Nakamura Misaki, Yanagisawa Takufumi, Okamura Yumiko, Fukuma Ryohei, Hirata Masayuki, Araki Toshihiko, Kamitani Yukiyasu, Yorifuji Shiro
Department of Functional Diagnostic Science, Osaka University Graduate School of Medicine Suita, Japan.
Department of Functional Diagnostic Science, Osaka University Graduate School of Medicine Suita, Japan ; Department of Neurosurgery, Osaka University Graduate School of Medicine Suita, Japan ; Department of Neuroinformatics, ATR Computational Neuroscience Laboratories Kyoto, Japan ; Japan Science and Technology Agency, Precursory Research for Embryonic Science and Technology Osaka, Japan.
Front Hum Neurosci. 2015 Nov 4;9:609. doi: 10.3389/fnhum.2015.00609. eCollection 2015.
Humans recognize body parts in categories. Previous studies have shown that responses in the fusiform body area (FBA) and extrastriate body area (EBA) are evoked by the perception of the human body, when presented either as whole or as isolated parts. These responses occur approximately 190 ms after body images are visualized. The extent to which body-sensitive responses show specificity for different body part categories remains to be largely clarified. We used a decoding method to quantify neural responses associated with the perception of different categories of body parts. Nine subjects underwent measurements of their brain activities by magnetoencephalography (MEG) while viewing 14 images of feet, hands, mouths, and objects. We decoded categories of the presented images from the MEG signals using a support vector machine (SVM) and calculated their accuracy by 10-fold cross-validation. For each subject, a response that appeared to be a body-sensitive response was observed and the MEG signals corresponding to the three types of body categories were classified based on the signals in the occipitotemporal cortex. The accuracy in decoding body-part categories (with a peak at approximately 48%) was above chance (33.3%) and significantly higher than that for random categories. According to the time course and location, the responses are suggested to be body-sensitive and to include information regarding the body-part category. Finally, this non-invasive method can decode category information of a visual object with high temporal and spatial resolution and this result may have a significant impact in the field of brain-machine interface research.
人类以类别来识别身体部位。先前的研究表明，当人体以整体或孤立部分呈现时，梭状身体区（FBA）和纹外身体区（EBA）会因对人体的感知而产生反应。这些反应在身体图像可视化后约190毫秒出现。身体敏感反应对不同身体部位类别的特异性程度在很大程度上仍有待阐明。我们使用一种解码方法来量化与不同身体部位类别感知相关的神经反应。九名受试者在观看14张脚、手、嘴和物体的图像时，通过脑磁图（MEG）测量他们的大脑活动。我们使用支持向量机（SVM）从MEG信号中解码呈现图像的类别，并通过10倍交叉验证计算其准确率。对于每个受试者，观察到一种似乎是身体敏感的反应，并根据枕颞叶皮层中的信号对与三种身体类别相对应的MEG信号进行分类。解码身体部位类别的准确率（峰值约为48%）高于机遇水平（33.3%），且显著高于随机类别。根据时间进程和位置，这些反应被认为是身体敏感的，并包含有关身体部位类别的信息。最后，这种非侵入性方法可以以高时间和空间分辨率解码视觉对象的类别信息，这一结果可能会对脑机接口研究领域产生重大影响。
