Monoaminergic variation, cortical structure, and disaster trauma interact to shape emotional vulnerability
Sato, D. X.; Makino, T.; Katahira, K.; Yu, Z.; Tomita, H.; Mugikura, S.; Kinoshita, K.; Kawata, M.
Show abstract
Emotional vulnerability arises from the interplay of genetic variation, cortical network capacity, and subjective processing of adverse experiences, yet these components are rarely examined together in population-scale datasets. Monoaminergic signaling offers an opportunity for understanding such interactions because variation in related genes shapes sensitivity to environmental inputs. Among them, the vesicular monoamine transporter 1 (VMAT1) Thr136Ile variant is notable for its functional effects and its long-term maintenance at intermediate frequencies, a pattern consistent with context-dependent selection. These properties make it an informative marker for investigating how genetic sensitivity interacts with neurobiological and experiential factors to shape affective functioning. Using data from up to 9,625 participants in the Tohoku Medical Megabank Project, a cohort established after the 2011 Great East Japan Earthquake, we investigated how monoaminergic variation, cortical morphology, and traumatic memory shape affective functioning. We found associations between the Thr136Ile variant and negative affectivity and depressive symptoms, consistent with prior reports of heightened emotional reactivity associated with the 136Thr allele. However, magnitude of these effects was substantially amplified in specific experiential and neurobiological contexts. Subjective trauma sensitivity, capturing the discrepancy between earthquake disruption and current traumatic memory, displayed a genotype-dependent association with depressive symptoms, and this relationship was strongest when parietal or insular cortical surface area was smaller. Together, these results identify a multilevel pathway through which monoaminergic variation contributes to emotional vulnerability by interacting with both cortical network capacity and trauma processing. The context-dependent influence of monoaminergic variation may further contribute to maintenance of affective diversity in human populations.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Transcriptome signatures of the medial prefrontal cortex underlying GABAergic control of resilience to chronic stress exposure 93%
- Structural brain alterations associated with suicidal thoughts and behaviors in young people: results across 21 international studies from the ENIGMA Suicidal Thoughts and Behaviours consortium 93%
- The Common Genetic Architecture of Anxiety Disorders 93%
Similar papers in this journal
- Integrating HiTOP and RDoC Frameworks Part II: Shared and Distinct Biological Mechanisms of Externalizing and Internalizing Psychopathology 95%
- Serotonin and childhood maltreatment interact to shape brain architecture and anxious avoidant behavior, a TPH2 imaging genetics approach 94%
- Dual-systems models of the genetic architecture of impulsive personality traits: Neurogenetic evidence of distinct but related factors 94%
Similar papers in this journal
- Contributions of PTSD polygenic risk and environmental stress to suicidality in preadolescents 94%
- Higher polygenic scores for empathy increase posttraumatic stress severity in response to certain traumatic events 93%
- Acute stress blunts prediction error signals in the dorsal striatum during reinforcement learning 92%
Similar papers in this journal
- Novel polygenic risk score as a translational tool linking depression-related changes in the corticolimbic transcriptome with neural face processing and anhedonic symptoms 95%
- Sex differences in the genetic regulation of the blood transcriptome response to glucocorticoid receptor activation 94%
- Genetic factors influencing a neurobiological substrate for psychiatric disorders 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.