Oxytocin modulates the temporal dynamics of resting EEG networks

Abstract

Oxytocin is a key modulator of social interaction, but we possess little knowledge of its underlying effects on neuropsychological processes. We used a spatio-temporal EEG microstates analysis to reveal oxytocin’s effects on the temporal dynamics of intrinsically generated activity in neural networks. Given oxytocin’s known anxiolytic effects, we hypothesized that it increases the temporal stability of the four archetypal EEG resting networks. Eighty-six male participants had received oxytocin or placebo intranasally before we recorded their resting EEG. As hypothesized, oxytocin globally increased the average duration of the four archetypal resting networks and specifically decreased the occurrence and coverage of an autonomic processing-related network to benefit greater coverage of an attention-related network. Moreover, these neurophysiological changes were more pronounced in participants with high anxiety levels and strong subjectively experienced effects of the oxytocin administration. In sum, our study shows that oxytocin reduces rapid switching among neural resting networks by increasing their temporal stability. Specifically, it seems to reduce the brain’s need for preparing the internally-oriented processing of autonomic information, thus enabling the externally-oriented processing of social information. Changes in the temporal dynamics of resting networks might underlie oxytocin’s anxiolytic effects - potentially informing innovative psychobiological treatment strategies.

Intrinsic, dynamic and effective connectivity among large-scale brain networks modulated by oxytocin

“…In support of this conceptualization higher temporal flexibility 347 of the brain has been associated with the motor-skill learning ability (Bassett et al, 2011(Bassett et al, , 2013(Bassett et al, , 348 2015 and general intelligence (Zhang et al, 2016) and has been found to be impaired across 349 psychiatric disorders characterized by cognitive and social impairments (Zhang et al, 2016). 350 However, in contrast to a previous pharmacological EEG study (Schiller et al, 2019), we found 351 no evidence of OXT influencing brain temporal state switching frequency. This may perhaps 352 reflect the fact that the peptide has no reported effects on either motor learning or general 353 intelligence, although since it does influence learning with social feedback and rewards 354 (Hurlemann et al, 2010;Hu et al, 2015) and it is possible that it might influence switching 355 frequency during performance of such social learning tasks.…”

EEG microstate features according to performance on a mental arithmetic task

“…This is also consistent with the results of Seitzman et al showing that microstate type D is task-positive, and microstate type C is task-negative 27 . In both groups, the mean duration and mean occurrence changed, which suggests that there was a change in temporal stability due to task execution 28 . However, poor performers had a different degree of change compared to good performers.…”

Temporal dynamics of resting EEG networks are associated with prosociality

“…Our measurement protocol consisted of 20-s eyes open periods followed by 40-s eyes closed periods, repeated five times. This resting state paradigm has been routinely used in resting EEG research 16,17,27,77 in order to minimize fluctuations in participants' vigilance state. Participants can become drowsy already after 3 min of recording resting state brain activity, if there is no alternation of eyes-open/eyes-closed periods 78 .…”

“…More specifically, we used a spatio-temporal analysis approach to cluster the resting EEG signal into a circumscribed number of scalp electrical potential topographies that remain stable for certain time periods (ca. 50-120 ms) before dynamically changing into a different topography that remains stable again [24][25][26][27][28] . One has referred to these periods with stable topographies as "microstates" and one has interpreted transitions between microstates to represent sequential coordinated activity of different, distributed neural networks.…”