Lee Eunji, Kim Ji-Hyun, Park Jaeseok, Kim Sung-Phil, Shin Taehoon
Department of Mechanical and Biomedical Engineering, Ewha W. University, Seoul, Republic of Korea.
Graduate Program in Smart Factory, Ewha W. University, Seoul, Republic of Korea.
Front Neurosci. 2025 Jun 19;19:1606801. doi: 10.3389/fnins.2025.1606801. eCollection 2025.
Aristotle illusion is a well-known tactile illusion which causes the perception of one object as two. EEG analysis was employed to investigate the neural correlates of Aristotle illusion, yet was limited due to low spatial resolution of EEG. This study aimed to identify brain regions involved in the Aristotle illusion using functional magnetic resonance imaging (fMRI) and deep learning-based analysis of fMRI data.
While three types of tactile stimuli (Aristotle, Reverse, Asynchronous) were applied to thirty participants' fingers, we collected fMRI data, and recorded the number of stimuli each participant perceived. Four convolutional neural network (CNN) models were trained for perception-based classification tasks (the occurrence of Aristotle illusion vs. Reverse illusion, the occurrence vs. absence of Reverse illusion), and stimulus-based classification tasks (Aristotle vs. Reverse, Reverse vs. Asynchronous, and Aristotle vs. Asynchronous).
Simple fully convolution network (SFCN) achieved the highest classification accuracy of 68.4% for the occurrence of Aristotle illusion vs. Reverse illusion, and 80.1% for the occurrence vs. absence of Reverse illusion. For stimulus-based classification tasks, all CNN models yielded accuracies around 50% failing to distinguish among the three types of applied stimuli. Gradient-weighted class activation mapping (Grad-CAM) analysis revealed salient brain regions-of-interest (ROIs) for the perception-based classification tasks, including the somatosensory cortex and parietal regions.
Our findings demonstrate that perception-driven neural responses are classifiable using fMRI-based CNN models. Saliency analysis of the trained CNNs reveals the involvement of the somatosensory cortex and parietal regions in making classification decisions, consistent with previous research. Other salient ROIs include orbitofrontal cortex, middle temporal pole, supplementary motor area, and middle cingulate cortex.
亚里士多德错觉是一种著名的触觉错觉,会导致将一个物体感知为两个。脑电图(EEG)分析曾被用于研究亚里士多德错觉的神经关联,但由于EEG的空间分辨率较低而受到限制。本研究旨在使用功能磁共振成像(fMRI)和基于深度学习的fMRI数据分析来确定参与亚里士多德错觉的脑区。
在对30名参与者的手指施加三种类型的触觉刺激(亚里士多德错觉、反向错觉、异步错觉)时,我们收集了fMRI数据,并记录了每个参与者感知到的刺激数量。训练了四个卷积神经网络(CNN)模型用于基于感知的分类任务(亚里士多德错觉与反向错觉的发生、反向错觉的发生与不发生)以及基于刺激的分类任务(亚里士多德错觉与反向错觉、反向错觉与异步错觉、亚里士多德错觉与异步错觉)。
简单全卷积网络(SFCN)在亚里士多德错觉与反向错觉发生的分类任务中达到了最高分类准确率68.4%,在反向错觉发生与不发生的分类任务中达到了80.1%。对于基于刺激的分类任务,所有CNN模型的准确率均在50%左右,无法区分三种应用刺激。梯度加权类激活映射(Grad-CAM)分析揭示了基于感知的分类任务中显著的脑区兴趣点(ROI),包括体感皮层和顶叶区域。
我们的研究结果表明,基于fMRI的CNN模型可对感知驱动的神经反应进行分类。对训练后的CNN进行的显著性分析揭示了体感皮层和顶叶区域参与了分类决策,这与先前的研究一致。其他显著的ROI包括眶额皮层、颞中极、辅助运动区和扣带中央回。