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.

神经学家努力理解大脑网络中支配的因果机制 感知、认知功能、决策和行为。为了理解这些机制， 研究人员试图将大脑区域(所谓的功能)之间的统计关联 连通性)转化为因果关系，这引发了一个问题，即一个大脑区域是否有直接的 对其他脑区记录的生理活动的影响。这种因果关系被称为 有效连接。有效的连接对于了解不同区域的神经活动如何 是有因果关系的。这也为今后的实验设计提供了重要依据 通过干预特定的神经过程来影响某些认知结果。有几种方法 已开发用于识别和估计有效连通性，包括格兰杰因果关系和 动态因果模型。然而，这些现有的方法由于以下原因容易受到虚假关联的影响 在神经成像数据中发现的共享的网络、时间和空间依赖结构。此外， 他们通常不会明确说明对不可测量的混杂因素的假设，比如是否以及如何 在确定有效时，许多未被观察到的影响多个大脑区域的神经活动是允许的 连通性。这两个常见的虚假和有偏见的发现来源很容易误导我们的 对有效连接的理解，导致设计不佳的实验或干预措施 改善认知功能。这项建议的目标是开发可靠和健壮的因果推理 推断导致共同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对 神经成像(例如，功能磁共振成像)数据中存在偏见和误导性发现的潜在来源，以及提供 比通常依赖单个p值的现有方法更可靠、更健壮的推理工具 从假定的参数模型中的单个参数。这项初步研究将为今后的工作铺平道路。 独立基金将进一步研究多个大脑区域之间的有效连接 对于虚假发现的许多来源都是可靠的。