# Mitigating the statistical bias due to anatomical variation in pediatric fNIRS

> **NIH NIH R21** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $407,268

## Abstract

Title: Mitigating the statistical bias due to anatomical variation in pediatric fNIRS
Abstract: Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging method that records
changes in blood oxygenation in the brain using optical sensors placed on the scalp. Although this technique is
widely used for functional imaging, this approach does not provide any direct anatomical information about the
structure of the head, such as layer thicknesses and brain depth beneath the sensors. As a result, the
magnitude of fNIRS signals is confounded by variations in this unknown underlying anatomy which introduces
the potential for statistical bias when comparing across groups and in longitudinal studies. This is particularly
relevant for pediatric and developmental studies where this anatomy is expected to systematically vary across
sample groups. Currently in the fNIRS field, corrections for this unknown anatomy have been proposed using
semi-empirical formulas, look-up tables, or off-the-shelf statistical mediation models such as mixed-effects
modeling. However, these corrections are not able to account for the complexity of this problem and unable to
model heterogeneity across subjects and spatial locations. In this work, we propose to develop a statistical
correction method based on the construction of a database of distributions of these corrections across
demographics and spatial locations on the head obtained from analysis of existing pediatric anatomical MRI
data. We propose to use these distributions as priors in the analysis of fNIRS group level data to model bias
and uncertainty introduced by unknown anatomical structures.
Aim 1. Characterize how key anatomical and demographic factors influence fNIRS measurements.
Aim 2. Development and characterization of our proposed novel anatomical statistical distribution
model.
Aim 3. Integrate the proposed model into our existing open source fNIRS toolbox.

## Key facts

- **NIH application ID:** 10987825
- **Project number:** 1R21HD110747-01A1
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Theodore James Huppert
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $407,268
- **Award type:** 1
- **Project period:** 2024-09-20 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10987825

## Citation

> US National Institutes of Health, RePORTER application 10987825, Mitigating the statistical bias due to anatomical variation in pediatric fNIRS (1R21HD110747-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10987825. Licensed CC0.

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