Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA.
Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA.
Neuroimage. 2023 Sep;278:120300. doi: 10.1016/j.neuroimage.2023.120300. Epub 2023 Jul 29.
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.
脑活动流模型估计任务诱发活动在脑连接上的移动，以帮助解释网络产生的任务功能。活动流模型已被证明能够在广泛的大脑区域和任务条件下准确生成任务诱发的大脑激活。然而，鉴于用于参数化活动流模型的标准功能连接测量存在因果解释的已知问题，这些模型的解释能力有限。我们在这里表明，基于因果原理的功能/有效连接（FC）测量有助于对活动流模型进行机制解释。我们从简单到复杂的 FC 测量进行，每个测量都增加了反映因果原则的算法细节。这反映了许多神经科学家对减少 FC 测量复杂性的偏好（以最小化假设、最小化计算时间，并完全理解和轻松传达方法细节），这可能会与因果有效性产生权衡。我们从 Pearson 相关系数（当前领域的标准）开始，以保持与该领域的最大相关性，使用模拟和实证 fMRI 数据来估计一系列 FC 测量的因果有效性。最后，我们将基于因果的 FC 的活动流模型应用于背外侧前额叶皮层（DLPFC）区域，证明了分布式因果网络机制对其在工作记忆任务中强烈激活的贡献。值得注意的是，这个完全分布式的模型能够解释传统上认为主要依赖于区域内（即非分布式）递归过程的 DLPFC 工作记忆效应。总之，这些结果表明，使用因果 FC 方法对活动流模型进行参数化可以识别人类大脑中认知计算的网络机制，具有很大的潜力。
