Prosody production networks are modulated by sensory cues and social context

Prosodic signatures of ASD severity and developmental delay in preschoolers

Atypical prosody in speech production is a core feature of Autism Spectrum Disorder (ASD) that can impact everyday life communication. Because the ability to modulate prosody develops around the age of speech acquisition, it might be affected by ASD symptoms and developmental delays that emerge at the same period. Here, we investigated the existence of a prosodic signature of developmental level and ASD symptom severity in a sample of 74 autistic preschoolers. We first developed an original diarization pipeline to extract preschoolers' vocalizations from recordings of naturalistic social interactions. Using this novel approach, we then found a robust voice quality signature of ASD developmental difficulties in preschoolers. Furthermore, some prosodic measures were associated with one year later outcome in participants who had not acquired speech yet. Altogether, our results highlight the potential benefits of automatized diarization algorithms and prosodic metrics for digital phenotyping in psychiatry, helping clinicians establish early diagnosis and prognosis.

A novel beamformer-based imaging of phase-amplitude coupling (BIPAC) unveiling the inter-regional connectivity of emotional prosody processing in women with primary dysmenorrhea

Objective. Neural communication or the interactions of brain regions play a key role in the formation of functional neural networks. A type of neural communication can be measured in the form of phase-amplitude coupling (PAC), which is the coupling between the phase of low-frequency oscillations and the amplitude of high-frequency oscillations. This paper presents a beamformer-based imaging method, beamformer-based imaging of PAC (BIPAC), to quantify the strength of PAC between a seed region and other brain regions.Approach. A dipole is used to model the ensemble of neural activity within a group of nearby neurons and represents a mixture of multiple source components of cortical activity. From ensemble activity at each brain location, the source component with the strongest coupling to the seed activity is extracted, while unrelated components are suppressed to enhance the sensitivity of coupled-source estimation.Main results. In evaluations using simulation data sets, BIPAC proved advantageous with regard to estimation accuracy in source localization, orientation, and coupling strength. BIPAC was also applied to the analysis of magnetoencephalographic signals recorded from women with primary dysmenorrhea in an implicit emotional prosody experiment. In response to negative emotional prosody, auditory areas revealed strong PAC with the ventral auditory stream and occipitoparietal areas in the theta-gamma and alpha-gamma bands, which may respectively indicate the recruitment of auditory sensory memory and attention reorientation. Moreover, patients with more severe pain experience appeared to have stronger coupling between auditory areas and temporoparietal regions.Significance. Our findings indicate that the implicit processing of emotional prosody is altered by menstrual pain experience. The proposed BIPAC is feasible and applicable to imaging inter-regional connectivity based on cross-frequency coupling estimates. The experimental results also demonstrate that BIPAC is capable of revealing autonomous brain processing and neurodynamics, which are more subtle than active and attended task-driven processing.

Supramodal neural networks support top-down processing of social signals

The perception of facial and vocal stimuli is driven by sensory input and cognitive top‐down influences. Important top‐down influences are attentional focus and supramodal social memory representations. The present study investigated the neural networks underlying these top‐down processes and their role in social stimulus classification. In a neuroimaging study with 45 healthy participants, we employed a social adaptation of the Implicit Association Test. Attentional focus was modified via the classification task, which compared two domains of social perception (emotion and gender), using the exactly same stimulus set. Supramodal memory representations were addressed via congruency of the target categories for the classification of auditory and visual social stimuli (voices and faces). Functional magnetic resonance imaging identified attention‐specific and supramodal networks. Emotion classification networks included bilateral anterior insula, pre‐supplementary motor area, and right inferior frontal gyrus. They were pure attention‐driven and independent from stimulus modality or congruency of the target concepts. No neural contribution of supramodal memory representations could be revealed for emotion classification. In contrast, gender classification relied on supramodal memory representations in rostral anterior cingulate and ventromedial prefrontal cortices. In summary, different domains of social perception involve different top‐down processes which take place in clearly distinguishable neural networks.

Vocomotor and Social Brain Networks Work Together to Express Social Traits in Voices

Voice modulation is important when navigating social interactions-tone of voice in a business negotiation is very different from that used to comfort an upset child. While voluntary vocal behavior relies on a cortical vocomotor network, social voice modulation may require additional social cognitive processing. Using functional magnetic resonance imaging, we investigated the neural basis for social vocal control and whether it involves an interplay of vocal control and social processing networks. Twenty-four healthy adult participants modulated their voice to express social traits along the dimensions of the social trait space (affiliation and competence) or to express body size (control for vocal flexibility). Naïve listener ratings showed that vocal modulations were effective in evoking social trait ratings along the two primary dimensions of the social trait space. Whereas basic vocal modulation engaged the vocomotor network, social voice modulation specifically engaged social processing regions including the medial prefrontal cortex, superior temporal sulcus, and precuneus. Moreover, these regions showed task-relevant modulations in functional connectivity to the left inferior frontal gyrus, a core vocomotor control network area. These findings highlight the impact of the integration of vocal motor control and social information processing for socially meaningful voice modulation.