Department of Psychology, Carnegie Mellon University, and Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA.
Proc Natl Acad Sci U S A. 2011 Jun 14;108(24):9998-10003. doi: 10.1073/pnas.1102433108. Epub 2011 May 31.
Face individuation is one of the most impressive achievements of our visual system, and yet uncovering the neural mechanisms subserving this feat appears to elude traditional approaches to functional brain data analysis. The present study investigates the neural code of facial identity perception with the aim of ascertaining its distributed nature and informational basis. To this end, we use a sequence of multivariate pattern analyses applied to functional magnetic resonance imaging (fMRI) data. First, we combine information-based brain mapping and dynamic discrimination analysis to locate spatiotemporal patterns that support face classification at the individual level. This analysis reveals a network of fusiform and anterior temporal areas that carry information about facial identity and provides evidence that the fusiform face area responds with distinct patterns of activation to different face identities. Second, we assess the information structure of the network using recursive feature elimination. We find that diagnostic information is distributed evenly among anterior regions of the mapped network and that a right anterior region of the fusiform gyrus plays a central role within the information network mediating face individuation. These findings serve to map out and characterize a cortical system responsible for individuation. More generally, in the context of functionally defined networks, they provide an account of distributed processing grounded in information-based architectures.
面孔识别是我们视觉系统最令人印象深刻的成就之一，但揭示支持这一壮举的神经机制似乎回避了传统的功能脑数据分析方法。本研究旨在确定其分布式性质和信息基础，调查面孔身份感知的神经编码。为此，我们使用一系列适用于功能磁共振成像 (fMRI) 数据的多元模式分析。首先，我们结合基于信息的脑映射和动态判别分析来定位支持个体水平面孔分类的时空模式。该分析揭示了梭状回和颞前区的网络，这些区域携带关于面孔身份的信息，并提供了证据表明梭状回面孔区对不同的面孔身份有不同的激活模式。其次，我们使用递归特征消除来评估网络的信息结构。我们发现诊断信息在映射网络的前区均匀分布，而梭状回的右前区在介导面孔个体化的信息网络中起着核心作用。这些发现有助于描绘和表征负责个体化的皮质系统。更一般地说，在功能定义网络的背景下，它们提供了基于基于信息的架构的分布式处理的解释。
