Multivariate Pattern Analysis of fMRI Data Using Deep Neural Network

• Main Authors: Wang, Yi-Cheng, 王儀錚
• Other Authors: Chen,Yong-Sheng
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/uj48ud

碩士 === 國立交通大學 === 生醫工程研究所 === 106 === Multivariate pattern analysis (MVPA) has been widely used to reveal task-related information embedded in the spatial patterns of cortical activity. Compared to univariate approaches, multivariate methods combine signals from all voxels within regions of interest. In this thesis, we proposed a novel MVPA method based on deep neural networks. This method not only can obtain the higher classification accuracy than Support Vector Machine (SVM), but also can identify a group of voxels with discriminative capability. That is proposed method can obtain a global functional map which represents the information carried by the spatial pattern of activity within the whole brain. We applied proposed method to analyze a set of functional magnetic resonance imaging (fMRI) data and demonstrated the feasibility of the proposed method.