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发展 与行为或症状相关的电路。定义这些电路的网络产生功能 可以在个体中测量并且每个人都独有的表型。这样的工作成立 为理解脑功能和脑部疾病提供生物学基础的巨大希望， 允许我们表征生长，发展和衰老的轨迹，以根据患者对患者进行分类 它们的功能表型，最终有助于治疗决策并预测结果。 构建基于连接的预测模型，涉及3个不同的步骤：1） 连接矩阵总结了定义的节点/分析之间的连接； 2）随后的 关联步骤将边缘强度与感兴趣的行为或临床症状联系起来； 3）最后一个预测 验证的模型步骤，并确保模型概括和关联并不虚假。尽管 许多地图集都可以使用，尚无共识来定义建筑物节点的地图集 Connectome，使跨站点的模型和验证的共享变得困难。一秒钟，经常被忽视 问题是，支持一种行为的节点配置对于不同的行为可能不相同 由于大脑组织的功能灵活性。因此，虽然分析和大脑建模步骤 从历史上看，它们是单独处理的，它们不是独立的，不应得到这样的对待。 在这项工作中，我们将开发一种联合分割/脑形表型建模方法，该方法在统计上提供 强大的，分析性的稳健性和生物学上可解释的贝叶斯模型，这些模型不依赖于 选择初始地图集。我们将通过预测能力，可靠性和 可推广性，并与现有的最新方法相比。验证数据将包括健康 来自人类连接项目的成人数据和450个人的经诊断样本（添加后 这项研究中的150名受试者）在耶鲁大学收集，跨越从健康控制受试者到患有的范围 精神病。将开发16种行为度量和6个临床分数的规范模型 并与神经科学社区分享。 研究中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 该田间大约十几个任意地图杆抑制了模型的共享。这项工作将移动 通过改善脑 - 表型预测建模的方法，识别电路来向前发展。 支持行为，而没有先验地征收任意地图集的行为。结果可能会推动我们的 了解支持行为的大脑网络并影响广泛的精神病。 促进向研究社区发布通用模型将有助于理解如何使用 这些用于分配治疗和监测治疗反应的方法。