Abstract: Functional MRI brain map is characterized by precise positioning,but low signal-to-noise ratio and large volume of data.We analyse the hyper-parameter optimization of general regression model on fMRI brain data,based on the non-homogeneity validation method.To acknowledge the discrepancy in ability of different regression approaches（including ridge regression,support vector regression and Elman Recurrent Neural Network） we compare them over every feature rating of external environment.The original data is obtained from PBAIC2006.The experiment results show that this method has good stability and generalization;also it can be used in the field which lacks of knowledge of the relevant fields.At the same time,we find the linear method is simple,effective in the fMRI regression mission,and has higher predictive ability but lower calculating costs than other methods under many feature rating of external environment.

摘要： 脑功能核磁共振图像fMRI的特点是定位准确,但信噪比低、数据量大。对fMRI数据的泛回归模型的超参数寻优问题作了分析,提出基于非同质检验的超参数确认方法,重点比较了它在线性和非线性的回归方式（包括岭回归,支持向量回归,Elman递归神经网络）下针对不同外界环境特征的回归能力差异,实验所采用原始数据均来自PBAIC2006,结果表明,该方法在对相关领域知识较少依赖的前提下,具有较好的稳定性和泛化能力;同时在所涉及到的回归方法当中,线性方法的实现简单、有效,在计算代价上低于其他方法,对多种外界特征具有较高的预测能力。