Mechanist philosophers have examined several strategies scientists use for discovering causal mechanisms in neuroscience. Findings about the anatomical organization of the brain play a central role in several such strategies. Little attention has been paid, however, to the use of network analysis and causal modeling techniques for mechanism discovery. In particular, mechanist philosophers have not explored whether and how these strategies incorporate information about the anatomical organization of the brain. This paper clarifies these issues in the light of the distinction between structural, functional and effective connectivity. Specifically, we examine two quantitative strategies currently used for causal discovery from functional neuroimaging data: dynamic causal modeling and probabilistic graphical modeling. We show that dynamic causal modeling uses findings about the brain’s anatomical organization to improve the statistical estimation of parameters in an already specified causal model of the target brain mechanism. Probabilistic graphical modeling, in contrast, makes no appeal to the brain’s anatomical organization, but lays bare the conditions under which correlational data suffice to license reliable inferences about the causal organization of a target brain mechanism. The question of whether findings about the anatomical organization of the brain can and should constrain the inference of causal networks remains open, but we show how the tools supplied by graphical modeling methods help to address it.

中文翻译：

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使用网络分析和因果建模发现大脑机制

机械论哲学家研究了科学家用于发现神经科学因果机制的几种策略。关于大脑解剖组织的发现在几个这样的策略中起着核心作用。然而，很少有人关注使用网络分析和因果建模技术进行机制发现。特别是，机械论哲学家尚未探索这些策略是否以及如何结合有关大脑解剖组织的信息。本文根据结构、功能和有效连接之间的区别阐明了这些问题。具体来说，我们研究了目前用于从功能性神经影像数据中发现因果关系的两种定量策略：动态因果建模和概率图形建模。我们展示了动态因果建模使用有关大脑解剖组织的发现来改进目标大脑机制的已指定因果模型中参数的统计估计。相比之下，概率图形建模对大脑的解剖组织没有吸引力，但揭示了相关数据足以对目标大脑机制的因果组织进行可靠推断的条件。关于大脑解剖组织的发现是否可以而且应该限制因果网络的推断仍然是开放的，但我们展示了图形建模方法提供的工具如何帮助解决这个问题。