# Brain AnalyzIR: A software platform for improving scientific rigor in functional NIRS statistical analysis

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $335,299

## Abstract

Abstract
Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging modality that uses low-levels of
light to measure evoked hemodynamic changes in the brain. This technique has been growing in popularity
over the last several decades due its versatility and portability and the applicability of this technique in unique
experimental situations and subject populations, such as studies on children, infants, or using ecologically valid
experimental designs (walking, social interaction, etc). As the number of end-users in this field grows, it is
important to establish scientifically rigorous best practices for analysis and interpretation of these studies. A
fallacy of the fNIRS field has been the direct import of methods and interpretations from other modalities (e.g.
functional MRI) without proper adaptation and generalization for the fNIRS-specific noise and signal properties
of the data. Furthermore, to date, the development of many fNIRS methods has been based on ad-hoc
observations of these algorithms under specific datasets. As a result, end-users often use methods designed
for statistical assumptions that do not match their own data. Failure to use proper statistical models or unmet
assumptions often results in high false-positive rates and poor scientific rigor and this has been the case in
many prior fNIRS studies. The goal of this Biomedical Research Group (BRG-R01) project is to establish
current best practices for fNIRS analysis and an infrastructure for future development based on quantitative
comparisons of methodologies via receiver operator characteristics analysis, quantification of bias, etc. This
project will also establish an open-source fNIRS database to allow characterization and classification of the
various properties of fNIRS signals and to quantify their effect on statistical models. Our group has a long
history of fNIRS analysis and open-source software development over the last 15 years and is considered one
of the top labs in fNIRS analysis. The specific aims of this project are:
Aim 1. Development of an open fNIRS database and benchmarking platform for testing and characterizing the
development of new algorithms and statistical methods.
Aim 2. Determination of best practices for fNIRS analysis under general and categorized noise models.
Aim 3. Continued development and improvement of fNIRS-specific analysis models with focus on end-user
needs and feedback.
Aim4. Dissemination and training of methods.

## Key facts

- **NIH application ID:** 10436947
- **Project number:** 5R01EB028248-04
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Theodore James Huppert
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $335,299
- **Award type:** 5
- **Project period:** 2019-08-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10436947

## Citation

> US National Institutes of Health, RePORTER application 10436947, Brain AnalyzIR: A software platform for improving scientific rigor in functional NIRS statistical analysis (5R01EB028248-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10436947. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*