Yücel Meryem A, Anderson Jessica E, Rogers De'Ja, Hajirahimi Parisa, Farzam Parya, Gao Yuanyuan, Kaplan Rini I, Braun Emily J, Mukadam Nishaat, Duwadi Sudan, Carlton Laura, Beeler David, Butler Lindsay K, Carpenter Erin, Girnis Jaimie, Wilson John, Tripathi Vaibhav, Zhang Yiwen, Sorger Bettina, von Lühmann Alexander, Somers David C, Cronin-Golomb Alice, Kiran Swathi, Ellis Terry D, Boas David A
Neurophotonics Center, Boston University, Boston, MA, USA.
Department of Biomedical Engineering, Boston University, Boston, MA, USA.
Nat Hum Behav. 2025 Sep 2. doi: 10.1038/s41562-025-02274-7.
Functional near-infrared spectroscopy (fNIRS) is a promising neuroimaging method owing to its non-invasive nature and adaptability to real-world settings. However, fNIRS signal quality is sensitive to individual differences in biophysical factors such as hair and skin characteristics, which can considerably 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. Here we quantify the impact of hair properties and skin pigmentation, as well as head size, sex and age, on signal quality in n = 115 individuals. We provide recommendations for fNIRS researchers, including a suggested metadata table and guidance for cap and optode configurations, hair management techniques and strategies to optimize data collection across varied participants. This research will help to guide future hardware advances and methodological standards to overcome barriers to inclusivity in fNIRS studies.
功能近红外光谱技术（fNIRS）因其非侵入性以及对现实环境的适应性，是一种很有前景的神经成像方法。然而，fNIRS信号质量对头发和皮肤特征等生物物理因素的个体差异很敏感，这些因素会显著影响近红外光的吸收和散射。如果处理不当，这些因素可能会因对不同人群的信号质量产生不成比例的影响而使fNIRS研究产生偏差。在此，我们量化了头发特性、皮肤色素沉着以及头围、性别和年龄对115名个体信号质量的影响。我们为fNIRS研究人员提供了建议，包括一个建议的元数据表以及关于帽式和光极配置、头发管理技术和策略的指导，以优化不同参与者的数据收集。这项研究将有助于指导未来的硬件改进和方法标准，以克服fNIRS研究中的包容性障碍。
