Parkinson’s disease (PD) exhibits significant clinical heterogeneity, presenting challenges in the identification of reliable electroencephalogram (EEG) biomarkers. Machine learning techniques have been integrated with resting-state EEG for PD diagnosis, but their practicality is constrained by the interpretable features and the stochastic nature of resting-state EEG. The present study proposes a novel and interpretable deep learning model, graph signal processing-graph convolutional networks (GSP-GCNs), using event-related EEG data obtained from a specific task involving vocal pitch regulation for PD diagnosis. By incorporating both local and global information from single-hop and multi-hop networks, our proposed GSP-GCNs models achieved an averaged classification accuracy of 90.2%, exhibiting a significant improvement of 9.5% over other deep learning models. Moreover, the interpretability analysis revealed discriminative distributions of large-scale EEG networks and topographic map of microstate MS5 learned by our models, primarily located in the left ventral premotor cortex, superior temporal gyrus, and Broca’s area that are implicated in PD-related speech disorders, reflecting our GSP-GCN models’ ability to provide interpretable insights identifying distinctive EEG biomarkers from large-scale networks. These findings demonstrate the potential of interpretable deep learning models coupled with voice-related EEG signals for distinguishing PD patients from healthy controls with accuracy and elucidating the underlying neurobiological mechanisms.

Although working memory (WM) is considered as an emergent property of the speech perception and production systems, the role of WM in sensorimotor integration during speech processing is largely unknown. We conducted two event-related potential experiments with female and male young adults to investigate the contribution of WM to the neurobehavioural processing of altered auditory feedback during vocal production. A delayed match-to-sample task that required participants to indicate whether the pitch feedback perturbations they heard during vocalizations in test and sample sequences matched, elicited significantly larger vocal compensations, larger N1 responses in the left middle and superior temporal gyrus, and smaller P2 responses in the left middle and superior temporal gyrus, inferior parietal lobule, somatosensory cortex, right inferior frontal gyrus, and insula compared with a control task that did not require memory retention of the sequence of pitch perturbations. On the other hand, participants who underwent extensive auditory WM training produced suppressed vocal compensations that were correlated with improved auditory WM capacity, and enhanced P2 responses in the left middle frontal gyrus, inferior parietal lobule, right inferior frontal gyrus, and insula that were predicted by pretraining auditory WM capacity. These findings indicate that WM can enhance the perception of voice auditory feedback errors while inhibiting compensatory vocal behavior to prevent voice control from being excessively influenced by auditory feedback. This study provides the first evidence that auditory-motor integration for voice control can be modulated by top-down influences arising from WM, rather than modulated exclusively by bottom-up and automatic processes.SIGNIFICANCE STATEMENTOne outstanding question that remains unsolved in speech motor control is how the mismatch between predicted and actual voice auditory feedback is detected and corrected. The present study provides two lines of converging evidence, for the first time, that working memory cannot only enhance the perception of vocal feedback errors but also exert inhibitory control over vocal motor behavior. These findings represent a major advance in our understanding of the top-down modulatory mechanisms that support the detection and correction of prediction-feedback mismatches during sensorimotor control of speech production driven by working memory. Rather than being an exclusively bottom-up and automatic process, auditory-motor integration for voice control can be modulated by top-down influences arising from working memory.

To track the course of development in children's fine-grained perception of Mandarin tones, the present study explored how categorical perception (CP) of Mandarin tones emerges along age among 70 four- to seven-year-old children and 16 adults. Prominent discrimination peaks were found for both the child and the adult groups, and they were well aligned with the corresponding identification crossovers. Moreover, six-year-olds showed a much narrower width (i.e. a sharper slope) compared to younger children, and have already acquired adult-like identification competence of Mandarin high-level and mid-rising tones. Although the ability to discriminate within-category tone pairs did not change, the between-category discrimination accuracies were positively correlated with chronological ages among child participants. We assume that the perceptual refinement of Mandarin tones in young children may be driven by an accumulation of perceptual development from the tonal information of the ambient sound input.

In this investigation, a picture-character matching task using the event-related potential (ERP) technique was conducted to identify the temporal dynamics of orthographic and semantic activation during Chinese character recognition. The ERP results revealed that the impact of orthography occurred early and was long-lasting, whereas the semantic effect was comparatively late. In particular, the orthographic effect exhibited a larger occipital N170, smaller frontal P200, and larger N400 component for characters sharing orthographic information with paired pictures. The observed orthographic effect may reflect the processing of characters shifting from radical-level facilitation at the N170 and P200 to lexical-level competition at the N400. In contrast, a semantic effect was only found at the N400. Additionally, a significant orthography by semantic interaction was found at the N400, reflecting the relevance of orthography during semantic activation in Chinese character recognition which suggested that character meaning could be directly accessed from orthography for skilled readers.

Childhood autism spectrum disorder (ASD) can easily be misdiagnosed, due to the nonspecific social and communicational deficits associated with the disorder. The present study attempted to profile the mental development and visual attention toward emotion among preschool children with mild or moderate ASD who were attending mainstream kindergartens. A total of 21 children (17 boys and 4 girls) diagnosed with mild or moderate ASD selected from 5,178 kindergarteners from the Xi’an city were recruited. Another group of 21 typically developing (TD) children who were matched with age, gender, and class served as controls. All children were assessed using the Griffiths Mental Development Scales–Chinese (GDS-C), and their social visual attention was assessed during watching 20 ecologically valid film scenes by using eye tracking technique. The results showed that ASD children had lower mental development scores in the Locomotor, Personal-Social, Language, Performance, and Practical Reasoning subscales than the TD peers. Moreover, deficits in recognizing emotions from facial expressions based on naturalistic scene stimuli with voice were found for ASD children. The deficits were significantly correlated with their ability in social interaction and development quotient in ASD group. ASD children showed atypical eye-gaze pattern when compared to TD children during facial emotion expression task. Children with ASD had reduced visual attention to facial emotion expression, especially for the eye region. The findings confirmed the deficits of ASD children in real life multimodal of emotion recognition, and their atypical eye-gaze pattern for emotion recognition. Parents and teachers of children with mild or moderate ASD should make informed educational decisions according to their level of mental development. In addition, eye tracking technique might clinically help provide evidence diagnosing children with mild or moderate ASD.