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Curzel F, Tillmann B, Ferreri L. Lights on music cognition: A systematic and critical review of fNIRS applications and future perspectives. Brain Cogn 2024; 180:106200. [PMID: 38908228 DOI: 10.1016/j.bandc.2024.106200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 06/10/2024] [Accepted: 06/16/2024] [Indexed: 06/24/2024]
Abstract
Research investigating the neural processes related to music perception and production constitutes a well-established field within the cognitive neurosciences. While most neuroimaging tools have limitations in studying the complexity of musical experiences, functional Near-Infrared Spectroscopy (fNIRS) represents a promising, relatively new tool for studying music processes in both laboratory and ecological settings, which is also suitable for both typical and pathological populations across development. Here we systematically review fNIRS studies on music cognition, highlighting prospects and potentialities. We also include an overview of fNIRS basic theory, together with a brief comparison to characteristics of other neuroimaging tools. Fifty-nine studies meeting inclusion criteria (i.e., using fNIRS with music as the primary stimulus) are presented across five thematic sections. Critical discussion of methodology leads us to propose guidelines of good practices aiming for robust signal analyses and reproducibility. A continuously updated world map is proposed, including basic information from studies meeting the inclusion criteria. It provides an organized, accessible, and updatable reference database, which could serve as a catalyst for future collaborations within the community. In conclusion, fNIRS shows potential for investigating cognitive processes in music, particularly in ecological contexts and with special populations, aligning with current research priorities in music cognition.
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Affiliation(s)
- Federico Curzel
- Laboratoire d'Étude des Mécanismes Cognitifs (EMC), Université Lumière Lyon 2, Bron, Auvergne-Rhône-Alpes, 69500, France; Lyon Neuroscience Research Center (CRNL), INSERM, U1028, CNRS, UMR 5292, Université Claude Bernard Lyon1, Université de Lyon, Bron, Auvergne-Rhône-Alpes, 69500, France.
| | - Barbara Tillmann
- Lyon Neuroscience Research Center (CRNL), INSERM, U1028, CNRS, UMR 5292, Université Claude Bernard Lyon1, Université de Lyon, Bron, Auvergne-Rhône-Alpes, 69500, France; LEAD CNRS UMR5022, Université de Bourgogne-Franche Comté, Dijon, Bourgogne-Franche Comté 21000, France.
| | - Laura Ferreri
- Laboratoire d'Étude des Mécanismes Cognitifs (EMC), Université Lumière Lyon 2, Bron, Auvergne-Rhône-Alpes, 69500, France; Department of Brain and Behavioural Sciences, Università di Pavia, Pavia, Lombardia 27100, Italy.
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Čeko M, Hirshfield L, Doherty E, Southwell R, D'Mello SK. Cortical cognitive processing during reading captured using functional-near infrared spectroscopy. Sci Rep 2024; 14:19483. [PMID: 39174562 PMCID: PMC11341567 DOI: 10.1038/s41598-024-69630-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 08/07/2024] [Indexed: 08/24/2024] Open
Abstract
Neuroimaging studies using functional magnetic resonance imaging (fMRI) have provided unparalleled insights into the fundamental neural mechanisms underlying human cognitive processing, such as high-level linguistic processes during reading. Here, we build upon this prior work to capture sentence reading comprehension outside the MRI scanner using functional near infra-red spectroscopy (fNIRS) in a large sample of participants (n = 82). We observed increased task-related hemodynamic responses in prefrontal and temporal cortical regions during sentence-level reading relative to the control condition (a list of non-words), replicating prior fMRI work on cortical recruitment associated with high-level linguistic processing during reading comprehension. These results lay the groundwork towards developing adaptive systems to support novice readers and language learners by targeting the underlying cognitive processes. This work also contributes to bridging the gap between laboratory findings and more real-world applications in the realm of cognitive neuroscience.
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Affiliation(s)
- Marta Čeko
- Institute of Cognitive Science, University of Colorado Boulder, 1777 Exposition Drive, Boulder, CO, 80305, USA.
| | - Leanne Hirshfield
- Institute of Cognitive Science, University of Colorado Boulder, 1777 Exposition Drive, Boulder, CO, 80305, USA.
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA.
| | - Emily Doherty
- Institute of Cognitive Science, University of Colorado Boulder, 1777 Exposition Drive, Boulder, CO, 80305, USA
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA
| | - Rosy Southwell
- Institute of Cognitive Science, University of Colorado Boulder, 1777 Exposition Drive, Boulder, CO, 80305, USA
| | - Sidney K D'Mello
- Institute of Cognitive Science, University of Colorado Boulder, 1777 Exposition Drive, Boulder, CO, 80305, USA
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA
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Akhter J, Naseer N, Nazeer H, Khan H, Mirtaheri P. Enhancing Classification Accuracy with Integrated Contextual Gate Network: Deep Learning Approach for Functional Near-Infrared Spectroscopy Brain-Computer Interface Application. SENSORS (BASEL, SWITZERLAND) 2024; 24:3040. [PMID: 38793895 PMCID: PMC11125334 DOI: 10.3390/s24103040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 05/02/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024]
Abstract
Brain-computer interface (BCI) systems include signal acquisition, preprocessing, feature extraction, classification, and an application phase. In fNIRS-BCI systems, deep learning (DL) algorithms play a crucial role in enhancing accuracy. Unlike traditional machine learning (ML) classifiers, DL algorithms eliminate the need for manual feature extraction. DL neural networks automatically extract hidden patterns/features within a dataset to classify the data. In this study, a hand-gripping (closing and opening) two-class motor activity dataset from twenty healthy participants is acquired, and an integrated contextual gate network (ICGN) algorithm (proposed) is applied to that dataset to enhance the classification accuracy. The proposed algorithm extracts the features from the filtered data and generates the patterns based on the information from the previous cells within the network. Accordingly, classification is performed based on the similar generated patterns within the dataset. The accuracy of the proposed algorithm is compared with the long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM). The proposed ICGN algorithm yielded a classification accuracy of 91.23 ± 1.60%, which is significantly (p < 0.025) higher than the 84.89 ± 3.91 and 88.82 ± 1.96 achieved by LSTM and Bi-LSTM, respectively. An open access, three-class (right- and left-hand finger tapping and dominant foot tapping) dataset of 30 subjects is used to validate the proposed algorithm. The results show that ICGN can be efficiently used for the classification of two- and three-class problems in fNIRS-based BCI applications.
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Affiliation(s)
- Jamila Akhter
- Department of Mechatronics and Biomedical Engineering, Air University, Islamabad 44000, Pakistan; (J.A.); (H.N.)
| | - Noman Naseer
- Department of Mechatronics and Biomedical Engineering, Air University, Islamabad 44000, Pakistan; (J.A.); (H.N.)
| | - Hammad Nazeer
- Department of Mechatronics and Biomedical Engineering, Air University, Islamabad 44000, Pakistan; (J.A.); (H.N.)
| | - Haroon Khan
- Department of Mechanical, Electrical, and Chemical Engineering, OsloMet—Oslo Metropolitan University, 0176 Oslo, Norway; (H.K.); (P.M.)
| | - Peyman Mirtaheri
- Department of Mechanical, Electrical, and Chemical Engineering, OsloMet—Oslo Metropolitan University, 0176 Oslo, Norway; (H.K.); (P.M.)
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Li J, Li Y, Huang M, Li D, Wan T, Sun F, Zeng Q, Xu F, Wang J. The most fundamental and popular literature on functional near-infrared spectroscopy: a bibliometric analysis of the top 100 most cited articles. Front Neurol 2024; 15:1388306. [PMID: 38756218 PMCID: PMC11096499 DOI: 10.3389/fneur.2024.1388306] [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: 02/19/2024] [Accepted: 04/18/2024] [Indexed: 05/18/2024] Open
Abstract
Background Functional near infrared spectroscopy (fNIRS) has developed rapidly in recent years, and there are more and more studies on fNIRS. At present, there is no bibliometric analysis of the top 100 most cited articles on fNIRS research. Objective To identify the top 100 most cited articles on fNIRS and analyze those most fundamental and popular articles through bibliometric research methods. Methods The literature on fNIRS of web of science from 1990 to 2023 was searched and the top 100 most cited articles were identified by citations. Use the bibliometrix package in R studio and VOSviewer for data analysis and plotting to obtain the output characteristics and citation status of these 100 most cited articles, and analyze research trends in this field through keywords. Results A total of 9,424 articles were retrieved from web of science since 1990. The average citation number of the 100 articles was 457.4 (range from 260 to 1,366). Neuroimage published the most articles (n = 31). Villringer, A. from Leipzig University had the largest number of top 100 papers. Harvard University (n = 22) conducted most cited articles. The United States, Germany, Japan, and the United Kingdom had most cited articles, respectively. The most common keywords were near-infrared spectroscopy, activation, cerebral-blood-flow, brain, newborn-infants, oxygenation, cortex, fMRI, spectroscopy. The fund sources mostly came from National Institutes of Health Unitd States (NIH) and United States Department of Health Human Services (n = 28). Conclusion Neuroimage was the most popular journal. The top countries, institutions, and authors were the United States, Harvard University, and Villringer, A., respectively. Researchers and institutions from North America and Europe contributed the most. Near-infrared spectroscopy, activation, cerebral-blood-flow, brain, newborn-infants, oxygenation, cortex, fmri, spectroscopy, stimulation, blood-flow, light-propagation, infants, tissue comprise the future research directions and potential topic hotspots for fNIRS.
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Affiliation(s)
- Jiyang Li
- Rehabilitation Medicine Department, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Yang Li
- Rehabilitation Medicine Department, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Maomao Huang
- Rehabilitation Medicine Department, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Dan Li
- Rehabilitation Medicine Department, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Tenggang Wan
- Rehabilitation Medicine Department, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Fuhua Sun
- Rehabilitation Medicine Department, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Qiu Zeng
- Rehabilitation Medicine Department, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Fangyuan Xu
- Rehabilitation Medicine Department, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Jianxiong Wang
- Rehabilitation Medicine Department, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
- Rehabilitation Medicine and Engineering Key Laboratory of Luzhou, Luzhou, Sichuan, China
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Moffat R, Casale CE, Cross ES. Mobile fNIRS for exploring inter-brain synchrony across generations and time. FRONTIERS IN NEUROERGONOMICS 2024; 4:1260738. [PMID: 38234472 PMCID: PMC10790948 DOI: 10.3389/fnrgo.2023.1260738] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 12/01/2023] [Indexed: 01/19/2024]
Abstract
While still relatively rare, longitudinal hyperscanning studies are exceptionally valuable for documenting changes in inter-brain synchrony, which may in turn underpin how behaviors develop and evolve in social settings. The generalizability and ecological validity of this experimental approach hinges on the selected imaging technique being mobile-a requirement met by functional near-infrared spectroscopy (fNIRS). fNIRS has most frequently been used to examine the development of inter-brain synchrony and behavior in child-parent dyads. In this position paper, we contend that dedicating attention to longitudinal and intergenerational hyperscanning stands to benefit the fields of social and cognitive neuroscience more broadly. We argue that this approach is particularly relevant for understanding the neural mechanisms underpinning intergenerational social dynamics, and potentially for benchmarking progress in psychological and social interventions, many of which are situated in intergenerational contexts. In line with our position, we highlight areas of intergenerational research that stand to be enhanced by longitudinal hyperscanning with mobile devices, describe challenges that may arise from measuring across generations in the real world, and offer potential solutions.
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Affiliation(s)
- Ryssa Moffat
- Social Brain Sciences, ETH Zurich, Zurich, Switzerland
| | - Courtney E. Casale
- School of Psychological Sciences, Macquarie University, Sydney, NSW, Australia
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Flanagan K, Saikia MJ. Consumer-Grade Electroencephalogram and Functional Near-Infrared Spectroscopy Neurofeedback Technologies for Mental Health and Wellbeing. SENSORS (BASEL, SWITZERLAND) 2023; 23:8482. [PMID: 37896575 PMCID: PMC10610697 DOI: 10.3390/s23208482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 09/04/2023] [Accepted: 10/11/2023] [Indexed: 10/29/2023]
Abstract
Neurofeedback, utilizing an electroencephalogram (EEG) and/or a functional near-infrared spectroscopy (fNIRS) device, is a real-time measurement of brain activity directed toward controlling and optimizing brain function. This treatment has often been attributed to improvements in disorders such as ADHD, anxiety, depression, and epilepsy, among others. While there is evidence suggesting the efficacy of neurofeedback devices, the research is still inconclusive. The applicability of the measurements and parameters of consumer neurofeedback wearable devices has improved, but the literature on measurement techniques lacks rigorously controlled trials. This paper presents a survey and literary review of consumer neurofeedback devices and the direction toward clinical applications and diagnoses. Relevant devices are highlighted and compared for treatment parameters, structural composition, available software, and clinical appeal. Finally, a conclusion on future applications of these systems is discussed through the comparison of their advantages and drawbacks.
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Affiliation(s)
- Kira Flanagan
- Electrical Engineering, University of North Florida, Jacksonville, FL 32224, USA
- Biomedical Sensors and Systems Laboratory, University of North Florida, Jacksonville, FL 32224, USA
| | - Manob Jyoti Saikia
- Electrical Engineering, University of North Florida, Jacksonville, FL 32224, USA
- Biomedical Sensors and Systems Laboratory, University of North Florida, Jacksonville, FL 32224, USA
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