课题基金 / 基金详情

Biobehavioral Predictors of Illness Progression in Adolescent Depression

Biobehavioral Predictors of Illness Progression in Adolescent Depression
青少年抑郁症疾病进展的生物行为预测因子
批准号:
9907663
负责人:
Vilma Gabbay
金额:
$85.06万
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
未结题
起止时间:
2020-04-01 至 2025-01-31

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项目成果

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中文摘要
翻译
项目总结/摘要 青少年抑郁症是一个主要的公共卫生问题与显着的发病率和死亡率。我们 我知道青少年抑郁症的严重程度和过程有很大的不同,但其潜在的机制 仍然未知。这项提案解决了这一问题,旨在确定疾病的生物行为预测因子 在青少年中的轨迹,为影响其过程的治疗提供信息。我们的总体假设是奖励 功能障碍及其潜在的神经免疫机制有助于维持抑郁症, 青年我们提出了以下模型:(1)外周炎症(例如,细胞因子,犬尿氨酸)与 伴有快感缺乏;(2)炎症扩展至CNS,诱导氧化应激[↓谷胱甘肽(GSH,抗氧化剂)] 和GABA(抑制性神经递质)缺陷;(3)这种神经化学变化改变了奖赏回路, 导致快感缺乏,并导致抑郁症的发展。为了支持,我们记录了青少年 抑郁症的核心症状,只有快感缺乏--反映了奖励过程的缺陷--而不是易怒, 与更差的结局相关,包括自杀倾向和慢性。我们还报告了 循环细胞因子和犬尿氨酸与快感缺乏和奖赏神经回路。利用质子MR 1H-MRS结果显示,青少年前扣带皮层(ACC)GABA水平降低, 抑郁症是由快感缺乏症引起的。此外,使用功能磁共振成像,我们绘制了纹状体连接的关系, 并记录了奖励预期和成就的不同神经激活模式, 导致快感缺失的奖赏过程在初步研究中,我们发现ACC GABA和奖励- 在2年随访时,预期脑激活预测快感缺乏的严重程度。建立在我们令人信服的 到目前为止,我们提出了一个关于奖励过程和神经免疫机制的前瞻性研究, 目的是确定青少年抑郁过程的可改变的预测途径。我们将学习120 青少年抑郁症状。三个实验室综合临床评价和计算机化 将在基线、12个月和24个月随访时进行奖励任务会话。基线和12个月时,访视 将包括:a)用于犬尿氨酸代谢物的免疫血液收集、免疫细胞分析和细胞因子分泌 免疫刺激后(作为生物应激源); B)fMRI静息状态和任务检查不同的奖励 过程(例如,c)1H MRS探测ACC GABA和纹状体GSH, 奖励相关脑区炎症神经代谢结果的指标。结局指标将 包括快感缺乏严重程度(原发性)、沿着抑郁严重程度、功能、焦虑和自杀倾向。 原则性变量选择方法将解决多重比较问题。动态因果建模和 机器学习分类方法将探索所有检查因素之间的关系。
英文摘要
PROJECT SUMMARY / ABSTRACT Adolescent depression is a major public health concern associated with significant morbidity and mortality. We know that adolescent depression varies considerably in severity and course, but its underlying mechanisms remain unknown. This proposal addresses this concern, aiming to identify biobehavioral predictors of illness trajectory in adolescents to inform treatment influencing its course. Our overall hypothesis is that reward dysfunction and its underlying neuro-immunological mechanisms contribute to maintenance of depression in youth. We propose the following model: (1) peripheral inflammation (e.g., cytokines, kynurenines) is associated with anhedonia; (2) inflammation extends to the CNS, inducing oxidative stress [↓glutathione (GSH, antioxidant)] and GABA (inhibitory neurotransmitter) deficits; (3) such neuro-chemical changes alter the reward circuitry, leading to anhedonia, and contribute to depression progression. In support, we documented that of adolescent depression’s core symptoms, only anhedonia—reflecting deficits in reward processes—and not irritability was associated with worse outcome, including suicidality and chronicity. We also reported associations between circulatory cytokines and kynurenines with both anhedonia and reward neurocircuitry. Utilizing proton MR spectroscopy (1H MRS), we showed that decreased anterior cingulate cortex (ACC) GABA in adolescent depression was driven by anhedonia. Additionally, using fMRI, we mapped striatal-based connectivity in relation to anhedonia and documented distinct neural activation patterns of reward anticipation and attainment—both reward processes that contribute to anhedonia. In pilot studies, we found that ACC GABA and reward- anticipation brain activation predicted anhedonia severity at 2-year follow-up. Building upon our compelling findings to date, we propose a prospective study of reward processes and neuroimmunological mechanisms, with the goal to identify modifiable pathways predictive of depression course in adolescents. We will study 120 adolescents with depressive symptoms. Three in-laboratory comprehensive clinical evaluation and computerized reward task sessions will be done at baseline, 12-, and 24-month follow-ups. At baseline and 12 months, visits will include: a) immune blood collection for kynurenine metabolites, immune cell profiling, and cytokine secretion post immune stimulation (as a biological stressor); b) fMRI resting state and tasks examining distinct reward processes (e.g., anticipation, attainment, learning); and c) 1H MRS to probe ACC GABA and striatal GSH as indices for inflammatory neurometabolic consequences in reward-related brain regions. Outcome measures will include anhedonia severity (primary), along with depression severity, functioning, anxiety, and suicidality. Principled variable selection approach will address multiple comparison concerns. Dynamic causal modeling and machine learning classification methods will explore the relationships among all examined factors.
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会议论文
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