Name: Taylor James

Dissertation Defense Meeting

Date: Thursday July 22, 2021

Time: 11:00 AM

Location: https://bluejeans.com/487274552/0751 

Advisor: Audrey Duarte, Ph.D. (Georgia Tech)

Dissertation Committee members:

Thackery Brown, Ph.D. (Georgia Tech)

Vonetta Dotson, Ph.D. (Georgia State)

Ann Pearman, Ph.D. (Georgia Tech)

Paul Verhaeghen, Ph.D. (Georgia Tech)

Title: Neural Correlates of Emotional Memory as a Function of Age and Depressive Symptoms

Abstract: Age-related positivity effects are well established in the literature. Positivity effects in memory are represented as greater benefits for positive over neutral material and/or reductions in the benefits for negative over neutral material with age. However, it is unknown if positivity effects are limited to older adults without depressive symptoms. In the current fMRI study, individuals ages 18-76 with a range of depressive symptom severity were scanned as they rated the emotional intensity of positive, neutral, and negative images that were preceded by cues to signal the valence of the upcoming image. Participants subsequently completed a recognition memory task outside of the scanner. Behavioral, univariate, representational similarity, and functional connectivity analyses provided evidence for interactive effects between age and depressive symptoms. For instance, at low levels of depression, typical patterns in aging emerged: younger age was associated with better memory for negative than neutral images, and this memory benefit for negative material was reduced with older age. With increasing levels of depression, however, there was a reduction in the positivity effect, manifesting as improvements in negative relative to neutral memory. The neural data highlighted mechanisms that may underlie these interactive effects, including reductions in amygdala-prefrontal cortex functional connectivity associated with downregulation of negative affect. Together, these findings suggest that depressive symptoms in older adulthood reduce positivity effects through alterations in neural networks underlying emotion regulation.