Individualized brain systems and depression

个体化大脑系统和抑郁症

• 批准号: 10360953
• 负责人: Matthew D Sacchet
• 金额: $ 41万
• 依托单位: MCLEAN HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-06 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10360953
• 关键词: Address; Affective; Aftercare; Agreement; Amygdaloid structure; Anhedonia; Antidepressive Agents; Anxiety; Architecture; Arousal; Base of the Brain; Behavior; Behavioral; Brain; Brain Mapping; Bupropion; Cardiology; Clinical; Clinical Trials; Cognitive; Cognitive deficits; Complex; Corpus striatum structure; Data; Data Set; Databases; Development; Diagnosis; Diagnostic; Disease; Dopamine Uptake Inhibitors; Economic Burden; Exhibits; Foundations; Functional Magnetic Resonance Imaging; Future; Goals; Hippocampus (Brain); Individual; Individual Differences; Intervention; Lateral; Link; Machine Learning; Major Depressive Disorder; Maps; Medial; Medical; Mental Depression; Mental disorders; Methodology; Methods; Moods; Neuroanatomy; Norepinephrine; Oncology; Outcome; Parietal; Parietal Lobe; Patients; Pattern; Performance; Persons; Pharmacology; Pharmacotherapy; Plant Roots; Prediction of Response to Therapy; Prefrontal Cortex; Prevalence; Psyche structure; Psychiatry; Research; Selective Serotonin Reuptake Inhibitor; Sertraline; Stress; Symptoms; Syndrome; System; Testing; Translations; Treatment outcome; antagonist; anxiety spectrum disorders; anxious; base; behavioral outcome; behavioral response; biosignature; burden of illness; clinical care; comorbidity; data sharing; improved; individual variation; innovation; kappa opioid receptors; neural model; neuroimaging; organizational structure; precision medicine; predicting response; programs; public health relevance; relating to nervous system; response; social; theories; treatment response

PROJECT SUMMARY / ABSTRACT The goal of this proposal is to advance neural models of major depressive disorder (MDD). Prior studies of MDD and related conditions have relied on group-level information when making inferences about individual brains, and have yielded limited translation and clinical impact. Such group-level approaches are limited given robust evidence that the brain exhibits substantial individual variability in its organization. This proposal describes a computational psychiatry approach rooted in new computational neuroimaging methods that will provide improved detail in mapping the brains of individuals with MDD, including in relation to diagnostic status, symptom and behavioral profiles, and predicting treatment response. More specifically, the team proposes an advanced fMRI-based brain mapping approach that will be used to deeply characterize the rich organizational structure of functional brain systems at the level of individuals (yielding “individualized brain systems”). The proposed research will be completed by leveraging over 700 existing datasets acquired through data sharing. This proposal is feasible, in part due, to data sharing and the strong theoretical and methodological foundations provided by the PI and the team’s prior research. MDD is a particularly promising focus for this proposal given that it is (1) highly heterogeneous and thus an ideal target for mapping individual variability; (2) highly prevalent and the leading contributor to global disease burden; and that (3) fewer than one in three MDD patients remit after treatment. The Specific Aims of this proposal are to: (1) Map individualized brain systems in MDD; (2) Characterize relations between individualized brain systems and core MDD symptoms and behavioral deficits; and, finally, to (3) Explicate predictive relations between individualized brain systems and MDD clinical trial outcomes to three mechanistically distinct treatments. In addition to theory-driven studies, this proposal includes the development of a complementary data-driven machine learning approach that will use only individualized brain system features to make clinically meaningful predictions about specific patients. This will include predicting diagnostic status, symptom and behavioral profiles, and treatment outcomes. Precision medicine has considerably impacted several medical fields, including cardiology and oncology. We have yet to see similar developments in psychiatry, given, in part, due to the challenge of mapping relations among clinical features of mental illness and the brain. The development of computational neuroimaging approaches, including those in the current proposal, now provide new opportunities to address this challenge and translational gap.

项目摘要 /摘要 该提案的目的是推进重度抑郁症（MDD）的神经模型。先前的研究 推断个人时，MDD及相关条件有助于小组级信息 大脑，并产生了有限的翻译和临床影响。这样的小组级方法受到限制 强有力的证据表明大脑在组织中表现出很大的个人变异性。该提案描述了 一种植根于新计算神经影像学方法的计算精神病学方法 改进了绘制MDD个体的大脑的细节，包括与诊断状态有关 和行为特征，并预测治疗反应。 更具体地说，团队提出了一种基于fMRI的高级大脑映射方法，该方法将用于 深刻地描述了功能性脑系统的丰富组织结构 （产生“个性化的大脑系统”）。拟议的研究将通过利用超过700 通过数据共享获取的现有数据集。该建议是可行的，部分原因是数据共享和 PI和团队先前的研究提供了强大的理论和方法论基础。 MDD是一个 鉴于（1）高度异质，因此是该提议的理想目标，特别是 映射个人变异性； （2）高度普遍，是全球疾病伯恩的主要贡献者；那 （3）治疗后不到三分之一的MDD患者份额。 该提案的具体目的是：（1）MDD中的个性化大脑系统； （2）表征 个性化的大脑系统与核心MDD符号与行为定义的关系；最后，到 （3）将个性化大脑系统与MDD临床试验之间的阐明预测关系分为三个 机械上不同的治疗方法。除了以理论为导向的研究外，该建议还包括发展 完工的数据驱动机器学习方法，该方法将仅使用个性化的大脑系统 对特定患者的临床有意义预测的特征。这将包括预测诊断 状态，症状和行为特征以及治疗结果。 精密医学已经深思熟虑地影响了几个医学领域，包括心脏病学和肿瘤学。我们 尚未看到精神病学的类似发展，部分原因是绘制关系的挑战 在精神疾病和大脑的临床特征中。计算神经影像学的发展 方法，包括当前提案中的方法，现在为应对这一挑战提供了新的机会 并翻译差距。