Preprocessing and Analysis of fNIRS Data

A modified Beer–Lambert equation was used to convert raw fNIRS data to deoxyhemoglobin and oxyhemoglobin concentrations, and wavelet detrending was applied to these values. A fourth-degree polynomial was used to model and remove the baseline drift from the raw signal. For each participant, channels were automatically removed from the analysis if the root mean square of the raw data trace was 10 times that of the average for that participant. Comparisons between “clean” and “raw” data refer to data that did or did not undergo global mean removal, respectively. To generate the “clean” data, global systemic effects were removed using a spatial filter¹⁴ (link) prior to hemodynamic modeling. The assumption underlying the use of a spatial filter is that neural activity due to the task, in this case related to finger movements, would result in activity localized to the contralateral motor cortex. Therefore, any activity present across a larger area of the brain is most likely due to global systemic effects. The algorithm used here¹⁴ (link) utilizes PCA and a high-pass Gaussian spatial filter to remove components of the data that are present throughout the brain. Raw and clean data were reshaped into $4\times 4\times 4\times 133$ images, and SPM8 was used for first-level general linear model (GLM) analysis.