Sensory–motor networks involved in speech production and motor control: An fMRI study

Abstract

Speaking is one of the most complex motor behaviors developed to facilitate human communication. The underlying neural mechanisms of speech involve sensory-motor interactions that incorporate feedback information for online monitoring and control of produced speech sounds. In the present study, we adopted an auditory feedback pitch perturbation paradigm and combined it with functional magnetic resonance imaging (fMRI) recordings in order to identify brain areas involved in speech production and motor control. Subjects underwent fMRI scanning while they produced a steady vowel sound /a/ (speaking) or listened to the playback of their own vowel production (playback). During each condition, the auditory feedback from vowel production was either normal (no perturbation) or perturbed by an upward (+600 cents) pitch shift stimulus randomly. Analysis of BOLD responses during speaking (with and without shift) vs. rest revealed activation of a complex network including bilateral superior temporal gyrus (STG), Heschl's gyrus, precentral gyrus, supplementary motor area (SMA), Rolandic operculum, postcentral gyrus and right inferior frontal gyrus (IFG). Performance correlation analysis showed that the subjects produced compensatory vocal responses that significantly correlated with BOLD response increases in bilateral STG and left precentral gyrus. However, during playback, the activation network was limited to cortical auditory areas including bilateral STG and Heschl's gyrus. Moreover, the contrast between speaking vs. playback highlighted a distinct functional network that included bilateral precentral gyrus, SMA, IFG, postcentral gyrus and insula. These findings suggest that speech motor control involves feedback error detection in sensory (e.g. auditory) cortices that subsequently activate motor-related areas for the adjustment of speech parameters during speaking.

The effects of stimulus complexity on the preattentive processing of self-generated and nonself voices: An ERP study

“…Consistent with this hypothesis, previous studies demonstrated that latency is normally increased when processing more versus less complex stimuli (Pakarinen et al, 2013;Polich, 2007;Pulvermüller & Shtyrov, 2006). Furthermore, robust evidence shows that the STG is particularly sensitive to Berrors^in one's own voice, acting as a more general deviance detector, both in active vocal production or passive-listening conditions (Behroozmand et al, 2015;Parkinson et al, 2012;Zheng et al, 2010). Indeed, auditory error cells, whose activity reflects the mismatch between sensory prediction and the incoming feedback, are thought to be located in the STG (planum temporal and posterior STG; Golfinopoulos et al, 2010).…”

“…Since the MMN is generated within supratemporal auditory cortical regions (Alho et al, 1996;Näätänen et al, 2007), this might suggest that the STG plays an important role in detecting deviance in SGVand NSV stimuli, even when participants' attention is focused elsewhere. Given the differential processing of one's own voice during speech production versus passive listening (Behroozmand et al, 2015;Golfinopoulos et al, 2010;Parkinson et al, 2012;Zheng et al, 2010;Zheng et al, 2013), the faster detection of a nonself than of a self-generated voice stimulus observed in our study might be specific of passivelistening contexts, wherein the reduced activation of sensorymotor mechanisms signals the biological relevance of detecting a nonself voice stimulus violating a regular and highly predictable auditory background. Moreover, the left STG has been consistently implicated in the processing of linguistic content and in the extraction of speech meaning conveyed by voice signals (e.g., Belin et al, 2011;Binder, 2000;DeWitt & Rauschecker, 2012;Obleser, Zimmermann, Van Meter, & Rauschecker, 2007).…”

“…This would allow more efficient control of more complex linguistic messages by the vocal selfmonitoring system, and in the presence of nonlinguistic selfgenerated vocalizations, more resources would be available to process nonself voice stimuli. Nonetheless, since recent neuroimaging evidence has shown that different neural processes are involved in how one's own voice is processed during speech production versus passive listening to the same vocal sounds (Behroozmand et al, 2015;Golfinopoulos, Tourville, & Guenther, 2010: Parkinson et al, 2012Zheng, Munhall, & Johnsrude, 2010;Zheng et al, 2013), extending these findings to the realm of online vocal self-monitoring processes remains merely speculative. In particular, studies have demonstrated that, compared with a passive-listening condition, selfgenerated voice feedback during vocal production recruits additional sensory-motor brain regions, which are otherwise not activated in response to the passive-listening condition (Behroozmand et al, 2015;Parkinson et al, 2012;Zheng et al, 2010;Zheng et al, 2013).…”

Altered resting‐state functional connectivity of the putamen and internal globus pallidus is related to speech impairment in Parkinson's disease

“…STG serves as functional integration area with partial overlap between speech perception and production mechanisms (Price, 2012). This functional overlap makes STG uniquely suited to detect and integrate auditory feedback during speech production (Behroozmand et al., 2015, 2016; Hickok & Poeppel, 2007; Parkinson et al., 2012; Paus, Perry, Zatorre, Worsley, & Evans, 1996; Tourville & Guenther, 2011; Tourville, Reilly, & Guenther, 2008). The STG cluster identified in the present study corresponds closely to an anterolateral region of Heschl's gyrus that electrocorticography data has linked to online voice error correction following rapid perturbations in auditory feedback (Behroozmand et al., 2016).…”

Effects of Early and Late Bilingualism on Resting-State Functional Connectivity

“…In addition to the more synchronous activity between the left and right IFGpt, simultaneous bilinguals had greater connectivity between the IFGpt and a distributed system of cognitive control areas, including the dorsolateral prefrontal cortex and inferior parietal lobule, within the frontoparietal executive control circuit (Cole and Schneider, 2007;Vincent et al, 2008;Spreng et al, 2010;Wang et al, 2014;Behroozmand et al, 2015), a system of brain regions that manages cognitive function across a wide array of conditions and facilitates rapid changes in behavior that require enhanced attention. García-Pentó n (2013) recently identified increased efficiency of the frontoparietal subnetwork for early Spanish-Basque bilinguals, and there is emerging evidence that this control system persists in older bilingual adults (Grady et al, 2015).…”