start-ver=1.4 cd-journal=joma no-vol=5 cd-vols= no-issue=8 article-no= start-page=e02141 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2019 dt-pub=20190831 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Stroking hardness changes the perception of affective touch pleasantness across different skin sites en-subtitle= kn-subtitle= en-abstract= kn-abstract=Human unmyelinated tactile afferents (CT afferents) in hairy skin are thought to be involved in the transmission of affective aspects of touch. How the perception of affective touch differs across human skin has made substantial progress; however, the majority of previous studies have mainly focused on the relationship between stroking velocities and pleasantness ratings. Here, we investigate how stroking hardness affects the perception of affective touch. Affective tactile stimulation was given with four different hardness of brushes at three different forces, which were presented to either palm or forearm. To quantify the physical factors of the stimuli (brush hardness), ten na?ve, healthy participants assessed brush hardness using a seven-point scale. Based on these ten participants, five more participants were added to rate the hedonic value of brush stroking using a visual analogue scale (VAS). We found that pleasantness ratings over the skin resulted in a preference for light, soft stroking, which was rated as more pleasant when compared to heavy, hard stroking. Our results show that the hairy skin of the forearm is more susceptible to stroking hardness than the glabrous of the palm in terms of the perception of pleasantness. These findings of the current study extend the growing literature related to the effect of stroking characteristics on pleasantness ratings. en-copyright= kn-copyright= en-aut-name=YuJiabin en-aut-sei=Yu en-aut-mei=Jiabin kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=YangJiajia en-aut-sei=Yang en-aut-mei=Jiajia kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=YuYinghua en-aut-sei=Yu en-aut-mei=Yinghua kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=WuQiong en-aut-sei=Wu en-aut-mei=Qiong kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=TakahashiSatoshi en-aut-sei=Takahashi en-aut-mei=Satoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=EjimaYoshimichi en-aut-sei=Ejima en-aut-mei=Yoshimichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=WuJinglong en-aut-sei=Wu en-aut-mei=Jinglong kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= affil-num=1 en-affil=Cognitive Neuroscience Laboratory, Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=2 en-affil=Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=3 en-affil=Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=4 en-affil=Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=5 en-affil=Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=6 en-affil=Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=7 en-affil=Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University kn-affil= en-keyword=Affective tactile kn-keyword=Affective tactile en-keyword=CT afferents; Neuroscience kn-keyword=CT afferents; Neuroscience en-keyword=Physical factors kn-keyword=Physical factors en-keyword=Pleasantness ratings kn-keyword=Pleasantness ratings en-keyword=Stroking hardness. kn-keyword=Stroking hardness. END start-ver=1.4 cd-journal=joma no-vol=5 cd-vols= no-issue=8 article-no= start-page=e02141 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2019 dt-pub=201908 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Stroking hardness changes the perception of affective touch pleasantness across different skin sites en-subtitle= kn-subtitle= en-abstract= kn-abstract=Human unmyelinated tactile afferents (CT afferents) in hairy skin are thought to be involved in the transmission of affective aspects of touch. How the perception of affective touch differs across human skin has made substantial progress; however, the majority of previous studies have mainly focused on the relationship between stroking velocities and pleasantness ratings. Here, we investigate how stroking hardness affects the perception of affective touch. Affective tactile stimulation was given with four different hardness of brushes a three different forces, which were presented to either palm or forearm. To quantify the physical factors of the stimuli (brush hardness), ten naive, healthy participants assessed brush hardness using a seven-point scale. Based on these ten participants, five more participants were added to rate the hedonic value of brush stroking using a visual analogue scale (VAS). We found that pleasantness ratings over the skin resulted in a preference for light, soft stroking, which was rated as more pleasant when compared to heavy, hard stroking. Our results show that the hairy skin of the forearm is more susceptible to stroking hardness than the glabrous of the palm in terms of the perception of pleasantness. These findings of the current study extend the growing literature related to the effect of stroking characteristics on pleasantness ratings. en-copyright= kn-copyright= en-aut-name=YuJiabin en-aut-sei=Yu en-aut-mei=Jiabin kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=YangJiajia en-aut-sei=Yang en-aut-mei=Jiajia kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=YuYinghua en-aut-sei=Yu en-aut-mei=Yinghua kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=WuQiong en-aut-sei=Wu en-aut-mei=Qiong kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=TakahashiSatoshi en-aut-sei=Takahashi en-aut-mei=Satoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=EjimaYoshimichi en-aut-sei=Ejima en-aut-mei=Yoshimichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=WuJinglong en-aut-sei=Wu en-aut-mei=Jinglong kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= affil-num=1 en-affil=Cognitive Neuroscience Laboratory, Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=2 en-affil=Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=3 en-affil=Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=4 en-affil=Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=5 en-affil=Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=6 en-affil=Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=7 en-affil=Cognitive Neuroscience Laboratory, Graduate School of Interdisciplinary, Science and Engineering in Health Systems, Okayama University kn-affil= en-keyword=Neuroscience kn-keyword=Neuroscience en-keyword=Pleasantness ratings kn-keyword=Pleasantness ratings en-keyword=Affective tactile kn-keyword=Affective tactile en-keyword=Physical factors kn-keyword=Physical factors en-keyword=CT afferents kn-keyword=CT afferents en-keyword=Stroking hardness kn-keyword=Stroking hardness END start-ver=1.4 cd-journal=joma no-vol= cd-vols= no-issue= article-no= start-page=e02033 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2021 dt-pub=20210119 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Functional heterogeneity in the left lateral posterior parietal cortex during visual and haptic crossmodal dot-surface matching en-subtitle= kn-subtitle= en-abstract= kn-abstract=Background
Vision and touch are thought to contribute information to object perception in an independent but complementary manner. The left lateral posterior parietal cortex (LPPC) has long been associated with multisensory information processing, and it plays an important role in visual and haptic crossmodal information retrieval. However, it remains unclear how LPPC subregions are involved in visuo]haptic crossmodal retrieval processing.
Methods
In the present study, we used an fMRI experiment with a crossmodal delayed match]to]sample paradigm to reveal the functional role of LPPC subregions related to unimodal and crossmodal dot]surface retrieval.
Results
The visual]to]haptic condition enhanced the activity of the left inferior parietal lobule relative to the haptic unimodal condition, whereas the inverse condition enhanced the activity of the left superior parietal lobule. By contrast, activation of the left intraparietal sulcus did not differ significantly between the crossmodal and unimodal conditions. Seed]based resting connectivity analysis revealed that these three left LPPC subregions engaged distinct networks, confirming their different functions in crossmodal retrieval processing.
Conclusion
Taken together, the findings suggest that functional heterogeneity of the left LPPC during visuo]haptic crossmodal dot]surface retrieval processing reflects that the left LPPC does not simply contribute to retrieval of past information; rather, each subregion has a specific functional role in resolving different task requirements. en-copyright= kn-copyright= en-aut-name=YangJiajia en-aut-sei=Yang en-aut-mei=Jiajia kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=YuYinghua en-aut-sei=Yu en-aut-mei=Yinghua kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=ShigemasuHiroaki en-aut-sei=Shigemasu en-aut-mei=Hiroaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=KadotaHiroshi en-aut-sei=Kadota en-aut-mei=Hiroshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=NakaharaKiyoshi en-aut-sei=Nakahara en-aut-mei=Kiyoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=KochiyamaTakanori en-aut-sei=Kochiyama en-aut-mei=Takanori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=EjimaYoshimichi en-aut-sei=Ejima en-aut-mei=Yoshimichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=WuJinglong en-aut-sei=Wu en-aut-mei=Jinglong kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= affil-num=1 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=2 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=3 en-affil=Kochi University of Technology kn-affil= affil-num=4 en-affil=Kochi University of Technology kn-affil= affil-num=5 en-affil=Kochi University of Technology kn-affil= affil-num=6 en-affil=ATR Brain Activity Imaging Center kn-affil= affil-num=7 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=8 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= en-keyword=crossmodal processing kn-keyword=crossmodal processing en-keyword=fMRI kn-keyword=fMRI en-keyword=haptic dot-surface matching kn-keyword=haptic dot-surface matching en-keyword=lateral posterior parietal cortex kn-keyword=lateral posterior parietal cortex en-keyword=memory retrieval kn-keyword=memory retrieval END start-ver=1.4 cd-journal=joma no-vol=5 cd-vols= no-issue=5 article-no= start-page=eaav9053 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2019 dt-pub=20190501 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Layer-specific activation of sensory input and predictive feedback in the human primary somatosensory cortex en-subtitle= kn-subtitle= en-abstract= kn-abstract=When humans perceive a sensation, their brains integrate inputs from sensory receptors and process them based on their expectations. The mechanisms of this predictive coding in the human somatosensory system are not fully understood. We fill a basic gap in our understanding of the predictive processing of somatosensation by examining the layer-specific activity in sensory input and predictive feedback in the human primary somatosensory cortex (S1). We acquired submillimeter functional magnetic resonance imaging data at 7T (n = 10) during a task of perceived, predictable, and unpredictable touching sequences. We demonstrate that the sensory input from thalamic projects preferentially activates the middle layer, while the superficial and deep layers in S1 are more engaged for cortico-cortical predictive feedback input. These findings are pivotal to understanding the mechanisms of tactile prediction processing in the human somatosensory cortex. en-copyright= kn-copyright= en-aut-name=YuYinghua en-aut-sei=Yu en-aut-mei=Yinghua kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=HuberLaurentius en-aut-sei=Huber en-aut-mei=Laurentius kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=YangJiajia en-aut-sei=Yang en-aut-mei=Jiajia kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=JangrawDavid C. en-aut-sei=Jangraw en-aut-mei=David C. kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=HandwerkerDaniel A. en-aut-sei=Handwerker en-aut-mei=Daniel A. kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=MolfesePeter J. en-aut-sei=Molfese en-aut-mei=Peter J. kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=ChenGang en-aut-sei=Chen en-aut-mei=Gang kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=EjimaYoshimichi en-aut-sei=Ejima en-aut-mei=Yoshimichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=WuJinglong en-aut-sei=Wu en-aut-mei=Jinglong kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=BandettiniPeter A. en-aut-sei=Bandettini en-aut-mei=Peter A. kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= affil-num=1 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems,Okayama University kn-affil= affil-num=2 en-affil=Section on Functional Imaging Methods, National Institute of Mental Health kn-affil= affil-num=3 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems,Okayama University kn-affil= affil-num=4 en-affil=Section on Functional Imaging Methods, National Institute of Mental Health kn-affil= affil-num=5 en-affil=Section on Functional Imaging Methods, National Institute of Mental Health kn-affil= affil-num=6 en-affil=Section on Functional Imaging Methods, National Institute of Mental Health kn-affil= affil-num=7 en-affil=Scientific and Statistical Computing Core, National Institute of Mental Health kn-affil= affil-num=8 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems,Okayama University kn-affil= affil-num=9 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems,Okayama University kn-affil= affil-num=10 en-affil=Section on Functional Imaging Methods, National Institute of Mental Health kn-affil= END