# Developing Best Practices for Inclusion in fNIRS Research: Equity for Participants With Afro‐Textured Hair

## TL;DR

This paper presents best practices for including participants with Afro-textured hair in fNIRS studies to promote equity in neuroimaging research.

## Contribution

The study introduces culturally responsive techniques to improve sensor contact and inclusion of Black participants in fNIRS research.

## Key findings

- Signal quality improved by 50% on average using the developed hair preparation techniques.
- The greatest improvements were observed in anterior brain regions.
- Participants with Afro-textured hair were successfully included in a diverse dataset.

## Abstract

Functional near‐infrared spectroscopy (fNIRS) is a popular optical neuroimaging method; however, participants with Afro‐textured (i.e., dark, coarse, curly) hair are often excluded due to difficulty obtaining sensor–scalp contact. Grounded in lived experience and sociocultural literature, we aimed to develop and evaluate culturally responsive best practices for participant interaction and hair preparation to increase Black participant inclusion in fNIRS research. First, we developed an intake survey, guidelines for researcher staffing and training, and a suite of customizable hair preparation techniques that prioritize participant comfort and hair integrity. We then evaluated these techniques with 19 Black participants (11 adults, eight children) with varying hair types/styles; methods included braiding cornrows around the intended optode montage, using gels and clips to part hair, and various ways of increasing tension to promote sensor–scalp contact. On average, signal quality improved by 50%, with the greatest improvements in anterior regions. While signal quality was not perfect, it was generally improved to the point of acceptability and inclusion in a racially/ethnically diverse dataset (with hair type/color as covariates). We conclude with recommendations for increasing awareness of racial bias in neuroimaging, greater diversity in research teams, and a more inclusive approach for working with diverse populations.