title: Statistical analysis of fNIRS data: a comprehensive review.
abstract: Functional near-infrared spectroscopy (fNIRS) is a non-invasive method to measure brain activities using the changes of optical absorption in the brain through the intact skull. fNIRS has many advantages over other neuroimaging modalities such as positron emission tomography (PET), functional magnetic resonance imaging (fMRI), or magnetoencephalography (MEG), since it can directly measure blood oxygenation level changes related to neural activation with high temporal resolution. However, fNIRS signals are highly corrupted by measurement noises and physiology-based systemic interference. Careful statistical analyses are therefore required to extract neuronal activity-related signals from fNIRS data. In this paper, we provide an extensive review of historical developments of statistical analyses of fNIRS signal, which include motion artifact correction, short source-detector separation correction, principal component analysis (PCA)/independent component analysis (ICA), false discovery rate (FDR), serially-correlated errors, as well as inference techniques such as the standard t-test, F-test, analysis of variance (ANOVA), and statistical parameter mapping (SPM) framework. In addition, to provide a unified view of various existing inference techniques, we explain a linear mixed effect model with restricted maximum likelihood (ReML) variance estimation, and show that most of the existing inference methods for fNIRS analysis can be derived as special cases. Some of the open issues in statistical analysis are also described.
authors.affiliation: Bio Imaging & Signal Processing Lab., Dept. of Bio & Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 373-1 Guseong-dong, Yuseong-gu, Daejon 305-701, Republic of Korea; Rotman Research Institute at Baycrest Centre, University of Toronto, Toronto, Ontario M6A 2E1, Canada.
authors.forename: Sungho
authors.initials: S
authors.lastname: Tak
authors.forename: Jong Chul
authors.initials: JC
authors.lastname: Ye
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D059907:Functional Neuroimaging / Q000379:methods / Q000706:statistics & numerical data*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D012039:Regional Blood Flow / Q000502:physiology; D012867:Skin / Q000098:blood supply; D019265:Spectroscopy, Near-Infrared / Q000379:methods / Q000706:statistics & numerical data*
keywords: Correlation analysis; Data-driven analysis; GLM; Group analysis; Multi-level analysis; Multiple comparison; Spectral analysis; Statistical parameter mapping; fNIRS; t-Test