Causal Inference for Neuroimaging

摘要：功能磁共振成像(FMRI)等新技术帮助我们提高了 了解人脑的功能。区分因果关系和虚假关系是非常有趣的， 对基础神经科学和在医学、药物开发和临床实践中的应用都很重要。 神经学家想知道实验刺激如何影响大脑功能，神经活动如何不同 大脑区域是因果联系的，以及神经活动如何调节刺激和刺激之间的关系 结果。研究人员应用各种统计方法(例如，格兰杰因果关系，动态因果模型， 结构方程模型和定向图形模型)到fMRI数据，解释所产生的关联 作为效果，往往是不适当的。这是一个大问题。这笔赠款的上一个资金周期奠定了 在神经科学研究中，利用潜力，对随意推理的原则性方法的基础工作 因果推断的统计文献中使用的结果符号。在这里，我们试图扩大框架- 在那里开展的工作是为了更好地研究中介，其中一个或多个大脑区域的神经活动进行中介 治疗-结果关系和有效的连接，其中不同区域的激活是因果关系 已链接。首先，我们发现了新的因果效应，并构建了一个新的全脑模型，它将有助于 在更现实的假设下研究区域级别的本地化、调解和有效连接 比在对fMRI数据建模时通常所做的要多。此外，我们还创建了一种新的行为统计方法- ING高维中介分析表明，IdentifiES网络可能中介 治疗和结果。最后，以工具变量和结构方程模型的文献为基础。 ELS，我们开发了研究中介和有效连接的新方法，在应用外部 使用最新开发的技术进行神经刺激，如经颅磁刺激(TMS)和 经颅直流电刺激(TDC)开始在理论上和理论上都得到广泛应用。 科学和应用背景。我们将这些方法应用于创伤后应激障碍研究的fMRI数据， 热痛和社会评价。

Abstract: New technologies such as functional magnetic resonance imaging (fMRI) have aided our ability to understand human brain function. Distinguishing causal from spurious relationships is of great interest and importance for both basic neuroscience and applications in medicine, drug development, and clinical practice. Neuroscientists want to know how experimental stimuli affect brain function, how neural activity in different brain regions are causally linked, and how neural activity mediates the relationship between a stimulus and an outcome. Researchers apply various statistical procedures (e.g., Granger causality, dynamic causal models, structural equation models, and directed graphical models) to fMRI data, interpreting the resulting associations as effects, often inappropriately. This is a major problem. The previous funding cycle of this grant laid the groundwork for a principled approach to casual inference in neuroscience research, employing the potential outcomes notation used in the statistical literature on causal inference. Here we seek to extend the frame- work developed there to better study mediation, where neural activity in one or more brain regions mediates a treatment-outcome relationship, and effective connectivity, where activations in different regions are causally linked. To begin, we define new causal effects and construct a new whole brain model that will facilitate the study of localization, mediation, and effective connectivity at the regional level under assumptions more realistic than those typically made in modeling fMRI data. Further, we create a new statistical method for conduct- ing high-dimensional mediation analysis that identifies networks that may mediate the relationship between a treatment and outcome. Finally, building on the literature on instrumental variables and structural equation mod- els, we develop new methods for studying mediation and effective connectivity after the application of external neurostimulation using recently developed technologies such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) that are starting to see widespread use in both theoretical neuro- science and applied contexts. We apply the methods to fMRI data from studies of post-traumatic stress disorder, thermal pain and social evaluation.