Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low-levels of light (650–900 nm) to measure changes in cerebral blood volume and oxygenation. Over the last several decades, this technique has been utilized in a growing number of functional and resting-state brain studies. The lower operation cost, portability, and versatility of this method make it an alternative to methods such as functional magnetic resonance imaging for studies in pediatric and special populations and for studies without the confining limitations of a supine and motionless acquisition setup. However, the analysis of fNIRS data poses several challenges stemming from the unique physics of the technique, the unique statistical properties of data, and the growing diversity of non-traditional experimental designs being utilized in studies due to the flexibility of this technology. For these reasons, specific analysis methods for this technology must be developed. In this paper, we introduce the NIRS Brain AnalyzIR toolbox as an open-source Matlab-based analysis package for fNIRS data management, pre-processing, and first- and second-level (i.e., single subject and group-level) statistical analysis. Here, we describe the basic architectural format of this toolbox, which is based on the object-oriented programming paradigm. We also detail the algorithms for several of the major components of the toolbox including statistical analysis, probe registration, image reconstruction, and region-of-interest based statistics.

Functional near-infrared spectroscopy (fNIRS) is an emerging low-cost noninvasive neuroimaging technique that measures cortical bloodflow. While fNIRS has gained interest as a potential alternative to fMRI for use with clinical and pediatric populations, it remains unclear whether fNIRS has the necessary sensitivity to serve as a replacement for fMRI. The present study set out to examine whether fNIRS has the sensitivity to detect linear changes in activation and functional connectivity in response to cognitive load, and functional connectivity changes when transitioning from a task-free resting state to a task. Sixteen young adult subjects were scanned with a continuous-wave fNIRS system during a 10-minute resting-state scan followed by a letter n-back task with three load conditions. Five optical probes were placed over frontal and parietal cortices, covering bilateral dorsolateral PFC (dlPFC), bilateral ventrolateral PFC (vlPFC), frontopolar cortex (FP), and bilateral parietal cortex. Activation was found to scale linearly with working memory load in bilateral prefrontal cortex. Functional connectivity increased with increasing n-back loads for fronto-parietal, interhemispheric dlPFC, and local connections. Functional connectivity differed between the resting state scan and the n-back scan, with fronto-parietal connectivity greater during the n-back, and interhemispheric vlPFC connectivity greater during rest. These results demonstrate that fNIRS is sensitive to both cognitive load and state, suggesting that fNIRS is well-suited to explore the full complement of neuroimaging research questions and will serve as a viable alternative to fMRI.

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique that provides the opportunity to noninvasively monitor hemodynamic activity within the human brain. First demonstrated by Jobsis,¹ the fNIRS technique has been applied to a growing number of populations and experimental studies over the last three decades (reviewed in Refs. 2 and 3). This technique uses diffuse optical measurements in the red to near-infrared range (650 to 900 nm) to measure changes in cerebral blood oxygenation and concentration. These signals are recorded between optical light sources and detectors that are placed on the scalp over the region-of-interest. At the typical source–detector separations used for functional brain imaging of around 25 to 35 mm, light from the scalp can penetrate into the first few millimeters of the cerebral tissue, which allows measurements of many cortical cognitive areas of the brain.² However, since these measurements are made across layers of the highly vascularized skin and skull, fNIRS measurements are often heavily contaminated by these superficial physiological signals (e.g., blood pressure, respiratory, and cardiac signals).^4,5 These superficial systemic physiological noises can be misinterpreted as brain activity and can drive both high false positive and false negative estimates.⁶ Furthermore, these errors are particularly troublesome when there is a possibility of a task-induced systemic change accompanying the evoked brain signals, as may be the case in studies requiring physical movement or inducing anxiety resulting in respiratory and heart rate changes.⁵

Shared intentionality, or collaborative interactions in which individuals have a shared goal and must coordinate their efforts, is a core component of human interaction. However, the biological bases of shared intentionality and, specifically, the processes by which the brain adjusts to the sharing of common goals, remain largely unknown. Using functional near infrared spectroscopy (fNIRS), coordination of cerebral hemodynamic activation was found in subject pairs when completing a puzzle together in contrast to a condition in which subjects completed identical but individual puzzles (same intention without shared intentionality). Interpersonal neural coordination was also greater when completing a puzzle together compared to two control conditions including the observation of another pair completing the same puzzle task or watching a movie with a partner (shared experience). Further, permutation testing revealed that the time course of neural activation of one subject predicted that of their partner, but not that of others completing the identical puzzle in different partner sets. Results indicate unique brain-to-brain coupling specific to shared intentionality beyond what has been previously found by investigating the fundamentals of social exchange.