The structure of information in the brain is crucial to cognitive function. The representational space of a brain region can be identified through Representational Similarity Analysis (RSA) applied to functional magnetic resonance imaging (fMRI) data. In its classic form, RSA collapses the time-series of each condition, eliminating fluctuations in similarity over time. We propose a method for identifying representational connectivity (RC) networks, which share fluctuations in representational strength, in an analogous manner to functional connectivity (FC), which tracks fluctuations in BOLD signal, and informational connectivity, which tracks fluctuations in pattern discriminability. We utilize jackknife resampling, a statistical technique in which observations are removed in turn to determine their influence. We applied the jackknife technique to an existing fMRI dataset collected as participants viewed videos of animals (Nastase et al., 2017). We used ventral temporal cortex (VT) as a seed region, and compared the resulting network to a second-order RSA, in which brain regions' representational spaces are compared, and to the network identified through FC. The novel representational connectivity analysis identified a network comprising regions associated with lower-level visual processing, spatial cognition, perceptual-motor integration, and visual attention, indicating that these regions shared fluctuations in representational similarity strength with VT. RC, second-order RSA and FC identified areas unique to each method, indicating that analyzing shared fluctuations in the strength of representational similarity reveals previously undetectable networks of regions. The RC analysis thus offers a new way to understand representational similarity at the network level.

中文翻译：

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代表性连接分析：通过折刀重采样识别代表性强度共享变化的网络

大脑中信息的结构对于认知功能至关重要。可以通过应用于功能磁共振成像（fMRI）数据的代表性相似性分析（RSA）来识别大脑区域的代表性空间。RSA以其经典形式折叠每个条件的时间序列，消除了相似性随时间的波动。我们提出了一种识别代表性连接（RC）网络的方法，该方法以与功能连接（FC）类似的方式（其跟踪BOLD信号的波动）和信息连接（其跟踪模式可分辨性的波动）来共享代表强度的波动。我们采用折刀重采样技术，一种统计技术，其中依次删除观测值以确定其影响。我们将折刀技术应用于现有的fMRI数据集，参与者在观看动物的视频时会对其进行收集（Nastase et al。，2017）。我们使用腹侧颞叶皮质（VT）作为种子区域，并将生成的网络与二阶RSA（将大脑区域的代表空间进行比较）和通过FC识别的网络进行比较。新颖的代表性连通性分析确定了一个网络，该网络包括与较低级别的视觉处理，空间认知，知觉运动整合和视觉注意相关的区域，表明这些区域与VT共享了代表相似强度的波动。每种方法特有的RC，二阶RSA和FC识别区域，表明分析代表性相似性强度的共享波动揭示了以前无法检测到的区域网络。因此，RC分析提供了一种新的方式来理解网络级别的表示相似性。