An integrative Bayesian approach for linking brain to behavioral phenotype

将大脑与行为表型联系起来的综合贝叶斯方法

• 批准号: 10718215
• 负责人: R Todd Constable
• 金额: $ 60.6万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10718215
• 关键词: Adult; Aging; Area; Atlases; Bayesian Method; Bayesian Modeling; Behavior; Behavioral; Biological; Brain; Brain Diseases; Categories; Clinical; Cognitive; Communities; Consensus; Data; Data Set; Development; Diagnosis; Diagnostic; Dimensions; Disease; Elasticity; Ensure; Evaluation; Functional Magnetic Resonance Imaging; Growth; Heterogeneity; Human; Individual; Joints; Link; Magnetic Resonance Imaging; Measures; Medicine; Mental disorders; Methodology; Methods; Mind; Modeling; Monitor; Neurobiology; Neurosciences; Patients; Performance; Phenotype; Population; Reproducibility; Research; Research Domain Criteria; Resources; Running; Sampling; Site; Subgroup; Symptoms; System; Tissues; United States National Institutes of Health; Validation; Variant; Work; behavior measurement; behavior prediction; behavioral phenotyping; brain behavior; connectome; connectome based predictive modeling; data sharing; flexibility; human data; improved; interest; novel strategies; outcome prediction; patient subsets; predictive modeling; psychiatric symptom; support network; treatment group; treatment response

Abstract Recent advances in human connectome research have led to the development of models that reveal the brain circuits associated with behaviors or symptoms. The networks that define these circuits yield functional phenotypes that can be measured in individuals and are unique to each individual. Such work holds tremendous promise for providing a biological basis for understanding brain function and brain disorders, it allows us to characterize trajectories of growth, development, and aging, to categorize patients according to their functional phenotype, ultimately aiding treatment decisions, and predicting outcomes. Building such connectome based predictive models, involves 3 distinct steps: 1) construction of the connectivity matrix summarizing the connections across the defined nodes/parcellation; 2) a subsequent association step linking edge strength to the behavior or clinical symptom of interest; 3) and finally a predictive model step for validation and to ensure the models generalize and the associations are not spurious. While many atlases are available, there has been no consensus on which atlas to use to define the nodes in building the connectome, making the sharing of models and validation across sites difficult. A second, often overlooked problem, is that the node configuration supporting one behavior may not be the same for a different behavior due to the functional flexibility in brain organization. Thus, while the parcellation and brain modeling steps have historically been treated separately, they are not independent and should not be treated as such. In this work we will develop a joint parcellation/brain-phenotype modeling approach that provides statistically powerful, analytically robust, and biologically interpretable Bayesian models that are not dependent upon the choice of the initial atlas. We will validate the models through measures of predictive power, reliability, and generalizability, and compare to existing state-of-the-art methods. Data for validation will include the healthy adult data from the human connectome project and a transdiagnostic sample of 450 individuals (after adding 150 subjects in this study) collected at Yale, spanning a range from healthy control subjects to those with psychiatric illnesses. Normative models for 16 behavioral measures and 6 clinical scores will be developed and shared with the neuroscience community. A key aspect of validation and reproducibility in research is the sharing of data and models. The use of approximately a dozen or so arbitrary atlases in the field inhibits the sharing of models. This work will move the field forward by improving the methodology of brain-phenotype predictive modeling, identifying the circuits supporting behavior, without a priori imposition of an arbitrary atlas. The results could advance our understanding of the brain networks supporting behavior and impact a wide range of psychiatric illnesses. Facilitating the release of generalized models to the research community will aid in understanding how to use these methods for assigning treatments and monitoring the response to treatment.

抽象的 人类连接组研究的最新进展导致了揭示大脑的模型的开发 与行为或症状相关的电路。定义这些电路的网络产生功能 表型可以在个体中测量并且对每个个体来说都是独特的。这样的工作持有 它为理解大脑功能和大脑疾病提供生物学基础提供了巨大的希望 使我们能够描绘生长、发育和衰老的轨迹，并根据以下因素对患者进行分类： 它们的功能表型，最终帮助治疗决策并预测结果。 构建这种基于连接组的预测模型涉及 3 个不同的步骤：1）构建 连接矩阵总结了定义的节点/分区之间的连接； 2）随后的 将边缘强度与感兴趣的行为或临床症状联系起来的关联步骤； 3）最后是预测 用于验证的模型步骤，并确保模型具有概括性并且关联不是虚假的。尽管 有许多图集可供使用，但对于使用哪个图集来定义构建中的节点尚未达成共识 连接组，使得跨站点共享模型和验证变得困难。第二个经常被忽视的 问题是，支持一种行为的节点配置对于不同的行为可能不同 由于大脑组织的功能灵活性。因此，虽然分割和大脑建模步骤 它们历来被分开对待，但它们并不独立，也不应被如此对待。 在这项工作中，我们将开发一种联合分割/大脑表型建模方法，提供统计数据 强大的、分析稳健的、生物学上可解释的贝叶斯模型，不依赖于 初始图集的选择。我们将通过预测能力、可靠性和 普遍性，并与现有的最先进的方法进行比较。验证数据将包括健康的 来自人类连接组项目的成人数据和 450 人的跨诊断样本（添加后 本研究中的 150 名受试者是在耶鲁大学收集的，涵盖从健康对照受试者到患有糖尿病的受试者 精神疾病。将开发 16 项行为测量和 6 项临床评分的规范模型 并与神经科学界分享。 研究验证和可重复性的一个关键方面是数据和模型的共享。使用 该领域大约有十几个任意图集阻碍了模型的共享。这部作品将动 通过改进大脑表型预测模型的方法、识别回路来推动该领域的发展 支持行为，而不是预先强加任意图集。结果可以推进我们的 了解支持行为并影响多种精神疾病的大脑网络。 促进向研究界发布通用模型将有助于理解如何使用 这些方法用于分配治疗和监测治疗反应。