Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal.
Curr Opin Neurobiol. 2020 Dec;65:59-69. doi: 10.1016/j.conb.2020.09.009. Epub 2020 Nov 1.
The brain is composed of many functionally distinct areas. This organization supports distributed processing, and requires the coordination of signals across areas. Our understanding of how populations of neurons in different areas interact with each other is still in its infancy. As the availability of recordings from large populations of neurons across multiple brain areas increases, so does the need for statistical methods that are well suited for dissecting and interrogating these recordings. Here we review multivariate statistical methods that have been, or could be, applied to this class of recordings. By leveraging population responses, these methods can provide a rich description of inter-areal interactions. At the same time, these methods can introduce interpretational challenges. We thus conclude by discussing how to interpret the outputs of these methods to further our understanding of inter-areal interactions.
大脑由许多功能不同的区域组成。这种组织支持分布式处理，并需要跨区域协调信号。我们对不同区域的神经元群体如何相互作用的理解还处于起步阶段。随着来自多个脑区的大量神经元记录的可用性的增加，我们需要能够很好地分析和研究这些记录的统计方法。在这里，我们回顾了已经或可能应用于这类记录的多元统计方法。通过利用群体反应，这些方法可以提供区域间相互作用的丰富描述。同时，这些方法也可能带来解释上的挑战。因此，我们最后讨论了如何解释这些方法的输出，以进一步了解区域间的相互作用。
