Karimi-Rouzbahani Hamid, Woolgar Alexandra, Henson Richard, Nili Hamed
MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom.
Department of Computing, Macquarie University, Sydney, NSW, Australia.
Front Neurosci. 2022 Mar 10;16:755988. doi: 10.3389/fnins.2022.755988. eCollection 2022.
Brain connectivity analyses have conventionally relied on statistical relationship between one-dimensional summaries of activation in different brain areas. However, summarizing activation patterns within each area to a single dimension ignores the potential statistical dependencies between their multi-dimensional activity patterns. Representational Connectivity Analyses (RCA) is a method that quantifies the relationship between multi-dimensional patterns of activity without reducing the dimensionality of the data. We consider two variants of RCA. In model-free RCA, the goal is to quantify the shared information for two brain regions. In model-based RCA, one tests whether two regions have shared information about a specific aspect of the stimuli/task, as defined by a model. However, this is a new approach and the potential caveats of model-free and model-based RCA are still understudied. We first explain how model-based RCA detects connectivity through the lens of models, and then present three scenarios where model-based and model-free RCA give discrepant results. These conflicting results complicate the interpretation of functional connectivity. We highlight the challenges in three scenarios: complex intermediate models, common patterns across regions, and transformation of representational structure across brain regions. The article is accompanied by scripts (https://osf.io/3nxfa/) that reproduce the results. In each case, we suggest potential ways to mitigate the difficulties caused by inconsistent results. The results of this study shed light on some understudied aspects of RCA, and allow researchers to use the method more effectively.
传统上，脑连接性分析依赖于不同脑区激活的一维汇总之间的统计关系。然而，将每个区域内的激活模式汇总为一个维度忽略了它们多维活动模式之间潜在的统计依赖性。表征连接性分析（RCA）是一种在不降低数据维度的情况下量化多维活动模式之间关系的方法。我们考虑RCA的两种变体。在无模型RCA中，目标是量化两个脑区的共享信息。在基于模型的RCA中，人们测试两个区域是否具有关于由模型定义的刺激/任务特定方面的共享信息。然而，这是一种新方法，无模型和基于模型的RCA的潜在警告仍未得到充分研究。我们首先解释基于模型的RCA如何通过模型视角检测连接性，然后呈现三种基于模型和无模型RCA给出不同结果的情况。这些相互矛盾的结果使功能连接性的解释变得复杂。我们强调了三种情况下的挑战：复杂的中间模型、跨区域的共同模式以及跨脑区表征结构的转换。本文配有重现结果的脚本（https://osf.io/3nxfa/）。在每种情况下，我们都提出了减轻结果不一致所带来困难的潜在方法。这项研究的结果揭示了RCA一些尚未充分研究的方面，并使研究人员能够更有效地使用该方法。
