bioRxivNeurosciencePreprint
Spatiotemporal mapping of the neural markers of prediction error processing across multisensory and unisensory modalities
Abstract
Prediction errors in the brain are indexed by two event-related potentials - MMN and P300, which are elicited upon violation of regularity in the occurrence of repetitive stimuli. While MMN reflects the brains ability to perform automatic comparisons between consecutive stimuli and provides an electrophysiological index of sensory error detection, P300 is associated with cognitive processes such as update in working memory. Till date, there has been extensive research on the roles of MMN and P300 individually, because of their potential to be used as clinical markers of consciousness and attention, respectively. However, the relationship between these two ERPs, specifically in terms of their underlying cortical generators, in context of prediction error propagation along the hierarchical brain across multiple modalities is an open question. Our objective in this article is two-fold. First, we reconfirm previous reports regarding the generators of MMN and P300 in sensor space through source-space analysis using an accurate individual subject level co-registration of MRI and EEG data collected from healthy humans. We demonstrate that in multisensory environments, MMN and P300 markers represent "modality-specific" and "modality-independent" information processing, respectively. Advancing an earlier understanding that multisensory contexts speed up early sensory processing, our study reveals that this temporal facilitation extends to even the later components of prediction error processing, using custom-designed experiments that allow comparisons across different modality combinations. Such knowledge can be of immense value in clinical research for determining the stages of various treatments in aging, schizophrenia and depression, and their efficacy on cognitive function.
- organism: human
- assay: electronic health records
- research focus: cell type annotation
- approach: statistical model
- Citations
- 0
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