Current Status and Issues Regarding Pre-processing of fNIRS Neuroimaging Data: An Investigation of Diverse Signal Filtering Methods Within a General Linear Model Framework

TLDR

Functional near‑infrared spectroscopy studies exhibit substantial heterogeneity in analysis and pre‑processing, and many lack complete methodological descriptions, hindering replication and comparison. The authors aimed to survey typical reporting practices in fNIRS papers and to establish a common preprocessing protocol to guide standardization. They reviewed 110 cognitive‑neuroscience fNIRS articles from 2016 and conducted simulation analyses with synthetic data to optimize the filtering step before applying a GLM for statistical inference. The results revealed widespread omission of key methodological details, great variability in filtering approaches, and that a 1000th‑order band‑pass FIR filter most effectively removes noise and recovers hemodynamic responses in a GLM framework, providing preliminary recommendations for improving fNIRS analysis and reporting.

Abstract

Functional near-infrared spectroscopy (fNIRS) research articles show a large heterogeneity in the analysis approaches and pre-processing procedures. Additionally, there is often a lack of a complete description of the methods applied, necessary for study replication or for results comparison. The aims of this paper were (i) to review and investigate which information is generally included in published fNIRS papers, and (ii) to define a signal pre-processing procedure to set a common ground for standardization guidelines. To this goal, we have reviewed 110 fNIRS articles published in 2016 in the field of cognitive neuroscience, and performed a simulation analysis with synthetic fNIRS data to optimize the signal filtering step before applying the GLM method for statistical inference. Our results highlight the fact that many papers lack important information, and there is a large variability in the filtering methods used. Our simulations demonstrated that the optimal approach to remove noise and recover the hemodynamic response from fNIRS data in a GLM framework is to use a 1000th order band-pass Finite Impulse Response filter. Based on these results, we give preliminary recommendations as to the first step toward improving the analysis of fNIRS data and dissemination of the results.