NIRS-KIT: a MATLAB toolbox for both resting-state and task fNIRS data analysis

Abstract. Significance: Functional near-infrared spectroscopy (fNIRS) has been widely used to probe human brain function during task state and resting state. However, the existing analysis toolboxes mainly focus on task activation analysis, few software packages can assist resting-state fNIRS studies. Aim: We aimed to provide a versatile and easy-to-use toolbox to perform analysis for both resting state and task fNIRS. Approach: We developed a MATLAB toolbox called NIRS-KIT that works for both resting-state analysis and task activation detection. Results: NIRS-KIT implements common and necessary processing steps for performing fNIRS data analysis, including data preparation, quality control, preprocessing, individual-level analysis, group-level statistics with several popular statistical models, and multiple comparison correction methods, and finally results visualization. For resting-state fNIRS analysis, functional connectivity analysis, graph theory-based network analysis, and amplitude of low-frequency fluctuations analysis are provided. Additionally, NIRS-KIT also supports activation analysis for task fNIRS. Conclusions: NIRS-KIT offers an open source tool for researchers to analyze resting-state and/or task fNIRS data in one suite. It contains several key features: (1) good compatibility, supporting multiple fNIRS recording systems, data formats of NIRS-SPM and Homer2, and the shared near-infrared spectroscopy format data format recommended by the fNIRS society; (2) flexibility, supporting customized preprocessing scripts; (3) ease-to-use, allowing processing fNIRS signals in batch manner with user-friendly graphical user interfaces; and (4) feature-packed data viewing and result visualization. We anticipate that this NIRS-KIT will facilitate the development of the fNIRS field.

[1]  Clara A. Scholl,et al.  Synchronized delta oscillations correlate with the resting-state functional MRI signal , 2007, Proceedings of the National Academy of Sciences.

[2]  Hiroki Sato,et al.  A NIRS–fMRI investigation of prefrontal cortex activity during a working memory task , 2013, NeuroImage.

[3]  Xi-Nian Zuo,et al.  REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing , 2011, PloS one.

[4]  Haijing Niu,et al.  The development of functional network organization in early childhood and early adolescence: A resting-state fNIRS study , 2018, Developmental Cognitive Neuroscience.

[5]  David A. Boas,et al.  A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans , 2006, NeuroImage.

[6]  Klaus-Robert Müller,et al.  A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy , 2019, NeuroImage.

[7]  Ying Han,et al.  Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: A resting-state fMRI study , 2011, NeuroImage.

[8]  Sheng Zhang,et al.  Changes in cerebral morphometry and amplitude of low-frequency fluctuations of BOLD signals during healthy aging: correlation with inhibitory control , 2013, Brain Structure and Function.

[9]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Bharat B. Biswal,et al.  Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity , 2010, NeuroImage.

[11]  Quan Zhang,et al.  Adaptive filtering to reduce global interference in non-invasive NIRS measures of brain activation: How well and when does it work? , 2009, NeuroImage.

[12]  Thomas E. Nichols,et al.  Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate , 2002, NeuroImage.

[13]  J. Hirsch,et al.  The present and future use of functional near‐infrared spectroscopy (fNIRS) for cognitive neuroscience , 2018, Annals of the New York Academy of Sciences.

[14]  Sergiu Groppa,et al.  Dynamics of the human brain network revealed by time-frequency effective connectivity in fNIRS. , 2017, Biomedical optics express.

[15]  D. Boas,et al.  HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain. , 2009, Applied optics.

[16]  Tülay Adali,et al.  Independent Component Analysis by Entropy Bound Minimization , 2010, IEEE Transactions on Signal Processing.

[17]  Huilin Zhu,et al.  Decreased functional connectivity and disrupted neural network in the prefrontal cortex of affective disorders: A resting-state fNIRS study. , 2017, Journal of affective disorders.

[18]  Toru Yamada,et al.  Separation of fNIRS Signals into Functional and Systemic Components Based on Differences in Hemodynamic Modalities , 2012, PloS one.

[19]  H. Jasper,et al.  The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

[20]  M. Ferrari,et al.  A Mini-Review on Functional Near-Infrared Spectroscopy (fNIRS): Where Do We Stand, and Where Should We Go? , 2019, Photonics.

[21]  Chaogan Yan,et al.  DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI , 2010, Front. Syst. Neurosci..

[22]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[23]  Jinrui Zhang,et al.  FC-NIRS: A Functional Connectivity Analysis Tool for Near-Infrared Spectroscopy Data , 2015, BioMed research international.

[24]  C. Kranczioch,et al.  Signal Processing in fNIRS: A Case for the Removal of Systemic Activity for Single Trial Data , 2019, Front. Hum. Neurosci..

[25]  Lauren L Emberson,et al.  Isolating the effects of surface vasculature in infant neuroimaging using short-distance optical channels: a combination of local and global effects , 2016, Neurophotonics.

[26]  Lian Duan,et al.  Quantitative comparison of resting-state functional connectivity derived from fNIRS and fMRI: A simultaneous recording study , 2012, NeuroImage.

[27]  Qi Dong,et al.  Applications of Resting-State fNIRS in the Developing Brain: A Review From the Connectome Perspective , 2020, Frontiers in Neuroscience.

[28]  Gary H. Glover,et al.  A quantitative comparison of NIRS and fMRI across multiple cognitive tasks , 2011, NeuroImage.

[29]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[30]  David A. Boas,et al.  A Quantitative Comparison of Simultaneous BOLD fMRI and NIRS Recordings during Functional Brain Activation , 2002, NeuroImage.

[31]  Massimo Marchiori,et al.  Economic small-world behavior in weighted networks , 2003 .

[32]  Mary E. Meyerand,et al.  The influence of spatial resolution and smoothing on the detectability of resting-state and task fMRI , 2014, NeuroImage.

[33]  Guy A. Dumont,et al.  Analyzing the resting state functional connectivity in the human language system using near infrared spectroscopy , 2014, Front. Hum. Neurosci..

[34]  Wei Liu,et al.  Assessment of trait anxiety and prediction of changes in state anxiety using functional brain imaging: A test–retest study , 2016, NeuroImage.

[35]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[36]  Hanli Liu,et al.  Exploring brain functional connectivity in rest and sleep states: a fNIRS study , 2018, Scientific Reports.

[37]  Meryem A Yücel,et al.  Functional Near Infrared Spectroscopy: Enabling Routine Functional Brain Imaging. , 2017, Current opinion in biomedical engineering.

[38]  Marco Ferrari,et al.  Functional Near-Infrared Spectroscopy (fNIRS) for Assessing Cerebral Cortex Function During Human Behavior in Natural/Social Situations: A Concise Review , 2019 .

[39]  B. T. Thomas Yeo,et al.  Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: Issues and recommendations , 2017, NeuroImage.

[40]  V. Haughton,et al.  Mapping functionally related regions of brain with functional connectivity MR imaging. , 2000, AJNR. American journal of neuroradiology.

[41]  Ilias Tachtsidis,et al.  Current Status and Issues Regarding Pre-processing of fNIRS Neuroimaging Data: An Investigation of Diverse Signal Filtering Methods Within a General Linear Model Framework , 2019, Front. Hum. Neurosci..

[42]  Martin Wolf,et al.  A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology , 2014, NeuroImage.

[43]  Xu Cui,et al.  Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics , 2010, NeuroImage.

[44]  Masa-aki Sato,et al.  Reduction of global interference of scalp-hemodynamics in functional near-infrared spectroscopy using short distance probes , 2016, NeuroImage.

[45]  Masako Okamoto,et al.  Automated cortical projection of head-surface locations for transcranial functional brain mapping , 2005, NeuroImage.

[46]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[47]  Karl J. Friston.  Functional and Effective Connectivity: A Review , 2011, Brain Connect..

[48]  M. V. D. Heuvel,et al.  Exploring the brain network: A review on resting-state fMRI functional connectivity , 2010, European Neuropsychopharmacology.

[49]  David A Boas,et al.  Eigenvector-based spatial filtering for reduction of physiological interference in diffuse optical imaging. , 2005, Journal of biomedical optics.

[50]  Chaozhe Zhu,et al.  An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF , 2008, Journal of Neuroscience Methods.

[51]  É. Moulines,et al.  Second Order Blind Separation of Temporally Correlated Sources , 1993 .

[52]  Bharat B. Biswal,et al.  The oscillating brain: Complex and reliable , 2010, NeuroImage.

[53]  Yong He,et al.  Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. , 2007, Brain & development.

[54]  Zhen Yuan,et al.  Effective Connectivity of the Fronto-Parietal Network during the Tangram Task in a Natural Environment , 2019, Neuroscience.

[55]  J. Jean Chen,et al.  Intrinsic Frequencies of the Resting-State fMRI Signal: The Frequency Dependence of Functional Connectivity and the Effect of Mode Mixing , 2019, Front. Neurosci..

[56]  Meryem A Yücel,et al.  Short separation regression improves statistical significance and better localizes the hemodynamic response obtained by near-infrared spectroscopy for tasks with differing autonomic responses , 2015, Neurophotonics.

[57]  Masako Okamoto,et al.  Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10–20 system oriented for transcranial functional brain mapping , 2004, NeuroImage.

[58]  Chandan J. Vaidya,et al.  Temporal Derivative Distribution Repair (TDDR): A motion correction method for fNIRS , 2019, NeuroImage.

[59]  Sungho Tak,et al.  NIRS-SPM: Statistical parametric mapping for near-infrared spectroscopy , 2009, NeuroImage.

[60]  Qitao Tan,et al.  Effective Connectivity Analysis of the Brain Network in Drivers during Actual Driving Using Near-Infrared Spectroscopy , 2017, Front. Behav. Neurosci..

[61]  Aaron T. Buss,et al.  Validating an image-based fNIRS approach with fMRI and a working memory task , 2017, NeuroImage.

[62]  Andreas Daffertshofer,et al.  Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory , 2010, PloS one.

[63]  Yufeng Zang,et al.  Functional connectivity as revealed by independent component analysis of resting-state fNIRS measurements , 2010, NeuroImage.

[64]  Marco Ferrari,et al.  A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application , 2012, NeuroImage.

[65]  Eric Moreau,et al.  A generalization of joint-diagonalization criteria for source separation , 2001, IEEE Trans. Signal Process..

[66]  Norihiro Sadato,et al.  A NIRS–fMRI study of resting state network , 2012, NeuroImage.

[67]  Atsushi Maki,et al.  Tutorial on platform for optical topography analysis tools , 2016, Neurophotonics.

[68]  C. Pipper,et al.  [''R"--project for statistical computing]. , 2008, Ugeskrift for laeger.

[69]  Yong He,et al.  GRETNA: a graph theoretical network analysis toolbox for imaging connectomics , 2015, Front. Hum. Neurosci..

[70]  Yong He,et al.  BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics , 2013, PloS one.

[71]  J. Kim,et al.  Investigation of brain functional connectivity in patients with mild cognitive impairment: A functional near‐infrared spectroscopy (fNIRS) study , 2019, Journal of biophotonics.

[72]  I. Miyai,et al.  Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis. , 2007, Journal of biomedical optics.

[73]  Hanli Liu,et al.  EasyTopo: A toolbox for rapid diffuse optical topography based on a standard template of brain atlas , 2013, Photonics West - Biomedical Optics.

[74]  Felix Scholkmann,et al.  Signal Processing in Functional Near-Infrared Spectroscopy (fNIRS): Methodological Differences Lead to Different Statistical Results , 2018, Front. Hum. Neurosci..

[75]  Archana K. Singh,et al.  Spatial registration of multichannel multi-subject fNIRS data to MNI space without MRI , 2005, NeuroImage.

[76]  D. Delpy,et al.  System for long-term measurement of cerebral blood and tissue oxygenation on newborn infants by near infra-red transillumination , 1988, Medical and Biological Engineering and Computing.

[77]  Ross E. Vanderwert,et al.  The use of near-infrared spectroscopy in the study of typical and atypical development , 2014, NeuroImage.

[78]  Y. Zang,et al.  RESTplus: an improved toolkit for resting-state functional magnetic resonance imaging data processing. , 2019, Science bulletin.

[79]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[80]  D. Boas,et al.  Non-invasive neuroimaging using near-infrared light , 2002, Biological Psychiatry.

[81]  David A. Boas,et al.  Improved recovery of the hemodynamic response in diffuse optical imaging using short optode separations and state-space modeling , 2011, NeuroImage.

[82]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[83]  V Latora,et al.  Efficient behavior of small-world networks. , 2001, Physical review letters.

[84]  Chaozhe Zhu,et al.  Use of fNIRS to assess resting state functional connectivity , 2010, Journal of Neuroscience Methods.

[85]  Harry L. Graber,et al.  nirsLAB: A Computing Environment for fNIRS Neuroimaging Data Analysis , 2014 .