FC-NIRS: A Functional Connectivity Analysis Tool for Near-Infrared Spectroscopy Data

Functional near-infrared spectroscopy (fNIRS), a promising noninvasive imaging technique, has recently become an increasingly popular tool in resting-state brain functional connectivity (FC) studies. However, the corresponding software packages for FC analysis are still lacking. To facilitate fNIRS-based human functional connectome studies, we developed a MATLAB software package called “functional connectivity analysis tool for near-infrared spectroscopy data” (FC-NIRS). This package includes the main functions of fNIRS data preprocessing, quality control, FC calculation, and network analysis. Because this software has a friendly graphical user interface (GUI), FC-NIRS allows researchers to perform data analysis in an easy, flexible, and quick way. Furthermore, FC-NIRS can accomplish batch processing during data processing and analysis, thereby greatly reducing the time cost of addressing a large number of datasets. Extensive experimental results using real human brain imaging confirm the viability of the toolbox. This novel toolbox is expected to substantially facilitate fNIRS-data-based human functional connectome studies.

[1]  Tomer Fekete,et al.  The NIRS Analysis Package: Noise Reduction and Statistical Inference , 2011, PloS one.

[2]  I. Dan,et al.  Sound to Language: Different Cortical Processing for First and Second Languages in Elementary School Children as Revealed by a Large-Scale Study Using fNIRS , 2011, Cerebral cortex.

[3]  Bharat B. Biswal,et al.  Determination of Dominant Frequency of Resting-State Brain Interaction within One Functional System , 2012, PloS one.

[4]  Olaf Sporns,et al.  The Human Connectome: A Structural Description of the Human Brain , 2005, PLoS Comput. Biol..

[5]  Guillaume Flandin,et al.  Functional optical signal analysis: a software tool for near-infrared spectroscopy data processing incorporating statistical parametric mapping. , 2007, Journal of biomedical optics.

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

[7]  Yong He,et al.  Revealing Topological Organization of Human Brain Functional Networks with Resting-State Functional near Infrared Spectroscopy , 2012, PloS one.

[8]  Shuntaro Sasai,et al.  Frequency-specific functional connectivity in the brain during resting state revealed by NIRS , 2011, NeuroImage.

[9]  G. Dumont,et al.  Wavelet based motion artifact removal for Functional Near Infrared Spectroscopy , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[10]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[11]  G. Taga,et al.  Brain imaging in awake infants by near-infrared optical topography , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Hellmuth Obrig,et al.  A wearable multi-channel fNIRS system for brain imaging in freely moving subjects , 2014, NeuroImage.

[13]  Abraham Z. Snyder,et al.  Resting-state functional connectivity in the human brain revealed with diffuse optical tomography , 2009, NeuroImage.

[14]  J. Mehler,et al.  The neonate brain detects speech structure , 2008, Proceedings of the National Academy of Sciences.

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

[16]  Sungho Tak,et al.  Statistical analysis of fNIRS data: A comprehensive review , 2014, NeuroImage.

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

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

[19]  E. Niebuhr,et al.  Down's syndrome , 1974, Humangenetik.

[20]  A. Eke,et al.  The modified Beer–Lambert law revisited , 2006, Physics in medicine and biology.

[21]  Brian R. White,et al.  Bedside optical imaging of occipital resting-state functional connectivity in neonates , 2012, NeuroImage.

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

[23]  Yong He,et al.  Test-Retest Reliability of Graph Metrics in Functional Brain Networks: A Resting-State fNIRS Study , 2013, PloS one.

[24]  G. Taga,et al.  Development of Global Cortical Networks in Early Infancy , 2010, The Journal of Neuroscience.

[25]  Naoto Takahashi,et al.  Functional connectivity of the cortex of term and preterm infants and infants with Down's syndrome , 2014, NeuroImage.

[26]  Quan Zhang,et al.  Near-Infrared Neuroimaging with NinPy , 2009, Front. Neuroinform..

[27]  Haijing Niu,et al.  Resting-state functional connectivity assessed with two diffuse optical tomographic systems. , 2011, Journal of biomedical optics.

[28]  Yong He,et al.  Resting-State Functional Brain Connectivity : Lessons from Functional Near-Infrared Spectroscopy , 2022 .

[29]  Hamid Dehghani,et al.  Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography , 2007, Proceedings of the National Academy of Sciences.

[30]  Bharat B. Biswal,et al.  Detecting resting-state functional connectivity in the language system using functional near-infrared spectroscopy. , 2010, Journal of biomedical optics.

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

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

[33]  Fumitaka Homae,et al.  Prefrontal cortical involvement in young infants' analysis of novelty. , 2009, Cerebral cortex.

[34]  B. Biswal,et al.  Characterizing variation in the functional connectome: promise and pitfalls , 2012, Trends in Cognitive Sciences.

[35]  M Wolf,et al.  How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation , 2010, Physiological measurement.