PMID: 38235250 PMCID: PMC10790897 DOI: 10.3389/fnrgo.2021.663368] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 07/26/2021] [Indexed: 01/19/2024]
Abstract
Background: Sex differences in neuromuscular fatigue is well-documented, however the underlying mechanisms remain understudied, particularly for the aging population. Objective: This study investigated sex differences in fatigability of the upper and lower extremity of older adults using a neuroergonomics approach. Methods: Thirty community-dwelling older adults (65 years or older; 15 M, 15 F) performed intermittent submaximal fatiguing handgrip and knee extension exercises until voluntary exhaustion on separate days. Muscle activity from prime muscles of the hand/arm and knee extensors were monitored using electromyography, neural activity from the frontal, motor, and sensory areas were monitored using functional near infrared spectroscopy, and force output were obtained. Results: While older males were stronger than females across both muscle groups, they exhibited longer endurance times and greater strength loss during knee extension exercises. These lower extremity findings were associated with greater force complexity over time and concomitant increase in left motor and right sensory motor regions. While fatigability during handgrip exercises was comparable across sexes, older females exhibited concurrent increases in the activation of the ipsilateral motor regions over time. Discussion: We identified differences in the underlying central neural strategies adopted by males and females in maintaining downstream motor outputs during handgrip fatigue that were not evident with traditional ergonomics measures. Additionally, enhanced neural activation in males during knee exercises that accompanied longer time to exhaustion point to potential rehabilitation/exercise strategies to improve neuromotor outcomes in more fatigable older adults.
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Affiliation(s)
- Ranjana K. Mehta
- Wm. Michael Barnes '64 Department of Industrial & Systems Engineering, Texas A&M University, College Station, TX, United States
| | - Joohyun Rhee
- Department of Environmental and Occupational Health, Texas A&M University, College Station, TX, United States
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30
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Liang Z, Tian H, Yang HC, Arimitsu T, Takahashi T, Sassaroli A, Fantini S, Niu H, Minagawa Y, Tong Y. Tracking Brain Development From Neonates to the Elderly by Hemoglobin Phase Measurement Using Functional Near-Infrared Spectroscopy. IEEE J Biomed Health Inform 2021; 25:2497-2509. [PMID: 33493123 DOI: 10.1109/jbhi.2021.3053900] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The biological and neurological processes during the lifespan are dynamic with significant alterations associated with different stages of life. The phase and coupling of oxy-hemoglobin (Δ[HbO]) and deoxy-hemoglobin concentration changes (Δ[Hb]) measured by functional near-infrared spectroscopy (fNIRS) are shown to characterize the neurovascular and metabolic development of infants. However, the changes in phase and coupling across the human lifespan remain mostly unknown. Here, fNIRS measurements of Δ[HbO] and Δ[Hb] conducted at two sites on different age populations (from newborns to elderly) were combined. Firstly, we assessed the influence of random noise on the calculation of the phase difference and phase-locking index (PLI) in fNIRS measurement. The results showed that the phase difference is close to π as the noise intensity approaches -8 dB, and the coupling strength (i.e., PLI) presents a u-shape curve as the noise increase. Secondly, phase difference and PLI in the frequency range 0.01-0.10 Hz were calculated after denoising. It showed that the phase difference increases from newborns to 3-4-month-olds babies. This phase difference persists throughout adulthood until finally being disrupted in the old age. The children's PLI is the highest, followed by that of adults. These two groups' PLI are significantly higher than those of infants and the elderly (p < 0.001). Lastly, a hemodynamic model was used to explain the observations and found close associations with cerebral autoregulation and speed of blood flow. These results demonstrate that the phase-related parameters measured by fNIRS can be used to study the brain and assess brain health throughout the lifespan.
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31
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Abujelala M, Karthikeyan R, Tyagi O, Du J, Mehta RK. Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics. Brain Sci 2021; 11:885. [PMID: 34209388 PMCID: PMC8304323 DOI: 10.3390/brainsci11070885] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 06/26/2021] [Accepted: 06/28/2021] [Indexed: 12/02/2022] Open
Abstract
The nature of firefighters` duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees' safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm's performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.
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Affiliation(s)
- Maher Abujelala
- Department of Industrial & Systems Engineering, Texas A & M University, College Station, TX 77843, USA;
| | - Rohith Karthikeyan
- Department of Mechanical Engineering, Texas A & M University, College Station, TX 77843, USA;
| | - Oshin Tyagi
- Department of Industrial & Systems Engineering, Texas A & M University, College Station, TX 77843, USA;
| | - Jing Du
- Department of Civil and Coastal Engineering, Engineering School of Sustainable Infrastructure and Environment (ESSIE), Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32611, USA;
| | - Ranjana K. Mehta
- Department of Industrial & Systems Engineering, Texas A & M University, College Station, TX 77843, USA;
- Department of Mechanical Engineering, Texas A & M University, College Station, TX 77843, USA;
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Chen T, Zhao C, Pan X, Qu J, Wei J, Li C, Liang Y, Zhang X. Decoding different working memory states during an operation span task from prefrontal fNIRS signals. BIOMEDICAL OPTICS EXPRESS 2021; 12:3495-3511. [PMID: 34221675 PMCID: PMC8221954 DOI: 10.1364/boe.426731] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 05/07/2021] [Accepted: 05/10/2021] [Indexed: 06/13/2023]
Abstract
We propose an effective and practical decoding method of different mental states for potential applications for the design of brain-computer interfaces, prediction of cognitive behaviour, and investigation of cognitive mechanism. Functional near infrared spectroscopy (fNIRS) signals that interrogated the prefrontal and parietal cortices and were evaluated by generalized linear model were recorded when nineteen healthy adults performed the operation span (OSPAN) task. The oxygenated hemoglobin changes during OSPAN, response, and rest periods were classified with a support vector machine (SVM). The relevance vector regression algorithm was utilized for prediction of cognitive performance based on multidomain features of fNIRS signals from the OSPAN task. We acquired decent classification accuracies for OSPAN vs. response (above 91.2%) and for OSPAN vs. rest (above 94.7%). Eight of the ten cognitive testing scores could be predicted from the combination of OSPAN and response features, which indicated the brain hemodynamic responses contain meaningful information suitable for predicting cognitive performance.
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Affiliation(s)
- Ting Chen
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Cui Zhao
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Xingyu Pan
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Junda Qu
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Jing Wei
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Chunlin Li
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Ying Liang
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Xu Zhang
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
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Slutter MWJ, Thammasan N, Poel M. Exploring the Brain Activity Related to Missing Penalty Kicks: An fNIRS Study. FRONTIERS IN COMPUTER SCIENCE 2021. [DOI: 10.3389/fcomp.2021.661466] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
At vital moments in professional soccer matches, penalties were often missed. Psychological factors, such as anxiety and pressure, are among the critical causes of the mistakes, commonly known as choking under pressure. Nevertheless, the factors have not been fully explored. In this study, we used functional near-infrared spectroscopy (fNIRS) to investigate the influence of the brain on this process. An in-situ study was set-up (N = 22), in which each participant took 15 penalties under three different pressure conditions: without a goalkeeper, with an amiable goalkeeper, and with a competitive goalkeeper. Both experienced and inexperienced soccer players were recruited, and the brain activation was compared across groups. Besides, fNIRS activation was compared between sessions that participants felt anxious against sessions without anxiety report, and between penalty-scoring and -missing sessions. The results show that the task-relevant brain region, the motor cortex, was more activated when players were not experiencing performance anxiety. The activation of task-irrelevant areas was shown to be related to players experiencing anxiety and missing penalties, especially the prefrontal cortex (PFC). More particularly, an overall higher activation of the PFC and an increase of PFC lateral asymmetry were related to anxious players and missed penalties, which can be caused by players' worries about the consequences of scoring or missing the penalty kicks. When experienced players were feeling anxious, their left temporal cortex activation increased, which could be an indication that experienced overthink the situation and neglect their automated skills. Besides, the left temporal cortex activation is higher when inexperienced players succeeded to score a penalty. Overall, the results of this study are in line with the neural efficiency theory and demonstrate the feasibility and ecological validity to detect neurological clues relevant to anxiety and performance from fNIRS recordings in the field.
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Gemignani J, Gervain J. Comparing different pre-processing routines for infant fNIRS data. Dev Cogn Neurosci 2021; 48:100943. [PMID: 33735718 PMCID: PMC7985709 DOI: 10.1016/j.dcn.2021.100943] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 02/25/2021] [Accepted: 03/09/2021] [Indexed: 01/24/2023] Open
Abstract
Functional Near Infrared Spectroscopy (fNIRS) is an important neuroimaging technique in cognitive developmental neuroscience. Nevertheless, there is no general consensus yet about best pre-processing practices. This issue is highly relevant, especially since the development and variability of the infant hemodynamic response (HRF) is not fully known. Systematic comparisons between analysis methods are thus necessary. We investigated the performance of five different pipelines, selected on the basis of a systematic search of the infant NIRS literature, in two experiments. In Experiment 1, we used synthetic data to compare the recovered HRFs with the true HRF and to assess the robustness of each method against increasing levels of noise. In Experiment 2, we analyzed experimental data from a published study, which assessed the neural correlates of artificial grammar processing in newborns. We found that with motion artifact correction (as opposed to rejection) a larger number of trials were retained, but HRF amplitude was often strongly reduced. By contrast, artifact rejection resulted in a high exclusion rate but preserved adequately the characteristics of the HRF. We also found that the performance of all pipelines declined as the noise increased, but significantly less so than if no pre-processing was applied. Finally, we found no difference between running the pre-processing on optical density or concentration change data. These results suggest that pre-processing should thus be optimized as a function of the specific quality issues a give dataset exhibits.
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Affiliation(s)
- Jessica Gemignani
- Department of Developmental Psychology and Socialisation, University of Padova, Padova, Italy; Integrative Neuroscience and Cognition Center, CNRS & University of Paris, Paris, France.
| | - Judit Gervain
- Department of Developmental Psychology and Socialisation, University of Padova, Padova, Italy; Integrative Neuroscience and Cognition Center, CNRS & University of Paris, Paris, France
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Chiarelli AM, Perpetuini D, Croce P, Filippini C, Cardone D, Rotunno L, Anzoletti N, Zito M, Zappasodi F, Merla A. Evidence of Neurovascular Un-Coupling in Mild Alzheimer's Disease through Multimodal EEG-fNIRS and Multivariate Analysis of Resting-State Data. Biomedicines 2021; 9:biomedicines9040337. [PMID: 33810484 PMCID: PMC8066873 DOI: 10.3390/biomedicines9040337] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 03/20/2021] [Accepted: 03/23/2021] [Indexed: 12/18/2022] Open
Abstract
Alzheimer’s disease (AD) is associated with modifications in cerebral blood perfusion and autoregulation. Hence, neurovascular coupling (NC) alteration could become a biomarker of the disease. NC might be assessed in clinical settings through multimodal electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Multimodal EEG-fNIRS was recorded at rest in an ambulatory setting to assess NC and to evaluate the sensitivity and specificity of the methodology to AD. Global NC was evaluated with a general linear model (GLM) framework by regressing whole-head EEG power envelopes in three frequency bands (theta, alpha and beta) with average fNIRS oxy- and deoxy-hemoglobin concentration changes in the frontal and prefrontal cortices. NC was lower in AD compared to healthy controls (HC) with significant differences in the linkage of theta and alpha bands with oxy- and deoxy-hemoglobin, respectively (p = 0.028 and p = 0.020). Importantly, standalone EEG and fNIRS metrics did not highlight differences between AD and HC. Furthermore, a multivariate data-driven analysis of NC between the three frequency bands and the two hemoglobin species delivered a cross-validated classification performance of AD and HC with an Area Under the Curve, AUC = 0.905 (p = 2.17 × 10−5). The findings demonstrate that EEG-fNIRS may indeed represent a powerful ecological tool for clinical evaluation of NC and early identification of AD.
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Affiliation(s)
- Antonio M. Chiarelli
- Department of Neuroscience, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, Faculty of Medicine, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (D.P.); (P.C.); (C.F.); (D.C.); (F.Z.); (A.M.)
- Correspondence: ; Tel.: +39-087-1355-6954
| | - David Perpetuini
- Department of Neuroscience, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, Faculty of Medicine, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (D.P.); (P.C.); (C.F.); (D.C.); (F.Z.); (A.M.)
| | - Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, Faculty of Medicine, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (D.P.); (P.C.); (C.F.); (D.C.); (F.Z.); (A.M.)
| | - Chiara Filippini
- Department of Neuroscience, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, Faculty of Medicine, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (D.P.); (P.C.); (C.F.); (D.C.); (F.Z.); (A.M.)
| | - Daniela Cardone
- Department of Neuroscience, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, Faculty of Medicine, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (D.P.); (P.C.); (C.F.); (D.C.); (F.Z.); (A.M.)
| | - Ludovica Rotunno
- Department of Medicine and Science of Ageing, Faculty of Medicine, University G. d’Annunzio of Chieti-Pescara, Via Dei Vestini 31, 66100 Chieti, Italy; (L.R.); (N.A.); (M.Z.)
| | - Nelson Anzoletti
- Department of Medicine and Science of Ageing, Faculty of Medicine, University G. d’Annunzio of Chieti-Pescara, Via Dei Vestini 31, 66100 Chieti, Italy; (L.R.); (N.A.); (M.Z.)
| | - Michele Zito
- Department of Medicine and Science of Ageing, Faculty of Medicine, University G. d’Annunzio of Chieti-Pescara, Via Dei Vestini 31, 66100 Chieti, Italy; (L.R.); (N.A.); (M.Z.)
| | - Filippo Zappasodi
- Department of Neuroscience, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, Faculty of Medicine, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (D.P.); (P.C.); (C.F.); (D.C.); (F.Z.); (A.M.)
| | - Arcangelo Merla
- Department of Neuroscience, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, Faculty of Medicine, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (D.P.); (P.C.); (C.F.); (D.C.); (F.Z.); (A.M.)
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36
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Perpetuini D, Chiarelli AM, Filippini C, Cardone D, Croce P, Rotunno L, Anzoletti N, Zito M, Zappasodi F, Merla A. Working Memory Decline in Alzheimer's Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E1380. [PMID: 33279924 PMCID: PMC7762102 DOI: 10.3390/e22121380] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 11/30/2020] [Accepted: 12/03/2020] [Indexed: 12/14/2022]
Abstract
Alzheimer's disease (AD) is characterized by working memory (WM) failures that can be assessed at early stages through administering clinical tests. Ecological neuroimaging, such as Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS), may be employed during these tests to support AD early diagnosis within clinical settings. Multimodal EEG-fNIRS could measure brain activity along with neurovascular coupling (NC) and detect their modifications associated with AD. Data analysis procedures based on signal complexity are suitable to estimate electrical and hemodynamic brain activity or their mutual information (NC) during non-structured experimental paradigms. In this study, sample entropy of whole-head EEG and frontal/prefrontal cortex fNIRS was evaluated to assess brain activity in early AD and healthy controls (HC) during WM tasks (i.e., Rey-Osterrieth complex figure and Raven's progressive matrices). Moreover, conditional entropy between EEG and fNIRS was evaluated as indicative of NC. The findings demonstrated the capability of complexity analysis of multimodal EEG-fNIRS to detect WM decline in AD. Furthermore, a multivariate data-driven analysis, performed on these entropy metrics and based on the General Linear Model, allowed classifying AD and HC with an AUC up to 0.88. EEG-fNIRS may represent a powerful tool for the clinical evaluation of WM decline in early AD.
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Affiliation(s)
- David Perpetuini
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Antonio Maria Chiarelli
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Chiara Filippini
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Daniela Cardone
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Pierpaolo Croce
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Ludovica Rotunno
- Department of Medicine and Science of Ageing, University G. D’Annunzio, Via Dei Vestini 31, 66100 Chieti, Italy; (L.R.); (N.A.); (M.Z.)
| | - Nelson Anzoletti
- Department of Medicine and Science of Ageing, University G. D’Annunzio, Via Dei Vestini 31, 66100 Chieti, Italy; (L.R.); (N.A.); (M.Z.)
| | - Michele Zito
- Department of Medicine and Science of Ageing, University G. D’Annunzio, Via Dei Vestini 31, 66100 Chieti, Italy; (L.R.); (N.A.); (M.Z.)
| | - Filippo Zappasodi
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Arcangelo Merla
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
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Gratton G, Weaver SR, Burley CV, Low KA, Maclin EL, Johns PW, Pham QS, Lucas SJE, Fabiani M, Rendeiro C. Dietary flavanols improve cerebral cortical oxygenation and cognition in healthy adults. Sci Rep 2020; 10:19409. [PMID: 33235219 PMCID: PMC7687895 DOI: 10.1038/s41598-020-76160-9] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 10/08/2020] [Indexed: 12/22/2022] Open
Abstract
Cocoa flavanols protect humans against vascular disease, as evidenced by improvements in peripheral endothelial function, likely through nitric oxide signalling. Emerging evidence also suggests that flavanol-rich diets protect against cognitive aging, but mechanisms remain elusive. In a randomized double-blind within-subject acute study in healthy young adults, we link these two lines of research by showing, for the first time, that flavanol intake leads to faster and greater brain oxygenation responses to hypercapnia, as well as higher performance only when cognitive demand is high. Individual difference analyses further show that participants who benefit from flavanols intake during hypercapnia are also those who do so in the cognitive challenge. These data support the hypothesis that similar vascular mechanisms underlie both the peripheral and cerebral effects of flavanols. They further show the importance of studies combining physiological and graded cognitive challenges in young adults to investigate the actions of dietary flavanols on brain function.
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Affiliation(s)
- Gabriele Gratton
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Psychology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Samuel R Weaver
- School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, B15 2TT, UK
| | - Claire V Burley
- School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, B15 2TT, UK
| | - Kathy A Low
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Edward L Maclin
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Paul W Johns
- Abbott-Nutrition Division, Research and Development, 3300 Stelzer Road, Columbus, OH, 43219, USA
| | - Quang S Pham
- Abbott-Nutrition Division, Research and Development, 3300 Stelzer Road, Columbus, OH, 43219, USA
| | - Samuel J E Lucas
- School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, B15 2TT, UK
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK
| | - Monica Fabiani
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Psychology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Catarina Rendeiro
- School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, B15 2TT, UK.
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK.
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Tyagi O, Zhu Y, Johnson C, Mehta RK, Sasangohar F, Erraguntla M, Qaraqe K. Neural Signatures of Handgrip Fatigue in Type 1 Diabetic Men and Women. Front Hum Neurosci 2020; 14:564969. [PMID: 33240061 PMCID: PMC7680760 DOI: 10.3389/fnhum.2020.564969] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 10/14/2020] [Indexed: 01/22/2023] Open
Abstract
Type 1 diabetes (T1D) is associated with reduced muscular strength and greater muscle fatigability. Along with changes in muscular mechanisms, T1D is also linked to structural changes in the brain. How the neurophysiological mechanisms underlying muscle fatigue is altered with T1D and sex related differences of these mechanisms are still not well investigated. The aim of this study was to determine the impact of T1D on the neural correlates of handgrip fatigue and examine sex and T1D related differences in neuromuscular performance parameters, neural activation and functional connectivity patterns between the motor regions of the brain. Forty-two adults, balanced by condition (healthy vs T1D) and sex (male vs female), and performed submaximal isometric handgrip contractions until voluntary exhaustion. Initial strength, endurance time, strength loss, force variability, and complexity measures were collected. Additionally, hemodynamic responses from motor-function related cortical regions, using functional near-infrared spectroscopy (fNIRS), were obtained. Overall, females exhibited lower initial strength (p < 0.0001), and greater strength loss (p = 0.023) than males. While initial strength was significantly lower in the T1D group (p = 0.012) compared to the healthy group, endurance times and strength loss were comparable between the two groups. Force complexity, measured as approximate entropy, was found to be lower throughout the experiment for the T1D group (p = 0.0378), indicating lower online motor adaptability. Although, T1D and healthy groups fatigued similarly, only the T1D group exhibited increased neural activation in the left (p = 0.095) and right (p = 0.072) supplementary motor areas (SMA) over time. A sex × condition × fatigue interaction effect (p = 0.044) showed that while increased activation was observed in both T1D females and healthy males from the Early to Middle phase, this was not observed in healthy females or T1D males. These findings demonstrate that T1D adults had lower adaptability to fatigue which they compensated for by increasing neural effort. This study highlights the importance of examining both neural and motor performance signatures when investigating the impact of chronic conditions on neuromuscular fatigue. Additionally, the findings have implications for developing intervention strategies for training, rehabilitation, and ergonomics considerations for individuals with chronic conditions.
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Affiliation(s)
- Oshin Tyagi
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States
| | - Yibo Zhu
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States
| | - Connor Johnson
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States
| | - Ranjana K. Mehta
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States
| | - Farzan Sasangohar
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States
| | - Madhav Erraguntla
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States
| | - Khalid Qaraqe
- Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Doha, Qatar
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Steinmetzger K, Shen Z, Riedel H, Rupp A. Auditory cortex activity measured using functional near-infrared spectroscopy (fNIRS) appears to be susceptible to masking by cortical blood stealing. Hear Res 2020; 396:108069. [DOI: 10.1016/j.heares.2020.108069] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 08/24/2020] [Accepted: 08/31/2020] [Indexed: 01/21/2023]
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40
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Sherafati A, Snyder AZ, Eggebrecht AT, Bergonzi KM, Burns‐Yocum TM, Lugar HM, Ferradal SL, Robichaux‐Viehoever A, Smyser CD, Palanca BJ, Hershey T, Culver JP. Global motion detection and censoring in high-density diffuse optical tomography. Hum Brain Mapp 2020; 41:4093-4112. [PMID: 32648643 PMCID: PMC8022277 DOI: 10.1002/hbm.25111] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 06/05/2020] [Accepted: 06/10/2020] [Indexed: 12/30/2022] Open
Abstract
Motion-induced artifacts can significantly corrupt optical neuroimaging, as in most neuroimaging modalities. For high-density diffuse optical tomography (HD-DOT) with hundreds to thousands of source-detector pair measurements, motion detection methods are underdeveloped relative to both functional magnetic resonance imaging (fMRI) and standard functional near-infrared spectroscopy (fNIRS). This limitation restricts the application of HD-DOT in many challenging imaging situations and subject populations (e.g., bedside monitoring and children). Here, we evaluated a new motion detection method for multi-channel optical imaging systems that leverages spatial patterns across measurement channels. Specifically, we introduced a global variance of temporal derivatives (GVTD) metric as a motion detection index. We showed that GVTD strongly correlates with external measures of motion and has high sensitivity and specificity to instructed motion-with an area under the receiver operator characteristic curve of 0.88, calculated based on five different types of instructed motion. Additionally, we showed that applying GVTD-based motion censoring on both hearing words task and resting state HD-DOT data with natural head motion results in an improved spatial similarity to fMRI mapping. We then compared the GVTD similarity scores with several commonly used motion correction methods described in the fNIRS literature, including correlation-based signal improvement (CBSI), temporal derivative distribution repair (TDDR), wavelet filtering, and targeted principal component analysis (tPCA). We find that GVTD motion censoring on HD-DOT data outperforms other methods and results in spatial maps more similar to those of matched fMRI data.
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Affiliation(s)
- Arefeh Sherafati
- Department of PhysicsWashington University in St. LouisSt. LouisMissouriUSA
| | - Abraham Z. Snyder
- Department of RadiologyWashington University School of Medicine in StSt. LouisMissouriUSA
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
| | - Adam T. Eggebrecht
- Department of RadiologyWashington University School of Medicine in StSt. LouisMissouriUSA
- Department of Biomedical EngineeringWashington University School in St. LouisSt. LouisMissouriUSA
- Division of Biology and Biomedical SciencesWashington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | | | - Tracy M. Burns‐Yocum
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonIndianaUSA
| | - Heather M. Lugar
- Department of PsychiatryWashington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | - Silvina L. Ferradal
- Department Of Intelligent Systems EngineeringIndiana UniversityBloomingtonIndianaUSA
| | | | - Christopher D. Smyser
- Department of RadiologyWashington University School of Medicine in StSt. LouisMissouriUSA
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
- Department of PediatricsWashington University in St. LouisSt. LouisMissouriUSA
| | - Ben J. Palanca
- Department of AnesthesiologyWashington University School of Medicine in St. Louis, St. LouisMissouriUSA
| | - Tamara Hershey
- Department of RadiologyWashington University School of Medicine in StSt. LouisMissouriUSA
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonIndianaUSA
| | - Joseph P. Culver
- Department of PhysicsWashington University in St. LouisSt. LouisMissouriUSA
- Department of RadiologyWashington University School of Medicine in StSt. LouisMissouriUSA
- Department of Biomedical EngineeringWashington University School in St. LouisSt. LouisMissouriUSA
- Division of Biology and Biomedical SciencesWashington University School of Medicine in St. LouisSt. LouisMissouriUSA
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41
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Forcione M, Chiarelli AM, Perpetuini D, Davies DJ, O’Halloran P, Hacker D, Merla A, Belli A. Tomographic Task-Related Functional Near-Infrared Spectroscopy in Acute Sport-Related Concussion: An Observational Case Study. Int J Mol Sci 2020; 21:E6273. [PMID: 32872557 PMCID: PMC7503954 DOI: 10.3390/ijms21176273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 08/26/2020] [Accepted: 08/28/2020] [Indexed: 12/04/2022] Open
Abstract
Making decisions regarding return-to-play after sport-related concussion (SRC) based on resolution of symptoms alone can expose contact-sport athletes to further injury before their recovery is complete. Task-related functional near-infrared spectroscopy (fNIRS) could be used to scan for abnormalities in the brain activation patterns of SRC athletes and help clinicians to manage their return-to-play. This study aims to show a proof of concept of mapping brain activation, using tomographic task-related fNIRS, as part of the clinical assessment of acute SRC patients. A high-density frequency-domain optical device was used to scan 2 SRC patients, within 72 h from injury, during the execution of 3 neurocognitive tests used in clinical practice. The optical data were resolved into a tomographic reconstruction of the brain functional activation pattern, using diffuse optical tomography. Moreover, brain activity was inferred using single-subject statistical analyses. The advantages and limitations of the introduction of this optical technique into the clinical assessment of acute SRC patients are discussed.
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Affiliation(s)
- Mario Forcione
- National Institute for Health Research Surgical Reconstruction and Microbiology Research Centre (NIHR-SRMRC), University Hospitals Birmingham NHS Foundation Trust, Mindelsohn Way, Birmingham B15 2TH, UK; (D.J.D.); (A.B.)
- Neuroscience & Ophthalmology Research Group, Institute of Inflammation & Ageing, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK;
| | - Antonio Maria Chiarelli
- Imaging and Clinical Sciences, Department of Neuroscience, University G. D’Annunzio of Chieti-Pescara, Institute for Advanced Biomedical Technologies, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (D.P.); (A.M.)
| | - David Perpetuini
- Imaging and Clinical Sciences, Department of Neuroscience, University G. D’Annunzio of Chieti-Pescara, Institute for Advanced Biomedical Technologies, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (D.P.); (A.M.)
| | - David James Davies
- National Institute for Health Research Surgical Reconstruction and Microbiology Research Centre (NIHR-SRMRC), University Hospitals Birmingham NHS Foundation Trust, Mindelsohn Way, Birmingham B15 2TH, UK; (D.J.D.); (A.B.)
- Neuroscience & Ophthalmology Research Group, Institute of Inflammation & Ageing, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK;
| | - Patrick O’Halloran
- Neuroscience & Ophthalmology Research Group, Institute of Inflammation & Ageing, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK;
| | - David Hacker
- Clinical Neuropsychology, University Hospitals Birmingham NHS Foundation Trust, Mindelsohn Way, Birmingham B15 2TH, UK;
| | - Arcangelo Merla
- Imaging and Clinical Sciences, Department of Neuroscience, University G. D’Annunzio of Chieti-Pescara, Institute for Advanced Biomedical Technologies, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (D.P.); (A.M.)
| | - Antonio Belli
- National Institute for Health Research Surgical Reconstruction and Microbiology Research Centre (NIHR-SRMRC), University Hospitals Birmingham NHS Foundation Trust, Mindelsohn Way, Birmingham B15 2TH, UK; (D.J.D.); (A.B.)
- Neuroscience & Ophthalmology Research Group, Institute of Inflammation & Ageing, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK;
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42
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Butler LK, Kiran S, Tager-Flusberg H. Functional Near-Infrared Spectroscopy in the Study of Speech and Language Impairment Across the Life Span: A Systematic Review. AMERICAN JOURNAL OF SPEECH-LANGUAGE PATHOLOGY 2020; 29:1674-1701. [PMID: 32640168 PMCID: PMC7893520 DOI: 10.1044/2020_ajslp-19-00050] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Purpose Functional brain imaging is playing an increasingly important role in the diagnosis and treatment of communication disorders, yet many populations and settings are incompatible with functional magnetic resonance imaging and other commonly used techniques. We conducted a systematic review of neuroimaging studies using functional near-infrared spectroscopy (fNIRS) with individuals with speech or language impairment across the life span. We aimed to answer the following question: To what extent has fNIRS been used to investigate the neural correlates of speech-language impairment? Method This systematic review was preregistered with PROSPERO, the international prospective register of systematic reviews (CRD42019136464). We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol for preferred reporting items for systematic reviews. The database searches were conducted between February and March of 2019 with the following search terms: (a) fNIRS or functional near-infrared spectroscopy or NIRS or near-infrared spectroscopy, (b) speech or language, and (c) disorder or impairment or delay. Results We found 34 fNIRS studies that involved individuals with speech or language impairment across nine categories: (a) autism spectrum disorders; (b) developmental speech and language disorders; (c) cochlear implantation and deafness; (d) dementia, dementia of the Alzheimer's type, and mild cognitive impairment; (e) locked-in syndrome; (f) neurologic speech disorders/dysarthria; (g) stroke/aphasia; (h) stuttering; and (i) traumatic brain injury. Conclusions Though it is not without inherent challenges, fNIRS may have advantages over other neuroimaging techniques in the areas of speech and language impairment. fNIRS has clinical applications that may lead to improved early and differential diagnosis, increase our understanding of response to treatment, improve neuroprosthetic functioning, and advance neurofeedback.
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Affiliation(s)
- Lindsay K. Butler
- Sargent College of Health and Rehabilitation Sciences, Boston University, MA
| | - Swathi Kiran
- Sargent College of Health and Rehabilitation Sciences, Boston University, MA
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43
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Zhao H, Brigadoi S, Chitnis D, Vita ED, Castellaro M, Powell S, Everdell NL, Cooper RJ. A wide field-of-view, modular, high-density diffuse optical tomography system for minimally constrained three-dimensional functional neuroimaging. BIOMEDICAL OPTICS EXPRESS 2020; 11:4110-4129. [PMID: 32923032 PMCID: PMC7449732 DOI: 10.1364/boe.394914] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 06/03/2020] [Accepted: 06/09/2020] [Indexed: 05/09/2023]
Abstract
The ability to produce high-quality images of human brain function in any environment and during unconstrained movement of the subject has long been a goal of neuroimaging research. Diffuse optical tomography, which uses the intensity of back-scattered near-infrared light from multiple source-detector pairs to image changes in haemoglobin concentrations in the brain, is uniquely placed to achieve this goal. Here, we describe a new generation of modular, fibre-less, high-density diffuse optical tomography technology that provides exceptional sensitivity, a large dynamic range, a field-of-view sufficient to cover approximately one-third of the adult scalp, and also incorporates dedicated motion sensing into each module. Using in-vivo measures, we demonstrate a noise-equivalent power of 318 fW, and an effective dynamic range of 142 dB. We describe the application of this system to a novel somatomotor neuroimaging paradigm that involves subjects walking and texting on a smartphone. Our results demonstrate that wearable high-density diffuse optical tomography permits three-dimensional imaging of the human brain function during overt movement of the subject; images of somatomotor cortical activation can be obtained while subjects move in a relatively unconstrained manner, and these images are in good agreement with those obtained while the subjects remain stationary. The scalable nature of the technology we described here paves the way for the routine acquisition of high-quality, three-dimensional, whole-cortex diffuse optical tomography images of cerebral haemodynamics, both inside and outside of the laboratory environment, which has profound implications for neuroscience.
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Affiliation(s)
- Hubin Zhao
- DOT-HUB, Biomedical Optics Research Laboratory, Department of Medical Physics and Biomedical Engineering, University College London, London, WC1E 6BT, UK
| | - Sabrina Brigadoi
- Department of Developmental and Social Psychology, University of Padova, Padova, Italy
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Danial Chitnis
- School of Engineering, Institute for Integrated Micro and Nano Systems, University of Edinburgh, Edinburgh, EH9 3FF, UK
| | - Enrico De Vita
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Marco Castellaro
- Department of Information Engineering, University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Samuel Powell
- Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Nicholas L. Everdell
- DOT-HUB, Biomedical Optics Research Laboratory, Department of Medical Physics and Biomedical Engineering, University College London, London, WC1E 6BT, UK
| | - Robert J. Cooper
- DOT-HUB, Biomedical Optics Research Laboratory, Department of Medical Physics and Biomedical Engineering, University College London, London, WC1E 6BT, UK
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44
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On Your Mark, Get Set, Self-Control, Go: A Differentiated View on the Cortical Hemodynamics of Self-Control during Sprint Start. Brain Sci 2020; 10:brainsci10080494. [PMID: 32751179 PMCID: PMC7463617 DOI: 10.3390/brainsci10080494] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 07/24/2020] [Accepted: 07/27/2020] [Indexed: 11/17/2022] Open
Abstract
Most sports are self-control demanding. For example, during a sprint start, athletes have to respond as fast as possible to the start signal (action initiation) while suppressing the urge to start too early (action inhibition). Here, we examined the cortical hemodynamic response to these demands by measuring activity in the two lateral prefrontal cortices (lPFC), a central area for self-control processes. We analyzed activity within subregions of the lPFC, while subjects performed a sprint start, and we assessed if activation varied as a function of hemisphere and gender. In a counterbalanced within-subject design, 39 participants (age: mean (M) = 22.44, standard deviation (SD) = 5.28, 22 women) completed four sprint start conditions (blocks). In each block, participants focused on inhibition (avoid false start), initiation (start fast), no start (do not start) and a combined condition (start fast; avoid false start). We show that oxyhemoglobin in the lPFC increased after the set signal and this increase did not differ between experimental conditions. Increased activation was primarily observed in ventral areas of the lPFC, but only in males, and this increase did not vary between hemispheres. This study provides further support for the involvement of the ventral lPFC during a sprint start, while highlighting gender differences in the processing of sprint start-induced self-control demands.
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45
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Li Y, Ma Y, Ma S, Liang Z, Xu F, Tong Y, Frederick BD, Yin S, Li X. Asymmetry of peripheral vascular biomarkers in ischemic stroke patients, assessed using NIRS. JOURNAL OF BIOMEDICAL OPTICS 2020; 25:1-16. [PMID: 32562389 PMCID: PMC7306490 DOI: 10.1117/1.jbo.25.6.065001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 06/05/2020] [Indexed: 05/27/2023]
Abstract
SIGNIFICANCE Low-frequency oscillations (LFOs) ranging from 0.01 to 0.15 Hz are common in functional imaging studies. Some of these LFOs are non-neuronal and are correlated with autonomic physiological processes. AIM We investigate the relationships between systemic low-frequency oscillations (sLFOs) measured at different peripheral sites during resting states in ischemic stroke patients. APPROACH Twenty-seven ischemic stroke patients (ages 44 to 90; 20 male and 7 female) were recruited for the study. During the experiments, fluctuations in oxyhemoglobin concentration were measured in the left and right toes, fingertips, and earlobes using a multichannel near-infrared spectroscopy instrument. We applied cross-correlation and frequency component analyses on the sLFO data. RESULTS The results showed that embolization broke the symmetry of the sLFO transmission and that the damage was not limited to the local area but spread throughout the body. Among six peripheral sites, the power spectrum width of the earlobes was significantly larger than that of the fingers and toes. This indicates that the earlobes may contain more physiological information. Finally, the results of fuzzy clustering verified that sLFOs can serve as perfusion biomarkers to differentiate stroke from healthy subjects. CONCLUSIONS The high correlation values and corresponding delays in sLFOs support the hypothesis that (1) the correlation characteristics of sLFOs in stroke patients are different from those of healthy subjects. These characteristics can reflect patient condition, to an extent. Embolization in ischemic stroke patients breaks the symmetry of the body's sLFO transmission, disrupting the balance of blood circulation. (2) sLFOs can be used as perfusion biomarkers to differentiate ischemic stroke patients from healthy subjects. Studying these signals can explicate the overall feedback/influence of pericentral interactions. Finally, peripheral sLFOs have been shown to be an effective and accurate tool for assessing peripheral blood circulation and vascular integrity in ischemic stroke patients.
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Affiliation(s)
- Yingwei Li
- Yanshan University, School of Information Science and Engineering, Qinhuangdao, China
- McLean Hospital, McLean Imaging Center, Belmont, Massachusetts, United States
| | - Yunfei Ma
- Yanshan University, School of Information Science and Engineering, Qinhuangdao, China
| | - Shaoqing Ma
- Yanshan University, School of Information Science and Engineering, Qinhuangdao, China
| | - Zhenhu Liang
- Yanshan University, School of Information Science and Engineering, Qinhuangdao, China
| | - Fang Xu
- PLA Rocket Force Characteristic Medical Center, Department of Neurology, Beijing, China
| | - Yunjie Tong
- Purdue University, Weldon School of Biomedical Engineering, West Lafayette, Indiana, United States
| | - Blaise deB Frederick
- McLean Hospital, McLean Imaging Center, Belmont, Massachusetts, United States
- Harvard Medical School, Department of Psychiatry, Boston, Massachusetts, United States
| | - Shimin Yin
- PLA Rocket Force Characteristic Medical Center, Department of Neurology, Beijing, China
| | - Xiaoli Li
- Beijing Normal University, Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing, China
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46
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Chiarelli AM, Perpetuini D, Croce P, Greco G, Mistretta L, Rizzo R, Vinciguerra V, Romeo MF, Zappasodi F, Merla A, Fallica PG, Edlinger G, Ortner R, Giaconia GC. Fiberless, Multi-Channel fNIRS-EEG System Based on Silicon Photomultipliers: Towards Sensitive and Ecological Mapping of Brain Activity and Neurovascular Coupling. SENSORS (BASEL, SWITZERLAND) 2020; 20:E2831. [PMID: 32429372 PMCID: PMC7285196 DOI: 10.3390/s20102831] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/08/2020] [Accepted: 05/13/2020] [Indexed: 11/17/2022]
Abstract
Portable neuroimaging technologies can be employed for long-term monitoring of neurophysiological and neuropathological states. Functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) are highly suited for such a purpose. Their multimodal integration allows the evaluation of hemodynamic and electrical brain activity together with neurovascular coupling. An innovative fNIRS-EEG system is here presented. The system integrated a novel continuous-wave fNIRS component and a modified commercial EEG device. fNIRS probing relied on fiberless technology based on light emitting diodes and silicon photomultipliers (SiPMs). SiPMs are sensitive semiconductor detectors, whose large detection area maximizes photon harvesting from the scalp and overcomes limitations of fiberless technology. To optimize the signal-to-noise ratio and avoid fNIRS-EEG interference, a digital lock-in was implemented for fNIRS signal acquisition. A benchtop characterization of the fNIRS component showed its high performances with a noise equivalent power below 1 pW. Moreover, the fNIRS-EEG device was tested in vivo during tasks stimulating visual, motor and pre-frontal cortices. Finally, the capabilities to perform ecological recordings were assessed in clinical settings on one Alzheimer's Disease patient during long-lasting cognitive tests. The system can pave the way to portable technologies for accurate evaluation of multimodal brain activity, allowing their extensive employment in ecological environments and clinical practice.
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Affiliation(s)
- Antonio Maria Chiarelli
- Department of Neuroscience, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (D.P.); (P.C.); (F.Z.); (A.M.)
| | - David Perpetuini
- Department of Neuroscience, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (D.P.); (P.C.); (F.Z.); (A.M.)
| | - Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (D.P.); (P.C.); (F.Z.); (A.M.)
| | - Giuseppe Greco
- Department of Energy, Engineering and Mathematical Models, University of Palermo, Viale delle Scienze 9, 90128 Palermo, Italy; (G.G.); (L.M.); (R.R.); (G.C.G.)
| | - Leonardo Mistretta
- Department of Energy, Engineering and Mathematical Models, University of Palermo, Viale delle Scienze 9, 90128 Palermo, Italy; (G.G.); (L.M.); (R.R.); (G.C.G.)
| | - Raimondo Rizzo
- Department of Energy, Engineering and Mathematical Models, University of Palermo, Viale delle Scienze 9, 90128 Palermo, Italy; (G.G.); (L.M.); (R.R.); (G.C.G.)
| | - Vincenzo Vinciguerra
- ADG R&D, STMicroelectronics s.r.l., Stradale Primosole 50, 95121 Catania, Italy; (V.V.); (M.F.R.); (P.G.F.)
| | - Mario Francesco Romeo
- ADG R&D, STMicroelectronics s.r.l., Stradale Primosole 50, 95121 Catania, Italy; (V.V.); (M.F.R.); (P.G.F.)
| | - Filippo Zappasodi
- Department of Neuroscience, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (D.P.); (P.C.); (F.Z.); (A.M.)
| | - Arcangelo Merla
- Department of Neuroscience, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (D.P.); (P.C.); (F.Z.); (A.M.)
| | - Pier Giorgio Fallica
- ADG R&D, STMicroelectronics s.r.l., Stradale Primosole 50, 95121 Catania, Italy; (V.V.); (M.F.R.); (P.G.F.)
| | - Günter Edlinger
- Guger Technologies OG, Herbersteinstrasse 60, 8020 Graz, Austria;
| | - Rupert Ortner
- g.tec Medical Engineering Spain S.L., Calle Plom 5-7, 08038 Barcelona, Spain;
| | - Giuseppe Costantino Giaconia
- Department of Energy, Engineering and Mathematical Models, University of Palermo, Viale delle Scienze 9, 90128 Palermo, Italy; (G.G.); (L.M.); (R.R.); (G.C.G.)
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47
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Lee CW, Blanco B, Dempsey L, Chalia M, Hebden JC, Caballero-Gaudes C, Austin T, Cooper RJ. Sleep State Modulates Resting-State Functional Connectivity in Neonates. Front Neurosci 2020; 14:347. [PMID: 32362811 PMCID: PMC7180180 DOI: 10.3389/fnins.2020.00347] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Accepted: 03/23/2020] [Indexed: 01/26/2023] Open
Abstract
The spontaneous cerebral activity that gives rise to resting-state networks (RSNs) has been extensively studied in infants in recent years. However, the influence of sleep state on the presence of observable RSNs has yet to be formally investigated in the infant population, despite evidence that sleep modulates resting-state functional connectivity in adults. This effect could be extremely important, as most infant neuroimaging studies rely on the neonate to remain asleep throughout data acquisition. In this study, we combine functional near-infrared spectroscopy with electroencephalography to simultaneously monitor sleep state and investigate RSNs in a cohort of healthy term born neonates. During active sleep (AS) and quiet sleep (QS) our newborn neonates show functional connectivity patterns spatially consistent with previously reported RSN structures. Our three independent functional connectivity analyses revealed stronger interhemispheric connectivity during AS than during QS. In turn, within hemisphere short-range functional connectivity seems to be enhanced during QS. These findings underline the importance of sleep state monitoring in the investigation of RSNs.
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Affiliation(s)
- Chuen Wai Lee
- neoLAB, The Evelyn Perinatal Imaging Centre, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.,Neonatal Intensive Care Unit, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Borja Blanco
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Sciences, University College London, London, United Kingdom.,Basque Center on Cognition, Brain and Language, Donostia/San Sebastián, Spain
| | - Laura Dempsey
- neoLAB, The Evelyn Perinatal Imaging Centre, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.,DOT-HUB, Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Sciences, University College London, London, United Kingdom
| | - Maria Chalia
- neoLAB, The Evelyn Perinatal Imaging Centre, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.,Neonatal Intensive Care Unit, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Jeremy C Hebden
- neoLAB, The Evelyn Perinatal Imaging Centre, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.,DOT-HUB, Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Sciences, University College London, London, United Kingdom
| | | | - Topun Austin
- neoLAB, The Evelyn Perinatal Imaging Centre, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.,Neonatal Intensive Care Unit, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.,DOT-HUB, Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Sciences, University College London, London, United Kingdom
| | - Robert J Cooper
- neoLAB, The Evelyn Perinatal Imaging Centre, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.,DOT-HUB, Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Sciences, University College London, London, United Kingdom
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48
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Zahabi M, Abdul Razak AM, Shortz AE, Mehta RK, Manser M. Evaluating advanced driver-assistance system trainings using driver performance, attention allocation, and neural efficiency measures. APPLIED ERGONOMICS 2020; 84:103036. [PMID: 31987518 DOI: 10.1016/j.apergo.2019.103036] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2019] [Revised: 12/01/2019] [Accepted: 12/14/2019] [Indexed: 05/27/2023]
Abstract
There are about 44 million licensed older drivers in the U.S. Older adults have higher crash rates and fatalities as compared to middle-aged and young drivers, which might be associated with degradations in sensory, cognitive, and physical capabilities. Advanced driver-assistance systems (ADAS) have the potential to substantially improve safety by removing some of driver vehicle control responsibilities. However, a critical aspect of providing ADAS is educating drivers on their operational characteristics and continued use. Twenty older adults participated in a driving simulation study assessing the effectiveness of video-based and demonstration-based training protocols in learning ADAS considering gender differences. The findings revealed video-based training to be more effective than demonstration-based training in improving driver performance and reducing off-road visual attention allocation and mental workload. In addition, female drivers required lower investment of mental effort (higher neural efficiency) to maintain the performance relative to males and they were less distracted by ADAS. However, male drivers were faster in activating ADAS as compared to females since they were monitoring the status of ADAS features more frequently while driving. The findings of this study provided an empirical support for using video-based approach for learning ADAS in older adults to improve driver safety and supported previous findings on older adults' learning that as age increases there is a tendency to prefer more passive and observational learning methods.
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Affiliation(s)
- Maryam Zahabi
- Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA.
| | | | - Ashley E Shortz
- Department of Environmental and Occupational Health, Texas A&M University, College Station, TX, USA
| | - Ranjana K Mehta
- Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA
| | - Michael Manser
- Texas A&M Transportation Institute, College Station, TX, USA
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49
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Kong TS, Gratton C, Low KA, Tan CH, Chiarelli AM, Fletcher MA, Zimmerman B, Maclin EL, Sutton BP, Gratton G, Fabiani M. Age-related differences in functional brain network segregation are consistent with a cascade of cerebrovascular, structural, and cognitive effects. Netw Neurosci 2020; 4:89-114. [PMID: 32043045 PMCID: PMC7006874 DOI: 10.1162/netn_a_00110] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 09/21/2019] [Indexed: 01/09/2023] Open
Abstract
Age-related declines in cognition are associated with widespread structural and functional brain changes, including changes in resting-state functional connectivity and gray and white matter status. Recently we have shown that the elasticity of cerebral arteries also explains some of the variance in cognitive and brain health in aging. Here, we investigated how network segregation, cerebral arterial elasticity (measured with pulse-DOT-the arterial pulse based on diffuse optical tomography) and gray and white matter status jointly account for age-related differences in cognitive performance. We hypothesized that at least some of the variance in brain and cognitive aging is linked to reduced cerebrovascular elasticity, leading to increased cortical atrophy and white matter abnormalities, which, in turn, are linked to reduced network segregation and decreases in cognitive performance. Pairwise comparisons between these variables are consistent with an exploratory hierarchical model linking them, especially when focusing on association network segregation (compared with segregation in sensorimotor networks). These findings suggest that preventing or slowing age-related changes in one or more of these factors may induce a neurophysiological cascade beneficial for preserving cognition in aging.
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Affiliation(s)
- Tania S. Kong
- Beckman Institute, University of Illinois at Urbana-Champaign, IL, USA
- Psychology Department, University of Illinois at Urbana-Champaign, IL, USA
| | - Caterina Gratton
- Department of Psychology, Northwestern University, IL, USA
- Department of Neurology, Northwestern University, IL, USA
| | - Kathy A. Low
- Beckman Institute, University of Illinois at Urbana-Champaign, IL, USA
| | - Chin Hong Tan
- Beckman Institute, University of Illinois at Urbana-Champaign, IL, USA
- Division of Psychology, Nanyang Technological University, Singapore
- Department of Pharmacology, National University of Singapore, Singapore
| | - Antonio M. Chiarelli
- Beckman Institute, University of Illinois at Urbana-Champaign, IL, USA
- Department of Neuroscience, Imaging and Clinical Sciences, University G. D’Annunzio of Chieti-Pescara, Chieti, Italy
| | - Mark A. Fletcher
- Beckman Institute, University of Illinois at Urbana-Champaign, IL, USA
| | | | - Edward L. Maclin
- Beckman Institute, University of Illinois at Urbana-Champaign, IL, USA
| | - Bradley P. Sutton
- Beckman Institute, University of Illinois at Urbana-Champaign, IL, USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, IL, USA
| | - Gabriele Gratton
- Beckman Institute, University of Illinois at Urbana-Champaign, IL, USA
- Psychology Department, University of Illinois at Urbana-Champaign, IL, USA
| | - Monica Fabiani
- Beckman Institute, University of Illinois at Urbana-Champaign, IL, USA
- Psychology Department, University of Illinois at Urbana-Champaign, IL, USA
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Chiarelli AM, Giaconia GC, Perpetuini D, Greco G, Mistretta L, Rizzo R, Vinciguerra V, Romeo MF, Merla A, Fallica PG. Wearable, Fiber-less, Multi-Channel System for Continuous Wave Functional Near Infrared Spectroscopy Based on Silicon Photomultipliers Detectors and Lock-In Amplification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:60-66. [PMID: 31945845 DOI: 10.1109/embc.2019.8857206] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Development and in-vivo validation of a Continuous Wave (CW) functional Near Infrared Spectroscopy (fNIRS) system is presented. The system is wearable, fiber-less, multi-channel (16×16, 256 channels) and expandable and it relies on silicon photomultipliers (SiPMs) for light detection. SiPMs are inexpensive, low voltage and resilient semiconductor light detectors, whose performances are analogous to photomultiplier tubes (PMTs). The advantage of SiPMs with respect to PMTs is that they allow direct contact with the scalp and avoidance of optical fibers. In fact, the coupling of SiPMs and light emitting diodes (LEDs) allows the transfer of the analog signals to and from the scalp through thin electric cables that greatly increase the system flexibility. Moreover, the optical probes, mechanically resembling electroencephalographic electrodes, are robust against motion artifacts. In order to increase the signal-to-noise-ratio (SNR) of the fNIRS acquisition and to decrease ambient noise contamination, a digital lock-in technique was implemented through LEDs modulation and SiPMs signal processing chain. In-vivo validation proved the system capabilities of detecting functional brain activity in the sensorimotor cortices. When compared to other state-of-the-art wearable fNIRS systems, the single photon sensitivity and dynamic range of SiPMs can exploit the long and variable interoptode distances needed for estimation of brain functional hemodynamics using CW-fNIRS.
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