An integrative Bayesian approach for linking brain to behavioral phenotype

摘要 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回路。定义这些电路的网络产生功能 表型可以在个体中测量，并且对每个个体都是唯一的。这类工作经得起推敲 它为了解大脑功能和大脑疾病提供了生物学基础，具有巨大的希望 使我们能够描述生长、发育和衰老的轨迹，根据以下情况对患者进行分类 他们的功能表型，最终帮助治疗决定，并预测结果。 构建这种基于连接体的预测模型包括3个不同的步骤：1)构建 汇总定义的节点/分区之间的连接的连接性矩阵；2)后续 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)最后预测 用于验证的模型步骤，以确保模型泛化并且关联不是虚假的。而当 有许多地图集可用，但对于使用哪种地图集来定义建筑物中的节点还没有达成共识 连接体，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 问题在于，对于不同的行为，支持一种行为的节点配置可能不同 由于大脑组织的功能灵活性。因此，虽然分割和大脑建模的步骤 在历史上，它们被分开对待，它们不是独立的，不应被视为独立。 在这项工作中，我们将开发一种联合分割/脑表型建模方法，以提供统计上的 强大的、分析稳健的、生物上可解释的贝叶斯模型，不依赖于 初始地图集的选择。我们将通过预测能力、可靠性和 普适性，并与现有的最先进的方法进行比较。验证的数据将包括健康的 来自人类连接组项目的成人数据和450人的跨诊断样本(在添加 这项研究中的150名受试者)收集在耶鲁大学，范围从健康对照受试者到有 精神疾病。将开发16个行为测量和6个临床评分的标准模型 并与神经科学界分享。 研究中验证和重复性的一个关键方面是数据和模型的共享。对.的使用 该领域中大约有十几个随意的地图集阻止了模型的共享。这项工作将会 通过改进脑-表型预测建模的方法，识别电路来向前推进场 支持行为，而不是事先强加的任意地图集。结果可能会推进我们的 了解支持行为的大脑网络，并影响广泛的精神疾病。 促进向研究社区发布通用模型将有助于理解如何使用 这些方法用于分配治疗和监测治疗反应。

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.