Individualized brain systems and depression

个体化大脑系统和抑郁症

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要该提案的目标是推进重度抑郁症 (MDD) 的神经模型的先前研究。 MDD 和相关条件在对个人进行推断时依赖于群体级别的信息鉴于这种群体层面的方法是有限的，并且产生了有限的转化和临床影响。该提议描述了大脑在其组织中表现出巨大个体差异的有力证据。一种植根于新的计算神经成像方法的计算精神病学方法，该方法将提供改善重度抑郁症患者大脑图谱的细节，包括与诊断状态、症状相关的细节和行为概况，并预测治疗反应。更具体地说，该团队提出了一种先进的基于功能磁共振成像的大脑绘图方法，该方法将用于深入表征个体水平上功能性大脑系统的丰富组织结构（产生“个性化大脑系统”）。拟议的研究将通过利用 700 多个来完成。通过数据共享获得的现有数据集是可行的，部分原因在于数据共享和 PI 和团队之前的研究提供了强大的理论和方法基础。鉴于该提案具有高度异质性，因此是一个理想的目标，因此该提案特别有希望成为焦点绘制个体变异性；(2) 高度普遍且是全球疾病负担的主要因素； (3) 不到三分之一的MDD患者在治疗后症状缓解。该提案的具体目标是： (1) 绘制 MDD 中的个体化大脑系统图 (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； (3) 向三个人阐明个体化大脑系统与 MDD 临床试验结果之间的预测关系除了理论驱动的研究之外，该提案还包括开发。一种互补的数据驱动机器学习方法，仅使用个性化的大脑系统对特定患者做出有临床意义的预测的功能，其中包括预测诊断。状态、症状和行为概况以及治疗结果。精准医学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。精神病学方面尚未看到类似的发展，部分原因是映射关系的挑战精神疾病和大脑的临床特征之间的关系。计算神经影像学的发展。包括当前提案中的方法在内的方法现在为应对这一挑战提供了新的机会和翻译差距。