Functional brain responses are strongly influenced by connectivity. Recently, we demonstrated a major example of this: category discriminability within occipitotemporal cortex (OTC) is enhanced for voxel sets that share strong functional connectivity to distal brain areas, relative to those that share lesser connectivity. That is, within OTC regions, sets of ‘most-connected’ voxels show improved multivoxel pattern discriminability for tool-, face-, and place stimuli relative to voxels with weaker connectivity to the wider brain. However, understanding whether these effects generalize to other domains (e.g. body perception network), and across different levels of the visual processing streams (e.g. dorsal-as well as ventral stream areas) is an important extension of this work. Here, we show that this so-called connectivity-guided decoding (CGD) effect broadly generalizes across a wide range of categories (tools, faces, bodies, hands, places). This effect is robust across dorsal stream areas, but less consistent in earlier ventral stream areas. In the latter regions, category discriminability is generally very high, suggesting that extraction of category-relevant visual properties is less reliant on connectivity to downstream areas. Further, CGD effects are primarily expressed in a category-specific manner: For example, within the network of tool regions, discriminability of tool information is greater than non-tool information. The connectivity-guided decoding approach shown here provides a novel demonstration of the crucial relationship between wider brain connectivity and complex local-level functional responses at different levels of the visual processing streams. Further, this approach generates testable new hypotheses about the relationships between connectivity and local selectivity.

中文翻译：

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表征强连接体素中视觉分类信息的可辨别性

大脑功能反应受到连通性的强烈影响。最近，我们展示了一个重要的例子：枕颞皮层（OTC）内的类别辨别力对于与远端大脑区域具有较强功能连接的体素集而言，相对于那些具有较低连接性的体素集而言得到了增强。也就是说，在 OTC 区域内，相对于与更广泛的大脑连接较弱的体素，“连接最紧密”的体素组显示出工具、面部和位置刺激的多体素模式辨别能力得到改善。然而，了解这些效应是否推广到其他领域（例如身体感知网络）以及不同级别的视觉处理流（例如背侧和腹侧流区域）是这项工作的重要延伸。在这里，我们展示了这种所谓的连接引导解码（CGD）效应广泛地推广到各种类别（工具、面部、身体、手、地点）。这种效应在背流区域中很强烈，但在早期的腹流区域中不太一致。在后面的区域中，类别可辨别性通常非常高，这表明类别相关视觉属性的提取不太依赖于与下游区域的连接。此外，CGD效应主要以类别特定的方式表达：例如，在工具区域的网络内，工具信息的可区分性大于非工具信息。这里显示的连接引导解码方法提供了一种新颖的演示，展示了更广泛的大脑连接和视觉处理流不同级别的复杂局部功能响应之间的重要关系。此外，这种方法产生了关于连通性和局部选择性之间关系的可测试的新假设。