Multivariate pattern dependence.
Multivariate pattern dependence.
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DOI：
10.1371/journal.pcbi.1005799
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发表时间：
2017-11
影响因子：
4.3
通讯作者：
Saxe R
中科院分区：
文献类型：
--
作者：
Anzellotti S;Caramazza A;Saxe R
当我们执行认知任务时，多个大脑区域都会参与其中。了解这些区域如何相互作用是揭示行为神经基础的基本步骤。大多数关于大脑区域之间相互作用的研究都集中在这些区域的单变量反应上。然而，正如多元模式分析所示，细粒度的响应模式编码了重要信息。在本文中，我们介绍并应用多元模式依赖性（MVPD）：一种根据人类反应模式之间的多元关系来研究人类大脑区域之间的统计依赖性的技术。 MVPD 将每个大脑区域的反应描述为特定区域多维空间中的轨迹，并对这些轨迹之间的多元关系进行建模。我们将 MVPD 应用于颞上沟 (pSTS) 和梭状面部区域 (FFA)，使用探照灯方法揭示这些种子区域与大脑其他部分之间的相互作用。在两个不同的实验中，MVPD 发现了标准功能连接未检测到的显着统计依赖性。此外，MVPD 在解释单个体素响应的独立方差方面优于单变量连接。最后，MVPD 发现了与 FFA 不同表征子空间相关的不同连接概况：FFA 的第一个主成分显示了与面部低级属性处理中涉及的枕叶和顶叶区域的差异连接，而第二和第三个成分则显示了与面部身份不变表示处理中涉及的前颞区的差异连接。人类行为由交换信息以完成任务的大脑区域系统支持。大脑区域之间的这种信息交换导致了随着时间的推移，它们的反应之间存在统计关系。最有可能的是，这些关系不仅将两个大脑区域的平均反应联系起来，而且还联系了它们更精细的空间模式。分析更精细的响应模式是单个区域内响应研究的关键进展，并且可以用来研究区域间的相互作用。为了捕获两个大脑区域之间的总体统计关系，我们需要描述每个区域相对于最能解释该区域随时间变化的维度的响应。这些维度可能因地区而异。我们引入了一种方法，其中每个区域的响应都根据最能解释其响应的区域特定维度来表征，并且区域之间的关系使用多元线性模型进行建模。我们证明，与两个不同实验中的标准功能连接相比，这种方法可以更好地解释数据，并且我们用它来发现梭状面部区域内的多个维度，这些维度与大脑的其他部分具有不同的连接配置文件。
When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD): a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS) and to the fusiform face area (FFA), using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity. Human behavior is supported by systems of brain regions that exchange information to complete a task. This exchange of information between brain regions leads to statistical relationships between their responses over time. Most likely, these relationships do not link only the mean responses in two brain regions, but also their finer spatial patterns. Analyzing finer response patterns has been a key advance in the study of responses within individual regions, and can be leveraged to study between-region interactions. To capture the overall statistical relationship between two brain regions, we need to describe each region’s responses with respect to dimensions that best account for the variation in that region over time. These dimensions can be different from region to region. We introduce an approach in which each region’s responses are characterized in terms of region-specific dimensions that best account for its responses, and the relationships between regions are modeled with multivariate linear models. We demonstrate that this approach provides a better account of the data as compared to standard functional connectivity in two different experiments, and we use it to discover multiple dimensions within the fusiform face area that have different connectivity profiles with the rest of the brain.
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通讯作者：
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Saxe R
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通讯作者：
Caramazza, Alfonso