Kim Junsuk, Yeon Jiwon, Ryu Jaekyun, Park Jang-Yeon, Chung Soon-Cheol, Kim Sung-Phil
Department of Human Perception, Cognition and Action, Max Planck Institute for Biological CyberneticsTübingen, Germany.
Department of Brain and Cognitive Engineering, Korea UniversitySeoul, South Korea.
Front Hum Neurosci. 2017 Sep 4;11:445. doi: 10.3389/fnhum.2017.00445. eCollection 2017.
Our previous human fMRI study found brain activations correlated with tactile stickiness perception using the uni-variate general linear model (GLM) (Yeon et al., 2017). Here, we conducted an in-depth investigation on neural correlates of sticky sensations by employing a multivoxel pattern analysis (MVPA) on the same dataset. In particular, we statistically compared multi-variate neural activities in response to the three groups of sticky stimuli: A supra-threshold group including a set of sticky stimuli that evoked vivid sticky perception; an infra-threshold group including another set of sticky stimuli that barely evoked sticky perception; and a sham group including acrylic stimuli with no physically sticky property. Searchlight MVPAs were performed to search for local activity patterns carrying neural information of stickiness perception. Similar to the uni-variate GLM results, significant multi-variate neural activity patterns were identified in postcentral gyrus, subcortical (basal ganglia and thalamus), and insula areas (insula and adjacent areas). Moreover, MVPAs revealed that activity patterns in posterior parietal cortex discriminated the perceptual intensities of stickiness, which was not present in the uni-variate analysis. Next, we applied a principal component analysis (PCA) to the voxel response patterns within identified clusters so as to find low-dimensional neural representations of stickiness intensities. Follow-up clustering analyses clearly showed separate neural grouping configurations between the Supra- and Infra-threshold groups. Interestingly, this neural categorization was in line with the perceptual grouping pattern obtained from the psychophysical data. Our findings thus suggest that different stickiness intensities would elicit distinct neural activity patterns in the human brain and may provide a neural basis for the perception and categorization of tactile stickiness.
我们之前的人类功能磁共振成像研究使用单变量通用线性模型(GLM)发现了与触觉粘性感知相关的大脑激活(Yeon等人,2017年)。在此,我们通过对同一数据集进行多体素模式分析(MVPA),对粘性感觉的神经相关性进行了深入研究。具体而言,我们对三组粘性刺激的多变量神经活动进行了统计比较:一个超阈值组,包括一组能引起生动粘性感知的粘性刺激;一个亚阈值组,包括另一组几乎不会引起粘性感知的粘性刺激;以及一个假刺激组,包括没有物理粘性特性的丙烯酸刺激。进行了搜索光MVPA以寻找携带粘性感知神经信息的局部活动模式。与单变量GLM结果类似,在中央后回、皮层下(基底神经节和丘脑)以及脑岛区域(脑岛和相邻区域)发现了显著的多变量神经活动模式。此外,MVPA显示顶叶后皮质的活动模式区分了粘性的感知强度,这在单变量分析中并不存在。接下来,我们对已识别簇内的体素反应模式应用主成分分析(PCA),以找到粘性强度的低维神经表征。后续的聚类分析清楚地显示了超阈值组和亚阈值组之间不同的神经分组配置。有趣的是,这种神经分类与从心理物理学数据中获得的感知分组模式一致。因此,我们的研究结果表明,不同的粘性强度会在人脑中引发不同的神经活动模式,并可能为触觉粘性的感知和分类提供神经基础。