[Psychogenic NonEpileptic Seizures: Current Knowledge and Contributions of the Study of Emotions]. / Crises psychogènes non épileptiques : état des connaissances et apports de l'évaluation des traitements émotionnels.

Psychogenic nonepileptic seizures (PNES) are paroxysmal attacks that can imitate epileptic seizures but do not have a neurological origin. There has been mounting interest these last few years to unravel psychological and neuronal factors that contribute to the development of PNES. The objective of this review is twofold. First, we examine recent contributions of clinical and researches studies to define the main features of PNES. Then, we focus on the possible link between changes in processing of emotional information and the onset of PNES. In this article, we identify promising directions for future research and argue that affective neuroscience may provide original findings to better understand this disease.

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

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.