The capacity to produce and understand written language is a uniquely human skill that exists on a continuum, and foundational to other facets of human cognition. Multivariate classifiers based on support vector machines (SVM) have provided much insight into the networks underlying reading skill beyond what traditional univariate methods can tell us. Shallow models like SVM require large amounts of data, and this problem is compounded when functional connections, which increase exponentially with network size, are predictors of interest. Data reduction using independent component analyses (ICA) mitigates this problem, but conventionally assumes linear relationships. Multilayer feedforward networks, in contrast, readily find optimal low-dimensional encodings of complex patterns that include complex nonlinear or conditional relationships. Samples of poor and highly-skilled young readers were selected from two open access data sets using rhyming and mental multiplication tasks, respectively. Functional connectivity was computed for the rhyming task within a functionally-defined reading network and used to train multilayer feedforward classifier models to simultaneously associate functional connectivity patterns with lexicality (word vs. pseudoword) and reading skill (poor vs. highly-skilled). Classifiers identified validation set lexicality with significantly better than chance accuracy, and reading skill with near-ceiling accuracy. Critically, a series of replications used pre-trained rhyming-task models to classify reading skill from mental multiplication task participants' connectivity with near-ceiling accuracy. The novel deep learning approach presented here provides the clearest demonstration to date that reading-skill dependent functional connectivity within the reading network influences brain processing dynamics across cognitive domains.

产生和理解书面语言的能力是连续存在的唯一人类技能，并且是人类认知的其他方面的基础。基于支持向量机（SVM）的多变量分类器已经提供了对网络基础阅读技能的深入了解，这超出了传统的单变量方法可以告诉我们的范围。像SVM这样的浅层模型需要大量数据，而当随着网络规模呈指数增长的功能连接成为人们关注的预测因素时，这个问题就变得更加复杂。使用独立成分分析（ICA）进行数据缩减可缓解此问题，但通常采用线性关系。相反，多层前馈网络很容易找到复杂模式的最佳低维编码，其中包括复杂的非线性或条件关系。从分别使用押韵和智力乘法任务的两个开放获取数据集中选择了贫困和高技能的年轻读者样本。在功能定义的阅读网络中针对押韵任务计算了功能连通性，并用于训练多层前馈分类器模型，以同时将功能连通性模式与词汇能力（单词与伪单词）和阅读技能（贫穷与高技能）相关联。分类器确定的验证集词法要比机会准确度好得多，而阅读技巧则具有接近上限的准确度。至关重要的是，一系列复制使用了预先训练的押韵任务模型，以接近天花板的准确性对来自心理乘法任务参与者连接的阅读技能进行了分类。