Inclusivity in fNIRS Studies: Quantifying the Impact of Hair and Skin Characteristics on Signal Quality with Practical Recommendations for Improvement

Meryem A Yücel*, Jessica E Anderson, De'Ja Rogers, Parisa Hajirahimi, Parya Farzam, Yuanyuan Gao, Rini I Kaplan, Emily J Braun, Nishaat Mukadam, Sudan Duwadi, Laura Carlton, David Beeler, Lindsay K Butler, Erin Carpenter, Jaimie Girnis, John Wilson, Vaibhav Tripathi, Yiwen Zhang, Bettina Sorger, Alexander von LühmannDavid C Somers, Alice Cronin-Golomb, Swathi Kiran, Terry D Ellis, David A Boas

*Corresponding author for this work

Research output: Working paper / PreprintPreprint

Abstract

Functional Near-Infrared Spectroscopy (fNIRS) holds transformative potential for research and clinical applications in neuroscience due to its non-invasive nature and adaptability to real-world settings. However, despite its promise, fNIRS signal quality is sensitive to individual differences in biophysical factors such as hair and skin characteristics, which can significantly impact the absorption and scattering of near-infrared light. If not properly addressed, these factors risk biasing fNIRS research by disproportionately affecting signal quality across diverse populations. Our results quantify the impact of various hair properties, skin pigmentation as well as head size, sex and age on signal quality, providing quantitative guidance for future hardware advances and methodological standards to help overcome these critical barriers to inclusivity in fNIRS studies. We provide actionable guidelines for fNIRS researchers, including a suggested metadata table and recommendations for cap and optode configurations, hair management techniques, and strategies to optimize data collection across varied participants. This research paves the way for the development of more inclusive fNIRS technologies, fostering broader applicability and improved interpretability of neuroimaging data in diverse populations.
Original languageEnglish
PublisherCold Spring Harbor Laboratory - bioRxiv
Number of pages38
DOIs
Publication statusPublished - 28 Oct 2024

Keywords

  • Functional Near-Infrared Spectroscopy
  • hair characteristics
  • inclusivity
  • signal quality
  • skin pigmentation

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