Attachment Patterns Trigger Differential Neural Signature of Emotional Processing in Adolescents

Abstract

BackgroundResearch suggests that individuals with different attachment patterns process social information differently, especially in terms of facial emotion recognition. However, few studies have explored social information processes in adolescents. This study examined the behavioral and ERP correlates of emotional processing in adolescents with different attachment orientations (insecure attachment group and secure attachment group; IAG and SAG, respectively). This study also explored the association of these correlates to individual neuropsychological profiles.Methodology/Principal FindingsWe used a modified version of the dual valence task (DVT), in which participants classify stimuli (faces and words) according to emotional valence (positive or negative). Results showed that the IAG performed significantly worse than SAG on tests of executive function (EF attention, processing speed, visuospatial abilities and cognitive flexibility). In the behavioral DVT, the IAG presented lower performance and accuracy. The IAG also exhibited slower RTs for stimuli with negative valence. Compared to the SAG, the IAG showed a negative bias for faces; a larger P1 and attenuated N170 component over the right hemisphere was observed. A negative bias was also observed in the IAG for word stimuli, which was demonstrated by comparing the N170 amplitude of the IAG with the valence of the SAG. Finally, the amplitude of the N170 elicited by the facial stimuli correlated with EF in both groups (and negative valence with EF in the IAG).Conclusions/SignificanceOur results suggest that individuals with different attachment patterns process key emotional information and corresponding EF differently. This is evidenced by an early modulation of ERP components’ amplitudes, which are correlated with behavioral and neuropsychological effects. In brief, attachments patterns appear to impact multiple domains, such as emotional processing and EFs.

Brain signatures of moral sensitivity in adolescents with early social deprivation

“…Based on previous studies of moral evaluations and empathic responses, we expected to observe major activity in the vmPFC, dlPFC, pSTS, amygdala, and insula for the intentionally-harmed-persons condition811. Based on a prior study60, we expected higher activity in the vmPFC, dlPFC, and amygdala for CG than DG, and higher activity in the right pSTS/TPJ for DG than CG. We expected these effects to occur in two different time windows: an early time window between 150 and 300 ms after stimulus onset, and a late time window between 600 and 800 ms after stimulus onset.…”

Attachment Representations and Brain Asymmetry during the Processing of Autobiographical Emotional Memories in Late Adolescence

“…For example, analyzing ERP responses to infant emotional faces studies using narrative measures of attachment found neural correlates associated with attentional processing (i.e., N200, P3) to be less prominent in insecure mothers (Fraedrich et al, 2010; Leyh et al, 2016a). Accordingly, studies using self-report measures of attachment found decreased ERP amplitudes in response to emotional faces in avoidant subjects (Zhang et al, 2008), as well as a response bias in favor of positive stimuli (Chavis and Kisley, 2012), while others found insecurely attached individuals to be less able to accurately discriminate between different facial emotion expressions on a neurophysiological level (Escobar et al, 2013; for a review also see Gander and Buchheim, 2015). …”

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“…Face response ERPs were segmented to stimulus onset, beginning 200‐ms prestimulus and continuous for 1 000 ms after. Segments were filtered in EEGlab using a 0.3 to 30 Hz zero‐phase shift FIR bandpass filter, a filtering range selected based on its use in prior emotional face‐processing studies . The Automatic Artefact Removal toolbox was used to remove ocular and electromyographic artefacts via spatial filtering and blind source separation.…”

“…Segments were filtered in EEGlab 69 using a 0.3 to 30 Hz zero-phase shift FIR bandpass filter, a filtering range selected based on its use in prior emotional face-processing studies. [70][71][72] The Automatic Artefact Removal toolbox 73 was used to remove ocular and electromyographic artefacts via spatial filtering and blind source separation. Bad channels were identified and interpolated using the ERP PCA Toolkit.…”