Combining the intersubject correlation analysis and the multivariate distance matrix regression to evaluate associations between fNIRS signals and behavioral data from ecological experiments.

The development of methods to analyze data acquired using functional near-infrared spectroscopy (fNIRS) in experiments similar to real-life situations is of great value in modern applied neuroscience. One of the most used methods to analyze fNIRS signals consists of the application of the general linear model on the observed hemodynamic signals. However, it implies limitations on the experimental design that must be constrained by triggers related to the stimuli protocols (such as block design or event related). In this work, a novel methodology is proposed to overcome such restrictions and allow more flexible protocols. The method combines the intersubject correlation analysis and the multivariate distance matrix regression to evaluate the brain-behavior relationship of subjects submitted to experiments with no trigger-based protocols. Its applicability is demonstrated throughout a naturalistic experiment about emotions conveyed by music. Thirty-two participants freely listened to instrumental excerpts from the operatic repertoire and reported the valences of the emotions conveyed by the musical segments. The method was able to find a statistically significant correlation between the subjects' fNIRS signals and valences of their emotional responses, for the excerpt that evoked the most negative valence. This result illustrates the potential of this approach as an alternative method to analyze fNIRS signals from experiments in which block design or task-related paradigms might not be suitable.