Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, USA; Program in Neural Computation, Carnegie Mellon University and University of Pittsburgh, USA; Department of Neurological Surgery, University of Pittsburgh, USA.
Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, USA; Department of Neurological Surgery, University of Pittsburgh, USA.
Neuroimage. 2017 Nov 15;162:32-44. doi: 10.1016/j.neuroimage.2017.08.033. Epub 2017 Aug 13.
The lack of multivariate methods for decoding the representational content of interregional neural communication has left it difficult to know what information is represented in distributed brain circuit interactions. Here we present Multi-Connection Pattern Analysis (MCPA), which works by learning mappings between the activity patterns of the populations as a factor of the information being processed. These maps are used to predict the activity from one neural population based on the activity from the other population. Successful MCPA-based decoding indicates the involvement of distributed computational processing and provides a framework for probing the representational structure of the interaction. Simulations demonstrate the efficacy of MCPA in realistic circumstances. In addition, we demonstrate that MCPA can be applied to different signal modalities to evaluate a variety of hypothesis associated with information coding in neural communications. We apply MCPA to fMRI and human intracranial electrophysiological data to provide a proof-of-concept of the utility of this method for decoding individual natural images and faces in functional connectivity data. We further use a MCPA-based representational similarity analysis to illustrate how MCPA may be used to test computational models of information transfer among regions of the visual processing stream. Thus, MCPA can be used to assess the information represented in the coupled activity of interacting neural circuits and probe the underlying principles of information transformation between regions.
由于缺乏对区域间神经通讯的表示内容进行解码的多元方法，因此很难知道分布式大脑电路交互中表示了什么信息。在这里，我们提出了多连接模式分析（MCPA），它通过学习作为处理信息的因素的群体活动模式之间的映射来工作。这些映射用于基于另一个群体的活动来预测一个神经群体的活动。基于 MCPA 的成功解码表明涉及分布式计算处理，并为探究交互的表示结构提供了框架。模拟表明了 MCPA 在实际情况下的有效性。此外，我们证明 MCPA 可以应用于不同的信号模态，以评估与神经通讯中的信息编码相关的各种假设。我们将 MCPA 应用于 fMRI 和人类颅内电生理数据，以提供该方法用于解码功能连接数据中的单个自然图像和面孔的实用性的概念验证。我们进一步使用基于 MCPA 的表示相似性分析来说明 MCPA 如何用于测试视觉处理流区域之间信息传递的计算模型。因此，MCPA 可用于评估相互作用的神经电路的耦合活动中表示的信息，并探究区域之间信息转换的基本原理。
