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Estimating Functional Connectivity Symmetry between Oxy- and Deoxy-Haemoglobin: Implications for fNIRS Connectivity Analysis

The NIRS Brain AnalyzIR Toolbox

Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213-2536, USA
Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213-2536, USA
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213-2536, USA
Departments of Radiology and Bioengineering, University of Pittsburgh, Clinical Science Translational Institute, and Center for the Neural Basis of Cognition, Pittsburgh, PA 15213-2536, USA
Author to whom correspondence should be addressed.
Algorithms 2018, 11(5), 73;
Received: 30 March 2018 / Revised: 5 May 2018 / Accepted: 12 May 2018 / Published: 16 May 2018
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. View Full-Text
Keywords: Functional near-infrared spectroscopy; toolbox; statistical analysis Functional near-infrared spectroscopy; toolbox; statistical analysis
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MDPI and ACS Style

Santosa, H.; Zhai, X.; Fishburn, F.; Huppert, T. The NIRS Brain AnalyzIR Toolbox. Algorithms 2018, 11, 73.

AMA Style

Santosa H, Zhai X, Fishburn F, Huppert T. The NIRS Brain AnalyzIR Toolbox. Algorithms. 2018; 11(5):73.

Chicago/Turabian Style

Santosa, Hendrik, Xuetong Zhai, Frank Fishburn, and Theodore Huppert. 2018. "The NIRS Brain AnalyzIR Toolbox" Algorithms 11, no. 5: 73.

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