The NIRS Brain AnalyzIR Toolbox
Poster No:
2061
Submission Type:
Abstract Submission
Authors:
Hendrik Santosa1, Xuetong Zhai1, Theodore Huppert1
Institutions:
1University of Pittsburgh, Pittsburgh, PA
First Author:
Co-Author(s):
Introduction:
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. 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 [1], 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 [2] 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.
Methods:
The AnalyzIR toolbox is an open-source analysis package. This toolbox utilizes both custom namespace and class definitions written in MATLAB (MathWorks, Natick MA USA) language to provide an object-oriented programming interface to performing fNIRS analysis. The toolbox is maintained on a public BitBucket.org project (Atlassian Corp. Sydney Australia. www.bitbucket.org/huppertt/nirs-toolbox) as well as the NIH's NeuroImaging Tools & Resources Collaboratory (NITRC) (https://www.nitrc.org/projects/AnalyzIR). In addition, several demos or examples (e.g., fNIRS analysis, connectivity, group analysis, image reconstruction, registration, etc.) with explanations are provided in the toolbox download.
Results:
Object classes define context-specific methods, such as the drawing commands, which allow the same command (e.g.,) "draw" to act differently depending on the type of object (e.g., time-course, statistics variable, or reconstructed image) that is called upon (see Fig. 1).
Fig. 1. Example plot for data objects: (a) Example time series from 3 fNIRS channels with stimulus information shown along the bottom. An example of the same probe object shown in (b) 2D probe geometry; (c) 10–20 International System, and (d) registered 3D probe geometry is also demonstrated.
Fig. 1. Example plot for data objects: (a) Example time series from 3 fNIRS channels with stimulus information shown along the bottom. An example of the same probe object shown in (b) 2D probe geometry; (c) 10–20 International System, and (d) registered 3D probe geometry is also demonstrated.
·Fig. 1. Example plot for data objects: (a) Example time series (b) 2D probe geometry; (c) 10–20 International System, and (d) registered 3D probe geometry.
Conclusions:
FNIRS technology will continue to evolve alongside other modalities to improve our understanding of human brain function. This open-source toolbox allows other researchers to freely use, modify, or share, while respecting the original toolbox authorship. We welcome more interaction with other researchers from various modalities (e.g., fNIRS, fMRI, MEG, EEG) on this AnalyzIR toolbox with a particular focus on developing multimodal methods within this common framework. The toolbox already supports all four of these modalities in some form. It is our hope that this toolbox will continue to grow and advance with (but not restricted to) the NIRS field. In addition, we already implemented the modules of the short-separation measurements in the toolbox as a popular technique to reduce the systemic physiological noises in the fNIRS signal. We also encourage researchers to use ROC analysis when they are proposing new methods. Doing so will allow proper comparisons of the performance of the proposed method with existing method.
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis
Image Registration and Computational Anatomy
Motion Correction and Preprocessing 2
Novel Imaging Acquisition Methods:
Multi-Modal Imaging
NIRS 1
Keywords:
Data analysis
Design and Analysis
Near Infra-Red Spectroscopy (NIRS)
Statistical Methods
1|2Indicates the priority used for review
My abstract is being submitted as a Software Demonstration.
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using author date format
[2] Santosa, H. et al. (2018), “The NIRS brain AnalyzIR toolbox,” Algorithms, 11(5), 73.