Near-infrared spectroscopy (NIRS) is a noninvasive neuroimaging tool for studying evoked hemodynamic changes within the brain. By this technique, changes in the optical absorption of light are recorded over time and are used to estimate the functionally evoked changes in cerebral oxyhemoglobin and deoxyhemoglobin concentrations that result from local cerebral vascular and oxygen metabolic effects during brain activity. Over the past three decades this technology has continued to grow, and today NIRS studies have found many niche applications in the fields of psychology, physiology, and cerebral pathology. The growing popularity of this technique is in part associated with a lower cost and increased portability of NIRS equipment when compared with other imaging modalities, such as functional magnetic resonance imaging and positron emission tomography. With this increasing number of applications, new techniques for the processing, analysis, and interpretation of NIRS data are continually being developed. We review some of the time-series and functional analysis techniques that are currently used in NIRS studies, we describe the practical implementation of various signal processing techniques for removing physiological, instrumental, and motion-artifact noise from optical data, and we discuss the unique aspects of NIRS analysis in comparison with other brain imaging modalities. These methods are described within the context of the MATLAB-based graphical user interface program, HomER, which we have developed and distributed to facilitate the processing of optical functional brain data.

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 a noninvasive brain imaging technique that uses low levels of red to near-infrared light to measure changes in oxy- and deoxy-hemoglobin in the brain.¹ Biological tissue in the range of around 650 to 900 nm (termed the “near-infrared window”) has low intrinsic absorption and allows light to remain detectable after passing through up to centimeters of tissue. In this wavelength range, tissue is highly turbid (scattering), which results in a stochastic and diffuse path as the photons migrate through the tissue. fNIRS brain imaging is accomplished by placing light sources on the scalp of the head. These transmit light into the tissue, where it diffuses through the volume and can reach around 5 to 8 mm into the cortex of the brain before exiting the tissue and being sensed by light detectors placed generally between 2 and 3.5 cm from the source. Changes in the optical absorption of the tissue along this diffuse path from source to detector will change the intensity of light received by the detector, which is recorded as a time series, usually at sample rates of several hertz or above. Measurements at two or more optical wavelengths within this near-infrared window are used to provide a distinction between oxy- and deoxy-hemoglobin changes based on the intrinsic absorption profile differences of these two chromophores and the modified Beer–Lambert law.²

Functional optical brain imaging measurements require a fast image acquisition rate and coverage of a large area of the brain. A fast acquisition rate permits simple filtering of faster hemoglobin changes arising from, for example, cardiac pulsation from the relatively slow evoked hemodynamic response to brain activation. A spatial coverage not limited to the activation area but extended to areas not involved with the stimulation allows separation of evoked hemodynamic changes from global systemic oscillations of hemoglobin at similar frequencies, such as Mayer waves and respiration.¹ With this purpose in mind, we have developed a fast multichannel optical imaging system with a large number of channels and a parallel acquisition scheme to allow a fast acquisition rate with a sufficiently high signal-to-noise ratio (SNR) to characterize brain activation and the background physiological fluctuations.

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