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Human amygdala engagement moderated by early life stress exposure is a biobehavioral target for predicting recovery on antidepressants.早年生活应激暴露所调节的人类杏仁核参与是预测抗抑郁药疗效恢复的一个生物行为靶点。
Proc Natl Acad Sci U S A. 2016 Oct 18;113(42):11955-11960. doi: 10.1073/pnas.1606671113. Epub 2016 Oct 10.
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Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015.1990 - 2015年79种行为、环境与职业及代谢风险或风险群组的全球、区域和国家比较风险评估:全球疾病负担研究2015的系统分析
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Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015.1990 - 2015年全球、区域和国家310种疾病和损伤的发病率、患病率及伤残调整生命年:全球疾病负担研究2015的系统分析
Lancet. 2016 Oct 8;388(10053):1545-1602. doi: 10.1016/S0140-6736(16)31678-6.
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Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: a theoretical review of the evidence and future directions for clinical translation.基于大规模神经回路功能障碍定义抑郁和焦虑的生物型:证据的理论综述及临床转化的未来方向
Depress Anxiety. 2017 Jan;34(1):9-24. doi: 10.1002/da.22556. Epub 2016 Sep 21.
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Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents.体重指数与全因死亡率:四大洲239项前瞻性研究的个体参与者数据荟萃分析
Lancet. 2016 Aug 20;388(10046):776-86. doi: 10.1016/S0140-6736(16)30175-1. Epub 2016 Jul 13.
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Precision psychiatry: a neural circuit taxonomy for depression and anxiety.精准精神病学:抑郁症和焦虑症的神经回路分类法
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Effect of antidepressant treatment on cognitive impairments associated with depression: a randomised longitudinal study.抗抑郁治疗对抑郁症相关认知障碍的影响:一项随机纵向研究。
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Research aimed at improving both mood and weight (RAINBOW) in primary care: A type 1 hybrid design randomized controlled trial.初级保健中旨在改善情绪和体重的研究(RAINBOW):一项1型混合设计随机对照试验。
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Frontoparietal Activation During Response Inhibition Predicts Remission to Antidepressants in Patients With Major Depression.前额顶叶激活在反应抑制期间预测了重度抑郁症患者对抗抑郁药的缓解。
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ENGAGE 研究:整合神经影像学、虚拟现实和智能手机感应技术,以了解在精准医疗模式下自我调节来管理抑郁和肥胖的方法。

The ENGAGE study: Integrating neuroimaging, virtual reality and smartphone sensing to understand self-regulation for managing depression and obesity in a precision medicine model.

机构信息

Department of Psychiatry and Behavioral Sciences, Stanford University, 401 Quarry Road, Stanford, CA, United States; MIRECC VISN21, Veterans Affairs Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA, United States.

Department of Psychiatry and Behavioral Sciences, Stanford University, 401 Quarry Road, Stanford, CA, United States.

出版信息

Behav Res Ther. 2018 Feb;101:58-70. doi: 10.1016/j.brat.2017.09.012. Epub 2017 Oct 7.

DOI:10.1016/j.brat.2017.09.012
PMID:29074231
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8109191/
Abstract

Precision medicine models for personalizing achieving sustained behavior change are largely outside of current clinical practice. Yet, changing self-regulatory behaviors is fundamental to the self-management of complex lifestyle-related chronic conditions such as depression and obesity - two top contributors to the global burden of disease and disability. To optimize treatments and address these burdens, behavior change and self-regulation must be better understood in relation to their neurobiological underpinnings. Here, we present the conceptual framework and protocol for a novel study, "Engaging self-regulation targets to understand the mechanisms of behavior change and improve mood and weight outcomes (ENGAGE)". The ENGAGE study integrates neuroscience with behavioral science to better understand the self-regulation related mechanisms of behavior change for improving mood and weight outcomes among adults with comorbid depression and obesity. We collect assays of three self-regulation targets (emotion, cognition, and self-reflection) in multiple settings: neuroimaging and behavioral lab-based measures, virtual reality, and passive smartphone sampling. By connecting human neuroscience and behavioral science in this manner within the ENGAGE study, we develop a prototype for elucidating the underlying self-regulation mechanisms of behavior change outcomes and their application in optimizing intervention strategies for multiple chronic diseases.

摘要

个性化实现持续行为改变的精准医学模型在很大程度上还未应用于当前的临床实践。然而,改变自我调节行为对于管理与生活方式相关的复杂慢性病(如抑郁和肥胖)至关重要,因为这些疾病是全球疾病和残疾负担的主要原因。为了优化治疗方法并解决这些负担,必须更好地理解行为改变和自我调节与其神经生物学基础之间的关系。在这里,我们提出了一项新研究的概念框架和方案,即“通过参与自我调节目标来了解行为改变的机制,改善情绪和体重(ENGAGE)”。该 ENGAGE 研究将神经科学与行为科学相结合,以更好地理解与行为改变相关的自我调节机制,从而改善患有抑郁和肥胖症的成年人的情绪和体重。我们在多个环境中收集三个自我调节目标(情绪、认知和自我反思)的检测结果:神经影像学和行为实验室测量、虚拟现实和被动智能手机采样。通过在 ENGAGE 研究中以这种方式将人类神经科学和行为科学联系起来,我们开发出一种原型,用于阐明行为改变结果的潜在自我调节机制及其在优化多种慢性疾病的干预策略中的应用。