Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations.
Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations.
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DOI：
10.1016/j.neuroimage.2023.120300
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发表时间：
2023-09
期刊：
影响因子：
5.7
通讯作者：
Cole, Michael W.
中科院分区：
文献类型：
--
作者：
Sanchez-Romero, Ruben;Ito, Takuya;Mill, Ravi D.;Hanson, Stephen Jose;Cole, Michael W.
关键词：
大脑活动流模型估计任务诱发活动在大脑连接上的运动，以帮助解释网络生成的任务功能。活动流模型已经被证明可以在各种各样的大脑区域和任务条件下准确地产生任务诱发的大脑激活。然而，这些模型的解释力有限，已知的问题与因果解释的标准功能连接措施用于参数化活动流模型。我们在这里表明，功能/有效连接（FC）的措施接地因果关系的原则，促进活动流模型的机械解释。我们从简单到复杂的FC措施的进展，每个添加算法的细节反映因果关系的原则。这反映了许多神经科学家倾向于降低FC测量复杂性（以最小化假设，最小化计算时间，并充分理解和轻松传达方法细节），这可能与因果有效性相权衡。我们从皮尔逊相关性（当前的领域标准）开始，以保持最大程度的相关性，使用模拟和经验fMRI数据在一系列FC测量中估计因果有效性。最后，我们将基于CAST-FC的活动流建模应用于背外侧前额叶皮层区域（DLPFC），证明了分布式因果网络机制有助于其在工作记忆任务中的强烈激活。值得注意的是，这种完全分布式模型能够解释DLPFC工作记忆效应，传统上认为这种效应主要依赖于区域内（即，非分布式）循环过程。总之，这些结果揭示了使用因果FC方法参数化活动流模型以识别人脑中认知计算的网络机制的前景。
Brain activity flow models estimate the movement of task-evoked activity over brain connections to help explain network-generated task functionality. Activity flow models have been shown to accurately generate task-evoked brain activations across a wide variety of brain regions and task conditions. However, these models have had limited explanatory power, given known issues with causal interpretations of the standard functional connectivity measures used to parameterize activity flow models. We show here that functional/effective connectivity (FC) measures grounded in causal principles facilitate mechanistic interpretation of activity flow models. We progress from simple to complex FC measures, with each adding algorithmic details reflecting causal principles. This reflects many neuroscientists’ preference for reduced FC measure complexity (to minimize assumptions, minimize compute time, and fully comprehend and easily communicate methodological details), which potentially trades off with causal validity. We start with Pearson correlation (the current field standard) to remain maximally relevant to the field, estimating causal validity across a range of FC measures using simulations and empirical fMRI data. Finally, we apply causal-FC-based activity flow modeling to a dorsolateral prefrontal cortex region (DLPFC), demonstrating distributed causal network mechanisms contributing to its strong activation during a working memory task. Notably, this fully distributed model is able to account for DLPFC working memory effects traditionally thought to rely primarily on within-region (i.e., not distributed) recurrent processes. Together, these results reveal the promise of parameterizing activity flow models using causal FC methods to identify network mechanisms underlying cognitive computations in the human brain.
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