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Klein F. Optimizing spatial specificity and signal quality in fNIRS: an overview of potential challenges and possible options for improving the reliability of real-time applications. FRONTIERS IN NEUROERGONOMICS 2024; 5:1286586. [PMID: 38903906 PMCID: PMC11188482 DOI: 10.3389/fnrgo.2024.1286586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 04/29/2024] [Indexed: 06/22/2024]
Abstract
The optical brain imaging method functional near-infrared spectroscopy (fNIRS) is a promising tool for real-time applications such as neurofeedback and brain-computer interfaces. Its combination of spatial specificity and mobility makes it particularly attractive for clinical use, both at the bedside and in patients' homes. Despite these advantages, optimizing fNIRS for real-time use requires careful attention to two key aspects: ensuring good spatial specificity and maintaining high signal quality. While fNIRS detects superficial cortical brain regions, consistently and reliably targeting specific regions of interest can be challenging, particularly in studies that require repeated measurements. Variations in cap placement coupled with limited anatomical information may further reduce this accuracy. Furthermore, it is important to maintain good signal quality in real-time contexts to ensure that they reflect the true underlying brain activity. However, fNIRS signals are susceptible to contamination by cerebral and extracerebral systemic noise as well as motion artifacts. Insufficient real-time preprocessing can therefore cause the system to run on noise instead of brain activity. The aim of this review article is to help advance the progress of fNIRS-based real-time applications. It highlights the potential challenges in improving spatial specificity and signal quality, discusses possible options to overcome these challenges, and addresses further considerations relevant to real-time applications. By addressing these topics, the article aims to help improve the planning and execution of future real-time studies, thereby increasing their reliability and repeatability.
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Affiliation(s)
- Franziska Klein
- Biomedical Devices and Systems Group, R&D Division Health, OFFIS - Institute for Information Technology, Oldenburg, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
- Neurocognition and Functional Neurorehabilitation Group, Department of Psychology, University of Oldenburg, Oldenburg, Germany
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2
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Takahashi S, Takahashi D, Kuroiwa Y, Sakurai N, Kodama N. Construction and evaluation of a neurofeedback system using finger tapping and near-infrared spectroscopy. FRONTIERS IN NEUROIMAGING 2024; 3:1361513. [PMID: 38726042 PMCID: PMC11079114 DOI: 10.3389/fnimg.2024.1361513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 04/09/2024] [Indexed: 05/12/2024]
Abstract
Introduction Neurofeedback using near-infrared spectroscopy (NIRS) has been used in patients with stroke and other patients, but few studies have included older people or patients with cognitive impairment. Methods We constructed a NIRS-based neurofeedback system and used finger tapping to investigate whether neurofeedback can be implemented in older adults while finger tapping and whether brain activity improves in older adults and healthy participants. Our simple neurofeedback system was constructed using a portable wearable optical topography (WOT-HS) device. Brain activity was evaluated in 10 older and 31 healthy young individuals by measuring oxygenated hemoglobin concentration during finger tapping and neurofeedback implementation. Results During neurofeedback, the concentration of oxygenated hemoglobin increased in the prefrontal regions in both the young and older participants. Discussion The results of this study demonstrate the usefulness of neurofeedback using simple NIRS devices for older adults and its potential to mitigate cognitive decline.
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Affiliation(s)
- Shingo Takahashi
- Department of Healthcare Informatics, Faculty of Health and Welfare, Takasaki University of Health and Welfare, Takasaki, Japan
| | - Daishi Takahashi
- Department of Healthcare Informatics, Faculty of Health and Welfare, Takasaki University of Health and Welfare, Takasaki, Japan
| | - Yuki Kuroiwa
- Department of Healthcare Informatics, Faculty of Health and Welfare, Takasaki University of Health and Welfare, Takasaki, Japan
| | - Noriko Sakurai
- Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, Japan
| | - Naoki Kodama
- Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, Japan
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3
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Guevara E, Rivas-Ruvalcaba FJ, Kolosovas-Machuca ES, Ramírez-Elías M, de León Zapata RD, Ramirez-GarciaLuna JL, Rodríguez-Leyva I. Parkinson's disease patients show delayed hemodynamic changes in primary motor cortex in fine motor tasks and decreased resting-state interhemispheric functional connectivity: a functional near-infrared spectroscopy study. NEUROPHOTONICS 2024; 11:025004. [PMID: 38812966 PMCID: PMC11135928 DOI: 10.1117/1.nph.11.2.025004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 05/10/2024] [Accepted: 05/10/2024] [Indexed: 05/31/2024]
Abstract
Significance People with Parkinson's disease (PD) experience changes in fine motor skills, which is viewed as one of the hallmark signs of this disease. Due to its non-invasive nature and portability, functional near-infrared spectroscopy (fNIRS) is a promising tool for assessing changes related to fine motor skills. Aim We aim to compare activation patterns in the primary motor cortex using fNIRS, comparing volunteers with PD and sex- and age-matched control participants during a fine motor task and walking. Moreover, inter and intrahemispheric functional connectivity (FC) was investigated during the resting state. Approach We used fNIRS to measure the hemodynamic changes in the primary motor cortex elicited by a finger-tapping task in 20 PD patients and 20 controls matched for age, sex, education, and body mass index. In addition, a two-minute walking task was carried out. Resting-state FC was also assessed. Results Patients with PD showed delayed hypoactivation in the motor cortex during the fine motor task with the dominant hand and delayed hyperactivation with the non-dominant hand. The findings also revealed significant correlations among various measures of hemodynamic activity in the motor cortex using fNIRS and different cognitive and clinical variables. There were no significant differences between patients with PD and controls during the walking task. However, there were significant differences in interhemispheric connectivity between PD patients and control participants, with a statistically significant decrease in PD patients compared with control participants. Conclusions Decreased interhemispheric FC and delayed activity in the primary motor cortex elicited by a fine motor task may one day serve as one of the many potential neuroimaging biomarkers for diagnosing PD.
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Affiliation(s)
- Edgar Guevara
- CONAHCYT-Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico
- Universidad Autónoma de San Luis Potosí, Coordinación para la Innovación y Aplicación de la Ciencia y la Tecnología, San Luis Potosí, Mexico
| | - Francisco Javier Rivas-Ruvalcaba
- Hospital Central “Dr. Ignacio Morones Prieto”, Universidad Autónoma de San Luis Potosí, Faculty of Medicine, Neurology Service, San Luis Potosí, Mexico
| | - Eleazar Samuel Kolosovas-Machuca
- Universidad Autónoma de San Luis Potosí, Coordinación para la Innovación y Aplicación de la Ciencia y la Tecnología, San Luis Potosí, Mexico
- Universidad Autónoma de San Luis Potosí, Faculty of Science, San Luis Potosí, Mexico
| | - Miguel Ramírez-Elías
- Universidad Autónoma de San Luis Potosí, Faculty of Science, San Luis Potosí, Mexico
| | | | - Jose Luis Ramirez-GarciaLuna
- Universidad Autónoma de San Luis Potosí, Coordinación para la Innovación y Aplicación de la Ciencia y la Tecnología, San Luis Potosí, Mexico
- Hospital Central “Dr. Ignacio Morones Prieto”, Universidad Autónoma de San Luis Potosí, Division of Surgery, Faculty of Medicine, San Luis Potosí, Mexico
| | - Ildefonso Rodríguez-Leyva
- Hospital Central “Dr. Ignacio Morones Prieto”, Universidad Autónoma de San Luis Potosí, Faculty of Medicine, Neurology Service, San Luis Potosí, Mexico
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4
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Ning M, Duwadi S, Yücel MA, von Lühmann A, Boas DA, Sen K. fNIRS dataset during complex scene analysis. Front Hum Neurosci 2024; 18:1329086. [PMID: 38576451 PMCID: PMC10991699 DOI: 10.3389/fnhum.2024.1329086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 03/06/2024] [Indexed: 04/06/2024] Open
Affiliation(s)
- Matthew Ning
- Department of Biomedical Engineering, Neurophotonics Center, Boston University, Boston, MA, United States
| | - Sudan Duwadi
- Department of Biomedical Engineering, Neurophotonics Center, Boston University, Boston, MA, United States
| | - Meryem A. Yücel
- Department of Biomedical Engineering, Neurophotonics Center, Boston University, Boston, MA, United States
| | - Alexander von Lühmann
- Department of Biomedical Engineering, Neurophotonics Center, Boston University, Boston, MA, United States
- BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
- Intelligent Biomedical Sensing (IBS) Lab, Technical University Berlin, Berlin, Germany
| | - David A. Boas
- Department of Biomedical Engineering, Neurophotonics Center, Boston University, Boston, MA, United States
| | - Kamal Sen
- Department of Biomedical Engineering, Neurophotonics Center, Boston University, Boston, MA, United States
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Gao C, Li T. Gender specificity of frontal activity based on fNIRS in distinguishing bipolar depression population from health control. JOURNAL OF BIOPHOTONICS 2024; 17:e202300346. [PMID: 37934196 DOI: 10.1002/jbio.202300346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 10/19/2023] [Accepted: 10/24/2023] [Indexed: 11/08/2023]
Abstract
Bipolar depression (BD) is a chronic psychiatric disorder characterized by recurring bouts of bipolar mania or hypomania followed by depression. In this essay, we used the functional near-infrared spectroscopy to investigate the frontal function of BD in males and females, which included a total of 43 BD patients and 28 healthy subjects. The hemodynamic response associated with the task was estimated using the generalized linear model (GLM) approach. Wavelet transforms coherence and Granger causality (GC) methods were employed to calculate brain connectivity. GLM and GC results revealed that female patients were more distinguishable from healthy controls than males. Additionally, the correlation between BD scores and GLM results showed that the brain activation of male subjects was affected by their anxiety levels. This study suggests that traditional diagnostic methods for BD may not be as sensitive in men as in women.
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Affiliation(s)
- Chenyang Gao
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Ting Li
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
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Ning M, Duwadi S, Yücel MA, Von Lühmann A, Boas DA, Sen K. fNIRS Dataset During Complex Scene Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.23.576715. [PMID: 38328139 PMCID: PMC10849700 DOI: 10.1101/2024.01.23.576715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
When analyzing complex scenes, humans often focus their attention on an object at a particular spatial location. The ability to decode the attended spatial location would facilitate brain computer interfaces for complex scene analysis (CSA). Here, we investigated capability of functional near-infrared spectroscopy (fNIRS) to decode audio-visual spatial attention in the presence of competing stimuli from multiple locations. We targeted dorsal frontoparietal network including frontal eye field (FEF) and intra-parietal sulcus (IPS) as well as superior temporal gyrus/planum temporal (STG/PT). They all were shown in previous functional magnetic resonance imaging (fMRI) studies to be activated by auditory, visual, or audio-visual spatial tasks. To date, fNIRS has not been applied to decode auditory and visual-spatial attention during CSA, and thus, no such dataset exists yet. This report provides an open-access fNIRS dataset that can be used to develop, test, and compare machine learning algorithms for classifying attended locations based on the fNIRS signals on a single trial basis.
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Affiliation(s)
- Matthew Ning
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Sudan Duwadi
- Neurophotonics Center, Department of Biomedical Engineering, Boston University
| | - Meryem A. Yücel
- Neurophotonics Center, Department of Biomedical Engineering, Boston University
| | - Alexander Von Lühmann
- Neurophotonics Center, Department of Biomedical Engineering, Boston University
- BIFOLD – Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany
- Intelligent Biomedical Sensing (IBS) Lab, Technische Universität Berlin, 10587 Berlin, Germany
| | - David A. Boas
- Neurophotonics Center, Department of Biomedical Engineering, Boston University
| | - Kamal Sen
- Neurophotonics Center, Department of Biomedical Engineering, Boston University
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Peng K, Karunakaran KD, Green S, Borsook D. Machines, mathematics, and modules: the potential to provide real-time metrics for pain under anesthesia. NEUROPHOTONICS 2024; 11:010701. [PMID: 38389718 PMCID: PMC10883389 DOI: 10.1117/1.nph.11.1.010701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 01/08/2024] [Accepted: 01/16/2024] [Indexed: 02/24/2024]
Abstract
The brain-based assessments under anesthesia have provided the ability to evaluate pain/nociception during surgery and the potential to prevent long-term evolution of chronic pain. Prior studies have shown that the functional near-infrared spectroscopy (fNIRS)-measured changes in cortical regions such as the primary somatosensory and the polar frontal cortices show consistent response to evoked and ongoing pain in awake, sedated, and anesthetized patients. We take this basic approach and integrate it into a potential framework that could provide real-time measures of pain/nociception during the peri-surgical period. This application could have significant implications for providing analgesia during surgery, a practice that currently lacks quantitative evidence to guide patient tailored pain management. Through a simple readout of "pain" or "no pain," the proposed system could diminish or eliminate levels of intraoperative, early post-operative, and potentially, the transition to chronic post-surgical pain. The system, when validated, could also be applied to measures of analgesic efficacy in the clinic.
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Affiliation(s)
- Ke Peng
- University of Manitoba, Department of Electrical and Computer Engineering, Price Faculty of Engineering, Winnipeg, Manitoba, Canada
| | - Keerthana Deepti Karunakaran
- Massachusetts General Hospital, Harvard Medical School, Department of Psychiatry, Boston, Massachusetts, United States
| | - Stephen Green
- Massachusetts Institute of Technology, Department of Mechanical Engineering, Boston, Massachusetts, United States
| | - David Borsook
- Massachusetts General Hospital, Harvard Medical School, Department of Psychiatry, Boston, Massachusetts, United States
- Massachusetts General Hospital, Harvard Medical School, Department of Radiology, Boston, Massachusetts, United States
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Lingelbach K, Gado S, Wirzberger M, Vukelić M. Workload-dependent hemispheric asymmetries during the emotion-cognition interaction: a close-to-naturalistic fNIRS study. FRONTIERS IN NEUROERGONOMICS 2023; 4:1273810. [PMID: 38234490 PMCID: PMC10790862 DOI: 10.3389/fnrgo.2023.1273810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 10/23/2023] [Indexed: 01/19/2024]
Abstract
Introduction We investigated brain activation patterns of interacting emotional distractions and cognitive processes in a close-to-naturalistic functional near-infrared spectroscopy (fNIRS) study. Methods Eighteen participants engaged in a monitoring-control task, mimicking common air traffic controller requirements. The scenario entailed experiencing both low and high workload, while concurrently being exposed to emotional speech distractions of positive, negative, and neutral valence. Results Our investigation identified hemispheric asymmetries in prefrontal cortex (PFC) activity during the presentation of negative and positive emotional speech distractions at different workload levels. Thereby, in particular, activation in the left inferior frontal gyrus (IFG) and orbitofrontal cortex (OFC) seems to play a crucial role. Brain activation patterns revealed a cross-over interaction indicating workload-dependent left hemispheric inhibition processes during negative distractions and high workload. For positive emotional distractions under low workload, we observed left-hemispheric PFC recruitment potentially associated with speech-related processes. Furthermore, we found a workload-independent negativity bias for neutral distractions, showing brain activation patterns similar to those of negative distractions. Discussion In conclusion, lateralized hemispheric processing, regulating emotional speech distractions and integrating emotional and cognitive processes, is influenced by workload levels and stimulus characteristics. These findings advance our understanding of the factors modulating hemispheric asymmetries during the processing and inhibition of emotional distractions, as well as the interplay between emotion and cognition. Moreover, they emphasize the significance of exploring emotion-cognition interactions in more naturalistic settings to gain a deeper understanding of their implications in real-world application scenarios (e.g., working and learning environments).
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Affiliation(s)
- Katharina Lingelbach
- Applied Neurocognitive Systems, Fraunhofer Institute for Industrial Engineering IAO, Stuttgart, Germany
- Applied Neurocognitive Psychology, Carl von Ossietzky University, Oldenburg, Germany
| | - Sabrina Gado
- Experimental Clinical Psychology, Department of Psychology, University of Würzburg, Würzburg, Germany
| | - Maria Wirzberger
- Department of Teaching and Learning with Intelligent Systems, University of Stuttgart, Stuttgart, Germany
- LEAD Graduate School and Research Network, University of Tübingen, Tübingen, Germany
| | - Mathias Vukelić
- Applied Neurocognitive Systems, Fraunhofer Institute for Industrial Engineering IAO, Stuttgart, Germany
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Shin J. Feasibility of local interpretable model-agnostic explanations (LIME) algorithm as an effective and interpretable feature selection method: comparative fNIRS study. Biomed Eng Lett 2023; 13:689-703. [PMID: 37873000 PMCID: PMC10590353 DOI: 10.1007/s13534-023-00291-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 05/23/2023] [Accepted: 05/25/2023] [Indexed: 10/25/2023] Open
Abstract
Many feature selection methods have been evaluated in functional near-infrared spectroscopy (fNIRS)-related studies. The local interpretable model-agnostic explanation (LIME) algorithm is a feature selection method for fNIRS datasets that has not yet been validated; the demand for its validation is increasing. To this end, we assessed the feature selection performance of LIME for fNIRS datasets in terms of classification accuracy. A comparative analysis was conducted for the benchmark (classification accuracy obtained without applying any feature selection method), LIME, two filter-based methods (minimum-redundancy maximum-relevance and t-test), and one wrapper-based method (sequential forward selection). To ensure the fairness and reliability of the performance evaluation, several open-access fNIRS datasets were used. The analysis revealed that LIME greatly outperformed the other feature selection methods in most cases and could achieve a statistically significantly better classification accuracy than that of the benchmark methods. These findings implied the effectiveness of LIME as a feature selection approach for fNIRS datasets.
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Affiliation(s)
- Jaeyoung Shin
- Department of Electronic Engineering, Wonkwang University, Iksan, 54538 Republic of Korea
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Wu D, Chang C, Yang J, Luo J, Xie S, Li H. Habit-DisHabit Design with a Quadratic Equation: A Better Model of the Hemodynamic Changes in Preschoolers during the Dimension Change Card Sorting Task. CHILDREN (BASEL, SWITZERLAND) 2023; 10:1574. [PMID: 37761535 PMCID: PMC10528280 DOI: 10.3390/children10091574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/13/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023]
Abstract
General linear modeling (GLM) has been widely employed to estimate the hemodynamic changes observed by functional near infrared spectroscopy (fNIRS) technology, which are found to be nonlinear rather than linear, however. Therefore, GLM might not be appropriate for modeling the hemodynamic changes evoked by cognitive processing in developmental neurocognitive studies. There is an urgent need to identify a better statistical model to fit into the nonlinear fNIRS data. This study addressed this need by developing a quadratic equation model to reanalyze the existing fNIRS data (N = 38, Mage = 5.0 years, SD = 0.69 years, 17 girls) collected from the mixed-order design Dimensional Change Card Sort (DCCS) task and verified the model with a new set of data with the Habit-DisHabit design. First, comparing the quadratic and cubic modeling results of the mixed-order design data indicated that the proposed quadratic equation was better than GLM and cubic regression to model the oxygenated hemoglobin (HbO) changes in this task. Second, applying this quadratic model with the Habit-DisHabit design data verified its suitability and indicated that the new design was more effective in identifying the neural correlates of cognitive shifting than the mixed-order design. These findings jointly indicate that Habit-DisHabit Design with a quadratic equation might better model the hemodynamic changes in preschoolers during the DCCS task.
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Affiliation(s)
- Dandan Wu
- Faculty of Education and Human Development, The Education University of Hong Kong, Hong Kong SAR, China;
| | - Chunqi Chang
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518055, China; (C.C.); (J.Y.)
| | - Jinfeng Yang
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518055, China; (C.C.); (J.Y.)
| | - Jiutong Luo
- Faculty of Education, Shenzhen University, Shenzhen 518060, China; (J.L.); (S.X.)
- Faculty of Education, Beijing Normal University, Beijing 100875, China
| | - Sha Xie
- Faculty of Education, Shenzhen University, Shenzhen 518060, China; (J.L.); (S.X.)
| | - Hui Li
- Faculty of Education and Human Development, The Education University of Hong Kong, Hong Kong SAR, China;
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Chan JY, Hssayeni MD, Wilcox T, Ghoraani B. Exploring the feasibility of tensor decomposition for analysis of fNIRS signals: a comparative study with grand averaging method. Front Neurosci 2023; 17:1180293. [PMID: 37638308 PMCID: PMC10448703 DOI: 10.3389/fnins.2023.1180293] [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: 03/06/2023] [Accepted: 07/27/2023] [Indexed: 08/29/2023] Open
Abstract
The analysis of functional near-infrared spectroscopy (fNIRS) signals has not kept pace with the increased use of fNIRS in the behavioral and brain sciences. The popular grand averaging method collapses the oxygenated hemoglobin data within a predefined time of interest window and across multiple channels within a region of interest, potentially leading to a loss of important temporal and spatial information. On the other hand, the tensor decomposition method can reveal patterns in the data without making prior assumptions of the hemodynamic response and without losing temporal and spatial information. The aim of the current study was to examine whether the tensor decomposition method could identify significant effects and novel patterns compared to the commonly used grand averaging method for fNIRS signal analysis. We used two infant fNIRS datasets and applied tensor decomposition (i.e., canonical polyadic and Tucker decompositions) to analyze the significant differences in the hemodynamic response patterns across conditions. The codes are publicly available on GitHub. Bayesian analyses were performed to understand interaction effects. The results from the tensor decomposition method replicated the findings from the grand averaging method and uncovered additional patterns not detected by the grand averaging method. Our findings demonstrate that tensor decomposition is a feasible alternative method for analyzing fNIRS signals, offering a more comprehensive understanding of the data and its underlying patterns.
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Affiliation(s)
- Jasmine Y. Chan
- Department of Psychology, Florida Atlantic University, Boca Raton, FL, United States
| | - Murtadha D. Hssayeni
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, United States
- Department of Computer Engineering, University of Technology, Baghdad, Iraq
| | - Teresa Wilcox
- Department of Psychology, Florida Atlantic University, Boca Raton, FL, United States
| | - Behnaz Ghoraani
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, United States
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Peng T, Esmaelpoor J, Mao D, Lee OW, Haneman M, Balasubramanian G, Wunderlich J, McKay CM. A Parametric Model for Characterizing Time-Variant Single Trials of Block-Design fNIRS Experiments. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082885 DOI: 10.1109/embc40787.2023.10340107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Block-design is a popular experimental paradigm for functional near-infrared spectroscopy (fNIRS). Traditional block-design analysis techniques such as generalized linear modeling (GLM) and waveform averaging (WA) assume that the brain is a time-invariant system. This is a flawed assumption. In this paper, we propose a parametric Gaussian model to quantify the time-variant behavior found across consecutive trials of block-design fNIRS experiments. Using simulated data at different signal-to-noise ratios (SNRs), we demonstrate that our proposed technique is capable of characterizing Gaussian-like fNIRS signal features with ≥3dB SNR. When used to fit recorded data from an auditory block-design experiment, model parameter values quantitatively revealed statistically significant changes in fNIRS responses across trials, consistent with visual inspection of data from individual trials. Our results suggest that our model effectively captures trial-to-trial differences in response, which enables researchers to study time-variant brain responses using block-design fNIRS experiments.
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Yoo SH, Huang G, Hong KS. Physiological Noise Filtering in Functional Near-Infrared Spectroscopy Signals Using Wavelet Transform and Long-Short Term Memory Networks. Bioengineering (Basel) 2023; 10:685. [PMID: 37370616 DOI: 10.3390/bioengineering10060685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 05/26/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023] Open
Abstract
Activated channels of functional near-infrared spectroscopy are typically identified using the desired hemodynamic response function (dHRF) generated by a trial period. However, this approach is not possible for an unknown trial period. In this paper, an innovative method not using the dHRF is proposed, which extracts fluctuating signals during the resting state using maximal overlap discrete wavelet transform, identifies low-frequency wavelets corresponding to physiological noise, trains them using long-short term memory networks, and predicts/subtracts them during the task session. The motivation for prediction is to maintain the phase information of physiological noise at the start time of a task, which is possible because the signal is extended from the resting state to the task session. This technique decomposes the resting state data into nine wavelets and uses the fifth to ninth wavelets for learning and prediction. In the eighth wavelet, the prediction error difference between the with and without dHRF from the 15-s prediction window appeared to be the largest. Considering the difficulty in removing physiological noise when the activation period is near the physiological noise, the proposed method can be an alternative solution when the conventional method is not applicable. In passive brain-computer interfaces, estimating the brain signal starting time is necessary.
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Affiliation(s)
- So-Hyeon Yoo
- School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, Republic of Korea
| | - Guanghao Huang
- Institute for Future, School of Automation, Qingdao University, Qingdao 266071, China
| | - Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, Republic of Korea
- Institute for Future, School of Automation, Qingdao University, Qingdao 266071, China
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Carius D, Herold F, Clauß M, Kaminski E, Wagemann F, Sterl C, Ragert P. Increased Cortical Activity in Novices Compared to Experts During Table Tennis: A Whole-Brain fNIRS Study Using Threshold-Free Cluster Enhancement Analysis. Brain Topogr 2023:10.1007/s10548-023-00963-y. [PMID: 37119404 DOI: 10.1007/s10548-023-00963-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 04/15/2023] [Indexed: 05/01/2023]
Abstract
There is a growing interest to understand the neural underpinnings of high-level sports performance including expertise-related differences in sport-specific skills. Here, we aimed to investigate whether expertise level and task complexity modulate the cortical hemodynamics of table tennis players. 35 right-handed table tennis players (17 experts/18 novices) were recruited and performed two table tennis strokes (forehand and backhand) and a randomized combination of them. Cortical hemodynamics, as a proxy for cortical activity, were recorded using functional near-infrared spectroscopy, and the behavioral performance (i.e., target accuracy) was assessed via video recordings. Expertise- and task-related differences in cortical hemodynamics were analyzed using nonparametric threshold-free cluster enhancement. In all conditions, table tennis experts showed a higher target accuracy than novices. Furthermore, we observed expertise-related differences in widespread clusters compromising brain areas being associated with sensorimotor and multisensory integration. Novices exhibited, in general, higher activation in those areas as compared to experts. We also identified task-related differences in cortical activity including frontal, sensorimotor, and multisensory brain areas. The present findings provide empirical support for the neural efficiency hypothesis since table tennis experts as compared to novices utilized a lower amount of cortical resources to achieve superior behavioral performance. Furthermore, our findings suggest that the task complexity of different table tennis strokes is mirrored in distinct cortical activation patterns. Whether the latter findings can be useful to monitor or tailor sport-specific training interventions necessitates further investigations.
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Affiliation(s)
- Daniel Carius
- Department of Movement Neuroscience, Faculty of Sport Science, Leipzig University, 04109, Leipzig, Germany.
| | - Fabian Herold
- Faculty of Health Sciences, University of Potsdam, 14476, Potsdam, Germany
| | - Martina Clauß
- Department of Movement Neuroscience, Faculty of Sport Science, Leipzig University, 04109, Leipzig, Germany
| | - Elisabeth Kaminski
- Department of Movement Neuroscience, Faculty of Sport Science, Leipzig University, 04109, Leipzig, Germany
- Max Planck Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany
| | - Florian Wagemann
- Department of Movement Neuroscience, Faculty of Sport Science, Leipzig University, 04109, Leipzig, Germany
| | - Clemens Sterl
- Department of Movement Neuroscience, Faculty of Sport Science, Leipzig University, 04109, Leipzig, Germany
| | - Patrick Ragert
- Department of Movement Neuroscience, Faculty of Sport Science, Leipzig University, 04109, Leipzig, Germany
- Max Planck Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany
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15
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Cowdrick KR, Urner T, Sathialingam E, Fang Z, Quadri A, Turrentine K, Yup Lee S, Buckley EM. Agreement in cerebrovascular reactivity assessed with diffuse correlation spectroscopy across experimental paradigms improves with short separation regression. NEUROPHOTONICS 2023; 10:025002. [PMID: 37034012 PMCID: PMC10079775 DOI: 10.1117/1.nph.10.2.025002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 03/07/2023] [Indexed: 06/19/2023]
Abstract
Significance Cerebrovascular reactivity (CVR), i.e., the ability of cerebral vasculature to dilate or constrict in response to vasoactive stimuli, is a biomarker of vascular health. Exogenous administration of inhaled carbon dioxide, i.e., hypercapnia (HC), remains the "gold-standard" intervention to assess CVR. More tolerable paradigms that enable CVR quantification when HC is difficult/contraindicated have been proposed. However, because these paradigms feature mechanistic differences in action, an assessment of agreement of these more tolerable paradigms to HC is needed. Aim We aim to determine the agreement of CVR assessed during HC, breath-hold (BH), and resting state (RS) paradigms. Approach Healthy adults were subject to HC, BH, and RS paradigms. End tidal carbon dioxide (EtCO2) and cerebral blood flow (CBF, assessed with diffuse correlation spectroscopy) were monitored continuously. CVR (%/mmHg) was quantified via linear regression of CBF versus EtCO2 or via a general linear model (GLM) that was used to minimize the influence of systemic and extracerebral signal contributions. Results Strong agreement ( CCC ≥ 0.69 ; R ≥ 0.76 ) among CVR paradigms was demonstrated when utilizing a GLM to regress out systemic/extracerebral signal contributions. Linear regression alone showed poor agreement across paradigms ( CCC ≤ 0.35 ; R ≤ 0.45 ). Conclusions More tolerable experimental paradigms coupled with regression of systemic/extracerebral signal contributions may offer a viable alternative to HC for assessing CVR.
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Affiliation(s)
- Kyle R. Cowdrick
- Georgia Institute of Technology and Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Tara Urner
- Georgia Institute of Technology and Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Eashani Sathialingam
- Georgia Institute of Technology and Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Zhou Fang
- Georgia Institute of Technology and Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Ayesha Quadri
- Children’s Healthcare of Atlanta and Emory University School of Medicine, Department of Pediatrics, Atlanta, Georgia, United States
- Morehouse School of Medicine, Atlanta, Georgia, United States
| | - Katherine Turrentine
- Children’s Healthcare of Atlanta and Emory University School of Medicine, Department of Pediatrics, Atlanta, Georgia, United States
| | - Seung Yup Lee
- Georgia Institute of Technology and Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
- Kennesaw State University, Department of Electrical and Computer Engineering, Marietta, Georgia, United States
| | - Erin M. Buckley
- Georgia Institute of Technology and Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
- Children’s Healthcare of Atlanta and Emory University School of Medicine, Department of Pediatrics, Atlanta, Georgia, United States
- Children’s Healthcare of Atlanta, Children’s Research Scholar, Atlanta, Georgia, United States
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16
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Single-leg stance on a challenging surface can enhance cortical activation in the right hemisphere - A case study. Heliyon 2023; 9:e13628. [PMID: 36846707 PMCID: PMC9950900 DOI: 10.1016/j.heliyon.2023.e13628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 02/02/2023] [Accepted: 02/06/2023] [Indexed: 02/12/2023] Open
Abstract
Maintaining body balance, whether static or dynamic, is critical in performing everyday activities and developing and optimizing basic motor skills. This study investigates how a professional alpine skier's brain activates on the contralateral side during a single-leg stance. Continuous-wave functional near-infrared spectroscopy (fNIRS) signals were recorded with sixteen sources and detectors over the motor cortex to investigate brain hemodynamics. Three different tasks were performed: barefooted walk (BFW), right-leg stance (RLS), and left-leg stance (LLS). The signal processing pipeline includes channel rejection, the conversation of raw intensities into hemoglobin concentration changes using modified Beer-Lambert law, baseline zero-adjustments, z-normalization, and temporal filtration. The hemodynamic brain signal was estimated using a general linear model with a 2-gamma function. Measured activations (t-values) with p-value <0.05 were only considered as statistically significant active channels. Compared to all other conditions, BFW has the lowest brain activation. LLS is associated with more contralateral brain activation than RLS. During LLS, higher brain activation was observed across all brain regions. The right hemisphere has comparatively more activated regions-of-interest. Higher ΔHbO demands in the dorsolateral prefrontal, pre-motor, supplementary motor cortex, and primary motor cortex were observed in the right hemisphere relative to the left which explains higher energy demands for balancing during LLS. Broca's temporal lobe was also activated during both LLS and RLS. Comparing the results with BFW- which is considered the most realistic walking condition-, it is concluded that higher demands of ΔHbO predict higher motor control demands for balancing. The participant struggled with balance during the LLS, showing higher ΔHbO in both hemispheres compared to two other conditions, which indicates the higher requirement for motor control to maintain balance. A post-physiotherapy exercise program is expected to improve balance during LLS, leading to fewer changes to ΔHbO.
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17
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Klein F, Lührs M, Benitez-Andonegui A, Roehn P, Kranczioch C. Performance comparison of systemic activity correction in functional near-infrared spectroscopy for methods with and without short distance channels. NEUROPHOTONICS 2023; 10:013503. [PMID: 36248616 PMCID: PMC9555616 DOI: 10.1117/1.nph.10.1.013503] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 07/25/2022] [Indexed: 05/20/2023]
Abstract
Significance: Functional near-infrared spectroscopy (fNIRS) is a promising tool for neurofeedback (NFB) or brain-computer interfaces (BCIs). However, fNIRS signals are typically highly contaminated by systemic activity (SA) artifacts, and, if not properly corrected, NFB or BCIs run the risk of being based on noise instead of brain activity. This risk can likely be reduced by correcting for SA, in particular when short-distance channels (SDCs) are available. Literature comparing correction methods with and without SDCs is still sparse, specifically comparisons considering single trials are lacking. Aim: This study aimed at comparing the performance of SA correction methods with and without SDCs. Approach: Semisimulated and real motor task data of healthy older adults were used. Correction methods without SDCs included a simple and a more advanced spatial filter. Correction methods with SDCs included a regression approach considering only the closest SDC and two GLM-based methods, one including all eight SDCs and one using only two a priori selected SDCs as regressors. All methods were compared with data uncorrected for SA and correction performance was assessed with quality measures quantifying signal improvement and spatial specificity at single trial level. Results: All correction methods were found to improve signal quality and enhance spatial specificity as compared with the uncorrected data. Methods with SDCs usually outperformed methods without SDCs. Correction methods without SDCs tended to overcorrect the data. However, the exact pattern of results and the degree of differences observable between correction methods varied between semisimulated and real data, and also between quality measures. Conclusions: Overall, results confirmed that both Δ [ HbO ] and Δ [ HbR ] are affected by SA and that correction methods with SDCs outperform methods without SDCs. Nonetheless, improvements in signal quality can also be achieved without SDCs and should therefore be given priority over not correcting for SA.
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Affiliation(s)
- Franziska Klein
- Carl von Ossietzky University of Oldenburg, Neurocognition and Functional Neurorehabilitation Group, Neuropsychology Lab, Oldenburg, Germany
- Address all correspondence to Franziska Klein,
| | - Michael Lührs
- Maastricht University, Faculty of Psychology and Neuroscience, Maastricht, The Netherlands
- Brain Innovation B.V., Maastricht, The Netherlands
| | | | - Pauline Roehn
- Carl von Ossietzky University of Oldenburg, Neurocognition and Functional Neurorehabilitation Group, Neuropsychology Lab, Oldenburg, Germany
| | - Cornelia Kranczioch
- Carl von Ossietzky University of Oldenburg, Neurocognition and Functional Neurorehabilitation Group, Neuropsychology Lab, Oldenburg, Germany
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18
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Li R, Hosseini H, Saggar M, Balters SC, Reiss AL. Current opinions on the present and future use of functional near-infrared spectroscopy in psychiatry. NEUROPHOTONICS 2023; 10:013505. [PMID: 36777700 PMCID: PMC9904322 DOI: 10.1117/1.nph.10.1.013505] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 01/13/2023] [Indexed: 05/19/2023]
Abstract
Functional near-infrared spectroscopy (fNIRS) is an optical imaging technique for assessing human brain activity by noninvasively measuring the fluctuation of cerebral oxygenated- and deoxygenated-hemoglobin concentrations associated with neuronal activity. Owing to its superior mobility, low cost, and good tolerance for motion, the past few decades have witnessed a rapid increase in the research and clinical use of fNIRS in a variety of psychiatric disorders. In this perspective article, we first briefly summarize the state-of-the-art concerning fNIRS research in psychiatry. In particular, we highlight the diverse applications of fNIRS in psychiatric research, the advanced development of fNIRS instruments, and novel fNIRS study designs for exploring brain activity associated with psychiatric disorders. We then discuss some of the open challenges and share our perspectives on the future of fNIRS in psychiatric research and clinical practice. We conclude that fNIRS holds promise for becoming a useful tool in clinical psychiatric settings with respect to developing closed-loop systems and improving individualized treatments and diagnostics.
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Affiliation(s)
- Rihui Li
- Stanford University, Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford, California, United States
| | - Hadi Hosseini
- Stanford University, Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford, California, United States
| | - Manish Saggar
- Stanford University, Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford, California, United States
| | - Stephanie Christina Balters
- Stanford University, Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford, California, United States
| | - Allan L. Reiss
- Stanford University, Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford, California, United States
- Stanford University, Department of Radiology and Pediatrics, Stanford, California, United States
- Stanford University, Department of Pediatrics, Stanford, California, United States
- Address all correspondence to Allan L. Reiss,
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19
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Udina C, Avtzi S, Mota-Foix M, Rosso AL, Ars J, Kobayashi Frisk L, Gregori-Pla C, Durduran T, Inzitari M. Dual-task related frontal cerebral blood flow changes in older adults with mild cognitive impairment: A functional diffuse correlation spectroscopy study. Front Aging Neurosci 2022; 14:958656. [PMID: 36605362 PMCID: PMC9807627 DOI: 10.3389/fnagi.2022.958656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 11/23/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction In a worldwide aging population with a high prevalence of motor and cognitive impairment, it is paramount to improve knowledge about underlying mechanisms of motor and cognitive function and their interplay in the aging processes. Methods We measured prefrontal cerebral blood flow (CBF) using functional diffuse correlation spectroscopy during motor and dual-task. We aimed to compare CBF changes among 49 older adults with and without mild cognitive impairment (MCI) during a dual-task paradigm (normal walk, 2- forward count walk, 3-backward count walk, obstacle negotiation, and heel tapping). Participants with MCI walked slower during the normal walk and obstacle negotiation compared to participants with normal cognition (NC), while gait speed during counting conditions was not different between the groups, therefore the dual-task cost was higher for participants with NC. We built a linear mixed effects model with CBF measures from the right and left prefrontal cortex. Results MCI (n = 34) showed a higher increase in CBF from the normal walk to the 2-forward count walk (estimate = 0.34, 95% CI [0.02, 0.66], p = 0.03) compared to participants with NC, related to a right- sided activation. Both groups showed a higher CBF during the 3-backward count walk compared to the normal walk, while only among MCI, CFB was higher during the 2-forward count walk. Discussion Our findings suggest a differential prefrontal hemodynamic pattern in older adults with MCI compared to their NC counterparts during the dual-task performance, possibly as a response to increasing attentional demand.
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Affiliation(s)
- Cristina Udina
- REFiT Barcelona Research Group, Parc Sanitari Pere Virgili and Vall d’Hebron Research Institute (VHIR), Barcelona, Spain,Medicine Department, Universitat Autònoma de Barcelona, Barcelona, Spain,*Correspondence: Cristina Udina,
| | - Stella Avtzi
- ICFO – Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Miriam Mota-Foix
- Statistics and Bioinformatics Unit, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain
| | - Andrea L. Rosso
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - Joan Ars
- REFiT Barcelona Research Group, Parc Sanitari Pere Virgili and Vall d’Hebron Research Institute (VHIR), Barcelona, Spain,Medicine Department, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Lisa Kobayashi Frisk
- ICFO – Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Clara Gregori-Pla
- ICFO – Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Turgut Durduran
- ICFO – Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Barcelona, Spain,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Marco Inzitari
- REFiT Barcelona Research Group, Parc Sanitari Pere Virgili and Vall d’Hebron Research Institute (VHIR), Barcelona, Spain,Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Barcelona, Spain
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20
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Hosni SMI, Borgheai SB, McLinden J, Zhu S, Huang X, Ostadabbas S, Shahriari Y. A Graph-Based Nonlinear Dynamic Characterization of Motor Imagery Toward an Enhanced Hybrid BCI. Neuroinformatics 2022; 20:1169-1189. [PMID: 35907174 DOI: 10.1007/s12021-022-09595-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/05/2022] [Indexed: 12/31/2022]
Abstract
Decoding neural responses from multimodal information sources, including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), has the transformative potential to advance hybrid brain-computer interfaces (hBCIs). However, existing modest performance improvement of hBCIs might be attributed to the lack of computational frameworks that exploit complementary synergistic properties in multimodal features. This study proposes a multimodal data fusion framework to represent and decode synergistic multimodal motor imagery (MI) neural responses. We hypothesize that exploiting EEG nonlinear dynamics adds a new informative dimension to the commonly combined EEG-fNIRS features and will ultimately increase the synergy between EEG and fNIRS features toward an enhanced hBCI. The EEG nonlinear dynamics were quantified by extracting graph-based recurrence quantification analysis (RQA) features to complement the commonly used spectral features for an enhanced multimodal configuration when combined with fNIRS. The high-dimensional multimodal features were further given to a feature selection algorithm relying on the least absolute shrinkage and selection operator (LASSO) for fused feature selection. Linear support vector machine (SVM) was then used to evaluate the framework. The mean hybrid classification performance improved by up to 15% and 4% compared to the unimodal EEG and fNIRS, respectively. The proposed graph-based framework substantially increased the contribution of EEG features for hBCI classification from 28.16% up to 52.9% when introduced the nonlinear dynamics and improved the performance by approximately 2%. These findings suggest that graph-based nonlinear dynamics can increase the synergy between EEG and fNIRS features for an enhanced MI response representation that is not dominated by a single modality.
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Affiliation(s)
- Sarah M I Hosni
- Department of Electrical, Computer & Biomedical Engineering, University of Rhode Island (URI), Kingston, RI, 02881, USA
| | - Seyyed B Borgheai
- Department of Electrical, Computer & Biomedical Engineering, University of Rhode Island (URI), Kingston, RI, 02881, USA
| | - John McLinden
- Department of Electrical, Computer & Biomedical Engineering, University of Rhode Island (URI), Kingston, RI, 02881, USA
| | - Shaotong Zhu
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115, USA
| | - Xiaofei Huang
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115, USA
| | - Sarah Ostadabbas
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115, USA
| | - Yalda Shahriari
- Department of Electrical, Computer & Biomedical Engineering, University of Rhode Island (URI), Kingston, RI, 02881, USA.
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21
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McLinden J, Borgheai B, Hosni S, Kumar C, Rahimi N, Shao M, Spencer KM, Shahriari Y. Individual-Specific Characterization of Event-Related Hemodynamic Responses during an Auditory Task: An Exploratory Study. Behav Brain Res 2022; 436:114074. [PMID: 36028001 DOI: 10.1016/j.bbr.2022.114074] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 08/11/2022] [Accepted: 08/21/2022] [Indexed: 11/24/2022]
Abstract
Functional near-infrared spectroscopy (fNIRS) has been established as an informative modality for understanding the hemodynamic-metabolic correlates of cortical auditory processing. To date, such knowledge has shown broad clinical applications in the diagnosis, treatment, and intervention procedures in disorders affecting auditory processing; however, exploration of the hemodynamic response to auditory tasks is yet incomplete. This holds particularly true in the context of auditory event-related fNIRS experiments, where preliminary work has shown the presence of valid responses while leaving the need for more comprehensive explorations of the hemodynamic correlates of event-related auditory processing. In this study, we apply an individual-specific approach to characterize fNIRS-based hemodynamic changes during an auditory task in healthy adults. Oxygenated hemoglobin (HbO2) concentration change time courses were acquired from eight participants. Independent component analysis (ICA) was then applied to isolate individual-specific class discriminative spatial filters, which were then applied to HbO2 time courses to extract auditory-related hemodynamic features. While six of eight participants produced significant class discriminative features before ICA-based spatial filtering, the proposed method identified significant auditory hemodynamic features in all participants. Furthermore, ICA-based filtering improved correlation between trial labels and extracted features in every participant. For the first time, this study demonstrates hemodynamic features important in experiments exploring auditory processing as well as the utility of individual-specific ICA-based spatial filtering in fNIRS-based feature extraction techniques in auditory experiments. These outcomes provide insights for future studies exploring auditory hemodynamic characteristics and may eventually provide a baseline framework for better understanding auditory response dysfunctions in clinical populations.
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Affiliation(s)
- J McLinden
- Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA
| | - B Borgheai
- Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA
| | - S Hosni
- Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA
| | - C Kumar
- Department of Computer and Information Science, University of Massachusetts Dartmouth, MA
| | - N Rahimi
- Department of Computer and Information Science, University of Massachusetts Dartmouth, MA
| | - M Shao
- Department of Computer and Information Science, University of Massachusetts Dartmouth, MA
| | - K M Spencer
- Department of Psychiatry, VA Boston Healthcare System and Harvard Medical School, Jamaica Plain, Boston, MA, USA
| | - Y Shahriari
- Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA.
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22
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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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23
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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: 42] [Impact Index Per Article: 21.0] [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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24
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Brain hemodynamic changes during sprint interval cycling exercise and recovery periods. Sci Sports 2022. [DOI: 10.1016/j.scispo.2022.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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25
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Varandas R, Lima R, Bermúdez I Badia S, Silva H, Gamboa H. Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning. SENSORS (BASEL, SWITZERLAND) 2022; 22:4010. [PMID: 35684626 PMCID: PMC9183003 DOI: 10.3390/s22114010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 05/20/2022] [Accepted: 05/24/2022] [Indexed: 06/15/2023]
Abstract
Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain-Computer Interfaces (BCI) allows for unobtrusively monitoring one's cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91 ± 13.67 %. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human-computer interaction variables.
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Affiliation(s)
- Rui Varandas
- LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal;
- PLUX Wireless Biosignals S.A., 1050-059 Lisboa, Portugal;
| | - Rodrigo Lima
- Departamento de Engenharia Informática, Universidade da Madeira & Madeira N-LINCS, 9020-105 Funchal, Portugal; (R.L.); (S.B.I.B.)
- NOVA Laboratory for Computer Science and Informatics, 2829-516 Caparica, Portugal
| | - Sergi Bermúdez I Badia
- Departamento de Engenharia Informática, Universidade da Madeira & Madeira N-LINCS, 9020-105 Funchal, Portugal; (R.L.); (S.B.I.B.)
- NOVA Laboratory for Computer Science and Informatics, 2829-516 Caparica, Portugal
| | - Hugo Silva
- PLUX Wireless Biosignals S.A., 1050-059 Lisboa, Portugal;
- Instituto de Telecomunicações (IT), 1049-001 Lisbon, Portugal
- Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
| | - Hugo Gamboa
- LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal;
- PLUX Wireless Biosignals S.A., 1050-059 Lisboa, Portugal;
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26
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Differentiation of task complexity in long-term memory retrieval using multifractal detrended fluctuation analysis of fNIRS recordings. Exp Brain Res 2022; 240:1701-1711. [DOI: 10.1007/s00221-022-06365-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 04/04/2022] [Indexed: 11/26/2022]
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27
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Wu KC, Tamborini D, Renna M, Peruch A, Huang Y, Martin A, Kaya K, Starkweather Z, Zavriyev AI, Carp SA, Salat DH, Franceschini MA. Open-source FlexNIRS: A low-cost, wireless and wearable cerebral health tracker. Neuroimage 2022; 256:119216. [PMID: 35452803 DOI: 10.1016/j.neuroimage.2022.119216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 03/30/2022] [Accepted: 04/13/2022] [Indexed: 11/26/2022] Open
Abstract
Currently, there is great interest in making neuroimaging widely accessible and thus expanding the sampling population for better understanding and preventing diseases. The use of wearable health devices has skyrocketed in recent years, allowing continuous assessment of physiological parameters in patients and research cohorts. While most health wearables monitor the heart, lungs and skeletal muscles, devices targeting the brain are currently lacking. To promote brain health in the general population, we developed a novel, low-cost wireless cerebral oximeter called FlexNIRS. The device has 4 LEDs and 3 photodiode detectors arranged in a symmetric geometry, which allows for a self-calibrated multi-distance method to recover cerebral hemoglobin oxygenation (SO2) at a rate of 100 Hz. The device is powered by a rechargeable battery and uses Bluetooth Low Energy (BLE) for wireless communication. We developed an Android application for portable data collection and real-time analysis and display. Characterization tests in phantoms and human participants show very low noise (noise-equivalent power <70 fW/√Hz) and robustness of SO2 quantification in vivo. The estimated cost is on the order of $50/unit for 1000 units, and our goal is to share the device with the research community following an open-source model. The low cost, ease-of-use, smart-phone readiness, accurate SO2 quantification, real time data quality feedback, and long battery life make prolonged monitoring feasible in low resource settings, including typically medically underserved communities, and enable new community and telehealth applications.
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Affiliation(s)
- Kuan-Cheng Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA; Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA 02215, USA.
| | - Davide Tamborini
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
| | - Marco Renna
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
| | - Adriano Peruch
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
| | - Yujing Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
| | - Alyssa Martin
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
| | - Kutlu Kaya
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
| | - Zachary Starkweather
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
| | - Alexander I Zavriyev
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
| | - Stefan A Carp
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
| | - David H Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
| | - Maria Angela Franceschini
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
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28
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Ortega-Martinez A, Von Lühmann A, Farzam P, Rogers D, Mugler EM, Boas DA, Yücel MA. Multivariate Kalman filter regression of confounding physiological signals for real-time classification of fNIRS data. NEUROPHOTONICS 2022; 9:025003. [PMID: 35692628 PMCID: PMC9174890 DOI: 10.1117/1.nph.9.2.025003] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 05/17/2022] [Indexed: 05/13/2023]
Abstract
Significance: Functional near-infrared spectroscopy (fNIRS) is a noninvasive technique for measuring hemodynamic changes in the human cortex related to neural function. Due to its potential for miniaturization and relatively low cost, fNIRS has been proposed for applications, such as brain-computer interfaces (BCIs). The relatively large magnitude of the signals produced by the extracerebral physiology compared with the ones produced by evoked neural activity makes real-time fNIRS signal interpretation challenging. Regression techniques incorporating physiologically relevant auxiliary signals such as short separation channels are typically used to separate the cerebral hemodynamic response from the confounding components in the signal. However, the coupling of the extra-cerebral signals is often noninstantaneous, and it is necessary to find the proper delay to optimize nuisance removal. Aim: We propose an implementation of the Kalman filter with time-embedded canonical correlation analysis for the real-time regression of fNIRS signals with multivariate nuisance regressors that take multiple delays into consideration. Approach: We tested our proposed method on a previously acquired finger tapping dataset with the purpose of classifying the neural responses as left or right. Results: We demonstrate computationally efficient real-time processing of 24-channel fNIRS data (400 samples per second per channel) with a two order of selective magnitude decrease in cardiac signal power and up to sixfold increase in the contrast-to-noise ratio compared with the nonregressed signals. Conclusion: The method provides a way to obtain better distinction of brain from non-brain signals in real time for BCI application with fNIRS.
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Affiliation(s)
| | - Alexander Von Lühmann
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
- Berlin Institute of Technology, Machine Learning Department, Berlin, Germany
| | - Parya Farzam
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
| | - De’Ja Rogers
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
| | - Emily M. Mugler
- Facebook Reality Labs Research, Menlo Park, California, United States
| | - David A. Boas
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
| | - Meryem A. Yücel
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
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29
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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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30
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Li J, Yan WJ, Wu Y, Tian XX, Zhang YW. Synaptosomal-Associated Protein 25 Gene Polymorphisms Affect Treatment Efficiency of Methylphenidate in Children With Attention-Deficit Hyperactivity Disorder: An fNIRS Study. Front Behav Neurosci 2022; 15:793643. [PMID: 35069142 PMCID: PMC8766417 DOI: 10.3389/fnbeh.2021.793643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/13/2021] [Indexed: 11/13/2022] Open
Abstract
Methylphenidate (MPH) is the first-line drug for the treatment of children with attention-deficit hyperactivity disorder (ADHD); however, individual curative effects of MPH vary. Many studies have demonstrated that synaptosomal-associated protein 25 (SNAP-25) gene MnlI polymorphisms may be related to the efficacy of MPH. However, the association between SNAP-25MnlI polymorphisms and changes in brain hemodynamic responses after MPH treatment is still unclear. This study used functional near-infrared spectroscopy (fNIRS) to preliminarily investigate the interaction of MPH treatment-related prefrontal inhibitory functional changes with the genotype status of the SNAP-25 gene in children with ADHD. In total, 38 children with ADHD aged 6.76–12.08 years were enrolled in this study and divided into the following two groups based on SNAP-25 gene MnlI polymorphisms: T/T genotype group (wild-type group, 27 children) and G allele carrier group (mutation group, 11 children). The averaged oxygenated hemoglobin concentration changes [Δavg oxy-Hb] and deoxyhemoglobin concentration changes [Δavg deoxy-Hb] in the frontal cortex before MPH treatment and after 1.5 h (post-MPH1.5h) and 4 weeks (post-MPH4w) of MPH treatments were monitored using fNIRS during the go/no-go task. SNAP-IV scores were evaluated both pre-MPH and post-MPH4w treatments. In the T/T genotype group, [Δavg oxy-Hb] in the dorsolateral prefrontal cortex was significantly higher after 4 weeks of MPH (post-MPH4W) treatment than pre-treatment; however, in the G allele group, no significant differences in [Δavg oxy-Hb] were observed between pre- and post-treatments. In the go/no-go task, the accuracy was significantly increased post-MPH4w treatment in the T/T genotype group, while no significant differences were observed in response time and accuracy of the “go” sand no-go task in the G allele group for pre-MPH, post-MPH1.5h, and post-MPH4w treatments. The T/T genotype group exhibited a significant decrease in SNAP-IV scores after MPH treatment, while the G allele group showed no significant difference. In conclusion, fNIRS data combined with SNAP-25 MnlI polymorphism analysis may be a useful biomarker for evaluating the effects of MPH in children with ADHD.
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Affiliation(s)
- Jie Li
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Pediatrics, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China
| | - Wen-Jie Yan
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yan Wu
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xin-Xin Tian
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yi-Wen Zhang
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Yi-Wen Zhang
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31
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Yang C, Zhang T, Huang K, Xiong M, Liu H, Wang P, Zhang Y. Increased both cortical activation and functional connectivity after transcranial direct current stimulation in patients with post-stroke: A functional near-infrared spectroscopy study. Front Psychiatry 2022; 13:1046849. [PMID: 36569623 PMCID: PMC9784914 DOI: 10.3389/fpsyt.2022.1046849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Previous studies have shown that cognitive impairment is common after stroke. Transcranial direct current stimulation (tDCS) is a promising tool for rehabilitating cognitive impairment. This study aimed to investigate the effects of tDCS on the rehabilitation of cognitive impairment in patients with stroke. METHODS Twenty-two mild-moderate post-stroke patients with cognitive impairments were treated with 14 tDCS sessions. A total of 14 healthy individuals were included in the control group. Cognitive function was assessed using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). Cortical activation was assessed using functional near-infrared spectroscopy (fNIRS) during the verbal fluency task (VFT). RESULTS The cognitive function of patients with stroke, as assessed by the MMSE and MoCA scores, was lower than that of healthy individuals but improved after tDCS. The cortical activation of patients with stroke was lower than that of healthy individuals in the left superior temporal cortex (lSTC), right superior temporal cortex (rSTC), right dorsolateral prefrontal cortex (rDLPFC), right ventrolateral prefrontal cortex (rVLPFC), and left ventrolateral prefrontal cortex (lVLPFC) cortical regions. Cortical activation increased in the lSTC cortex after tDCS. The functional connectivity (FC) between the cerebral hemispheres of patients with stroke was lower than that of healthy individuals but increased after tDCS. CONCLUSION The cognitive and brain functions of patients with mild-to-moderate stroke were damaged but recovered to a degree after tDCS. Increased cortical activation and increased FC between the bilateral cerebral hemispheres measured by fNIRS are promising biomarkers to assess the effectiveness of tDCS in stroke.
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Affiliation(s)
- Caihong Yang
- Department of Rehabilitation Medicine, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China.,School of Psychology, Central China Normal University, Wuhan, Hubei, China
| | - Tingyu Zhang
- Department of Rehabilitation Medicine, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Kaiqi Huang
- The Seventh Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Menghui Xiong
- Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Huiyu Liu
- Department of Rehabilitation Medicine, Yue Bei People's Hospital, Shaoguan, Guangdong, China
| | - Pu Wang
- Department of Rehabilitation Medicine, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China.,Department of Rehabilitation Medicine, Tianyang District People's Hospital, Baise, Guangxi, China
| | - Yan Zhang
- School of Educational Science, Huazhong University of Science and Technology, Wuhan, Hubei, China
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32
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von Lühmann A, Zheng Y, Ortega-Martinez A, Kiran S, Somers DC, Cronin-Golomb A, Awad LN, Ellis TD, Boas DA, Yücel MA. Towards Neuroscience of the Everyday World (NEW) using functional Near-Infrared Spectroscopy. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2021; 18:100272. [PMID: 33709044 PMCID: PMC7943029 DOI: 10.1016/j.cobme.2021.100272] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Functional Near-Infrared Spectroscopy (fNIRS) assesses human brain activity by noninvasively measuring changes of cerebral hemoglobin concentrations caused by modulation of neuronal activity. Recent progress in signal processing and advances in system design, such as miniaturization, wearability and system sensitivity, have strengthened fNIRS as a viable and cost-effective complement to functional Magnetic Resonance Imaging (fMRI), expanding the repertoire of experimental studies that can be performed by the neuroscience community. The availability of fNIRS and Electroencephalography (EEG) for routine, increasingly unconstrained, and mobile brain imaging is leading towards a new domain that we term "Neuroscience of the Everyday World" (NEW). In this light, we review recent advances in hardware, study design and signal processing, and discuss challenges and future directions towards achieving NEW.
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Affiliation(s)
- Alexander von Lühmann
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA 02215, USA
- NIRx Medical Technologies, Berlin 13355, Germany
| | - Yilei Zheng
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA 02215, USA
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China
| | | | - Swathi Kiran
- Department of Speech, Language, and Hearing, Boston University, Boston, MA 02215, USA
| | - David C. Somers
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA
| | - Alice Cronin-Golomb
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA
| | - Louis N. Awad
- College of Health and Rehabilitation Sciences, Sargent College, Boston University, Boston, MA 02215, USA
| | - Terry D. Ellis
- College of Health and Rehabilitation Sciences, Sargent College, Boston University, Boston, MA 02215, USA
| | - David A. Boas
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Meryem A. Yücel
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA 02215, USA
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Dans PW, Foglia SD, Nelson AJ. Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research. Brain Sci 2021; 11:606. [PMID: 34065136 PMCID: PMC8151801 DOI: 10.3390/brainsci11050606] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/05/2021] [Accepted: 05/07/2021] [Indexed: 12/26/2022] Open
Abstract
FNIRS pre-processing and processing methodologies are very important-how a researcher chooses to process their data can change the outcome of an experiment. The purpose of this review is to provide a guide on fNIRS pre-processing and processing techniques pertinent to the field of human motor control research. One hundred and twenty-three articles were selected from the motor control field and were examined on the basis of their fNIRS pre-processing and processing methodologies. Information was gathered about the most frequently used techniques in the field, which included frequency cutoff filters, wavelet filters, smoothing filters, and the general linear model (GLM). We discuss the methodologies of and considerations for these frequently used techniques, as well as those for some alternative techniques. Additionally, general considerations for processing are discussed.
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Affiliation(s)
- Patrick W. Dans
- Department of Kinesiology, McMaster University, Hamilton, ON L8S 4K1, Canada;
| | - Stevie D. Foglia
- School of Biomedical Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada;
| | - Aimee J. Nelson
- Department of Kinesiology, McMaster University, Hamilton, ON L8S 4K1, Canada;
- School of Biomedical Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada;
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Maymandi H, Perez Benitez JL, Gallegos-Funes F, Perez Benitez JA. A novel monitor for practical brain-computer interface applications based on visual evoked potential. BRAIN-COMPUTER INTERFACES 2021. [DOI: 10.1080/2326263x.2021.1900032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Hamidreza Maymandi
- Laboratorio de Electromagnetismo Aplicado (LENDE), Escuela Superior de Ingeniería Mecánica y Eléctrica (ESIME), Instituto Politécnico Nacional (IPN), CDMX, Mexico
| | - Jorge Luis Perez Benitez
- Laboratorio de Electromagnetismo Aplicado (LENDE), Escuela Superior de Ingeniería Mecánica y Eléctrica (ESIME), Instituto Politécnico Nacional (IPN), CDMX, Mexico
| | - F. Gallegos-Funes
- Laboratorio de Electromagnetismo Aplicado (LENDE), Escuela Superior de Ingeniería Mecánica y Eléctrica (ESIME), Instituto Politécnico Nacional (IPN), CDMX, Mexico
| | - J. A. Perez Benitez
- Laboratorio de Electromagnetismo Aplicado (LENDE), Escuela Superior de Ingeniería Mecánica y Eléctrica (ESIME), Instituto Politécnico Nacional (IPN), CDMX, Mexico
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Kwon J, Im CH. Subject-Independent Functional Near-Infrared Spectroscopy-Based Brain-Computer Interfaces Based on Convolutional Neural Networks. Front Hum Neurosci 2021; 15:646915. [PMID: 33776674 PMCID: PMC7994252 DOI: 10.3389/fnhum.2021.646915] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 02/19/2021] [Indexed: 11/22/2022] Open
Abstract
Functional near-infrared spectroscopy (fNIRS) has attracted increasing attention in the field of brain–computer interfaces (BCIs) owing to their advantages such as non-invasiveness, user safety, affordability, and portability. However, fNIRS signals are highly subject-specific and have low test-retest reliability. Therefore, individual calibration sessions need to be employed before each use of fNIRS-based BCI to achieve a sufficiently high performance for practical BCI applications. In this study, we propose a novel deep convolutional neural network (CNN)-based approach for implementing a subject-independent fNIRS-based BCI. A total of 18 participants performed the fNIRS-based BCI experiments, where the main goal of the experiments was to distinguish a mental arithmetic task from an idle state task. Leave-one-subject-out cross-validation was employed to evaluate the average classification accuracy of the proposed subject-independent fNIRS-based BCI. As a result, the average classification accuracy of the proposed method was reported to be 71.20 ± 8.74%, which was higher than the threshold accuracy for effective BCI communication (70%) as well as that obtained using conventional shrinkage linear discriminant analysis (65.74 ± 7.68%). To achieve a classification accuracy comparable to that of the proposed subject-independent fNIRS-based BCI, 24 training trials (of approximately 12 min) were necessary for the traditional subject-dependent fNIRS-based BCI. It is expected that our CNN-based approach would reduce the necessity of long-term individual calibration sessions, thereby enhancing the practicality of fNIRS-based BCIs significantly.
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Affiliation(s)
- Jinuk Kwon
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea.,Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Chang-Hwan Im
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea.,Department of Electronic Engineering, Hanyang University, Seoul, South Korea
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Cortical Activity Linked to Clocking in Deaf Adults: fNIRS Insights with Static and Animated Stimuli Presentation. Brain Sci 2021; 11:brainsci11020196. [PMID: 33562848 PMCID: PMC7914875 DOI: 10.3390/brainsci11020196] [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: 12/20/2020] [Revised: 01/28/2021] [Accepted: 02/02/2021] [Indexed: 11/16/2022] Open
Abstract
The question of the possible impact of deafness on temporal processing remains unanswered. Different findings, based on behavioral measures, show contradictory results. The goal of the present study is to analyze the brain activity underlying time estimation by using functional near infrared spectroscopy (fNIRS) techniques, which allow examination of the frontal, central and occipital cortical areas. A total of 37 participants (19 deaf) were recruited. The experimental task involved processing a road scene to determine whether the driver had time to safely execute a driving task, such as overtaking. The road scenes were presented in animated format, or in sequences of 3 static images showing the beginning, mid-point, and end of a situation. The latter presentation required a clocking mechanism to estimate the time between the samples to evaluate vehicle speed. The results show greater frontal region activity in deaf people, which suggests that more cognitive effort is needed to process these scenes. The central region, which is involved in clocking according to several studies, is particularly activated by the static presentation in deaf people during the estimation of time lapses. Exploration of the occipital region yielded no conclusive results. Our results on the frontal and central regions encourage further study of the neural basis of time processing and its links with auditory capacity.
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37
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Soekadar SR, Kohl SH, Mihara M, von Lühmann A. Optical brain imaging and its application to neurofeedback. Neuroimage Clin 2021; 30:102577. [PMID: 33545580 PMCID: PMC7868728 DOI: 10.1016/j.nicl.2021.102577] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 12/30/2020] [Accepted: 01/15/2021] [Indexed: 12/30/2022]
Abstract
Besides passive recording of brain electric or magnetic activity, also non-ionizing electromagnetic or optical radiation can be used for real-time brain imaging. Here, changes in the radiation's absorption or scattering allow for continuous in vivo assessment of regional neurometabolic and neurovascular activity. Besides magnetic resonance imaging (MRI), over the last years, also functional near-infrared spectroscopy (fNIRS) was successfully established in real-time metabolic brain imaging. In contrast to MRI, fNIRS is portable and can be applied at bedside or in everyday life environments, e.g., to restore communication and movement. Here we provide a comprehensive overview of the history and state-of-the-art of real-time optical brain imaging with a special emphasis on its clinical use towards neurofeedback and brain-computer interface (BCI) applications. Besides pointing to the most critical challenges in clinical use, also novel approaches that combine real-time optical neuroimaging with other recording modalities (e.g. electro- or magnetoencephalography) are described, and their use in the context of neuroergonomics, neuroenhancement or neuroadaptive systems discussed.
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Affiliation(s)
- Surjo R Soekadar
- Clinical Neurotechnology Laboratory, Dept. of Psychiatry and Psychotherapy, Neuroscience Research Center, Campus Charité Mitte (CCM), Charité - University Medicine of Berlin, Berlin, Germany.
| | - Simon H Kohl
- JARA-Institute Molecular Neuroscience and Neuroimaging (INM-11), Jülich Research Centre, Jülich, Germany; Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Medical Faculty, RWTH Aachen University, Germany
| | - Masahito Mihara
- Department of Neurology, Kawasaki Medical School, Kurashiki-City, Okayama, Japan
| | - Alexander von Lühmann
- Machine Learning Department, Computer Science, Technische Universität Berlin, Berlin, Germany; Neurophotonics Center, Biomedical Engineering, Boston University, Boston, USA
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Li H, Wu D, Yang J, Xie S, Luo J, Chang C. A Functional Near-Infrared Spectroscopy Examination of the Neural Correlates of Cognitive Shifting in Dimensional Change Card Sort Task. Front Hum Neurosci 2021; 14:561223. [PMID: 33551771 PMCID: PMC7859114 DOI: 10.3389/fnhum.2020.561223] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 12/17/2020] [Indexed: 11/13/2022] Open
Abstract
This study aims to examine the neural correlates of cognitive shifting during the Dimensional Change Card Sort Task (DCCS) task with functional near-infrared spectroscopy. Altogether 49 children completed the DCCS tasks, and 25 children (Mage = 68.66, SD = 5.3) passing all items were classified into the Switch group. Twenty children (M age = 62.05, SD = 8.13) committing more than one perseverative errors were grouped into the Perseverate group. The Switch group had Brodmann Area (BA) 9 and 10 activated in the pre-switch period and BA 6, 9, 10, 40, and 44 in the post-switch period. In contrast, the Perseverate group had BA 9 and 10 activated in the pre-switch period and BA 8, 9, 10 in the post-switch period. The general linear model results afford strong support to the "V-shape curve" hypothesis by identifying a significant decrease-increase cycle in BA 9 and 44, the neural correlations of cognitive shifting.
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Affiliation(s)
- Hui Li
- School of Education, Macquarie University, Sydney, NSW, Australia
| | - Dandan Wu
- School of Education, Macquarie University, Sydney, NSW, Australia
| | - Jinfeng Yang
- School of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Sha Xie
- Normal College, Shenzhen University, Shenzhen, China
| | - Jiutong Luo
- Faculty of Education, Beijing Normal University, Beijing, China
| | - Chunqi Chang
- School of Biomedical Engineering, Shenzhen University, Shenzhen, China
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39
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Noah JA, Zhang X, Dravida S, DiCocco C, Suzuki T, Aslin RN, Tachtsidis I, Hirsch J. Comparison of short-channel separation and spatial domain filtering for removal of non-neural components in functional near-infrared spectroscopy signals. NEUROPHOTONICS 2021; 8:015004. [PMID: 33598505 PMCID: PMC7881368 DOI: 10.1117/1.nph.8.1.015004] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 01/19/2021] [Indexed: 05/03/2023]
Abstract
Significance: With the increasing popularity of functional near-infrared spectroscopy (fNIRS), the need to determine localization of the source and nature of the signals has grown. Aim: We compare strategies for removal of non-neural signals for a finger-thumb tapping task, which shows responses in contralateral motor cortex and a visual checkerboard viewing task that produces activity within the occipital lobe. Approach: We compare temporal regression strategies using short-channel separation to a spatial principal component (PC) filter that removes global signals present in all channels. For short-channel temporal regression, we compare non-neural signal removal using first and combined first and second PCs from a broad distribution of short channels to limited distribution on the forehead. Results: Temporal regression of non-neural information from broadly distributed short channels did not differ from forehead-only distribution. Spatial PC filtering provides results similar to short-channel separation using the temporal domain. Utilizing both first and second PCs from short channels removes additional non-neural information. Conclusions: We conclude that short-channel information in the temporal domain and spatial domain regression filtering methods remove similar non-neural components represented in scalp hemodynamics from fNIRS signals and that either technique is sufficient to remove non-neural components.
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Affiliation(s)
- J. Adam Noah
- Yale School of Medicine, Department of Psychiatry, Brain Function Laboratory, New Haven, Connecticut, United States
| | - Xian Zhang
- Yale School of Medicine, Department of Psychiatry, Brain Function Laboratory, New Haven, Connecticut, United States
| | - Swethasri Dravida
- Yale School of Medicine, Interdepartmental Neuroscience Program New Haven, Connecticut, United States
| | - Courtney DiCocco
- Yale School of Medicine, Brain Function Laboratory, New Haven, Connecticut, United States
| | - Tatsuya Suzuki
- Meiji University, Graduate School of Science and Technology, Electrical Engineering Program, Kawasaki, Japan
- Meiji University, School of Science and Technology, Department of Electronics and Bioinformatics, Kawasaki, Japan
| | - Richard N. Aslin
- Haskins Laboratories, New Haven, Connecticut, United States
- Yale University, Department of Psychology, New Haven, Connecticut, United States
| | - Ilias Tachtsidis
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Joy Hirsch
- Yale School of Medicine, Department of Psychiatry, Brain Function Laboratory, New Haven, Connecticut, United States
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
- Yale School of Medicine, Department of Neuroscience, New Haven, Connecticut, United States
- Yale School of Medicine, Department of Comparative Medicine, New Haven, Connecticut, United States
- Address all correspondence to Joy Hirsch,
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40
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Yücel MA, Lühmann AV, Scholkmann F, Gervain J, Dan I, Ayaz H, Boas D, Cooper RJ, Culver J, Elwell CE, Eggebrecht A, Franceschini MA, Grova C, Homae F, Lesage F, Obrig H, Tachtsidis I, Tak S, Tong Y, Torricelli A, Wabnitz H, Wolf M. Best practices for fNIRS publications. NEUROPHOTONICS 2021; 8:012101. [PMID: 33442557 PMCID: PMC7793571 DOI: 10.1117/1.nph.8.1.012101] [Citation(s) in RCA: 123] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 12/02/2020] [Indexed: 05/09/2023]
Abstract
The application of functional near-infrared spectroscopy (fNIRS) in the neurosciences has been expanding over the last 40 years. Today, it is addressing a wide range of applications within different populations and utilizes a great variety of experimental paradigms. With the rapid growth and the diversification of research methods, some inconsistencies are appearing in the way in which methods are presented, which can make the interpretation and replication of studies unnecessarily challenging. The Society for Functional Near-Infrared Spectroscopy has thus been motivated to organize a representative (but not exhaustive) group of leaders in the field to build a consensus on the best practices for describing the methods utilized in fNIRS studies. Our paper has been designed to provide guidelines to help enhance the reliability, repeatability, and traceability of reported fNIRS studies and encourage best practices throughout the community. A checklist is provided to guide authors in the preparation of their manuscripts and to assist reviewers when evaluating fNIRS papers.
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Affiliation(s)
- Meryem A. Yücel
- Boston University, Neurophotonics Center, Biomedical Engineering, Boston, Massachusetts, United States
- Massachusetts General Hospital, Harvard Medical School, MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
- Address all correspondence to Meryem A. Yücel,
| | - Alexander v. Lühmann
- Boston University, Neurophotonics Center, Biomedical Engineering, Boston, Massachusetts, United States
- Massachusetts General Hospital, Harvard Medical School, MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
| | - Felix Scholkmann
- University Hospital Zurich, University of Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Neonatology Research, Zurich, Switzerland
- University of Bern, Institute for Complementary and Integrative Medicine, Bern, Switzerland
| | - Judit Gervain
- Université de Paris, CNRS, Integrative Neuroscience and Cognition Center, Paris, France
- Università di Padova, Department of Social and Developmental Psychology, Padua, Italy
| | - Ippeita Dan
- Chuo University, Faculty of Science and Engineering, Applied Cognitive Neuroscience Laboratory, Tokyo, Japan
| | - 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 Psychology, Philadelphia, Pennsylvania, United States
- Drexel University, Drexel Solutions Institute, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, Department of Family and Community Health, Philadelphia, Pennsylvania, United States
- Children’s Hospital of Philadelphia, Center for Injury Research and Prevention, Philadelphia, Pennsylvania, United States
| | - David Boas
- Boston University, Neurophotonics Center, Biomedical Engineering, Boston, Massachusetts, United States
| | - Robert J. Cooper
- University College London, DOT-HUB, Department of Medical Physics and Biomedical Engineering, Biomedical Optics Research Laboratory, London, United Kingdom
| | - Joseph Culver
- Washington University School of Medicine, Department of Radiology, St. Louis, Missouri, United States
| | - Clare E. Elwell
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Adam Eggebrecht
- Washington University School of Medicine, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States
| | - Maria A. Franceschini
- Massachusetts General Hospital, Harvard Medical School, MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
| | - Christophe Grova
- Concordia University, Department of Physics and PERFORM Centre, Multimodal Functional Imaging Lab, Montreal, Québec, Canada
- McGill University, Biomedical Engineering Department, Multimodal Functional Imaging Lab, Montreal, Québec, Canada
| | - Fumitaka Homae
- Tokyo Metropolitan University, Department of Language Sciences, Tokyo, Japan
| | - Frédéric Lesage
- Polytechnique Montréal, Department Electrical Engineering, Montreal, Canada
| | - Hellmuth Obrig
- University Hospital Leipzig, Max-Planck-Institute for Human Cognitive and Brain Sciences and Clinic for Cognitive Neurology, Leipzig, Germany
| | - Ilias Tachtsidis
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Sungho Tak
- Korea Basic Science Institute, Research Center for Bioconvergence Analysis, Ochang, Cheongju, Republic of Korea
| | - Yunjie Tong
- Weldon School of Biomedical Engineering Purdue University, West Lafayette, Indiana, United States
| | - Alessandro Torricelli
- Politecnico di Milano, Dipartimento di Fisica, Milan, Italy
- Consiglio Nazionale delle Ricerche, Istituto di Fotonica e Nanotecnologie, Milan, Italy
| | | | - Martin Wolf
- University Hospital Zurich, University of Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Neonatology Research, Zurich, Switzerland
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Zhang T, Zhang J, Huang J, Zheng Z, Wang P. Neural Activation via Acupuncture in Patients With Major Depressive Disorder: A Functional Near-Infrared Spectroscopy Study. Front Psychiatry 2021; 12:669533. [PMID: 34867499 PMCID: PMC8632864 DOI: 10.3389/fpsyt.2021.669533] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 10/05/2021] [Indexed: 02/05/2023] Open
Abstract
Background and Objective: Acupuncture is used as an alternative treatment for patients with major depressive disorder (MDD). The associated therapeutic effect of acupuncture is often attributed to its modulatory effect on the activity of the pre-frontal cortex (PFC), although the mechanism is not well-studied. We employed a repeated measures design to investigate the brain modulatory effect of acupuncture on the PFC in a group of patients with MDD and investigated whether the modulatory effect is influenced by the severity of the disease. Methods: A total of 47 patients diagnosed with MDD were enrolled in this functional near-infrared spectroscopy experiment. The severity of depressive symptoms was measured at baseline using the Hamilton Depression Rating Scale-24 (HAMD). The cortical activation in the bilateral PFC areas during a verbal fluency task (VFT) was measured before and after a single session of acupuncture in the Baihui acupoint. We further explored the potential correlation between the severity of MDD and task-related activation before and after acupuncture. Results: A single session of acupuncture significantly tended to enhance the activation level of the left frontopolar cortex in patients with severe depression during VFT, but a null effect was found in those with mild to moderate depression. Among patients with severe depression, a strong correlation was observed between HAMD scores and the change in VFT-related activation after acupuncture in the left dorsolateral PFC (DLPFC). Conclusion: A single session of acupuncture did not significantly modulate the activation of the left PFC in patients with mild to moderate depression; however, it demonstrated a tendency to enhance the activation of the frontopolar area in patients with severe depression. Among patients with severe depression, there is a correlation between the activation by acupuncture of left DLPFC during executive functioning and the severity of depressive symptoms, suggesting that the brain activity induced by acupuncture is likely to be influenced by the baseline disease severity in patients with MDD.
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Affiliation(s)
- Tingyu Zhang
- Department of Rehabilitation Medicine, The Seventh Affiliated Hospital Sun Yat-sen University, Shenzhen, China
| | - Jiaqi Zhang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Jiaxi Huang
- Mental Health Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Zhong Zheng
- Mental Health Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Pu Wang
- Department of Rehabilitation Medicine, The Seventh Affiliated Hospital Sun Yat-sen University, Shenzhen, China.,Guangdong Engineering and Technology Research Center for Rehabilitation Medicine and Translation, Guangzhou, China
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Ashlesh P, Deepak KK, Preet KK. Role of prefrontal cortex during Sudoku task: fNIRS study. Transl Neurosci 2020; 11:419-427. [PMID: 33335780 PMCID: PMC7718610 DOI: 10.1515/tnsci-2020-0147] [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: 06/15/2020] [Revised: 10/08/2020] [Accepted: 10/08/2020] [Indexed: 11/15/2022] Open
Abstract
Background Sudoku is a popular cognitively stimulating leisure-time activity. Many studies have been directed toward finding an algorithm to solve Sudoku, but the investigation of the neural substrates involved in Sudoku has been challenging. Methods Sudoku task was divided into two steps to understand the differential function of the prefrontal cortex (PFC) while applying heuristic rules. PFC activity was recorded at 16 optode locations using functional near infrared spectroscopy. Classical two-way analysis of variance as well as general linear model-based approach was used to analyze the data from 28 noise-free recordings obtained from right-handed participants. Results Post hoc analysis showed a significant increase in oxyhemoglobin concentrations and decrease in deoxyhemoglobin concentrations at all 16 optode locations during step 1 (3 × 3 subgrids) and step 2 (easy level 9 × 9 Sudoku) when compared with the rest (p < 0.0001). Contrasting the step 2 - step 1 revealed that medial regions of PFC were preferentially activated. Conclusion Both the medial and lateral regions of PFC are activated during Sudoku task. However, the medial regions of PFC play a differential role, especially when we consider searching and selecting the heuristic rules. Thus, Sudoku may be used for cognitive remediation training in neuropsychiatric disorders involving PFC.
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Affiliation(s)
- Patil Ashlesh
- Department of Physiology, All India Institute of Medical Sciences, Nagpur, India
| | - Kishore K Deepak
- Department of Physiology, All India Institute of Medical Sciences, New Delhi, India
| | - Kochhar Kanwal Preet
- Department of Physiology, All India Institute of Medical Sciences, New Delhi, India
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43
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von Lühmann A, Li X, Gilmore N, Boas DA, Yücel MA. Open Access Multimodal fNIRS Resting State Dataset With and Without Synthetic Hemodynamic Responses. Front Neurosci 2020; 14:579353. [PMID: 33132833 PMCID: PMC7550457 DOI: 10.3389/fnins.2020.579353] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 08/19/2020] [Indexed: 11/13/2022] Open
Affiliation(s)
- Alexander von Lühmann
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, United States
| | - Xinge Li
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, United States
| | - Natalie Gilmore
- Department of Speech, Language and Hearing Sciences, Boston University, Boston, MA, United States
| | - David A Boas
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, United States
| | - Meryem A Yücel
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, United States
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44
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Kohl SH, Mehler DMA, Lührs M, Thibault RT, Konrad K, Sorger B. The Potential of Functional Near-Infrared Spectroscopy-Based Neurofeedback-A Systematic Review and Recommendations for Best Practice. Front Neurosci 2020; 14:594. [PMID: 32848528 PMCID: PMC7396619 DOI: 10.3389/fnins.2020.00594] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 05/14/2020] [Indexed: 01/04/2023] Open
Abstract
Background: The effects of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI)-neurofeedback on brain activation and behaviors have been studied extensively in the past. More recently, researchers have begun to investigate the effects of functional near-infrared spectroscopy-based neurofeedback (fNIRS-neurofeedback). FNIRS is a functional neuroimaging technique based on brain hemodynamics, which is easy to use, portable, inexpensive, and has reduced sensitivity to movement artifacts. Method: We provide the first systematic review and database of fNIRS-neurofeedback studies, synthesizing findings from 22 peer-reviewed studies (including a total of N = 441 participants; 337 healthy, 104 patients). We (1) give a comprehensive overview of how fNIRS-neurofeedback training protocols were implemented, (2) review the online signal-processing methods used, (3) evaluate the quality of studies using pre-set methodological and reporting quality criteria and also present statistical sensitivity/power analyses, (4) investigate the effectiveness of fNIRS-neurofeedback in modulating brain activation, and (5) review its effectiveness in changing behavior in healthy and pathological populations. Results and discussion: (1–2) Published studies are heterogeneous (e.g., neurofeedback targets, investigated populations, applied training protocols, and methods). (3) Large randomized controlled trials are still lacking. In view of the novelty of the field, the quality of the published studies is moderate. We identified room for improvement in reporting important information and statistical power to detect realistic effects. (4) Several studies show that people can regulate hemodynamic signals from cortical brain regions with fNIRS-neurofeedback and (5) these studies indicate the feasibility of modulating motor control and prefrontal brain functioning in healthy participants and ameliorating symptoms in clinical populations (stroke, ADHD, autism, and social anxiety). However, valid conclusions about specificity or potential clinical utility are premature. Conclusion: Due to the advantages of practicability and relatively low cost, fNIRS-neurofeedback might provide a suitable and powerful alternative to EEG and fMRI neurofeedback and has great potential for clinical translation of neurofeedback. Together with more rigorous research and reporting practices, further methodological improvements may lead to a more solid understanding of fNIRS-neurofeedback. Future research will benefit from exploiting the advantages of fNIRS, which offers unique opportunities for neurofeedback research.
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Affiliation(s)
- Simon H Kohl
- JARA-Institute Molecular Neuroscience and Neuroimaging (INM-11), Jülich Research Centre, Jülich, Germany.,Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - David M A Mehler
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Michael Lührs
- Brain Innovation B.V., Research Department, Maastricht, Netherlands.,Faculty of Psychology and Neuroscience, Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Robert T Thibault
- School of Psychological Science, University of Bristol, Bristol, United Kingdom.,MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Kerstin Konrad
- JARA-Institute Molecular Neuroscience and Neuroimaging (INM-11), Jülich Research Centre, Jülich, Germany.,Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Bettina Sorger
- Faculty of Psychology and Neuroscience, Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands
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45
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Shin J. Random Subspace Ensemble Learning for Functional Near-Infrared Spectroscopy Brain-Computer Interfaces. Front Hum Neurosci 2020; 14:236. [PMID: 32765235 PMCID: PMC7379868 DOI: 10.3389/fnhum.2020.00236] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 05/28/2020] [Indexed: 12/28/2022] Open
Abstract
The feasibility of the random subspace ensemble learning method was explored to improve the performance of functional near-infrared spectroscopy-based brain-computer interfaces (fNIRS-BCIs). Feature vectors have been constructed using the temporal characteristics of concentration changes in fNIRS chromophores such as mean, slope, and variance to implement fNIRS-BCIs systems. The mean and slope, which are the most popular features in fNIRS-BCIs, were adopted. Linear support vector machine and linear discriminant analysis were employed, respectively, as a single strong learner and multiple weak learners. All features in every channel and available time window were employed to train the strong learner, and the feature subsets were selected at random to train multiple weak learners. It was determined that random subspace ensemble learning is beneficial to enhance the performance of fNIRS-BCIs.
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Affiliation(s)
- Jaeyoung Shin
- Department of Electronic Engineering, Wonkwang University, Iksan, South Korea
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46
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Santosa H, Zhai X, Fishburn F, Sparto PJ, Huppert TJ. Quantitative comparison of correction techniques for removing systemic physiological signal in functional near-infrared spectroscopy studies. NEUROPHOTONICS 2020; 7:035009. [PMID: 32995361 PMCID: PMC7511246 DOI: 10.1117/1.nph.7.3.035009] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 07/27/2020] [Indexed: 05/15/2023]
Abstract
Significance: Isolating task-evoked brain signals from background physiological noise (e.g., cardiac, respiratory, and blood pressure fluctuations) poses a major challenge for the analysis of functional near-infrared spectroscopy (fNIRS) data. Aim: The performance of several analytic methods to separate background physiological noise from brain activity including spatial and temporal filtering, regression, component analysis, and the use of short-separation (SS) measurements were quantitatively compared. Approach: Using experimentally recorded background signals (breath-hold task), receiver operating characteristics simulations were performed by adding various levels of additive synthetic "brain" responses in order to examine the sensitivity and specificity of several previously proposed analytic approaches. Results: We found that the use of SS fNIRS channels as regressors of no-interest within a linear regression model was the best performing approach examined. Furthermore, we found that the addition of all available SS data, including all recorded channels and both hemoglobin species, improved the method performance despite the additional degrees-of-freedom of the models. When SS data were not available, we found that principal component filtering using a separate baseline scan was the best alternative. Conclusions: The use of multiple SS measurements as regressors of no interest implemented in a robust, iteratively prewhitened, general linear model has the best performance of the tested existing methods.
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Affiliation(s)
- Hendrik Santosa
- University of Pittsburgh, Department of Radiology, Pittsburgh, Pennsylvania, United States
| | - Xuetong Zhai
- University of Pittsburgh, Department of Bioengineering, Pittsburgh, Pennsylvania, United States
| | - Frank Fishburn
- University of Pittsburgh, Department of Psychiatry, Pittsburgh, Pennsylvania, United States
| | - Patrick J. Sparto
- University of Pittsburgh, Department of Physical Therapy, Pittsburgh, Pennsylvania, United States
| | - Theodore J. Huppert
- University of Pittsburgh, Clinical Science Translational Institute, Pittsburgh, Pennsylvania, United States
- University of Pittsburgh, Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States
- University of Pittsburgh, Department of Electrical and Computer Engineering, Pittsburgh, Pennsylvania, United States
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