面向认知表现预测的时−空共变混合深度学习模型

Cognitive performance prediction has been an important topic for brain research. Functional magnetic resonance imaging is with high resolution in both spatial and temporal dimensions, which has the potential to support cognitive performance prediction. In order to address the problem that it is hard to characterize the spatiotemporal co-variation of brain data when predicting cognitive performance with functional magnetic resonance imaging data, inspired by the brain learning mechanism, a novel spatio-temporal co-variant hybrid deep learning framework has been presented here for evaluation the cognitive performance prediction, named as deep sparse recurrent autoencoder-recurrent fully connected net, to jointly minimize the loss function of the hybrid neural network models. The experimental results on the Human Connectome Project data set have shown that our proposed framework can predict cognitive performance and learn brain studying and memory-related neuroimaging features effectively and robustly, which can support predicting cognitive performance effectively.