Neural network of speech monitoring overlaps with overt speech production and comprehension networks: a sequential spatial and temporal ICA study

V.G. van de Ven*, F. Esposito, I.K. Christoffels

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

52 Citations (Web of Science)


The neural correlates of speech monitoring overlap with neural correlates of speech comprehension and production. However, it is unclear how these correlates are organized within functional connectivity networks, and how these networks interact to subserve speech monitoring. We applied spatial and temporal independent component analysis (sICA and tICA) to a functional magnetic resonance imaging (fMRI) experiment involving overt speech production, comprehension and monitoring. SICA and tICA respectively decompose fMRI data into spatial and temporal components that can be interpreted as distributed estimates of functional connectivity and concurrent temporal dynamics in one or more regions of fMRI activity. Using sICA we found multiple connectivity components that were associated with speech perception (auditory and left fronto-temporal components) and production (bilateral central sulcus and default-mode components), but not with speech monitoring. In order to further investigate if speech monitoring could be mapped in the auditory cortex as a unique temporal process, we applied tICA to voxels of the sICA auditory component. Amongst the temporal components we found a single, unique component that matched the speech monitoring temporal pattern. We used this temporal component as a new predictor for whole-brain activity and found that it correlated positively with bilateral auditory cortex, and negatively with the supplementary motor area (SMA). Psychophysiological interaction analysis of task and activity in bilateral auditory cortex and SMA showed that functional connectivity changed with task conditions. These results suggest that speech monitoring entails a dynamic coupling between different functional networks. Furthermore, we demonstrate that overt speech comprises multiple networks that are associated with specific speech-related processes. We conclude that the sequential combination of sICA and tICA is a powerful approach for the analysis of complex, overt speech tasks.
Original languageEnglish
Pages (from-to)1982-1991
Issue number4
Publication statusPublished - 1 Jan 2009

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