Commentary : current status and issues regarding pre-processing of fnirs neuroimaging data : an investigation of diverse signal filtering methods within a general linear model framework

Abstract

We read with great interest the manuscript from Pinti et al. (2019), which aimed to shed a light on one of the main open topics in neuroimaging: the definition of reproducible and standardized pipelines for the preprocessing of functional Near InfraRed Spectroscopy (fNIRS) signals. In particular, Pinti and colleagues focused on the filtering step, evidencing a high heterogeneity of filter types adopted and settings that could undermine the reproducibility of the studies.

Thanks to technological progress, a new generation of fNIRS devices can be used to collect brain activity signals within diverse settings and contexts (e.g., multi-modal and multi-person experimental designs; Azhari et al., 2019, 2020) and for diverse applications (e.g., to study the dynamics of the human brain network; Vergotte et al., 2017). The proliferation of use cases and applications brings up the possibility of fragmentation of the knowledge, unless the scientific community begins to adopt rigorous and standardized methods to allow comparability and reproducibility of the findings.