Abstract
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.
Highlights
Functional near-infrared spectroscopy is a non-invasive technique that uses low levels of red to near-infrared light (650–900 nm) to measure changes in the optical properties of tissue; those due to changes in blood/hemoglobin volume and oxygenation [1,2,3]
Within the AnalyzIR toolbox, data analysis is performed by a collection of processing modules, which are encapsulated within an abstract nirs.modules class definition
There are flags on the module to control the behavior of this filter to correct for motion or physiology
Summary
Functional near-infrared spectroscopy (fNIRS) is a non-invasive technique that uses low levels of red to near-infrared light (650–900 nm) to measure changes in the optical properties of tissue; those due to changes in blood/hemoglobin volume and oxygenation [1,2,3]. FNIRS studies have been expanded to child and infant populations (reviewed in [7]), to allow a range of motion (including fNIRS studies of brain activity during gait or balance [11]), and more “real-world” experience (reviewed in [12]) These studies create challenges to analysis such as the need to deal with complex sources of motion and/or physiological noise artifacts. The majority of fNIRS studies using tools and methods have either borrowed from other fields (primarily functional MRI) or have used modality-agnostic methods such as ordinary least-squares regression or methods coded in general programs such as statistical package for the social sciences (SPSS) [13] or statistical analysis system (SAS) [14] In general these methods are not designed to address the specific and unique features of fNIRS data and these make some assumptions that may not be optimal for the non-ideal noise structures and types of artifacts typically present in fNIRS signals. FNIRS analysis packages, which allows a head-to-head comparison of various analysis options
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