PDF(Pubmed)
Abstract：
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.
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).
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.
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.
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.
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).
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.
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.
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.
摘要：
鉴于青少年报告的抑郁症状患病率高，以及成年后经历精神疾病的相关风险，在个体水平上区分与负价相关的症状的轨迹对于在以后的生活中更好地了解其影响至关重要。
提出了一个纵向深度学习框架，识别自我报告和行为测量，以检测与NIMH研究领域标准（RDoC）的负价系统领域相关的抑郁症状。
适用于国家青少年酒精与神经发育联盟（NCANDA）621名参与者（年龄范围：12至17岁）的年度记录，深度学习框架识别负价症状的预测因子，其中包括较低的外向性，睡眠质量较差，执行控制功能受损和物质使用相关因素。
结果主要依赖于自我报告的测量，并且不提供有关潜在神经相关性的信息。此外，需要更大的样本来了解性别和其他人口统计学与经历负效价症状的风险相关的作用。
这些结果提供了有关青春期个体阴性效价症状预测因子的新信息，这对于理解抑郁症的发展和确定干预目标至关重要。重要的是,研究结果可以为青少年抑郁症的预防和治疗方法提供信息，专注于一组独特的可修改的调节剂，以包括睡眠卫生培训等因素，认知情绪疗法增强应对和可控性体验和/或物质使用干预。
提出了一个纵向深度学习框架，识别自我报告和行为测量，以检测与NIMH研究领域标准（RDoC）的负价系统领域相关的抑郁症状。
适用于国家青少年酒精与神经发育联盟（NCANDA）621名参与者（年龄范围：12至17岁）的年度记录，深度学习框架识别负价症状的预测因子，其中包括较低的外向性，睡眠质量较差，执行控制功能受损和物质使用相关因素。
结果主要依赖于自我报告的测量，并且不提供有关潜在神经相关性的信息。此外，需要更大的样本来了解性别和其他人口统计学与经历负效价症状的风险相关的作用。
这些结果提供了有关青春期个体阴性效价症状预测因子的新信息，这对于理解抑郁症的发展和确定干预目标至关重要。重要的是,研究结果可以为青少年抑郁症的预防和治疗方法提供信息，专注于一组独特的可修改的调节剂，以包括睡眠卫生培训等因素，认知情绪疗法增强应对和可控性体验和/或物质使用干预。
