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

通过规范模型绘制精神疾病中大脑微结构异常的异质性

• 批准号: 10658680
• 负责人: Nina Vanessa Kraguljac
• 金额: $ 60.28万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10658680
• 关键词: Age; Anatomy; Anisotropy; Antipsychotic Agents; Archives; Atlases; Autopsy; Brain; Brain region; Calibration; Characteristics; Chronic; Clinical; Code; Data; Data Pooling; Data Set; Decision Making; Diffusion Magnetic Resonance Imaging; Disease; Ecosystem; Genetic; Goals; Growth; Heterogeneity; Human; Image; Individual; Longevity; Maps; Measures; Medical; Mental disorders; Modeling; Neurites; Neurobiology; Noise; Normal Range; Online Systems; Onset of illness; Outcome; Output; Participant; Patients; Performance; Pharmaceutical Preparations; Phenotype; Physiological; Play; Population; Prediction of Response to Therapy; Probability; Psychiatry; Publishing; Quality Control; Reference Values; Research Domain Criteria; Role; Sampling; Schizophrenia; Severities; Structural defect; Structure; Symptoms; Techniques; Testing; Validation; Variant; Visualization; Work; biological systems; clinical predictors; clinical subtypes; clinical translation; clinically relevant; cohort; connectome data; data quality; deep learning; deficit syndrome; density; design; duration of untreated psychosis; early psychosis; experience; graphical user interface; gray matter; indexing; individual patient; individual variation; insight; interest; neuroimaging; patient population; precision medicine; predict clinical outcome; response; secondary analysis; sex; treatment response; virtual; white matter

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.

项目总结/摘要 精准医疗的核心前提是，个体独特的生理特征 在疾病易感性和对特定疗法的反应中发挥重要作用。方面取得进展 精神病学的精准医学必须超越基于群体的研究， 变异作为噪音，而是在生物学的正常范围内解释个体变异。 系统，这与研究领域标准[RDoC]范式一致。 关于将人类连接组数据用于以下目的的特别关注通知（NOSI） 二次分析我们建议使用规范建模（“大脑生长图表”）技术来生成一个 包含基于年龄和性别的整个生命周期大脑微观结构的综合参考图谱 使用公开可用的连接体数据（> 50，000个个体）， 数据集，年龄10-100岁）。我们选择了针对大脑微观结构，因为死后和神经成像 研究表明，几乎所有主要的精神疾病都涉及微结构。我们还将 创建一个基于Web的生态系统，其中包含相关的工作流程组件，包括一个选项， 将模型校准到新的数据集。除了将最终的模型和代码发布为XNUMX YTER Notebooks之外， 设计一个图形用户界面，以减少在命令行方面经验有限的神经科学家的障碍 编码和最大限度地发挥我们工作的影响。总之，这种方法将提供一个问题不可知的， 量化的框架来描述个体层面的变化，并给我们一个范式转变 有机会推进临床翻译，其中个别患者的大脑微观结构的信息可以 可以用来预测精神疾病的概率、临床结果或对治疗的反应。 我们将展示这种方法的价值，精神病学的应用，通过确定的程度和 早期精神病患者个体微结构偏差的临床相关性。我们选择了这个病人 人群，因为它在遗传和表型上具有异质性，疾病慢性化和 抗精神病药物的暴露是有限的，因为我们已经发现，微妙的微结构， 异常在疾病发作时已经存在，并且与临床和结果变量相关。使用 公开可用的连接体数据，我们首先将评估个体微结构变异的程度， 评估该患者人群中微结构偏离标准的异质性。然后我们将测试 个体微结构变化与关键临床变量之间的关系，并确定 个体的微结构变异可用于鉴定临床亚型。这是关键的一步， 走向精确精神病学，在那里，病人是基于他们独特的生理概念， 来自个体成像签名的特征和见解指导医疗决策。