% % This file was created by the Typo3 extension % sevenpack version 0.7.14 % % --- Timezone: CEST % Creation date: 2017-05-23 % Creation time: 10-51-21 % --- Number of references % 17 % @Article { YeonKRPCK2017, title = {Human Brain Activity Related to the Tactile Perception of Stickiness}, journal = {Frontiers in Human Neuroscience}, year = {2017}, month = {1}, volume = {11}, number = {8}, pages = {1-13}, abstract = {While the perception of stickiness serves as one of the fundamental dimensions for tactile sensation, little has been elucidated about the stickiness sensation and its neural correlates. The present study investigated how the human brain responds to perceived tactile sticky stimuli using functional magnetic resonance imaging (fMRI). To evoke tactile perception of stickiness with multiple intensities, we generated silicone stimuli with varying catalyst ratios. Also, an acrylic sham stimulus was prepared to present a condition with no sticky sensation. From the two psychophysics experiments–the methods of constant stimuli and the magnitude estimation—we could classify the silicone stimuli into two groups according to whether a sticky perception was evoked: the Supra-threshold group that evoked sticky perception and the Infra-threshold group that did not. In the Supra-threshold vs. Sham contrast analysis of the fMRI data using the general linear model (GLM), the contralateral primary somatosensory area (S1) and ipsilateral dorsolateral prefrontal cortex (DLPFC) showed significant activations in subjects, whereas no significant result was found in the Infra-threshold vs. Sham contrast. This result indicates that the perception of stickiness not only activates the somatosensory cortex, but also possibly induces higher cognitive processes. Also, the Supra- vs. Infra-threshold contrast analysis revealed significant activations in several subcortical regions, including the pallidum, putamen, caudate and thalamus, as well as in another region spanning the insula and temporal cortices. These brain regions, previously known to be related to tactile discrimination, may subserve the discrimination of different intensities of tactile stickiness. The present study unveils the human neural correlates of the tactile perception of stickiness and may contribute to broadening the understanding of neural mechanisms associated with tactile perception.}, web_url = {http://journal.frontiersin.org/article/10.3389/fnhum.2017.00008/pdf}, DOI = {10.3389/fnhum.2017.00008}, author = {Yeon, J and Kim, J and Ryu, J and Park, J-Y and Chung, S-C and Kim, S-P} } @Article { KimCCBK2016, title = {Neural Categorization of Vibrotactile Frequency in Flutter and Vibration Stimulations: an fMRI Study}, journal = {IEEE Transactions on Haptics}, year = {2016}, month = {12}, volume = {9}, number = {4}, pages = {455-464}, abstract = {As the use of wearable haptic devices with vibrating alert features is commonplace, an understanding of the perceptual categorization of vibrotactile frequencies has become important. This understanding can be substantially enhanced by unveiling how neural activity represents vibrotactile frequency information. Using functional magnetic resonance imaging (fMRI), this study investigated categorical clustering patterns of the frequency-dependent neural activity evoked by vibrotactile stimuli with gradually changing frequencies from 20 to 200 Hz. First, a searchlight multi-voxel pattern analysis (MVPA) was used to find brain regions exhibiting neural activities associated with frequency information. We found that the contralateral postcentral gyrus (S1) and the supramarginal gyrus (SMG) carried frequency-dependent information. Next, we applied multidimensional scaling (MDS) to find low-dimensional neural representations of different frequencies obtained from the multi-voxel activity patterns within these regions. The clustering analysis on the MDS results showed that neural activity patterns of 20-100 Hz and 120-200 Hz were divided into two distinct groups. Interestingly, this neural grouping conformed to the perceptual frequency categories found in the previous behavioral studies. Our findings therefore suggest that neural activity patterns in the somatosensory cortical regions may provide a neural basis for the perceptual categorization of vibrotactile frequency.}, department = {Department B{\"u}lthoff}, web_url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7523424}, DOI = {10.1109/TOH.2016.2593727}, author = {Kim, J and Chung, YG and Chung, S-C and B{\"u}lthoff, HH and Kim, S-P} } @Article { KimCCBK2016_2, title = {Decoding pressure stimulation locations on the fingers from human neural activation patterns}, journal = {NeuroReport}, year = {2016}, month = {11}, volume = {27}, number = {16}, pages = {1232–1236}, abstract = {In this functional MRI study, we investigated how the human brain activity represents tactile location information evoked by pressure stimulation on fingers. Using the searchlight multivoxel pattern analysis, we looked for local activity patterns that could be decoded into one of four stimulated finger locations. The supramarginal gyrus (SMG) and the thalamus were found to contain distinct multivoxel patterns corresponding to individual stimulated locations. In contrast, the univariate general linear model analysis contrasting stimulation against resting phases for each finger identified activations mainly in the primary somatosensory cortex (S1), but not in SMG or in thalamus. Our results indicate that S1 might be involved in the detection of the presence of pressure stimuli, whereas the SMG and the thalamus might play a role in identifying which finger is stimulated. This finding may provide additional evidence for hierarchical information processing in the human somatosensory areas.}, department = {Department B{\"u}lthoff}, web_url = {http://ovidsp.tx.ovid.com/sp-3.22.1b/ovidweb.cgi?\&S=CABCFPBFBJDDEBCCNCIKCGFBHLNPAA00\&WebLinkReturn=Full+Text\%3dL\%7cS.sh.22.23\%7c0\%7c00001756-201611010-00008\&PDFLink=FPDDNCFBCGCCBJ00\%7c\%2ffs047\%2fovft\%2flive\%2fgv024\%2f00001756\%2f00001756-201611010-00008\&PD}, DOI = {10.1097/WNR.0000000000000683}, author = {Kim, J and Chung, YG and Chung, S-C and B{\"u}lthoff, HH and Kim, S-P} } @Article { KimCPCWBK2015, title = {Decoding Accuracy in Supplementary Motor Cortex Correlates with Perceptual Sensitivity to Tactile Roughness}, journal = {PLoS ONE}, year = {2015}, month = {6}, volume = {10}, number = {6}, pages = {1-17}, abstract = {Perceptual sensitivity to tactile roughness varies across individuals for the same degree of roughness. A number of neurophysiological studies have investigated the neural substrates of tactile roughness perception, but the neural processing underlying the strong individual differences in perceptual roughness sensitivity remains unknown. In this study, we explored the human brain activation patterns associated with the behavioral discriminability of surface texture roughness using functional magnetic resonance imaging (fMRI). First, a whole-brain searchlight multi-voxel pattern analysis (MVPA) was used to find brain regions from which we could decode roughness information. The searchlight MVPA revealed four brain regions showing significant decoding results: the supplementary motor area (SMA), contralateral postcentral gyrus (S1), and superior portion of the bilateral temporal pole (STP). Next, we evaluated the behavioral roughness discrimination sensitivity of each individual using the just-noticeable difference (JND) and correlated this with the decoding accuracy in each of the four regions. We found that only the SMA showed a significant correlation between neuronal decoding accuracy and JND across individuals; Participants with a smaller JND (i.e., better discrimination ability) exhibited higher decoding accuracy from their voxel response patterns in the SMA. Our findings suggest that multivariate voxel response patterns presented in the SMA represent individual perceptual sensitivity to tactile roughness and people with greater perceptual sensitivity to tactile roughness are likely to have more distinct neural representations of different roughness levels in their SMA.}, department = {Department B{\"u}lthoff}, web_url = {http://www.plosone.org/article/fetchObject.action?uri=info:doi/10.1371/journal.pone.0129777\&representation=PDF}, DOI = {10.1371/journal.pone.0129777}, EPUB = {e0129777}, author = {Kim, J and Chung, YG and Park, J-Y and Chung, S-C and Wallraven, C and B{\"u}lthoff, HH and Kim, S-P} } @Article { KimSWLB2015, title = {Abstract Representations of Associated Emotions in the Human Brain}, journal = {Journal of Neuroscience}, year = {2015}, month = {4}, volume = {35}, number = {14}, pages = {5655-5663}, abstract = {Emotions can be aroused by various kinds of stimulus modalities. Recent neuroimaging studies indicate that several brain regions represent emotions at an abstract level, i.e., independently from the sensory cues from which they are perceived (e.g., face, body, or voice stimuli). If emotions are indeed represented at such an abstract level, then these abstract representations should also be activated by the memory of an emotional event. We tested this hypothesis by asking human participants to learn associations between emotional stimuli (videos of faces or bodies) and non-emotional stimuli (fractals). After successful learning, fMRI signals were recorded during the presentations of emotional stimuli and emotion-associated fractals. We tested whether emotions could be decoded from fMRI signals evoked by the fractal stimuli using a classifier trained on the responses to the emotional stimuli (and vice versa). This was implemented as a whole-brain searchlight, multivoxel activation pattern analysis, which revealed successful emotion decoding in four brain regions: posterior cingulate cortex (PCC), precuneus, MPFC, and angular gyrus. The same analysis run only on responses to emotional stimuli revealed clusters in PCC, precuneus, and MPFC. Multidimensional scaling analysis of the activation patterns revealed clear clustering of responses by emotion across stimulus types. Our results suggest that PCC, precuneus, and MPFC contain representations of emotions that can be evoked by stimuli that carry emotional information themselves or by stimuli that evoke memories of emotional stimuli, while angular gyrus is more likely to take part in emotional memory retrieval.}, department = {Department B{\"u}lthoff}, department2 = {Research Group Noppeney}, web_url = {http://www.jneurosci.org/content/35/14/5655.full.pdf+html}, DOI = {10.1523/JNEUROSCI.4059-14.2015}, author = {Kim, J and Schultz, J and Rohe, T and Wallraven, C and Lee, S-W and B{\"u}lthoff, HH} } @Article { KimMCCPBK2014, title = {Distributed functions of detection and discrimination of vibrotactile stimuli in the hierarchical human somatosensory system}, journal = {Frontiers in Human Neuroscience}, year = {2015}, month = {1}, volume = {8}, number = {1070}, pages = {1-10}, abstract = {According to the hierarchical view of human somatosensory network, somatic sensory information is relayed from the thalamus to primary somatosensory cortex (S1), and then distributed to adjacent cortical regions to perform further perceptual and cognitive functions. Although a number of neuroimaging studies have examined neuronal activity correlated with tactile stimuli, comparatively less attention has been devoted toward understanding how vibrotactile stimulus information is processed in the hierarchical somatosensory cortical network. To explore the hierarchical perspective of tactile information processing, we studied two cases: (a) discrimination between the locations of finger stimulation, and (b) detection of stimulation against no stimulation on individual fingers, using both standard general linear model (GLM) and searchlight multi-voxel pattern analysis (MVPA) techniques. These two cases were studied on the same data set resulting from a passive vibrotactile stimulation experiment. Our results showed that vibrotactile stimulus locations on fingers could be discriminated from measurements of human functional magnetic resonance imaging (fMRI). In particular, it was in case (a) where we observed activity in contralateral posterior parietal cortex (PPC) and supramarginal gyrus (SMG) but not in S1, while in case (b) we found significant cortical activations in S1 but not in PPC and SMG. These discrepant observations suggest the functional specialization with regard to vibrotactile stimulus locations, especially, the hierarchical information processing in the human somatosensory cortical areas. Our findings moreover support the general understanding that S1 is the main sensory receptive area for the sense of touch, and adjacent cortical regions (i.e., PPC and SMG) are in charge of a higher level of processing and may thus contribute most for the successful classification between stimulated finger locations.}, department = {Department B{\"u}lthoff}, web_url = {http://journal.frontiersin.org/Journal/10.3389/fnhum.2014.01070/pdf}, DOI = {10.3389/fnhum.2014.01070}, author = {Kim, J and M{\"u}ller, K-R and Chung, YG and Chung, S-C and Park, J-Y and B{\"u}lthoff, HH and Kim, S-P} } @Article { ChungKHKCCPK2013, title = {Frequency-dependent patterns of somatosensory cortical responses to vibrotactile stimulation in humans: A fMRI study}, journal = {Brain Research}, year = {2013}, month = {4}, volume = {1504}, pages = {47–57}, abstract = {In the human mechanosensation system, rapidly adapting afferents project sensory signals of flutter (5–50 Hz) to the contralateral primary somatosensory cortex (S1) and bilateral secondary somatosensory cortex (S2) whereas Pacinian afferents project sensory signals of vibration (50–400 Hz) to bilateral S2. However, it remains largely unknown how somatosensory cortical activity changes as a function of vibrotactile frequency. This functional magnetic resonance imaging (fMRI) study investigated frequency dependency of somatosensory cortical activity in humans by applying vibrotactile stimulation with various frequencies (20–200 Hz) to the index finger. We found more frequency-dependent voxels in the upper bank of the lateral sulcus (LS) of S2 than in S1 and the posterior parietal cortex of S2. Our statistical spatial clustering analysis showed that two groups of positively or negatively frequency-dependent voxels formed distinct clusters, most clearly in the LS. Using a cortical separability index, we reaffirmed that somatosensory cortical activity was most separable at 50 Hz, previously known to demarcate flutter and vibration. Our results suggest that the LS (S2) may play an important role in processing vibrotactile frequency information and that the somatosensory cortex may include spatially localized neural assemblies specialized to higher or lower vibrotactile frequency.}, web_url = {http://www.sciencedirect.com/science/article/pii/S0006899313001923}, DOI = {10.1016/j.brainres.2013.02.003}, author = {Chung, YG and Kim, J and Han, SW and Kim, H-S and Choi, MH and Chung, S-C and Park, J-Y and Kim, S-P} } @Article { SonFLKBR2012, title = {Human-Centered Design and Evaluation of Haptic Cueing for Teleoperation of Multiple Mobile Robots}, journal = {IEEE Transactions on Cybernetics}, year = {2013}, month = {4}, volume = {43}, number = {2}, pages = {597-609}, abstract = {In this paper, we investigate the effect of haptic cueing on a human operator's performance in the field of bilateral teleoperation of multiple mobile robots, particularly multiple unmanned aerial vehicles (UAVs). Two aspects of human performance are deemed important in this area, namely, the maneuverability of mobile robots and the perceptual sensitivity of the remote environment. We introduce metrics that allow us to address these aspects in two psychophysical studies, which are reported here. Three fundamental haptic cue types were evaluated. The Force cue conveys information on the proximity of the commanded trajectory to obstacles in the remote environment. The Velocity cue represents the mismatch between the commanded and actual velocities of the UAVs and can implicitly provide a rich amount of information regarding the actual behavior of the UAVs. Finally, the Velocity+Force cue is a linear combination of the two. Our experimental results show that, while maneuverability is best supported by the Force cue feedback, perceptual sensitivity is best served by the Velocity cue feedback. In addition, we show that large gains in the haptic feedbacks do not always guarantee an enhancement in the teleoperator's performance.}, url = {http://www.kyb.tuebingen.mpg.defileadmin/user_upload/files/publications/2012/2013a-SonFraChuKimBueRob.pdf}, department = {Department B{\"u}lthoff}, web_url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=6294459}, DOI = {10.1109/TSMCB.2012.2212884}, author = {Son, HI and Franchi, A and Chuang, LL and Kim, J and B{\"u}lthoff, HH and Robuffo Giordano, P} } @Inproceedings { KimCCPBK2013, title = {A multi-voxel pattern analysis of neural representation of vibrotactile location}, year = {2013}, month = {10}, pages = {1637-1640}, abstract = {Previous neural decoding studies have mainly focused on discrimination of activation patterns evoked by active movements. Nonetheless, comparatively, little attention has been devoted toward understanding how brain signals are observed with passive stimulus. In this study, we examined whether the stimulus locations on between fingers, one of the most fundamental features of passive vibrotactile stimulation, can be distinguished from human functional magnetic resonance imaging (fMRI) data. Whole brain searchlight multi-voxel pattern analysis (MVPA) has found two brain regions, which make a contribution to decode stimulus sites, in contralateral posterior parietal cortex (PPC) and contralateral secondary somatosensory cortex (S2). No significant area for the decoding of activity to stimulus site in primary somatosensory cortex (S1), which is well-developed brain region for finger somatotopy. On the other hand, a whole brain univariate group analysis has discovered activity in S1, not in PPC and S2 areas. These results suggest that PPC and S2 regions play a key role in the differentiation of passive vibrotactile stimulus locations, and thus decode tactile events from finger somatotopic.}, department = {Department B{\"u}lthoff}, web_url = {http://2013.iccas.org/}, publisher = {IEEE}, address = {Piscataway, NJ, USA}, event_place = {Gwangju, South Korea}, event_name = {13th International Conference on Control, Automation and Systems (ICCAS 2013)}, ISBN = {978-89-93215-05-2}, DOI = {10.1109/ICCAS.2013.6704194}, author = {Kim, J and Chung, YG and Chung, S-C and Park, J-Y and B{\"u}lthoff, HH and Kim, S-P} } @Inproceedings { ChungKKK2012, title = {Investigation of cortical activation patterns in response to the inter-digit vibrotactile stimulation}, year = {2012}, month = {10}, pages = {20138-2041}, abstract = {Mechanosensation includes the detection of mechanical stimuli from mechanoreceptors and sensory signal transduction to the somatosensory cortex through neural afferents. Previous studies reported the sensory signals of flutter (5-50 Hz) and vibration (50-400 Hz) traveled through separated neural afferents of rapidly adapting type 1 (RA) and rapidly adapting type 2 (PC) respectively, and they activated the primary (S1) and secondary (S2) somatosensory cortices with spatially distinct patterns. In this study, we investigated the cortical activation patterns of S1 and S2 in response to flutter and vibration delivered onto the tips of index (D2) and little (D5) digits using functional magnetic resonance imaging (fMRI). We demonstrated that D2 provided more apparent cortical activation patterns that the event of flutter stimulation elicited contralateral S1 and bilateral S2 activation while the event of vibration stimulation mainly elicited bilateral S2 activation due to the somatotopic neural projection mechanisms of the RA and PC afferents and the density distribution of mechanoreceptors.}, web_url = {http://ieeexplore.ieee.org/document/6393188/}, publisher = {IEEE}, address = {Piscataway, NJ, USA}, event_place = {Jeju, South Korea}, event_name = {12th International Conference on Control, Automation and Systems (ICCAS 2012)}, ISBN = {978-1-4673-2247-8}, author = {Chung, YG and Kim, J and Kim, H-S and Kim, S-P} } @Inproceedings { SonCKB2011, title = {Haptic Feedback Cues Can Improve Human Perceptual Awareness in Multi-Robots Teleoperation}, year = {2011}, month = {10}, pages = {1323-1328}, abstract = {The availability of additional force cues in haptic devices are often expected to improve control performance, over conditions that only provide visual feedback. However, there is little empirical evidence to show this to be true for the teleoperation control of remote vehicles (i.e., multiple unmanned aerial vehicles (UAVs)). In this paper, we show that force cues can increase one's sensitivity in discerning the presence of obstacles in the remote multi-UAVs' environment. Significant benefits, relative to a purely visual scenario, were achieved only when force cues were sufficiently amplified by large gains. In addition, force cues tended to provide stronger benefits when they were based on the UAVs' velocity information.}, department = {Department B{\"u}lthoff}, web_url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6106130}, publisher = {IEEE}, address = {Piscataway, NJ, USA}, event_place = {Gyeonggi-do, Korea}, event_name = {11th International Conference on Control, Automations and Systems (ICCAS 2011)}, ISBN = {978-1-4577-0835-0}, author = {Son, HI and Chuang, L and Kim, J and B{\"u}lthoff, HH} } @Inproceedings { SonCFKLLBR2011, title = {Measuring an Operator's Maneuverability Performance in the Haptic Teleoperation of Multiple Robots}, year = {2011}, month = {9}, pages = {3039-3046}, abstract = {In this paper, we investigate the maneuverability performance of human teleoperators on multi-robots. First, we propose that maneuverability performance can be assessed by a frequency response function that jointly considers the input force of the operator and the position errors of the multi-robot system that is being maneuvered. Doing so allows us to evaluate maneuverability performance in terms of the human teleoperator's interaction with the controlled system. This allowed us to effectively determine the suitability of different haptic cue algorithms in improving teleoperation maneuverability. Performance metrics based on the human teleoperator's frequency response function indicate that maneuverability performance is best supported by a haptic feedback algorithm which is based on an obstacle avoidance force.}, url = {http://www.kyb.tuebingen.mpg.defileadmin/user_upload/files/publications/2011/IROS-2011-Son.pdf}, department = {Department B{\"u}lthoff}, web_url = {http://www.iros2011.org/}, editor = {Amato, N.M.}, publisher = {IEEE}, address = {Piscatawy, NJ, USA}, event_place = {San Francisco, CA, USA}, event_name = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)}, ISBN = {978-1-61284-454-1}, DOI = {10.1109/IROS.2011.6048185}, author = {Son, HI and Chuang, LL and Franchi, A and Kim, J and Lee, D and Lee, S-W and B{\"u}lthoff, HH and Robuffo Giordano, P} } @Inproceedings { SonKCFRLB2011, title = {An Evaluation of Haptic Cues on the Tele-Operator's Perceptual Awareness of Multiple UAVs' Environments}, year = {2011}, month = {6}, pages = {149-154}, abstract = {The use of multiple unmanned aerial vehicles (UAVs) is increasingly being incorporated into a wide range of teleoperation applications. To date, relevant research has largely been focused on the development of appropriate control schemes. In this paper, we extend previous research by investigating how control performance could be improved by providing the teleoperator with haptic feedback cues. First, we describe a control scheme that allows a teleoperator to manipulate the flight of multiple UAVs in a remote environment. Next, we present three designs of haptic cue feedback that could increase the teleoperator's environmental awareness of such a remote environment. These cues are based on the UAVs' i) velocity information, ii) proximity to obstacles, and iii) a combination of these two sources of information. Finally, we present an experimental evaluation of these haptic cue designs. Our evaluation is based on the teleoperator's perceptual sensitivity to the physical environment inhabited by the multiple UAVs. We conclude that a teleoperator's perceptual sensitivity is best served by haptic feedback cues that are based on the velocity information of multiple UAVs.}, url = {http://www.kyb.tuebingen.mpg.defileadmin/user_upload/files/publications/2011/WHC-2011-Son.pdf}, department = {Department B{\"u}lthoff}, web_url = {http://www.haptics2011.org/en/}, editor = {Jones, L. , M. Harders, Y. Yokokohji}, publisher = {IEEE}, address = {Piscataway, NJ, USA}, event_place = {Istanbul, Turkey}, event_name = {IEEE 2011 World Haptics Conference (WHC 2011)}, ISBN = {978-1-4577-0299-0}, DOI = {10.1109/WHC.2011.5945477}, author = {Son, HI and Kim, J and Chuang, LL and Franchi, A and Robuffo Giordano, P and Lee, D and B{\"u}lthoff, HH} } @Poster { KimPYKPK2017, title = {Investigation of cortical activity related to perception of tactile hardness}, year = {2017}, month = {6}, day = {27}, number = {2153}, abstract = {Introduction: Tactile sensation is essential for humans to manipulate objects by hands. During object manipulation, many different physical properties of an object are sensed and processed by the human somatosensory system, supporting exquisite perceptual sensitivities [1]. Tactile sensation of different physical properties can be depicted in the several tactile perceptual dimensions, including roughness, hardness, stickiness and warmth [2]. To date, a number of human neuroimaging studies have unveiled neural mechanisms underlying roughness [3] and warmth perception [4]. Yet, relatively little is known about how the human brain subserves the perception of tactile hardness. Previous studies have suggested that slowly adapting type-1 (SA1) afferents are primarily responsible for perceiving hardness from the surface of an object [5] and the Brodmann areas (BA) 3b and 1 may contribute to perceiving hardness [6]. However, it remains elusive how the different levels of hardness are represented in the human brain during the dexterous manipulation of an object. Therefore, this study aims to investigate neural responses to tactile stimuli with the same shape and surface texture but different levels of hardness when people grip on the object with their hand. Functional magnetic resonance imaging (fMRI) is used to identify brain regions related with tactile hardness. Methods: Twelve right-handed subjects (8 female, mean age 23.1 years old) participated in the study. Experimental protocols were approved by the ethical committee of Ulsan National Institute of Science and Technology (UNISTIRB-15-16-A). Tactile stimuli with the same shape (oval) were prepared and grouped into four sets according to their hardness levels (level 1 to 4). Participants first performed a behavioral task in which they were given a pair of stimuli with eyes closed and asked to report the degree of a difference in hardness between them. Afterward, participants performed the fMRI experimental task in which they repetitively gripped on and released a given object (used in the behavioral task) for fifteen seconds followed by a nine-second rest. There were also trials in which participants executed the same grip-and-release motion without objects as a control task. Functional images (T2*-weighted gradient EPI, covering the whole depth of somatosensory area, TR = 3,000 ms, voxel size = 2.0 \(\times\) 2.0 \(\times\) 2.0 mm3) were obtained during the fMRI task using a Siemens 3T scanner (Magnetom TrioTim). The functional image analysis was performed using the general linear model (GLM) in SPM8 with a canonical hemodynamic response function to estimate blood-oxygen-level-dependent (BOLD) responses to each stimulus. Results: The analysis of behavioral experimental data showed that participants could correctly find differences in hardness levels among stimuli. The GLM analysis for individuals revealed activations in the contralateral postcentral gyrus in most participants modulated with different levels of hardness (p<0.001 uncorrected). Also, a random-effect group analysis of fMRI data revealed a cluster in the Rolandic operculum activated by the perception of tactile hardness (p<0.001 uncorrected). In addition, the cluster size and maximum activation peak was increased as the hardness level increased. Conclusions: Our study demonstrated that brain regions over the postcentral gyrus (S1) and Rolandic operculum might be related to the perception of tactile hardness. We also observed that the degree of activation in these regions, reflected by the size of the activated area (cluster size) and the level of activation (maximum peak) was proportional to the level of tactile hardness. Our results suggest that neural assemblies in the contralateral S1 and Roland operculum may play a role in sensing tactile hardness during dexterous object manipulation.}, department = {Department B{\"u}lthoff}, web_url = {https://ww5.aievolution.com/hbm1701/index.cfm?do=abs.viewAbs\&abs=1940}, event_place = {Vancouver, BC, Canada}, event_name = {23rd Annual Meeting of the Organization for Human Brain Mapping (OHBM 2017)}, author = {Kim, J and Park, Y and Yeon, J and Kim, J and Park, J-Y and Kim, S-P} } @Poster { KimCCPBK2015, title = {Multi-voxel patterns in the human brain associated with perceptual grouping of tactile frequencies}, year = {2015}, month = {6}, day = {17}, volume = {21}, number = {4292}, abstract = {Introduction: As the use of mobile devices (particularly, wearable devices with vibrating alert features) are becoming more widespread, investigations on perceptual grouping of vibrotactile stimuli with different features, such as vibrating frequencies, are becoming more important for the design of effective haptic user interfaces. Previous psychophysical studies demonstrated that human perceive vibration frequencies as three distinctive groups: 'slow motion' ranging from 1 to 3 Hz, 'fluttering' ranging from 10 to 70 Hz, and 'smooth vibration' ranging from 100 to 300 Hz [1, 2]. This perceptual grouping pattern has been mainly explained based on the different characteristics of the tactile sensory innervations [3, 4]. However, characteristics of tactile innervations and sensory afferents do not fully describe perceptual grouping of vibrotactile stimuli. For instance, a boundary frequency should be between 40 and 50 Hz according to the afferent characteristics, but perception of vibrotactile stimuli is rather discriminated between 70 and 100 Hz. Furthermore, perceptual grouping is more likely to be affected by the neural encoding of vibration frequencies in the central nervous system, in addition to the characteristics of afferents. Here, we therefore search for the brain regions carrying frequency discriminative information using the searchlight multi-voxel pattern analysis (MVPA) [5] and compare the neural representations of different frequencies with the perceptual grouping patterns using multidimensional scaling (MDS). Methods: Fourteen subjects participated in this study and experimental procedures were approved by the Korea University (KU-IRB-11-46-A-1). Vibrotactile stimuli whose frequency varied from 20 to 200 Hz with an increment of 20 Hz were delivered to the tip of the index finger of the right hand by a vibrotactile stimulation device. Subjects performed ten runs of two sessions (one run for each frequency). Each session consisted of two consecutive periods: a 30 s resting period followed by a 30 s stimulation period. Functional images (T2*-weighted gradient EPI, TR = 3 s, voxel size = 2.0 \(\times\) 2.0 \(\times\) 2.0 mm3) were obtained using a 3T scanner. An information-based analysis with a cubical searchlight was employed to find spatially localized neuronal patterns varying with tactile frequencies. Decoding accuracies evaluated by a 2-fold cross-validation procedure were allocated to the center voxel of each searchlight. Then, we computed a correlation-based dissimilarity matrix and used MDS to map the neural representations for each of ten different frequencies onto the 2D space. Results: A random-effects group analysis revealed that a cluster exhibited statistically significant decoding capabilities to differentiate distinct frequencies (p<0.0001 uncorrected, cluster size>50). This cluster covered the contralateral postcentral gyrus (S1) and the supramarginal gyrus (SMG). Mean decoding accuracy was 77.7 ± 13.8 \% and decoding accuracy results significantly exceeded the chance level (t13=7.5, p<0.01). The MDS analysis showed that neural representations of 20 and 200 Hz were mapped the farthest positions (i.e. located in opposite side). Moreover, hierarchical cluster analyses revealed that neural representations of each frequency were grouped into two clusters, one for 20-100Hz and the other for 120-200 Hz. Conclusions: In this study, we statistically assessed each set of multi-voxel patterns and revealed that contralateral S1 and SMG exhibited neural activity patterns specific to the vibration frequency discrimination. Results of MDS indicated that neural representations of 20\verb=~=100 Hz and 120\verb=~=200 Hz were divided into two distinct groups. This grouping pattern of neural representations is in line with the perceptual frequency categories suggested by previous studies [1, 2]. Our findings therefore suggest that the neural activity patterns in contralateral S1 and SMG may be closely related to perceptual grouping of vibrotactile frequency.}, department = {Department B{\"u}lthoff}, web_url = {http://www.humanbrainmapping.org/i4a/pages/index.cfm?pageID=3625}, event_place = {Honolulu, HI, USA}, event_name = {21st Annual Meeting of the Organization for Human Brain Mapping (OHBM 2015)}, author = {Kim, J and Chung, YG and Chung, S-C and Park, J-Y and B{\"u}lthoff, HH and Kim, S-P} } @Poster { KimBKCHCP2014, title = {A correlation study of behavioral and neural decoding performance for roughness discrimination}, year = {2014}, month = {6}, day = {11}, volume = {20}, number = {4116}, abstract = {Introduction: Recently multi-voxel pattern analysis (MVPA) has been introduced in the analysis of functional magnetic resonance imaging (fMRI) and allows us to examine distributed spatial patterns of neural activity in response to various tactile stimuli [1]. Taking advantage of its higher sensitivity [2], MVPA has been employed in a wide range of somatosensory research fields as a complement to the traditional univariate analysis. However, current research on tactile MVPA is mostly focused on delineating neuronal activation patterns in response to the tactile stimuli. Relatively less attention has been devoted towards understanding how neural activation patterns underlie diverse human behavioral outcomes during tactile manipulation tasks. In this study, we aim to investigate how the multi-voxel neural patterns varied with the behavioral discriminative performance in a roughness discrimination task. For this purpose, we search for the brain regions carrying roughness discriminative information using searchlight MVPA [3] and how each region correlates with the human behavioral performance. Methods: Sixteen subjects participated in this study approved by Korea University Institutional Review Board (KU-IRB-11-46-A-0). Anatomical (T1-weighted 3D MPRAGE) and functional images (T2*-weighted gradient EPI, TR = 3,000 ms, voxel size = 2.0\(\times\)2.0\(\times\)2.0 mm) were obtained using a Siemens 3T scanner (Magnetom TrioTim). Before the fMRI scanning, all the participants performed the behavioral roughness discrimination task. Five different roughness levels of aluminum-oxide abrasive papers (Sumitomo 3-M), which were validated and employed in the previous study [4], were used. In each trial of the task, the participants explored two randomly presented abrasive papers with the index fingertip of right hand and reported which of them felt rougher. Behavioral discriminative sensitivity was measured as the difference of roughness values between 25th and 75th percentile of a psychometric function. This was referred to the just noticeable difference (JND) [5]. An fMRI scanning consisted of five blocks with twenty trials. Each trial was made up two consecutive periods; an exploration for 6 s followed by a resting for 15 s. Following instructions, the participants explored a presented abrasive paper with their index fingertip of right hand. Brain signals were analyzed using a searchlight MVPA approach [3] and decoding accuracy of each significant cluster was obtained. Finally, we evaluated a correlation between the JND and the decoding accuracy using the Pearson correlation coefficient. Results: A random-effects group analysis revealed that four clusters exhibited statistically significant decoding capabilities to differentiate five distinct roughness levels (p<0.0001 uncorr., cluster size>30). These four clusters were located in the superior portion of the bilateral temporal pole (STP), supplementary motor area (SMA), and contralateral postcentral gyrus (S1). Decoding accuracies for roughness discrimination were significantly exceeded the chance level (=20\%) for every clusters (SMA: 40.3±4.4\%; contralateral S1: 38.0±6.7\%; contralateral STP: 33.6±4.7\%; ipsilateral STP: 33.1±3.7\%). Among these clusters, the significant Pearson correlation coefficient was obtained only for SMA (r=-0.547, p<0.05). Conclusions: In this study, we statistically assessed each set of multi-voxel patterns across the whole brain and revealed that bilateral STP, SMA, and contralateral S1 exhibited neural activity patterns specific to the roughness discrimination. Remarkably, decoding performance using SMA activity showed a significant correlation with the behavioral performance. The negative correlation in SMA indicates that individuals with higher decoding accuracy of roughness from SMA also show better performance in a roughness discrimination task. Our findings suggest that the pattern of activity in SMA may be closely related to the ability to discriminate tactile roughness.}, department = {Department B{\"u}lthoff}, web_url = {https://www.humanbrainmapping.org/i4a/pages/index.cfm?pageID=3565}, event_place = {Hamburg, Germany}, event_name = {20th Annual Meeting of the Organization for Human Brain Mapping (OHBM 2014)}, author = {Kim, J and B{\"u}lthoff, HH and Kim, S-P and Chung, YG and Han, SW and Chung, S-C and Park, J-Y} } @Poster { KimSRWWB2014, title = {Supramodal Representations of Associated Emotions}, year = {2014}, month = {6}, department = {Department B{\"u}lthoff}, web_url = {http://brain.korea.ac.kr/bce2014/?m=program}, event_place = {T{\"u}bingen, Germany}, event_name = {6th International Conference on Brain and Cognitive Engineering (BCE 2014)}, author = {Kim, J and Schultz, J and Rohe, T and Wallraven, C and W, S and B{\"u}lthoff, HH} }