High-level integrative networks: A resting-state fMRI investigation of reading and spelling

Gali Ellenblum, Jeremy J. Purcell, Xiaowei Song, Brenda Rapp

Research output: Contribution to journalArticlepeer-review

Abstract

Orthographic processing skills (reading and spelling) are evolutionarily recent and mastered late in development, providing an opportunity to investigate how the properties of the neural networks supporting skills of this type compare to those supporting evolutionarily older, well-established “reference” networks. Although there has been extensive research using task-based fMRI to study the neural substrates of reading, there has been very little using resting-state fMRI to examine the properties of orthographic networks. In this investigation using resting-state fMRI, we compare the within-network and across-network coherence properties of reading and spelling networks directly to these properties of reference networks, and we also compare the network properties of the key node of the orthographic networks—the visual word form area—to those of the other nodes of the orthographic and reference networks. Consistent with previous results, we find that orthographic processing networks do not exhibit certain basic network coherence properties displayed by other networks. However, we identify novel distinctive properties of the orthographic processing networks and establish that the visual word form area has unusually high levels of connectivity with a broad range of brain areas. These characteristics form the basis of our proposal that orthographic networks represent a class of “high-level integrative networks” with distinctive properties that allow them to recruit and integrate multiple, lower level processes.

Original languageEnglish (US)
Pages (from-to)961-977
Number of pages17
JournalJournal of cognitive neuroscience
Volume31
Issue number7
DOIs
StatePublished - 2019

ASJC Scopus subject areas

  • Cognitive Neuroscience

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