There is a substantial literature on the neurobiology of reading and dyslexia. Differences are often described in terms of individual regions or individual cognitive processes. However, there is a growing appreciation that the brain areas subserving reading are nested within larger functional systems, and new network analysis methods may provide greater insight into how reading difficulty arises. Yet, relatively few studies have adopted a principled network-based approach (e.g., connectomics) to studying reading. In this study, we combine data from previous reading literature, connectomics studies, and original data to investigate the relationship between network architecture and reading. First, we detailed the distribution of reading-related areas across many resting-state networks using meta-analytic data from NeuroSynth. Then, we tested whether individual differences in modularity, the brain’s tendency to segregate into resting-state networks, are related to reading skill. Finally, we determined whether brain areas that function atypically in dyslexia, as identified by previous meta-analyses, tend to be concentrated in hub regions. We found that most resting-state networks contributed to the reading network, including those subserving domain-general cognitive skills such as attention and executive function. There was also a positive relationship between the global modularity of an individual’s brain network and reading skill, with the visual, default mode and cingulo-opercular networks showing the highest correlations. Brain areas implicated in dyslexia were also significantly more likely to have a higher participation coefficient (connect to multiple resting-state networks) than other areas. These results contribute to the growing literature on the relationship between reading and brain network architecture. They suggest that an efficient network organization, i.e., one in which brain areas form cohesive resting-state networks, is important for skilled reading, and that dyslexia can be characterized by abnormal functioning of hub regions that map information between multiple systems. Overall, use of a connectomics framework opens up new possibilities for investigating reading difficulty, especially its commonalities across other neurodevelopmental disorders.

中文翻译：

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将网络框架应用于阅读和阅读障碍的神经生物学

有大量关于阅读和阅读障碍的神经生物学文献。差异通常根据单个区域或单个认知过程来描述。但是，越来越多的人意识到，将阅读的大脑区域嵌套在较大的功能系统中，并且新的网络分析方法可以提供关于阅读困难如何产生的更多见解。但是，很少有研究采用基于原理的基于网络的方法（例如，connectomics）来研究阅读。在这项研究中，我们结合了以前阅读文献，连接组学研究和原始数据中的数据，以研究网络体系结构与阅读之间的关系。首先，我们使用NeuroSynth的荟萃分析数据，详细说明了许多静息状态网络中与阅读相关的区域的分布。然后，我们测试了模块性的个体差异，大脑分离成静止状态网络的趋势是否与阅读能力有关。最后，我们确定了如先前的荟萃分析所确定的在阅读障碍中非典型功能的大脑区域是否倾向于集中在枢纽区域。我们发现，大多数静止状态网络都对阅读网络有所贡献，包括那些保留领域通用认知技能（例如注意力和执行功能）的网络。个人的大脑网络的整体模块性与阅读技能之间也存在正相关关系，其中视觉，默认模式和扣带-耳蜗网络之间的关系最为密切。与阅读障碍有关的大脑区域也比其他区域更可能具有更高的参与系数（连接到多个静止状态网络）。这些结果有助于有关阅读与大脑网络体系结构之间关系的文献不断增加。他们认为，有效的网络组织（即大脑区域形成凝聚的静止状态网络的组织）对于熟练的阅读很重要，并且诵读困难的特征可能在于在多个系统之间映射信息的枢纽区域的功能异常。总体而言，使用连接组学框架为研究阅读困难，尤其是其在其他神经发育障碍中的共性提供了新的可能性。这些结果有助于有关阅读与大脑网络体系结构之间关系的文献不断增加。他们认为，有效的网络组织（即大脑区域形成凝聚的静止状态网络的组织）对于熟练的阅读很重要，并且诵读困难的特征可能在于在多个系统之间映射信息的枢纽区域的功能异常。总体而言，使用连接组学框架为研究阅读困难，尤其是其在其他神经发育障碍中的共性提供了新的可能性。这些结果有助于有关阅读与大脑网络体系结构之间关系的文献不断增加。他们认为，有效的网络组织（即大脑区域形成凝聚的静止状态网络的组织）对于熟练的阅读很重要，并且诵读困难的特征可能在于在多个系统之间映射信息的枢纽区域的功能异常。总体而言，使用连接组学框架为研究阅读困难，尤其是其在其他神经发育障碍中的共性提供了新的可能性。且阅读障碍的特征可能是在多个系统之间映射信息的中心区域功能异常。总体而言，使用连接组学框架为研究阅读困难，尤其是其在其他神经发育障碍中的共性提供了新的可能性。且阅读障碍的特征可能是在多个系统之间映射信息的中心区域功能异常。总体而言，使用连接组学框架为研究阅读困难，尤其是其在其他神经发育障碍中的共性提供了新的可能性。