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There are age-related changes in neural connectivity during the encoding of positive, but not negative, information
Authors:Donna R Addis  Keely A Muscatell
Institution:a Department of Psychology, University of Auckland, New Zealand
b Department of Psychology, Boston College, Chestnut Hill, MA, USA
c Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
Abstract:

Introduction

Older adults often show sustained attention toward positive information and an improved memory for positive events. Little is known about the neural changes that may underlie these effects, although recent research has suggested that older adults may show differential recruitment of prefrontal regions during the successful encoding of emotional information. In the present study, effective connectivity analyses examined the network of regions that college-age and older adults recruited during the encoding of positive and negative images.

Methods

Participants viewed positive and negative images while undergoing a functional magnetic resonance imaging (fMRI) scan. Structural equation modeling was used to compare young and older adults' connectivity among regions of the emotional memory network while they encoded negative or positive items.

Results

Aging did not impact the connectivity among regions engaged during the encoding of negative information, but age differences did arise during the encoding of positive information. Most notably, in older adults, the ventromedial prefrontal cortex and amygdala strongly influenced hippocampal activity during the encoding of positive information. By contrast, in young adults, a strong thalamic influence on hippocampal activity was evident during encoding.

Conclusions

These findings suggest that older adults' “positivity effect” may arise from age-related changes in the interactions between affect-processing regions and the hippocampus during the encoding of positive information.
Keywords:Aging  Connectivity  Emotion  fMRI  Memory  Structural equation modeling
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