Cusack, Rhodri

Johnsrude, Ingrid

MacDonald, Ewen

Munhall, Kevin

Vicente-Grabovetsky, Alejandro

Zheng, Zane

The everyday act of speaking involves the complex processes of speech motor control. One important feature of such control is regulation of articulation when auditory concomitants of speech do not correspond to the intended motor gesture. While theoretical accounts of speech monitoring posit multiple functional components required for detection of errors in speech planning (e.g., Levelt, 1983), neuroimaging studies generally indicate either single brain regions sensitive to speech production errors, or small, discrete networks. Here we demonstrate that the complex system controlling speech is supported by a complex neural network that is involved in linguistic, motoric and sensory processing. With the aid of novel real-time acoustic analyses and representational similarity analyses of fMRI signals, our data show functionally differentiated networks underlying auditory feedback control of speech

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Downloaded from orbit.dtu.dk on: Dec 20, 2017Representational Similarity Analysis Reveals Heterogeneous Networks SupportingSpeech Motor ControlZheng, Zane; Cusack, Rhodri; Johnsrude, Ingrid ; MacDonald, Ewen; Munhall, Kevin; Vicente-Grabovetsky, AlejandroPublication date:2012Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Zheng, Z., Cusack, R., Johnsrude, I., MacDonald, E., Munhall, K., & Vicente-Grabovetsky, A. (2012).Representational Similarity Analysis Reveals Heterogeneous Networks Supporting Speech Motor Control.Poster session presented at 18th Annual Meeting of the Organization for Human Brain Mapping, Beijing, China.Representational Similarity Analysis Reveals Heterogeneous NetworksSupporting Speech Motor ControlPresented During:359 MT: Representational Similarity Analysis Reveals Heterogeneous Networks Supporting SpeechMotor Control Presented During: O-M3: LanguageMonday, June 11, 2012: 6:30 PM  - 6:45 PM Poster No:359 On Display:Monday, June 11 & Tuesday, June 12 Authors:Zane Zheng1, Rhodri Cusack2, Ingrid Johnsrude1, Ewen MacDonald3, Kevin Munhall1, AlejandroVicente-Grabovetsky2Institutions:1Queen's University, Kingston, Canada, 2Medical Research Council Cognition and Brain Sciences Unit,Cambridge, United Kingdom, 3Technical University of Denmark, Lyngby, DenmarkPoster Presenter(s):Zane Zheng   -  Contact Me Queen's University Kingston, CanadaIntroduction:The everyday act of speaking involves the complex processes of speech motor control. One importantfeature of such control is regulation of articulation when auditory concomitants of speech do notcorrespond to the intended motor gesture. While theoretical accounts of speech monitoring positmultiple functional components required for detection of errors in speech planning (e.g., Levelt, 1983),neuroimaging studies generally indicate either single brain regions sensitive to speech productionerrors, or small, discrete networks. Here we demonstrate that the complex system controlling speechis supported by a complex neural network that is involved in linguistic, motoric and sensoryprocessing. With the aid of novel real-time acoustic analyses and representational similarity analysesof fMRI signals, our data show functionally differentiated networks underlying auditory feedbackcontrol of speech.Methods:We developed a real-time speech-tracking system to deliver three types of auditory signal (clearspeech, formant-shifted speech, and signal-correlated noise), which served as temporally-gatedfeedback during production and as stimuli during listening in a 2x3 factorial design. Twenty subjectseither produce a monosyllabic word or listen during a 1600-msec silent period between successive1600-msec acquisitions in a rapid sparse-imaging paradigm. Each subject was scanned on 72 trialsfrom each of 6 conditions in pseudorandom order. Functional images were analyzed using SPM5 and acustom-made, modular toolbox (MVPaa). Three successive trials of the same condition were modeledas one regressor, leading to 24 ß estimates for each condition. A whole-brain searchlight analysis(Kriegeskorte et al. 2006) was performed on the ß images, comparing regressor-specific multivoxelpatterns within each 4mm-radius searchlight using Spearman correlation. The resulting data werecondensed into a 6x6 correlation matrix representing the similarity measures within and betweenconditions. A general linear model was then applied to the similarity measures to test hypothesis-driven predictions of pattern correlations. The resulting ß images were smoothed and entered intorandom-effects analyses. Clusters that survived the threshold of familywise error correction p < .05were deemed significant.Results:We searched for neural patterns that were consistent when processing load was specifically placed onWe searched for neural patterns that were consistent when processing load was specifically placed onputative systems subserving articulatory control. Clusters in which multivoxel patterns exhibited highsimilarity between two erroneous feedback types (i.e., formant-shifted feedback and noise feedback)but low similarity between either erroneous feedback and clear speech feedback during production(Fig. 1a) included bilateral cerebellum, right supplementary motor area, and right angular gyrus (Fig.2, cyan). These similarity patterns were not evident when assessed during listening (Fig. 1b),suggesting that this network encodes an 'error signal' during articulation irrespective of acousticfeatures of the error. The interaction (Fig. 1c) was confirmed using a paired t-test, which revealedadditional areas including bilateral superior temporal gyrus (STG), bilateral inferior frontal gyrus (IFG),and left precentral gyrus. We found that the significant response in the bilateral STG and IFG network(Fig. 2, magenta) was driven by the pattern similarity between the two speech-like stimuli, whereasthe response in the left precentral gyrus (Fig. 2, blue) was driven not by either, but by a combinationof error-sensitivity and speech-sensitivity (i.e., aspects of cyan and magenta networks). These resultswere confirmed by ANOVAs demonstrating that the regions exhibiting the interaction comprised 3functionally differentiated networks (p = .006). Conclusions:Our findings show for the first time that speech motor control relies on multiple, interactive, andfunctionally differentiated systems, offering insight into the complexity of the neural processessupporting speech movements.Language:Speech ProductionAbstract InformationReferencesLevelt, W.J. (1983), 'Monitoring and Self-repair in Speech', Cognition, vol. 14, no. 1, pp. 41-104.Kriegeskorte, N., Goebel, R., Bandettini, P. (2006), 'Information-based functional brain mapping',Proceedings of the National Academy of Sciences of the USA, vol. 103, no. 10, pp. 3863-3868.

Representational Similarity Analysis Reveals Heterogeneous Networks Supporting Speech Motor Control

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Representational Similarity Analysis Reveals Heterogeneous Networks Supporting Speech Motor Control

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