Impact of Hair and Skin Characteristics on Brain Imaging: Enhancing Inclusivity in fNIRS Research

Functional near-infrared spectroscopy (fNIRS) is an innovative, non-invasive neuroimaging technique that measures blood oxygenation levels associated with brain activity using near-infrared light. Unlike traditional methods such as fMRI, fNIRS offers the advantage of being more adaptable for use outside controlled laboratory environments, making it ideal for diverse research settings.

The core of fNIRS involves participants wearing a specialized cap fitted with optodes—light sources and detectors—that emit and measure near-infrared light reflected from the scalp. These measurements allow researchers to approximate blood oxygen levels in the brain’s outer regions. However, the effectiveness of fNIRS signals can be significantly influenced by various biophysical factors, including individual differences in hair and skin characteristics.

A recent study conducted by researchers at Boston University aimed to quantify how factors like hair density, color, type, and skin pigmentation impact the quality of signals obtained through fNIRS. This research is especially important as it addresses ongoing concerns about inclusivity in neuroimaging studies, which historically have underrepresented certain demographic groups.

The team analyzed data from over 100 participants while meticulously documenting their hair and skin attributes. They then employed statistical models to assess how each characteristic affected signal quality. Findings indicated that individuals with darker, denser hair and higher skin pigmentation—darker skin tones—tended to produce lower quality signals. These disparities highlight the potential for certain groups to be unintentionally excluded from neuroimaging research due to technical limitations.

Understanding these influences is crucial because it underscores the need for adaptable hardware, improved signal processing techniques, and transparent reporting practices. By addressing these factors, researchers can develop strategies to mitigate disparities, making fNIRS a more equitable tool for both research and clinical applications.

Looking ahead, the researchers are actively working on designing new hardware components, refining data analysis methods, and establishing best practices to enhance inclusivity. Their ultimate goal is to ensure that fNIRS technology benefits all populations—enabling more comprehensive neuroscience research, equitable clinical diagnostics, and accessible brain health monitoring.

In summary, this pioneering work reinforces the importance of considering biological diversity in neuroimaging. By acknowledging and mitigating the effects of hair and skin characteristics, the scientific community can move toward more inclusive and representative neuroimaging practices, broadening the reach and impact of brain research.