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

Paola Pinti,Antonia Hamilton,Paul Burgess,Ilias Tachtsidis,Felix Scholkmann

doi:10.3389/fnhum.2018.00505

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.

Highlights

The last few years have seen a rapid growth in the number of functional neuroimaging studies performed and published with functional near-infrared spectroscopy (Yücel et al, 2017). fNIRS has provided neuroscientists and clinicians with a novel and invaluable tool to study and monitor tissue oxygenation changes in the brain noninvasively
One of the major fields of application of fNIRS is cognitive neuroscience, where the mechanisms underlying brain functioning are typically investigated by monitoring the task or stimulus-evoked changes in the brain during the execution of cognitive tasks. fNIRS is well-suited to this application since it allows the study of cognition with very few physical constraints, allowing brain monitoring in a wide range of cognitive tasks, e.g., those including bodily movements, and in a variety of populations, e.g., infants, healthy adults, clinical patients
Note: If the authors stated in the paper that they used the Homer2 software package for their analysis and did not report any information about the filter type, we automatically considered they used a 3rd order Butterworth filter as this is the default option in the software

Summary

Introduction

The last few years have seen a rapid (almost exponential) growth in the number of functional neuroimaging studies performed and published with functional near-infrared spectroscopy (fNIRS) (Yücel et al, 2017). fNIRS has provided neuroscientists and clinicians with a novel and invaluable tool to study and monitor tissue oxygenation changes in the brain noninvasively. One of the major fields of application of fNIRS is cognitive neuroscience, where the mechanisms underlying brain functioning are typically investigated by monitoring the task or stimulus-evoked changes in the brain during the execution of cognitive tasks (see Pinti et al, 2018 for review). FNIRS is well-suited to this application since it allows the study of cognition with very few physical constraints, allowing brain monitoring in a wide range of cognitive tasks, e.g., those including bodily movements, and in a variety of populations, e.g., infants, healthy adults, clinical patients. A typical sequence of steps performed in a neuroscience with fNIRS is shown, usually comprising 4 main steps. A mixed block/eventrelated design can be employed (Petersen and Dubis, 2012)

Objectives

Methods

Results

Conclusion