Mapping heterogeneity of brain microstructural abnormalities in psychiatric disorders with normative modelling

项目摘要/摘要 精准医学的中心前提是个体独特的生理特征发挥作用 在疾病脆弱性和对特定疗法的反应方面发挥重要作用。取得进展，取得进展 精神病学中的精确医学必须超越忽视个人的基于群体的研究 变异是噪声，而是在生物学正常范围的背景下解释个体变异 系统，这与研究领域标准[RDoC]范例一致。 回应特别关注通知(NOSI)关于将人类连接组数据用于 二次分析我们建议使用标准化建模(脑生长曲线图)技术来生成 包含基于年龄和性别的寿命的大脑微观结构的综合参考图谱 使用公开可用的连接组数据的单个大脑区域的标准化信息(&gt；50,000人 数据集，年龄为10-100年)。我们选择以大脑微结构为目标是因为尸检和神经成像 研究表明，微结构参与了几乎所有主要的精神疾病。我们还将 创建包含相关工作流组件的基于Web的生态系统，包括以下选项 根据新的数据集校准模型。除了将最终模型和代码发布为Jupyter笔记本电脑外，我们还将 设计图形用户界面，以减少神经学家在命令行方面经验有限的障碍 并最大限度地发挥我们工作的影响。综上所述，这种方法将提供一种与问题无关的、 量化框架，描述个体层面的变化，并给我们提供一个范式转换 推进临床翻译的机会，其中单个患者的大脑微结构信息可以 被用来预测精神疾病、临床结果或治疗反应的可能性。 我们将展示这种方法在精神病学应用中的价值，方法是确定 早期精神病患者个体微结构偏差的临床意义。我们选择了这位病人 群体，因为它在遗传和表型上是异质性的，疾病的慢性病和 抗精神病药物的暴露是有限的，因为我们发现微妙的微结构 异常在发病时就已经存在，并与临床和结果变量有关。vbl.使用 可公开获得的连接体数据，我们将首先评估单个显微结构变异的程度和 评估患者群体微观结构偏离正常标准的异质性。然后我们将测试 个体微结构变异与关键临床变量之间的关系，并决定是否 个体微结构变异可用于识别临床亚型。这是关键的一步。 走向精确精神病学，根据患者独特的生理特征对患者进行概念化 个人成像签名的特征和洞察力指导医疗决策。

PROJECT SUMMARY/ ABSTRACT The central premise of precision medicine is that an individual’s unique physiologic characteristics play a significant role in disease vulnerability and response to specific therapies. To make progress towards precision medicine in psychiatry it is imperative to move beyond group-based studies that disregard individual variations as noise, and instead interpret individual variations in the context of the normal-range of biological systems, which is consistent with the Research Domain Criteria [RDoC] paradigm. In response to the Notice of Special Interest (NOSI) regarding the use of Human Connectome Data for Secondary Analysis we propose to use normative modeling (‘brain growth charting’) techniques to generate a comprehensive reference atlas for brain microstructure across the lifespan that contains age and sex based normative information for individual brain regions using publicly available connectome data (>50,000 individual datasets, age 10-100 years). We chose to target brain microstructure because postmortem and neuroimaging studies have implicated microstructural involvement in virtually all major psychiatric disorders. We will also create a web-based ecosystem that contains the relevant workflow components, including an option to calibrate models to new datasets. In addition to publishing final models and code as Jupyter notebooks, we will design a graphical user interface to reduce barriers for neuroscientists with limited experience in command line coding and to maximize the impact of our work. Taken together, this approach will provide a problem-agnostic, quantitative framework for characterizing variations at the individual level and give us a paradigm-shifting opportunity to advance clinical translation, where information of an individual patient’s brain microstructure can be leveraged to forecast the probability of a psychiatric condition, clinical outcomes, or response to treatment. We will showcase the value of this approach for psychiatric applications by determining the extent and clinical relevance of individual microstructure deviations in early psychosis patients. We chose this patient population because it is genetically and phenotypically heterogeneous, confounds of disease chronicity and antipsychotic medication exposure are limited, and because we have found that subtle microstructural abnormalities are already present at illness onset and associated with clinical and outcome variables. Using publicly available connectome data, we first will assess the extent of individual microstructure variations and assess heterogeneity in microstructural deviations from the norm in this patient population. We will then test the relationships between individual microstructure variations and key clinical variables, and determine if individual microstructural variations can be used to identify clinical subtypes. This constitutes a pivotal step towards precision psychiatry, where patients are conceptualized based on their unique physiologic characteristics, and insights from individual imaging signatures guide medical decision making.