Psychophysiological dynamics of emotional reactivity: Interindividual reactivity characterization and prediction by a machine learning approach.

ABSTRACT

The fast reaction of the autonomic nervous system (ANS) to an emotional challenge (EC) is the result of a functional coupling between parasympathetic (PNS) and sympathetic (SNS) branches. This coupling can be characterized by measures of cross-correlations between electrodermal activity (EDA) (under the influence of the SNS) and the RR interval (the interval between R peaks) (under the influence of the PNS and the SNS). Significant interindividual variability has previously been reported in SNS-PNS coupling in emotional situations, and the present study aimed to identify interindividual cross-correlation variability in ANS reactivity. We therefore studied EDA and the RR interval in 62 healthy subjects, recorded during a 24-minute EC. A Gaussian Mixture Model was used to cluster tonic EDA-RR cross-correlations during the EC. This identified two clusters that were characterized by significant or non-significant cross-correlations (SCC and NCC clusters, respectively). The SCC cluster reported higher negative emotion after the EC, while the NCC cluster reported higher scores on the Center for Epidemiologic Studies-Depression scale. The latter finding suggests that NCC is a pathological mood pattern with altered negative perception. Furthermore, a machine learning model that included three parameters indexing the functionality of both branches of the ANS, measured at baseline, predicted cluster membership. Our results are a first step in detecting dysfunctional ANS reactivity in general population.