Multivoxel patterns reveal functionally differentiated networks underlying auditory feedback processing of speech.

The everyday act of speaking involves the complex processes of speech motor control. An important component of control is monitoring, detection, and processing of errors when auditory feedback does not correspond to the intended motor gesture. Here we show, using fMRI and converging operations within a multivoxel pattern analysis framework, that this sensorimotor process is supported by functionally differentiated brain networks. During scanning, a real-time speech-tracking system was used to deliver two acoustically different types of distorted auditory feedback or unaltered feedback while human participants were vocalizing monosyllabic words, and to present the same auditory stimuli while participants were passively listening. Whole-brain analysis of neural-pattern similarity revealed three functional networks that were differentially sensitive to distorted auditory feedback during vocalization, compared with during passive listening. One network of regions appears to encode an "error signal" regardless of acoustic features of the error: this network, including right angular gyrus, right supplementary motor area, and bilateral cerebellum, yielded consistent neural patterns across acoustically different, distorted feedback types, only during articulation (not during passive listening). In contrast, a frontotemporal network appears sensitive to the speech features of auditory stimuli during passive listening; this preference for speech features was diminished when the same stimuli were presented as auditory concomitants of vocalization. A third network, showing a distinct functional pattern from the other two, appears to capture aspects of both neural response profiles. Together, our findings suggest that auditory feedback processing during speech motor control may rely on multiple, interactive, functionally differentiated neural systems.

Perceiving a stranger's voice as being one's own: a 'rubber voice' illusion?

We describe an illusion in which a stranger's voice, when presented as the auditory concomitant of a participant's own speech, is perceived as a modified version of their own voice. When the congruence between utterance and feedback breaks down, the illusion is also broken. Compared to a baseline condition in which participants heard their own voice as feedback, hearing a stranger's voice induced robust changes in the fundamental frequency (F0) of their production. Moreover, the shift in F0 appears to be feedback dependent, since shift patterns depended reliably on the relationship between the participant's own F0 and the stranger-voice F0. The shift in F0 was evident both when the illusion was present and after it was broken, suggesting that auditory feedback from production may be used separately for self-recognition and for vocal motor control. Our findings indicate that self-recognition of voices, like other body attributes, is malleable and context dependent.

Functional overlap between regions involved in speech perception and in monitoring one's own voice during speech production.

The fluency and the reliability of speech production suggest a mechanism that links motor commands and sensory feedback. Here, we examined the neural organization supporting such links by using fMRI to identify regions in which activity during speech production is modulated according to whether auditory feedback matches the predicted outcome or not and by examining the overlap with the network recruited during passive listening to speech sounds. We used real-time signal processing to compare brain activity when participants whispered a consonant-vowel-consonant word ("Ted") and either heard this clearly or heard voice-gated masking noise. We compared this to when they listened to yoked stimuli (identical recordings of "Ted" or noise) without speaking. Activity along the STS and superior temporal gyrus bilaterally was significantly greater if the auditory stimulus was (a) processed as the auditory concomitant of speaking and (b) did not match the predicted outcome (noise). The network exhibiting this Feedback Type x Production/Perception interaction includes a superior temporal gyrus/middle temporal gyrus region that is activated more when listening to speech than to noise. This is consistent with speech production and speech perception being linked in a control system that predicts the sensory outcome of speech acts and that processes an error signal in speech-sensitive regions when this and the sensory data do not match.

The functional specialization of the planum temporale.

The planum temporale (PT) is an anatomically heterogeneous area with several architectonic subdivisions and extensive connections with other parts of the brain. Here I review a functional MRI study investigating the role of a functionally defined area (Spt) within the left PT in speech motor processing and discuss the functional properties of PT regions in the context of findings from recent neurophysiological and neuroimaging studies.

Reducing inter-subject anatomical variation: effect of normalization method on sensitivity of functional magnetic resonance imaging data analysis in auditory cortex and the superior temporal region.

Conventional group analysis of functional MRI (fMRI) data usually involves spatial alignment of anatomy across participants by registering every brain image to an anatomical reference image. Due to the high degree of inter-subject anatomical variability, a low-resolution average anatomical model is typically used as the target template, and/or smoothing kernels are applied to the fMRI data to increase the overlap among subjects' image data. However, such smoothing can make it difficult to resolve small regions such as subregions of auditory cortex when anatomical morphology varies among subjects. Here, we use data from an auditory fMRI study to show that using a high-dimensional registration technique (HAMMER) results in an enhanced functional signal-to-noise ratio (fSNR) for functional data analysis within auditory regions, with more localized activation patterns. The technique is validated against DARTEL, a high-dimensional diffeomorphic registration, as well as against commonly used low-dimensional normalization techniques such as the techniques provided with SPM2 (cosine basis functions) and SPM5 (unified segmentation) software packages. We also systematically examine how spatial resolution of the template image and spatial smoothing of the functional data affect the results. Only the high-dimensional technique (HAMMER) appears to be able to capitalize on the excellent anatomical resolution of a single-subject reference template, and, as expected, smoothing increased fSNR, but at the cost of spatial resolution. In general, results demonstrate significant improvement in fSNR using HAMMER compared to analysis after normalization using DARTEL, or conventional normalization such as cosine basis function and unified segmentation in SPM, with more precisely localized activation foci, at least for activation in the region of auditory cortex.