Combining Multiple Functional Connectivity Methods to Improve Causal Inferences.
Combining Multiple Functional Connectivity Methods to Improve Causal Inferences.
复制标题
结合多种函数连接方法改进因果推理。
DOI：
10.1162/jocn_a_01580
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发表时间：
2021-03
影响因子：
3.2
通讯作者：
Cole MW
中科院分区：
文献类型：
--
作者：
Sanchez-Romero R;Cole MW
认知和行为来自大脑网络的相互作用，这表明因果相互作用应该是大脑功能研究的核心。然而，神经时间序列的功能连接（FC）方法之间的关系的特点是占主导地位的方法，评估双变量统计协会，而不是因果关系的相互作用。这种双变量方法导致大量的假阳性，因为它们没有考虑神经群体中的混杂因素（常见原因）。双变量皮尔逊相关（与功能性MRI）和相干性（与电生理方法）等方法的优势的一个主要原因可能是它们的简单性。因此，我们试图确定一个FC方法，这是简单的和改进的因果推理相对于最流行的方法。我们从部分相关性开始，通过神经网络模拟表明，相对于二元相关性，这大大提高了因果推理。然而，网络中碰撞器（共同效应）的存在导致了偏相关的假阳性，尽管这对于二元相关性来说不是问题。这使我们提出了一种新的组合功能连接方法（combinedFC），它结合了简单的二元和部分相关FC措施，使更有效的因果推理比单独。我们发布了一个工具箱，用于实现这种新的combinedFC方法，以促进基于FC的因果推理的改进。CombinedFC是一种通用的功能连接方法，可以同样适用于静止状态和基于任务的范例。
Cognition and behavior emerge from brain network interactions, suggesting that causal interactions should be central to the study of brain function. Yet approaches that characterize relationships among neural time series—functional connectivity (FC) methods—are dominated by methods that assess bivariate statistical associations rather than causal interactions. Such bivariate approaches result in substantial false positives since they do not account for confounders (common causes) among neural populations. A major reason for the dominance of methods such as bivariate Pearson correlation (with functional MRI) and coherence (with electrophysiological methods) may be their simplicity. Thus, we sought to identify an FC method that was both simple and improved causal inferences relative to the most popular methods. We started with partial correlation, showing with neural network simulations that this substantially improves causal inferences relative to bivariate correlation. However, the presence of colliders (common effects) in a network resulted in false positives with partial correlation, though this was not a problem for bivariate correlations. This led us to propose a new combined functional connectivity method (combinedFC) that incorporates simple bivariate and partial correlation FC measures to make more valid causal inferences than either alone. We release a toolbox for implementing this new combinedFC method to facilitate improvement of FC-based causal inferences. CombinedFC is a general method for functional connectivity and can be applied equally to resting-state and task-based paradigms.
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影响因子：
2.5
作者：
HOERL, AE;KENNARD, RW
通讯作者：
KENNARD, RW
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
--
作者：
Marinazzo D;Pellicoro M;Stramaglia S
通讯作者：
Stramaglia S
影响因子：
5.4
作者：
Hernán, MA;Hernández-Díaz, S;Robins, JM
通讯作者：
Robins, JM
影响因子：
5.7
作者：
Cole, Michael W.;Ito, Takuya;Cocuzza, Carrisa
通讯作者：
Cocuzza, Carrisa