Sensorimotor Representation of Speech Perception. Cross-Decoding of Place of Articulation Features during Selective Attention to Syllables in 7T fMRI

Role of articulatory motor networks in perceptual categorization of speech signals: a 7T fMRI study

Speech and language processing involve complex interactions between cortical areas necessary for articulatory movements and auditory perception and a range of areas through which these are connected and interact. Despite their fundamental importance, the precise mechanisms underlying these processes are not fully elucidated. We measured BOLD signals from normal hearing participants using high-field 7 Tesla fMRI with 1-mm isotropic voxel resolution. The subjects performed 2 speech perception tasks (discrimination and classification) and a speech production task during the scan. By employing univariate and multivariate pattern analyses, we identified the neural signatures associated with speech production and perception. The left precentral, premotor, and inferior frontal cortex regions showed significant activations that correlated with phoneme category variability during perceptual discrimination tasks. In addition, the perceived sound categories could be decoded from signals in a region of interest defined based on activation related to production task. The results support the hypothesis that articulatory motor networks in the left hemisphere, typically associated with speech production, may also play a critical role in the perceptual categorization of syllables. The study provides valuable insights into the intricate neural mechanisms that underlie speech processing.

Role of Articulatory Motor Networks in Perceptual Categorization of Speech Signals: A 7 T fMRI Study

BACKGROUND

The association between brain regions involved in speech production and those that play a role in speech perception is not yet fully understood. We compared speech production related brain activity with activations resulting from perceptual categorization of syllables using high field 7 Tesla functional magnetic resonance imaging (fMRI) at 1-mm isotropic voxel resolution, enabling high localization accuracy compared to previous studies.

METHODS

Blood oxygenation level dependent (BOLD) signals were obtained in 20 normal hearing subjects using a simultaneous multi-slice (SMS) 7T echo-planar imaging (EPI) acquisition with whole-head coverage and 1 mm isotropic resolution. In a speech production localizer task, subjects were asked to produce a silent lip-round vowel /u/ in response to the visual cue "U" or purse their lips when they saw the cue "P". In a phoneme discrimination task, subjects were presented with pairs of syllables, which were equiprobably identical or different along an 8-step continuum between the prototypic /ba/ and /da/ sounds. After the presentation of each stimulus pair, the subjects were asked to indicate whether the two syllables they heard were identical or different by pressing one of two buttons. In a phoneme classification task, the subjects heard only one syllable and asked to indicate whether it was /ba/ or /da/.

RESULTS

Univariate fMRI analyses using a parametric modulation approach suggested that left motor, premotor, and frontal cortex BOLD activations correlate with phoneme category variability in the /ba/-/da/ discrimination task. In contrast, the variability related to acoustic features of the phonemes were the highest in the right primary auditory cortex. Our multivariate pattern analysis (MVPA) suggested that left precentral/inferior frontal cortex areas, which were associated with speech production according to the localizer task, play a role also in perceptual categorization of the syllables.

CONCLUSIONS

The results support the hypothesis that articulatory motor networks in the left hemisphere that are activated during speech production could also have a role in perceptual categorization of syllables. Importantly, high voxel-resolution combined with advanced coil technology allowed us to pinpoint the exact brain regions involved in both perception and production tasks.

Improving the validity of neuroimaging decoding tests of invariant and configural neural representation

Many research questions in sensory neuroscience involve determining whether the neural representation of a stimulus property is invariant or specific to a particular stimulus context (e.g., Is object representation invariant to translation? Is the representation of a face feature specific to the context of other face features?). Between these two extremes, representations may also be context-tolerant or context-sensitive. Most neuroimaging studies have used operational tests in which a target property is inferred from a significant test against the null hypothesis of the opposite property. For example, the popular cross-classification test concludes that representations are invariant or tolerant when the null hypothesis of specificity is rejected. A recently developed neurocomputational theory suggests two insights regarding such tests. First, tests against the null of context-specificity, and for the alternative of context-invariance, are prone to false positives due to the way in which the underlying neural representations are transformed into indirect measurements in neuroimaging studies. Second, jointly performing tests against the nulls of invariance and specificity allows one to reach more precise and valid conclusions about the underlying representations, particularly when the null of invariance is tested using the fine-grained information from classifier decision variables rather than only accuracies (i.e., using the decoding separability test). Here, we provide empirical and computational evidence supporting both of these theoretical insights. In our empirical study, we use encoding of orientation and spatial position in primary visual cortex as a case study, as previous research has established that these properties are encoded in a context-sensitive way. Using fMRI decoding, we show that the cross-classification test produces false-positive conclusions of invariance, but that more valid conclusions can be reached by jointly performing tests against the null of invariance. The results of two simulations further support both of these conclusions. We conclude that more valid inferences about invariance or specificity of neural representations can be reached by jointly testing against both hypotheses, and using neurocomputational theory to guide the interpretation of results.

CEREBRUM-7T: Fast and Fully Volumetric Brain Segmentation of 7 Tesla MR Volumes

Ultra-high-field magnetic resonance imaging (MRI) enables sub-millimetre resolution imaging of the human brain, allowing the study of functional circuits of cortical layers at the meso-scale. An essential step in many functional and structural neuroimaging studies is segmentation, the operation of partitioning the MR images in anatomical structures. Despite recent efforts in brain imaging analysis, the literature lacks in accurate and fast methods for segmenting 7-tesla (7T) brain MRI. We here present CEREBRUM-7T, an optimised end-to-end convolutional neural network, which allows fully automatic segmentation of a whole 7T T1_w MRI brain volume at once, without partitioning the volume, pre-processing, nor aligning it to an atlas. The trained model is able to produce accurate multi-structure segmentation masks on six different classes plus background in only a few seconds. The experimental part, a combination of objective numerical evaluations and subjective analysis, confirms that the proposed solution outperforms the training labels it was trained on and is suitable for neuroimaging studies, such as layer functional MRI studies. Taking advantage of a fine-tuning operation on a reduced set of volumes, we also show how it is possible to effectively apply CEREBRUM-7T to different sites data. Furthermore, we release the code, 7T data, and other materials, including the training labels and the Turing test.

Combining Gamma With Alpha and Beta Power Modulation for Enhanced Cortical Mapping in Patients With Focal Epilepsy

About one third of patients with epilepsy have seizures refractory to the medical treatment. Electrical stimulation mapping (ESM) is the gold standard for the identification of “eloquent” areas prior to resection of epileptogenic tissue. However, it is time-consuming and may cause undesired side effects. Broadband gamma activity (55–200 Hz) recorded with extraoperative electrocorticography (ECoG) during cognitive tasks may be an alternative to ESM but until now has not proven of definitive clinical value. Considering their role in cognition, the alpha (8–12 Hz) and beta (15–25 Hz) bands could further improve the identification of eloquent cortex. We compared gamma, alpha and beta activity, and their combinations for the identification of eloquent cortical areas defined by ESM. Ten patients with intractable focal epilepsy (age: 35.9 ± 9.1 years, range: 22–48, 8 females, 9 right handed) participated in a delayed-match-to-sample task, where syllable sounds were compared to visually presented letters. We used a generalized linear model (GLM) approach to find the optimal weighting of each band for predicting ESM-defined categories and estimated the diagnostic ability by calculating the area under the receiver operating characteristic (ROC) curve. Gamma activity increased more in eloquent than in non-eloquent areas, whereas alpha and beta power decreased more in eloquent areas. Diagnostic ability of each band was close to 0.7 for all bands but depended on multiple factors including the time period of the cognitive task, the location of the electrodes and the patient’s degree of attention to the stimulus. We show that diagnostic ability can be increased by 3–5% by combining gamma and alpha and by 7.5–11% when gamma and beta were combined. We then show how ECoG power modulation from cognitive testing can be used to map the probability of eloquence in individual patients and how this probability map can be used in clinical settings to optimize ESM planning. We conclude that the combination of gamma and beta power modulation during cognitive testing can contribute to the identification of eloquent areas prior to ESM in patients with refractory focal epilepsy.

Electrophysiological Dynamics of Visual Speech Processing and the Role of Orofacial Effectors for Cross-Modal Predictions

The human brain generates predictions about future events. During face-to-face conversations, visemic information is used to predict upcoming auditory input. Recent studies suggest that the speech motor system plays a role in these cross-modal predictions, however, usually only audio-visual paradigms are employed. Here we tested whether speech sounds can be predicted on the basis of visemic information only, and to what extent interfering with orofacial articulatory effectors can affect these predictions. We registered EEG and employed N400 as an index of such predictions. Our results show that N400's amplitude was strongly modulated by visemic salience, coherent with cross-modal speech predictions. Additionally, N400 ceased to be evoked when syllables' visemes were presented backwards, suggesting that predictions occur only when the observed viseme matched an existing articuleme in the observer's speech motor system (i.e., the articulatory neural sequence required to produce a particular phoneme/viseme). Importantly, we found that interfering with the motor articulatory system strongly disrupted cross-modal predictions. We also observed a late P1000 that was evoked only for syllable-related visual stimuli, but whose amplitude was not modulated by interfering with the motor system. The present study provides further evidence of the importance of the speech production system for speech sounds predictions based on visemic information at the pre-lexical level. The implications of these results are discussed in the context of a hypothesized trimodal repertoire for speech, in which speech perception is conceived as a highly interactive process that involves not only your ears but also your eyes, lips and tongue.

What Acoustic Studies Tell Us About Vowels in Developing and Disordered Speech

Purpose Literature was reviewed on the development of vowels in children's speech and on vowel disorders in children and adults, with an emphasis on studies using acoustic methods. Method Searches were conducted with PubMed/MEDLINE, Google Scholar, CINAHL, HighWire Press, and legacy sources in retrieved articles. The primary search items included, but were not limited to, vowels, vowel development, vowel disorders, vowel formants, vowel therapy, vowel inherent spectral change, speech rhythm, and prosody. Results/Discussion The main conclusions reached in this review are that vowels are (a) important to speech intelligibility; (b) intrinsically dynamic; (c) refined in both perceptual and productive aspects beyond the age typically given for their phonetic mastery; (d) produced to compensate for articulatory and auditory perturbations; (e) influenced by language and dialect even in early childhood; (f) affected by a variety of speech, language, and hearing disorders in children and adults; (g) inadequately assessed by standardized articulation tests; and (h) characterized by at least three factors-articulatory configuration, extrinsic and intrinsic regulation of duration, and role in speech rhythm and prosody. Also discussed are stages in typical vowel ontogeny, acoustic characterization of rhotic vowels, a sensory-motor perspective on vowel production, and implications for clinical assessment of vowels.

The motor system's [modest] contribution to speech perception

Recent evidence suggests that the motor system may have a facilitatory role in speech perception during noisy listening conditions. Studies clearly show an association between activity in auditory and motor speech systems, but also hint at a causal role for the motor system in noisy speech perception. However, in the most compelling "causal" studies performance was only measured at a single signal-to-noise ratio (SNR). If listening conditions must be noisy to invoke causal motor involvement, then effects will be contingent on the SNR at which they are tested. We used articulatory suppression to disrupt motor-speech areas while measuring phonemic identification across a range of SNRs. As controls, we also measured phoneme identification during passive listening, mandible gesturing, and foot-tapping conditions. Two-parameter (threshold, slope) psychometric functions were fit to the data in each condition. Our findings indicate: (1) no effect of experimental task on psychometric function slopes; (2) a small effect of articulatory suppression, in particular, on psychometric function thresholds. The size of the latter effect was 1 dB (~5% correct) on average, suggesting, at best, a minor modulatory role of the speech motor system in perception.

Formant Space Reconstruction From Brain Activity in Frontal and Temporal Regions Coding for Heard Vowels

Classical studies have isolated a distributed network of temporal and frontal areas engaged in the neural representation of speech perception and production. With modern literature arguing against unique roles for these cortical regions, different theories have favored either neural code-sharing or cortical space-sharing, thus trying to explain the intertwined spatial and functional organization of motor and acoustic components across the fronto-temporal cortical network. In this context, the focus of attention has recently shifted toward specific model fitting, aimed at motor and/or acoustic space reconstruction in brain activity within the language network. Here, we tested a model based on acoustic properties (formants), and one based on motor properties (articulation parameters), where model-free decoding of evoked fMRI activity during perception, imagery, and production of vowels had been successful. Results revealed that phonological information organizes around formant structure during the perception of vowels; interestingly, such a model was reconstructed in a broad temporal region, outside of the primary auditory cortex, but also in the pars triangularis of the left inferior frontal gyrus. Conversely, articulatory features were not associated with brain activity in these regions. Overall, our results call for a degree of interdependence based on acoustic information, between the frontal and temporal ends of the language network.