AI Article Synopsis
- The study highlights the importance of understanding individual trajectories of depressive symptoms in adolescents, which can help predict future psychiatric disorders.
- A deep learning framework was used to analyze data from 621 participants aged 12 to 17, revealing predictors of negative valence symptoms like low extraversion and poor sleep quality.
- The findings stress the need for intervention strategies focused on modifiable factors such as sleep hygiene and cognitive-emotional therapy to prevent and treat depression in adolescents.
Article Abstract
Background: Given the high prevalence of depressive symptoms reported by adolescents and associated risk of experiencing psychiatric disorders as adults, differentiating the trajectories of the symptoms related to negative valence at an individual level could be crucial in gaining a better understanding of their effects later in life.
Methods: A longitudinal deep learning framework is presented, identifying self-reported and behavioral measurements that detect the depressive symptoms associated with the Negative Valence System domain of the NIMH Research Domain Criteria (RDoC).
Results: Applied to the annual records of 621 participants (age range: 12 to 17 years) of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA), the deep learning framework identifies predictors of negative valence symptoms, which include lower extraversion, poorer sleep quality, impaired executive control function and factors related to substance use.
Limitations: The results rely mainly on self-reported measures and do not provide information about the underlying neural correlates. Also, a larger sample is required to understand the role of sex and other demographics related to the risk of experiencing symptoms of negative valence.
Conclusions: These results provide new information about predictors of negative valence symptoms in individuals during adolescence that could be critical in understanding the development of depression and identifying targets for intervention. Importantly, findings can inform preventive and treatment approaches for depression in adolescents, focusing on a unique predictor set of modifiable modulators to include factors such as sleep hygiene training, cognitive-emotional therapy enhancing coping and controllability experience and/or substance use interventions.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10202130 | PMC |
| http://dx.doi.org/10.1016/j.jad.2022.06.002 | DOI Listing |
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