Background: Slow-adapting type I (SA-I) afferents deliver sensory signals to the somatosensory cortex during low-frequency (or static) mechanical stimulation. It has been reported that the somatosensory projection from SA-I afferents is effective and reliable for object grasping and manipulation. Despite a large number of neuroimaging studies on cortical activation responding to tactile stimuli mediated by SA-I afferents, how sensory information of such tactile stimuli flows over the somatosensory cortex remains poorly understood. In this study, we investigated tactile information processing of pressure stimuli between the primary (SI) and secondary (SII) somatosensory cortices by measuring effective connectivity using dynamic causal modeling (DCM). We applied pressure stimuli for 3 s to the right index fingertip of healthy participants and acquired functional magnetic resonance imaging (fMRI) data using a 3T MRI system. Results: DCM analysis revealed intra-hemispheric effective connectivity between the contralateral SI (cSI) and SII (cSII) characterized by both parallel (signal inputs to both cSI and cSII) and serial (signal transmission from cSI to cSII) pathways during pressure stimulation. DCM analysis also revealed inter-hemispheric effective connectivity among cSI, cSII, and the ipsilateral SII (iSII) characterized by serial (from cSI to cSII) and SII-level (from cSII to iSII) pathways during pressure stimulation. Conclusions: Our results support a hierarchical somatosensory network that underlies processing of low-frequency tactile information. The network consists of parallel inputs to both cSI and cSII (intra-hemispheric), followed by serial pathways from cSI to cSII (intra-hemispheric) and from cSII to iSII (inter-hemispheric). Importantly, our results suggest that both serial and parallel processing take place in tactile information processing of static mechanical stimuli as well as highlighting the contribution of callosal transfer to bilateral neuronal interactions in SII.
ASJC Scopus subject areas
- Cellular and Molecular Neuroscience