bioRxivNeurosciencePreprint
Prior information improves tactile representation in primary somatosensory cortex
Pegah KassraianiD, Finn RabeiD, Nadja EnziD, Marloes H. MaathuisiD, Nicole WenderothiD
Abstract
Perception and adaptive decision making rely on the integration of incoming sensory input with prior knowledge or expectations. While tactile stimuli play a significant role in shaping our perception and decision making, if and how prior information modulates the representation of tactile stimuli in early somatosensory cortices is largely unknown. Here, we employed functional magnetic resonance imaging (fMRI) and a vibrotactile detection paradigm to study the effect of prior information on tactile perception and tactile stimulus representation in early somatosensory areas. The supra-voxel somatotopic organization of early somatosensory areas allowed us to assess the effect of prior information on finger-specific representations. We found that vibrotactile stimuli congruent with expectations are associated with improved vibrotactile detection performance and a decrease of the mean blood-oxygen-level-dependent (BOLD) signal in the contralateral primary somatosensory cortex (S1). Concurrently, finger-specific activity associated with anticipated vibrotactile stimulation revealed higher multivariate decoding accuracies and better alignment with S1s somatotopic organization. In addition, we observed that prior information induced somatotopically organized activity in contralateral S1 even before tactile stimulation onset. The accuracy of the multivariate decoding of stimulus-specific expectations was therefore strongly associated with upcoming behavioral detection performance. Thus, our results reveal a role for S1 in the integration of upcoming tactile stimuli with prior information based on its somatotopic organization as well as the presence of behaviorally relevant activity in S1 before stimulation onset.
- organism: human
- assay: imaging
- research focus: cell-cell communication
- research focus: spatial domain detection
- research focus: differential expression
- approach: statistical model
- Citations
- 3
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