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Doherty EJ, Spencer CA, Burnison J, Čeko M, Chin J, Eloy L, Haring K, Kim P, Pittman D, Powers S, Pugh SL, Roumis D, Stephens JA, Yeh T, Hirshfield L. Interdisciplinary views of fNIRS: Current advancements, equity challenges, and an agenda for future needs of a diverse fNIRS research community. Front Integr Neurosci 2023; 17:1059679. [PMID: 36922983 PMCID: PMC10010439 DOI: 10.3389/fnint.2023.1059679] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 02/08/2023] [Indexed: 03/02/2023] Open
Abstract
Functional Near-Infrared Spectroscopy (fNIRS) is an innovative and promising neuroimaging modality for studying brain activity in real-world environments. While fNIRS has seen rapid advancements in hardware, software, and research applications since its emergence nearly 30 years ago, limitations still exist regarding all three areas, where existing practices contribute to greater bias within the neuroscience research community. We spotlight fNIRS through the lens of different end-application users, including the unique perspective of a fNIRS manufacturer, and report the challenges of using this technology across several research disciplines and populations. Through the review of different research domains where fNIRS is utilized, we identify and address the presence of bias, specifically due to the restraints of current fNIRS technology, limited diversity among sample populations, and the societal prejudice that infiltrates today's research. Finally, we provide resources for minimizing bias in neuroscience research and an application agenda for the future use of fNIRS that is equitable, diverse, and inclusive.
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Affiliation(s)
- Emily J. Doherty
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, United States
- Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, United States
| | - Cara A. Spencer
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, United States
- Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, United States
| | | | - Marta Čeko
- Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, United States
| | - Jenna Chin
- College of Arts, Humanities, and Social Sciences, Psychology, University of Denver, Denver, CO, United States
| | - Lucca Eloy
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, United States
- Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, United States
| | - Kerstin Haring
- Department of Computer Science, University of Denver, Denver, CO, United States
| | - Pilyoung Kim
- College of Arts, Humanities, and Social Sciences, Psychology, University of Denver, Denver, CO, United States
| | - Daniel Pittman
- Department of Computer Science, University of Denver, Denver, CO, United States
| | - Shannon Powers
- College of Arts, Humanities, and Social Sciences, Psychology, University of Denver, Denver, CO, United States
| | - Samuel L. Pugh
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, United States
- Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, United States
| | | | - Jaclyn A. Stephens
- Department of Occupational Therapy, Colorado State University, Fort Collins, CO, United States
| | - Tom Yeh
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, United States
| | - Leanne Hirshfield
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, United States
- Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, United States
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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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