Preprocessing Pipeline for fNIRS Data in Children

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique, largely used in paediatric research. However, there is not a standardized and widely accepted protocol for fNIRS data processing with potential effects on the reliability and replicability of the obtained results. The present study is within this framework aiming at the identification of an adequate pre-processing pipeline to be used for the analysis of children fNIRS datasets. The performance of five different motion correction techniques, based on the principal component analysis and on the wavelet filtering, was evaluated by analyzing fNIRS data recorded in 22 typically developing children (mean age 11.4 years). The results showed that the wavelet analysis combined with a moving average filter achieved the best performance, suggesting that this technique might become a gold-standard approach for motion artifacts correction in fNIRS children’s datasets.