Neural connectivity of voice control using structural equation modeling

Introduction: This study aims to model connectivity of neural regions involved in voice control. Here, we used structural equation modeling on a published dataset that employed the pitch shift paradigm. We hypothesized that our models would confirm differences in connectivity related to superior temporal gyrus during error processing of vocalization. Methods: We extracted time course data of eight regions included from 10 healthy subjects. A detailed description of subjects, MRI scanning procedures, imaging acquisition and data analysis can be found in Parkinson et al. 2012. Effective connectivity of regions activated during shift and no-shift paradigms was assessed using structural equation modeling techniques (AMOS version 19.0, SPSS, IBM). Results Consistent with our hypothesis, STG appears to play a crucial role in vocalization and error processing, showing increased participation of the right hemisphere during the shift condition than the no shift condition. Furthermore, left inferior frontal gyrus displays significant contribution to the modulation of vocal control through connections with PMC that change in response to the shift condition. Conclusions: Results indicated changes in connectivity of the voice network related to error detection and correction. Our models indicate hemispheric sensitivity to different elements of the auditory feedback and highlight the importance of examining network connectivity.