The Role of the Supplementary Motor Region in Overt Reading: Evidence for Differential Processing in SMA-Proper and Pre-SMA as a Function of Task Demands.

Abstract

A differentiation in function between the pre-SMA (i.e., cognitive load) and the SMA-proper (i.e., motor execution) has been described (Zhang et al., Cereb Cortex 22:99-111, 2012). These differential SMA functions may be influential in overt reading tasks. The present study examined the relationships between various segments of the SMA and overt reading through the modulation of task demands in an effort to explore the complexity of the print-to-speech network. Skilled reading adults (N = 15) took part in five overt reading tasks: pure regular word reading, pure exception word reading, mixed regular word and exception word reading, go/no-go reading with nonword foils and go/no-go reading with pseudohomophone foils. Five regions of interest that spanned the pre-SMA to the SMA-proper were isolated. Behaviour-function relationships were tested to examine the associations between performance (response time) and brain activity (percent signal change). Further, the coherence between feedforward (SMA) and feedback (supramarginal gyrus) regions were explored to further refine the print-to-speech network. We found that the pre-SMA was related to cognitively demanding tasks (go/no-go with pseudohomophones), whereas the SMA-proper was related to an automatized task (pure regular words). Notably, only those tasks that required information from the feedback system (i.e., mixed word lists, go/no-go tasks) showed connections between SMA regions and the supramarginal gyrus, which is in line with the role of feedback and feedforward systems in the print-to-speech network. Together, these results support the notion that the pre-SMA and SMA-proper are sensitive to reading tasks that differentially invoke higher cognitive resources (mixed word lists, go/no-go) versus automatized articulation (pure lists), respectively. We discuss our findings in the context of print-to-speech neural networks.