Return to search

Predicting Reappraisal Success with Innate Neural Connectivity Across the Adult Lifespan

Reappraisal — reinterpreting a situation to change emotional response — is an effective emotion regulation strategy that relies on cognitive control network activity, including engagement of the dorsolateral prefrontal cortex (dlPFC), to attenuate amygdala activity. Greater dlPFC-Amygdala functional connectivity predicts instructed reappraisal task success, and daily use of reappraisal for younger adults (Pico-Perez et al.., 2018) but not older adults (Opitz et al., 2012), while the connectivity of the vmPFC is predictive of physiological markers of ER success for all ages (Sakaki et al., 2016 & Urry et al., 2006). However, the relationship between Resting-State Functional Connectivity (RSFC) and reappraisal task success across the lifespan has yet to be investigated. Participants in the Cambridge Center for Aging Neuroscience study (N=299) completed an 8-minute resting-state fMRI scan. In each trial of an emotion regulation task, participants either viewed or reappraised a negative film and reported post-regulation positive affect. RSFC across bilateral amygdala and the mPFC, the left and the right dlPFC were calculated with Matlab’s CONN Toolbox. The hypothesis is that the strength of the amygdala-mPFC RSFC will predict lower negative and higher positive affect scores after reappraising, however, this study data failed to find evidence to support this hypothesis. The association between the amygdala-dlPFC RSFC and post-reappraisal negative affect scores was moderated by age. Positive affect was higher when there was a stronger negative RSFC in young and middle-aged adults, and this relationship was not significant at older ages (~72). Our results suggest that dlPFC-amygdala activity at rest may be a predictor of emotion regulation in younger and midlife adults but that dlPFC-amygdala activity may be less predictive of emotion regulation outcomes in later life.

Identiferoai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:masters_theses_2-2320
Date28 October 2022
CreatorsLongwell, Parker
PublisherScholarWorks@UMass Amherst
Source SetsUniversity of Massachusetts, Amherst
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceMasters Theses

Page generated in 0.002 seconds