Quantifying the impact of hair and skin characteristics on fNIRS signal quality for enhanced inclusivity
Yücel, M.A., Anderson, J.E., Rogers, D. et al. Quantifying the impact of hair and skin characteristics on fNIRS signal quality for enhanced inclusivity. Nat Hum Behav (2025). https://doi.org/10.1038/s41562-025-02274-7
Abstract
Semantic feature-based treatment (SFT), which engages the semantic network by repeatedly targeting retrieval of conceptual features to improve lexical-semantic access, has shown promise for facilitating generalization in aphasia rehabilitation. However, its capacity to drive broad improvement across cognitive-linguistic domains in bilinguals with aphasia (BWA) remains unclear. This study examined generalization effects (i.e., direct transfer, near transfer, and far transfer) following SFT in 48 Spanish-English BWA who took part in a randomized controlled trial. Participants received 40 h of SFT targeting word retrieval, with generalization assessed across three domains: naming of untrained items (direct transfer), semantic processing (near transfer), as well as global language ability and nonverbal abstract reasoning (far transfer). Results showed (i) robust improvements for trained and untrained naming targets, demonstrating direct transfer, (ii) near transfer effects for select semantic processing tasks, and (iii) far transfer limited to overall language function, with no gains in domain-general cognition. Notably, treatment benefits extended across languages, demonstrating cross-language generalization to multiple domains of language processing. Findings highlight SFT’s capacity to drive comprehensive language recovery in BWA, revealing broad generalization effects across languages and linguistic domains. Such effects underscore the importance of systematically examining generalization patterns to optimize rehabilitation outcomes.
Q&A with Meryem Yücel
What is this paper about?
The paper investigates how individual hair and skin characteristics affect the quality of fNIRS signals and provides practical recommendations to improve inclusivity in fNIRS studies. By systematically analyzing factors such as hair density, hair thickness, hair color, and skin pigmentation, the study identifies how these biophysical traits influence signal acquisition and hemodynamic measurements. The results are important because they highlight sources of variability that could bias neuroimaging outcomes and provide actionable guidelines for researchers to ensure more equitable and reliable fNIRS data across diverse populations.
How do the findings relate to the brain and recovery?
This paper investigates how individual biophysical differences—like hair thickness, hair color, skin pigmentation, head size, sex, and age—affect the quality of fNIRS signals. fNIRS measures changes in blood oxygenation and blood flow in the brain, which are directly linked to neural activity. High-quality, reliable measurements are essential to accurately understand how the brain functions in different contexts, including learning, cognitive tasks, or recovery after injury.
If certain groups of people (e.g., those with darker skin or thick hair) have lower signal quality due to these physical factors, studies may inadvertently underrepresent or mischaracterize their brain activity. By identifying these sources of variability and providing practical recommendations—such as improved cap fitting, optode placement, hair management strategies, and standardized metadata—the paper helps ensure that fNIRS can capture neural activity accurately across all individuals.
In terms of recovery, these findings are important because fNIRS is often used to monitor brain rehabilitation after stroke, traumatic brain injury, or neurodegenerative conditions. Optimizing signal quality across diverse participants ensures that clinicians and researchers can reliably track brain activity patterns, evaluate recovery progress, and tailor interventions for each individual, rather than having biased or incomplete data.