Individualized brain systems and depression

个体化大脑系统和抑郁症

• 批准号: 10704864
• 负责人: Matthew D Sacchet
• 金额: $ 42万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-21 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10704864
• 关键词: Address; Affect; Affective; Aftercare; Agreement; Amygdaloid structure; Anhedonia; Antidepressive Agents; Anxiety; Architecture; Arousal; Behavior; Behavioral; Brain; Brain Mapping; Bupropion; Cardiology; Clinical; Clinical Trials; Cognitive deficits; Complex; Corpus striatum structure; Data; Data Set; Databases; Development; Diagnosis; Diagnostic; Disease; Disease remission; Dopamine Uptake Inhibitors; Economic Burden; Exhibits; Foundations; Functional Magnetic Resonance Imaging; Future; Goals; Hippocampus; 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 Lobe; Patients; Pattern; Performance; Persons; Pharmacology; Pharmacotherapy; Prediction of Response to Therapy; Prefrontal Cortex; Prevalence; Psychiatry; Recurrence; Research; Selective Serotonin Reuptake Inhibitor; Sertraline; Stress; Symptoms; Syndrome; System; Testing; Translations; Treatment outcome; antagonist; anxiety spectrum disorders; anxious; behavioral outcome; behavioral response; biosignature; brain based; burden of illness; clinical care; cognitive control; comorbidity; data sharing; improved; individual variation; innovation; kappa opioid receptors; neural; neural model; neuroimaging; organizational structure; precision medicine; predicting response; programs; public health relevance; 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.

项目总结/摘要 该提案的目标是推进重度抑郁症（MDD）的神经模型。以前的研究 在对个人进行推断时，MDD和相关条件依赖于组级别的信息 大脑，并产生了有限的翻译和临床影响。这种小组一级的办法是有限的， 强有力的证据表明，大脑在其组织中表现出很大的个体差异。该提案描述了 一种基于新的计算神经成像方法的计算精神病学方法， 改善了MDD患者大脑图谱的细节，包括与诊断状态、症状 和行为特征，以及预测治疗反应。 更具体地说，该团队提出了一种先进的基于fMRI的大脑映射方法，该方法将用于 在个体水平上深入描述功能性大脑系统的丰富组织结构 （产生“个性化的大脑系统”）。这项研究将利用700多个 通过数据共享获得的现有数据集。这一建议是可行的，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基础。MDD是一种 考虑到它是（1）高度异质性的，因此是 绘制个体变异性;（2）高度流行，是全球疾病负担的主要贡献者;以及 (3)只有不到三分之一的MDD患者在治疗后缓解。 该提案的具体目标是：（1）绘制MDD中的个体化脑系统;（2）表征 个体化大脑系统与核心MDD症状和行为缺陷之间的关系;最后， (3)阐明个性化大脑系统与MDD临床试验结果之间的预测关系， 不同的治疗方法。除了理论驱动的研究外，该提案还包括开发 一种互补的数据驱动的机器学习方法，只使用个性化的大脑系统， 这些功能可以对特定患者进行有临床意义的预测。这将包括预测诊断 状态、症状和行为特征以及治疗结果。 精准医疗对包括心脏病学和肿瘤学在内的多个医学领域产生了重大影响。我们 在精神病学方面还没有看到类似的发展，部分原因是由于映射关系的挑战 在精神疾病和大脑的临床特征中。计算神经影像学的发展 包括本提案中的方法在内的各种方法现在为应对这一挑战提供了新的机会 和translational gap。