Tong, S. X. | Search bioRxiv

bioRxiv2025

An EEG Investigation of Neural Dynamics of Empathy Influenced by Congruent and Incongruent Pain Expressions in Autistic and Neurotypical Adults

Autistic individuals often show difficulties in empathy, but the underlying neural mechanisms of empathy in naturalistic contexts of pain have been less examined. This study employed a kinetic pain empathy paradigm, manipulating the congruence between pain expressions, i.e., body gestures and facial expressions based on a predictive coding framework. We collected EEG data from 51 autistic and 58 neurotypical adults during a pain observation task. Results indicated that autistic and neurotypical adults share a similar neural architecture for empathy processing and conflict resolution, involving an early stage of sensory arousal (i.e., N2 and theta) and a later stage of cognitive reappraisal (i.e., P3). However, the multivariate pattern analysis (MVPA) revealed nuanced but significant between-group differences in neural patterns. Compared to neurotypical peers, autistic adults demonstrated atypical processes in both empathy and conflict resolution. Specifically, they exhibited heightened early emotional arousal but expended greater cognitive effort to evaluate others pain. Autistic adults also showed increased alertness to unexpected sensory input and allocated more cognitive resources to resolve prediction errors from incongruent pairings. In contrast, neurotypical adults suppressed unnecessary cognitive efforts for meaningless errors. In summary, autistic adults may experience challenges in efficiently adjusting predictions to the external context, with their neural processing heavily depending on sensory input and less efficient in adapting cognitive resources to evaluate and respond to varied contextual demands.

neuroscience↗

bioRxiv2025

Neural hierarchy for coding articulatory dynamics in speech imagery and production

Mental imagery is a hallmark of human cognition, yet the neural mechanisms underlying these internal states remain poorly understood. Speech imagery--the internal simulation of speech without overt articulation--has been proposed to partially share neural substrates with actual speech articulation. However, the precise feature encoding and spatiotemporal dynamics of this neural architecture remain controversial, constraining the understanding of mental states and the development of reliable speech imagery decoders. Here, we leveraged high-resolution electrocorticography recordings to investigate the shared and modality-specific cortical coding of articulatory kinematic trajectories (AKTs) during speech imagery and articulation. Applying a linear model, we identified robust neural dynamics in frontoparietal cortex that encoded AKTs across both modalities. Shared neural populations across the middle premotor cortex, subcentral gyrus, and postcentral-supramarginal junction exhibited consistent spatiotemporal stability during the integrative articulatory planning. In contrast, modality-specific populations for speech imagery and articulation were somatotopically interleaved along the primary sensorimotor cortex, revealing a hierarchical spatiotemporal organization distinct from shared encoding regions. We further developed a generalized neural network to decode multi-population neural dynamics. The model achieved high syllable prediction accuracy for speech imagery (79% median accuracy), closely matching the performance of speech articulation (81%). This model robustly extrapolated AKT decoding to untrained syllables within each modality while demonstrating cross-modal generalization across shared populations. These findings uncover a somato-cognitive hierarchy linking high-level supramodal planning with modality-specific neural manifestation, revolutionizing an imagery-based brain-computer interface that directly decodes thoughts for synthetic telepathy.

neuroscience↗