Poster #61 - Difficulties in Emotion Regulation During Adolescence: Insights from Salience and Default Mode Network Connectivity Patterns

• In Event: 2-002 - Special Poster Session 05 with Continental Breakfast Reception
  In Poster Session: PS 05 - Interdisciplinary Section

Integrative Statement

The ability to modulate the intensity of one’s affective responses in order to adaptively fit the context or one’s goals is known as emotion regulation (ER; (Mazefsky et al., 2013). During adolescence, ER difficulties have been linked to various mental health outcomes, including depression, anxiety, and suicidal ideation (Weinberg and Klonsky, 2009). Examining resting state brain networks in adolescents at various risk for ER difficulties (i.e., Social Anxiety, Autism, and controls) may reveal neural signatures of ER deficits. Recent work has demonstrated that the salience network (SN), which relates to attention allocation, and default mode network (DMN), which underlies self-referential cognitions and inflexible thinking, may play a role in ER difficulties, i.e., repetitive negative thinking (Burrows et al., 2017). Thus, we aimed to determine whether SN-DMN connectivity patterns relate to ER difficulties in a transdiagnositc sample.
Participants completed an ER questionnaire, the Difficulties in ER Scale (DERS), and a resting state brain scan. The DERS has six subscales (i.e., Nonacceptance, Goals, Impulsivity, Awareness, ER Strategies, Clarity). Higher scores on all scales indicate greater difficulties in ER. Functional and structural Magnetic Resonance Imaging (MRI) were collected on a Siemens 3T scanner. Resting state images were collected with the following protocol: TR=2000ms; flip angle=90°; 37 slices; TE=25ms, FOV=64×64mm; voxel size=3.43×3.43×4mm. Resting-state preprocessing was conducted using the Configurable Pipeline Analysis of Connectomes (C-PAC; https://fcp-indi.github.io/). Friston’s 24-parameter correction was applied on all participants. Additionally, the time series data were censored for each volume that had greater than 0.8mm. After excluding two subjects with unstable parameter estimates due to excessive motion, the dataset consisted of 35 adolescents (Age: M[SD]= 15.45[1.74]; IQ: M[SD]= 107.03[13.17]). Data collection is ongoing.
Eight DMN and three SN nodes were chosen using Neurosynth (http://neurosynth.org/). Thus, a total of eleven 5mm spherical regions of interest (ROIs) were extracted for each participant. Group Iterative Multiple Model Estimation (GIMME), a graph theory approach (Gates and Molenaar, 2012), was applied on time series rest data in order to identify individual connectivity patterns. Subsequently, a community detection algorithm was used on the individual patterns to identify subgroups characterized by distinctive SN-DMN connectivity patterns.
All subjects were characterized by connections from the left insula (SN) to the paracingulate (DMN). Two data-driven subgroups were identified: Subgroup A was characterized by hyperconnectivity between SN-DMN nodes, while Subgroup B was characterized by hypoconnectivity on SN-DMN nodes. Data-driven subgroups did not differ in IQ (p>.51), or age (p=.08). To examine whether subgroups differed on difficulties in ER when controlling for age, a univariate ANCOVA was conducted. When controlling for age, data-driven subgroups differed on clarity of emotions (p=.05), but did not differ on any other DERS subscales (ps>.37).
These results suggest that functional connectivity between the SN-DMN may be associated with clarity of emotions during adolescence. More specifically, those with decreased SN-DMN connectivity demonstrated greater difficulties with emotional clarity, than those who had greater connectivity of SN-DMN nodes. In adolescence, SN-DMN hypoconnectivity may serve as a neural marker for difficulties with ER and an avenue to better-targeted treatments (e.g., mindfulness, neurofeedback).

Authors

• Ligia Antezana, Virginia Tech
  Presenting Author

• Charlotte Brown, Virginia Tech
  Non-Presenting Author

• Molly E Maurin, Virginia Tech
  Non-Presenting Author

• Jessica E. Voyack, Virginia Tech
  Non-Presenting Author

• Abigail Kirkpatrick, Virginia Tech
  Non-Presenting Author

• Nhi Ly, Virginia Tech
  Non-Presenting Author

• John A Richey, Virginia Tech
  Non-Presenting Author

• Marika Coffman, Virginia Tech
  Non-Presenting Author