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Abstract
Even though research in the field of functional near-infrared spectroscopy (fNIRS) has been performed for more than 20 years, consensus on signal processing methods is still lacking. A significant knowledge gap exists between established researchers and those entering the field. One major issue regularly observed in publications from researchers new to the field is the failure to consider possible signal contamination by hemodynamic changes unrelated to neurovascular coupling (i.e., scalp blood flow and systemic blood flow). This might be due to the fact that these researchers use the signal processing methods provided by the manufacturers of their measurement device without an advanced understanding of the performed steps. The aim of the present study was to investigate how different signal processing approaches (including and excluding approaches that partially correct for the possible signal contamination) affect the results of a typical functional neuroimaging study performed with fNIRS. In particular, we evaluated one standard signal processing method provided by a commercial company and compared it to three customized approaches. We thereby investigated the influence of the chosen method on the statistical outcome of a clinical data set (task-evoked motor cortex activity). No short-channels were used in the present study and therefore two types of multi-channel corrections based on multiple long-channels were applied. The choice of the signal processing method had a considerable influence on the outcome of the study. While methods that ignored the contamination of the fNIRS signals by task-evoked physiological noise yielded several significant hemodynamic responses over the whole head, the statistical significance of these findings disappeared when accounting for part of the contamination using a multi-channel regression. We conclude that adopting signal processing methods that correct for physiological confounding effects might yield more realistic results in cases where multi-distance measurements are not possible. Furthermore, we recommend using manufacturers’ standard signal processing methods only in case the user has an advanced understanding of every signal processing step performed.
Highlights
Optical neuroimaging based on functional near-infrared spectroscopy is a technique increasingly used to perform neuroscientific studies. fNIRS allows to measure changes in tissue hemodynamics and oxygenation on the human head non-invasively (Scholkmann et al, 2014)
The current study explores whether different signal processing methods applied to data from a clinical fNIRS protocol without short-distance channels leads to differences in outcome
Over the course of the last few years, a large body of evidence has been published regarding the contamination of the cortical hemodynamic signal acquired by fNIRS by cerebral and extracerebral hemodynamics not due to neurovascular coupling (Saager et al, 2011; Takahashi et al, 2011; Kirilina et al, 2012; Yamada et al, 2012; Gagnon et al, 2014; Tachtsidis and Scholkmann, 2016), and some were even addressed to researchers new to the field (Orihuela-Espina et al, 2010; Leff et al, 2011)
Summary
Optical neuroimaging based on functional near-infrared spectroscopy (fNIRS) is a technique increasingly used to perform neuroscientific studies. fNIRS allows to measure changes in tissue hemodynamics (blood perfusion) and oxygenation on the human head non-invasively (Scholkmann et al, 2014). No standardized and widely accepted signal processing method for fNIRS exists and no fNIRS guidelines article has been published yet, in contrast to fMRI for example (Strother, 2006; Poldrack et al, 2008; CaballeroGaudes and Reynolds, 2017) This can create the situation that novice users might mainly perform signal processing and data analysis with the tools provided by the commercial companies (like a ‘‘black box’’) which could lead to ‘‘false positives’’ or ‘‘false negatives’’ in the results (Tachtsidis and Scholkmann, 2016). By using the short-detector separation channels as a reference, the components of superficial interference in the long source-detector separation channels can be regressed out (Zhang et al, 2015)
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