Poster No:
2427
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 neuroimaging technique that uses low-levels of light (650-900 nm) to noninvasively measure changes in cerebral blood volume and oxygenation (Scholkmann, Kleiser et al. 2014). Over the last few years, this technique has been utilized in a growing number of resting-state and functional brain activity. However, the analysis of fNIRS data poses several challenges stemming from the unique physics of the technique, the unique statistical properties of data (Huppert 2016), and the growing diversity of non-traditional experimental design 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 poster, we introduce the NIRS Brain AnalyzIR toolbox as an open-source Matlab-based analysis package for fNIRS data management, pre-processing, first- and second-level statistical analysis. Here, we describe the basic architectural format of this toolbox, which is based on the object-oriented programming paradigm. Algorithms detail for several of the major components of the toolbox including statistical analysis, probe registration, image reconstruction, and region-of-interest based statistics have been previously described (Santosa, Zhai et al. 2018).
Methods:
Statistical Modules: The AnalyzIR toolbox contains number of processing modules for statistical analysis for both first- and higher-level models. These include time-series regression models (e.g., OLS (Huppert, Diamond et al. 2009) and AR-IRLS (Barker, Aarabi et al. 2013)), functional connectivity models (Santosa, Aarabi et al. 2017), image reconstruction methods (Zhai, Santosa et al. 2023), and higher order mixed effects and ANOVA models (Matlab: fitlme.m function).
Image Reconstruction Modules: Based on the optical forward model, the toolbox reconstructs the subject image by solving the underdetermined linear system between changes in concentrations of HbO2 and Hb in the tissue and the changes in optical density using a hierarchal Bayesian inverse model (Zhai, Santosa et al. 2023).
Connectivity and Hyper-Scanning Modules: The toolbox contains several modules for connectivity analysis and hyperscanning analysis (two or more subject are simultaneously recorded during an interactive task and the brain signals between subjects is analyzed). This analysis includes our proposed method for robust methods (Santosa, Aarabi et al. 2017).
Toolbox Utilities: The toolbox offers additional utilities for probe registration and adjusting for head size, region-of-interest analysis, and other features.
Results:
Architecture of Toolbox: The AnalyzIR toolbox is an open-source analysis package which utilizes both custom namespace and class definitions written in Matlab language to provide an object-oriented programming interface to performing fNIRS analysis. Pats of the toolbox interface to fNIRS forward models (models of the light propagation through tissue/brain) require separate download of the software such as NIRSFAST, Iso2mesh, MCextreme, MMC, tMCimg software packages. The toolbox currently also includes several functions for generating synthetic or semi-synthetic (experimental baseline data with synthetically added "evoked" response) data for testing purposes (ROC analysis) and offers several example scripts and tutorials including several full datasets that can be downloaded with code.
Graphical Interfaces: Although the toolbox is primarily a command-line interface, there are available several GUIs, such as nirs.viz.jobmanager, nirs.viz.nirsviewer, nirs.viz.StimUtil, etc.
Conclusions:
Future Direction: While the toolbox is written primarily for fNIRS data, it does provide limited support for EEG, MEG, and fMRI dense time-series data. We welcome more interaction with other researchers from various modalities on this toolbox with a particular focus on developing multimodal methods within this common framework.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Methods Development 2
Motion Correction and Preprocessing
Novel Imaging Acquisition Methods:
Multi-Modal Imaging
NIRS 1
Keywords:
Data analysis
Electroencephaolography (EEG)
Near Infra-Red Spectroscopy (NIRS)
Open-Source Software
OPTICAL
Optical Imaging Systems (OIS)
1|2Indicates the priority used for review
Provide references using author date format
Barker, J. W., A. Aarabi and T. J. Huppert (2013). "Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS." Biomedical Optics Express 4(8): 1366-1379.
Huppert, T. J. (2016). "Commentary on the statistical properties of noise and its implication on general linear models in functional near-infrared spectroscopy." Neurophotonics 3(1).
Huppert, T. J., S. G. Diamond, M. A. Franceschini and D. A. Boas (2009). "HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain." Applied Optics 48(10): D280-D298.
Santosa, H., A. Aarabi, S. B. Perlman and T. J. Huppert (2017). "Characterization and correction of the false-discovery rates in resting state connectivity using functional near-infrared spectroscopy." J Biomed Opt 22(5): 55002.
Santosa, H., X. Zhai, F. Fishburn and T. Huppert (2018). "The NIRS brain AnalyzIR toolbox." Algorithms 11(5): 73.
Scholkmann, F., S. Kleiser, A. J. Metz, R. Zimmermann, J. M. Pavia, U. Wolf and M. Wolf (2014). "A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology." NeuroImage 85: 6-27.
Zhai, X., H. Santosa, R. T. Krafty and T. J. Huppert (2023). "Brain space image reconstruction of functional near-infrared spectroscopy using a Bayesian adaptive fused sparse overlapping group lasso model." Neurophotonics 10(2): 023516.