受限波兹曼机联合稀疏近似的脑功能检测模型

Functional Connectivity Detection Method Based on Restricted Boltzmann Machine and Sparse Approximation

人脑功能连通性检测是神经科学研究的重要技术.使用受限制波兹曼机(Restricted Boltzmann Machine,RBM)对大量多被试功能磁共振(functional Magnetic Resonance Imaging,fMRI)数据进行建模可以检测人脑功能连接,但是不能有效检测单被试数据的功能连接.本文研究一种新颖的融合了稀疏近似与RBM技术的脑功能连通性检测模型,该模型充分利用fMRI数据的稀疏性,采用稀疏近似理论对fMRI数据进行空间域稀疏近似压缩,然后使用RBM建立模型,以检测脑功能连通性.实验结果表明,该融合模型可以有效地提取单被试数据的脑功能时间域混合模型及其相应的脑功能图谱,解决了RBM在单被试数据分析上的瓶颈.

The human brain functional connectivity detection is an important technique in neuroscience research. The restricted boltzmann machine （RBM）, modeling on a large amount of multi-subject functional magnetic resonance imaging （fMRI） data, it can discover the brain functional connectivity. However, the former method with restriction of the huge training data, it can not detect the functional connectivity on single-subject data effectively. In this research, a novel functional connectivity detection model taking advantage of the sparsity is presented, which is an effective combination of the spatial-domain sparse approximation theory and the RBM technique. The experimental results demonstrated that the proposed model could effectively discover both the temporal dynamic model and the corresponding spatial functional maps on the single-subject data, which settled the the bottleneck of RBM.