Euler Elastica Regularized Logistic Regression for Whole-Brain Decoding of fMRI Data
Euler Elastica Regularized Logistic Regression for Whole-Brain Decoding of fMRI Data
复制标题
用于 fMRI 数据全脑解码的 Euler Elastica 正则逻辑回归
DOI：
10.1109/tbme.2017.2756665
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发表时间：
2018-07-01
影响因子：
4.6
通讯作者：
Long, Zhiying
中科院分区：
文献类型：
--
作者：
Zhang, Chuncheng;Yao, Li;Long, Zhiying
目的：多变量模式分析方法已被广泛应用于功能磁共振成像(FMRI)数据中以了解脑状态。由于fMRI数据具有高特征、低样本的特点，机器学习方法被广泛地正则化，使用各种正则化方法来避免过度拟合。利用图像梯度的全变分(TV)和利用图像的梯度和曲率的Euler弹性(EE)都是空间结构的两种常用规则。与TV相比，EE规则能够克服TV规则偏爱分段恒定图像而不是分段平滑图像的缺点。在本研究中，我们首次将EE引入到基于fMRI的解码中，并提出了用于多类分类的EE正则化多项Logistic回归(EELR)算法。方法：我们对模拟和真实的fMRI数据进行了实验测试，以考察EELR的可行性和稳健性。将EELR与稀疏Logistic回归(SLR)和TV正则化LR(TVLR)进行比较。结果：EELR比TVLR和SLR具有更强的抗噪能力和更好的分类性能。此外，正向模型和权重模式显示，与TVLR相比，EELR检测到更大的大脑区域，这些区域对每项任务具有辨别能力，并由每项任务激活。结论：EELR不仅在脑解码方面有较好的表现，而且显示出有意义的辨别和激活模式。意义：这项研究表明，EELR在大脑解码和识别/激活模式检测方面具有很好的潜力。
Objective: Multivariate pattern analysis methods have been widely applied to functional magnetic resonance imaging (fMRI) data to decode brain states. Due to the "high features, low samples" in fMRI data, machine learning methods have been widely regularized using various regularizations to avoid overfitting. Both total variation (TV) using the gradients of images and Euler's elastica (EE) using the gradient and the curvature of images are the two popular regulations with spatial structures. In contrast to TV, EE regulation is able to overcome the disadvantage of TV regulation that favored piecewise constant images over piecewise smooth images. In this study, we introduced EE to fMRI-based decoding for the first time and proposed the EE regularized multinomial logistic regression (EELR) algorithm for multi-class classification. Methods: We performed experimental tests on both simulated and real fMRI data to investigate the feasibility and robustness of EELR. The performance of EELR was compared with sparse logistic regression (SLR) and TV regularized LR (TVLR). Results: The results showed that EELR was more robustness to noises and showed significantly higher classification performance than TVLR and SLR. Moreover, the forward models and weights patterns revealed that EELR detected larger brain regions that were discriminative to each task and activated by each task than TVLR. Conclusion: The results suggest that EELR not only performs well in brain decoding but also reveals meaningful discriminative and activation patterns. Significance: This study demonstrated that EELR showed promising potential in brain decoding and discriminative/activation pattern detection.