Algorithms · 2018 · 453 citations · 51 references
MedicineBiomedical EngineeringNeuroscienceBiomedical ImagingBiomedical Signal ProcessingNeurophysiologyBrain ImagingCerebral Blood FlowFunctional NeuroimagingInfrared SpectroscopyNear-infrared ImagingNear-infrared SpectroscopyAdvanced NeuroimagingBlood Oxygenation Level-dependent ContrastBlood Oxygenation Level-dependent ImagingBrain Function MappingBrain Functional ImagingFunctional Near-infrared SpectroscopySingle-subject Data Analysis
Functional near‑infrared spectroscopy (fNIRS) uses 650–900 nm light to noninvasively measure cerebral blood volume and oxygenation, offering a low‑cost, portable alternative to fMRI that has expanded into many functional and resting‑state studies but presents unique physics, statistical, and design challenges. The authors aim to develop dedicated analysis methods for fNIRS data. They present the NIRS Brain AnalyzIR toolbox, an open‑source MATLAB package built on object‑oriented design that implements algorithms for data management, preprocessing, statistical analysis, probe registration, image reconstruction, and ROI‑based statistics.
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.
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