面向认知表现预测的时-空共变混合深度学习模型

Spatio-temporal Co-variant Hybrid Deep Learning Framework for Cognitive Performance Prediction

认知表现预测已经成为当前大脑研究的重要课题.功能磁共振成像技术由于同时具有较好的时间和空间分辨率,有潜力为认知表现预测提供数据支持.为了解决基于功能磁共振成像数据对认知表现进行预测时大脑所具有的时-空共变难刻画问题,提出了一种新型基于大脑学习机制的时-空共变混合深度学习模型,即深度稀疏自编码器与循环全连接网络混合模型,以混合神经网络模型的损失函数误差作为认知表现预测能力的评价标准.在人类连接组项目数据集上的实验结果表明,提出的时-空共变混合模型能够有效和稳健地预测认知表现,并提取到与人脑学习、记忆相关的有意义的脑影像特征,从而为认知表现预测提供技术支持.

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.