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.

神经科学家努力了解脑网络中的因果机制 感知，认知功能，决策和行为。要了解这些机制， 研究人员试图翻译大脑区域之间的统计关联（所谓的功能 连通性）进入因果关系，提出了一个大脑区域是否直接的问题 对其他大脑区域记录的生理活性的影响。这种因果关系称为 有效的连通性。有效连通性对于学习不同地区的神经活动至关重要 与因果关系相关。它还提供了设计针对未来实验的重要证据 通过介入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的连通性，包括Granger因果关系和 动态因果建模。但是，这些现有方法很容易受到伪造的关联 神经影像数据中发现的共享网络，时间和空间依赖性结构。而且， 他们并不经常明确对未衡量混杂因素的假设，例如是否以及如何 在确定有效的情况下允许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。这两个伪造和有偏见的发现的共同来源很容易误导我们 了解有效连通性，导致设计较差的实验或干预措施 改善的认知功能。该提议的目的是开发可靠和强大的因果推论 推断大脑区域之间有效连通性的方法，以解释共享依赖性 结构以及无法衡量的混杂因素。这项拟议的研究将提高人们对 神经影像学（例如fMRI）数据中的偏见和误导性发现的潜在来源，并提供 与通常依赖单个p值的现有方法相比，更可靠，可靠的推论工具 从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 独立资金将进一步调查多个大脑区域之间的有效连通性 对许多虚假发现的强大来源。