Abstract
Background
Adolescents with depression exhibit negative biases in autobiographical memory with detrimental consequences for their self-concept and well-being. Investigating how adolescents relive positive autobiographical memories and activate the underlying neural networks could reveal mechanisms that drive such biases. This study investigated neural networks when reliving positive and neutral memories, and how neural activity is modulated by valence and vividness in adolescents with and without depression.
Methods
Adolescents (N = 69; n = 17 with depression) retrieved positive and neutral autobiographical memories. On a separate day, they relived these memories during fMRI scanning, and reported on pleasantness and vividness after reliving each memory. We used a multivariate, data-driven approach – event-related independent component analysis (eICA) – to characterize neural networks supporting autobiographical recollection.
Results
Adolescents with depression reported their positive memories as significantly less pleasant compared to healthy controls, while subjective vividness was unaffected. Using eICA, we identified a broad autobiographical memory network, and subnetworks related to reliving positive vs neutral memories. These subnetworks comprised a ‘self-referential processing network’ including medial prefrontal cortex, posterior cingulate cortex/precuneus, and temporoparietal junction, anti-correlating with parts of the central executive network and salience network. Adolescents with depression exhibited aberrant activation in this self-referential network, but only when reliving relatively ‘low’ pleasant memories.
Conclusions
Our findings provide first insights into how the quality of reliving autobiographical memories in adolescents with depression may relate to aberrant self-referential neural network activation, and underscore the potential of targeting memory reliving in therapeutic interventions to foster self-esteem and diminish depressive symptoms.
Adolescents with depression exhibit negative biases in autobiographical memory with detrimental consequences for their self-concept and well-being. Investigating how adolescents relive positive autobiographical memories and activate the underlying neural networks could reveal mechanisms that drive such biases. This study investigated neural networks when reliving positive and neutral memories, and how neural activity is modulated by valence and vividness in adolescents with and without depression.
Methods
Adolescents (N = 69; n = 17 with depression) retrieved positive and neutral autobiographical memories. On a separate day, they relived these memories during fMRI scanning, and reported on pleasantness and vividness after reliving each memory. We used a multivariate, data-driven approach – event-related independent component analysis (eICA) – to characterize neural networks supporting autobiographical recollection.
Results
Adolescents with depression reported their positive memories as significantly less pleasant compared to healthy controls, while subjective vividness was unaffected. Using eICA, we identified a broad autobiographical memory network, and subnetworks related to reliving positive vs neutral memories. These subnetworks comprised a ‘self-referential processing network’ including medial prefrontal cortex, posterior cingulate cortex/precuneus, and temporoparietal junction, anti-correlating with parts of the central executive network and salience network. Adolescents with depression exhibited aberrant activation in this self-referential network, but only when reliving relatively ‘low’ pleasant memories.
Conclusions
Our findings provide first insights into how the quality of reliving autobiographical memories in adolescents with depression may relate to aberrant self-referential neural network activation, and underscore the potential of targeting memory reliving in therapeutic interventions to foster self-esteem and diminish depressive symptoms.
| Original language | English |
|---|---|
| Pages (from-to) | 14-26 |
| Journal | Cortex |
| Volume | 168 |
| DOIs | |
| Publication status | Published - Nov 2023 |