Parkin Beth L, Hellyer Peter J, Leech Robert, Hampshire Adam
Institute of Cognitive Neuroscience, University College London, London WC1N 3AR, United Kingdom.
The Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, London SW7 2AZ, United Kingdom, and.
J Neurosci. 2015 May 20;35(20):7660-73. doi: 10.1523/JNEUROSCI.4956-14.2015.
A prominent hypothesis states that specialized neural modules within the human lateral frontopolar cortices (LFPCs) support "relational integration" (RI), the solving of complex problems using inter-related rules. However, it has been proposed that LFPC activity during RI could reflect the recruitment of additional "domain-general" resources when processing more difficult problems in general as opposed to RI specifically. Moreover, theoretical research with computational models has demonstrated that RI may be supported by dynamic processes that occur throughout distributed networks of brain regions as opposed to within a discrete computational module. Here, we present fMRI findings from a novel deductive reasoning paradigm that controls for general difficulty while manipulating RI demands. In accordance with the domain-general perspective, we observe an increase in frontoparietal activation during challenging problems in general as opposed to RI specifically. Nonetheless, when examining frontoparietal activity using analyses of phase synchrony and psychophysiological interactions, we observe increased network connectivity during RI alone. Moreover, dynamic causal modeling with Bayesian model selection identifies the LFPC as the effective connectivity source. Based on these results, we propose that during RI an increase in network connectivity and a decrease in network metastability allows rules that are coded throughout working memory systems to be dynamically bound. This change in connectivity state is top-down propagated via a hierarchical system of domain-general networks with the LFPC at the apex. In this manner, the functional network perspective reconciles key propositions of the globalist, modular, and computational accounts of RI within a single unified framework.
一个著名的假说是，人类外侧额极皮质（LFPCs）中的专门神经模块支持“关系整合”（RI），即使用相互关联的规则解决复杂问题。然而，有人提出，在关系整合过程中，LFPC的活动可能反映出在处理一般更困难的问题时（而非专门针对关系整合）额外“通用领域”资源的调用。此外，基于计算模型的理论研究表明，关系整合可能由大脑区域分布式网络中发生的动态过程支持，而非由离散的计算模块支持。在此，我们展示了一种新颖的演绎推理范式的功能磁共振成像（fMRI）结果，该范式在控制一般难度的同时操纵关系整合需求。根据通用领域的观点，我们观察到在一般具有挑战性的问题中（而非专门针对关系整合）额顶叶激活增加。尽管如此，当使用相位同步和心理生理交互分析来检查额顶叶活动时，我们仅在关系整合期间观察到网络连接性增加。此外，通过贝叶斯模型选择进行的动态因果建模将LFPC识别为有效连接源。基于这些结果，我们提出，在关系整合过程中，网络连接性的增加和网络亚稳定性的降低使得在整个工作记忆系统中编码的规则能够动态绑定。这种连接状态的变化通过以LFPC为顶点的通用领域网络层次系统自上而下传播。通过这种方式，功能网络视角在一个统一框架内协调了关系整合的整体主义、模块化和计算解释的关键命题。
