Understanding the Role of Speech Production in Reading: Evidence for a Print-to-Speech Neural Network Using Graphical Analysis

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Abstract

Objective: The neural circuitry associated with language processing is complex and dynamic. Graphical models are useful for studying complex neural networks as this method provides information about unique connectivity between regions within the context of the entire network of interest. Here, the authors explored the neural networks during covert reading to determine the role of feedforward and feedback loops in covert speech production. Method: Brain activity of skilled adult readers was assessed in real word and pseudoword reading tasks with functional MRI (fMRI). Results: The authors provide evidence for activity coherence in the feedforward system (inferior frontal gyrus—supplementary motor area) during real word reading and in the feedback system (supramarginal gyrus—precentral gyrus) during pseudoword reading. Graphical models provided evidence of an extensive, highly connected, neural network when individuals read real words that relied on coordination of the feedforward system. In contrast, when individuals read pseudowords the authors found a limited/restricted network that relied on coordination of the feedback system. Conclusion: Together, these results underscore the importance of considering multiple pathways and articulatory loops during language tasks and provide evidence for a print-to-speech neural network.

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