Reliable and robust causal inference approaches for effective connectivity research with fMRI data

可靠且稳健的因果推理方法，可利用功能磁共振成像数据进行有效的连通性研究

• 批准号: 10709066
• 负责人: Youjin Lee
• 金额: $ 40.27万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10709066
• 关键词: Affect; Awareness; Behavior; Brain; Brain region; Cognitive; Data; Decision Making; Dependence; Etiology; Functional Magnetic Resonance Imaging; Funding; Future; Goals; Intervention; Learning; Methods; Modeling; Nervous System Physiology; Neuraxis; Outcome; Perception; Physiological; Process; Research; Research Personnel; Source; Structure; Translating; causal model; cognitive function; design; experimental study; improved; neuroimaging; relating to nervous system; tool

Neuroscientists grapple with understanding the causal mechanisms within brain networks that govern perception, cognitive functions, decision-making, and behavior. To understand these mechanisms, researchers have attempted to translate statistical associations between brain regions (so-called functional connectivity) into causal relationships, raising the question of whether one brain region has a direct influence on the physiological activity recorded in other brain regions. Such causal relationships are called effective connectivity. Effective connectivity is essential in learning how neural activities in different regions are causally related. It also provides important evidence for designing future experiments aiming at affecting certain cognitive outcomes by intervening on specific neural processes. Several methods have been developed for identifying and estimating effective connectivity, including Granger causality and dynamic causal modeling. However, these existing methods are vulnerable to spurious associations due to the shared network, temporal, and spatial dependence structures found in neuroimaging data. Moreover, they are not often explicit about the assumptions on unmeasured confounders, such as whether and how much unobserved neural activities that affect multiple brain regions are allowed when determining effective connectivity. These two common sources of spurious and biased findings can readily mislead our understanding of effective connectivity, resulting in poorly designed experiments or interventions for improved cognitive functions. The goal of this proposal is to develop reliable and robust causal inference methods to infer effective connectivity between brain regions that account for the shared dependence structures as well as unmeasured confounding factors. This proposed research will raise awareness of potential sources of bias and misleading findings in neuroimaging (e.g., fMRI) data, as well as provide more reliable and robust inferential tools than existing methods that are often relying on a single p-value from a single parameter in the presumed parametric model. This pilot research will pave the way for future independent funding that will further investigate effective connectivity among multiple brain regions that are robust to many sources of spurious findings.

神经科学家努力理解控制大脑网络的因果机制 感知、认知功能、决策和行为。为了理解这些机制， 研究人员试图解释大脑区域之间的统计关联（所谓的功能性区域） 连接性）转化为因果关系，提出了一个大脑区域是否有直接关系的问题 对其他大脑区域记录的生理活动的影响。这种因果关系称为 有效的连接。有效的连接对于了解不同区域的神经活动如何至关重要 是有因果关系的。它还为设计未来的实验提供了重要证据 通过干预特定的神经过程来影响某些认知结果。几种方法有 被开发用于识别和估计有效连接，包括格兰杰因果关系和 动态因果建模。然而，这些现有方法很容易受到虚假关联的影响，因为 神经影像数据中发现的共享网络、时间和空间依赖结构。而且， 他们通常并不明确关于未测量的混杂因素的假设，例如是否以及如何 在确定有效时，允许影响多个大脑区域的许多未观察到的神经活动 连接性。这两个常见的虚假和有偏见的发现来源很容易误导我们 对有效连接的理解，导致实验或干预措施设计不当 改善认知功能。该提案的目标是开发可靠且稳健的因果推理 推断导致共享依赖的大脑区域之间有效连接的方法 结构以及未测量的混杂因素。这项拟议的研究将提高人们的认识 神经影像（例如功能磁共振成像）数据中潜在的偏见和误导性发现来源，并提供 比通常依赖于单个 p 值的现有方法更可靠、更稳健的推理工具 来自假定参数模型中的单个参数。这项试点研究将为未来铺平道路 独立资助将进一步研究多个大脑区域之间的有效连接 对许多虚假发现的来源都具有鲁棒性。