用于编码和解码模型的数据驱动的HRF估计
Data-driven HRF estimation for encoding and decoding models
摘要：
尽管通常使用规范的、数据独立的血液动力学响应函数（HRF），但是已知HRF的形状在脑区域和受试者之间变化。这表明，在对BOLD fMRI数据进行建模时，对该函数的数据驱动估计可能会导致更多的统计功效。然而，无约束估计的HRF可以产生高度不稳定的结果时，自由参数的数量是大的。我们开发了一种方法，用于联合估计的激活和HRF使用秩约束，使估计的HRF是平等的事件/条件，但允许它是不同的体素。模型估计导致一个优化问题，我们建议解决一个有效的拟牛顿法利用快速梯度计算。该模型被称为具有秩-1约束的GLM（R1-GLM），可以扩展到具有单独设计的GLM的设置，这已被证明可以提高大脑活动解码实验中的解码精度。我们比较了10种不同的HRF建模方法在两个不同的数据集的编码和解码得分。我们的研究结果表明，R1-GLM模型显着优于竞争的方法在编码和解码设置，定位它作为一个有吸引力的方法，无论是从精度和计算效率的角度来看。
英文摘要：
Despite the common usage of a canonical, data-independent, hemodynamic response function (HRF), it is known that the shape of the HRF varies across brain regions and subjects. This suggests that a data-driven estimation of this function could lead to more statistical power when modeling BOLD fMRI data. However, unconstrained estimation of the HRF can yield highly unstable results when the number of free parameters is large. We develop a method for the joint estimation of activation and HRF using a rank constraint causing the estimated HRF to be equal across events/conditions, yet permitting it to be different across voxels. Model estimation leads to an optimization problem that we propose to solve with an efficient quasi-Newton method exploiting fast gradient computations. This model, called GLM with Rank-1 constraint (R1-GLM), can be extended to the setting of GLM with separate designs which has been shown to improve decoding accuracy in brain activity decoding experiments. We compare 10 different HRF modeling methods in terms of encoding and decoding score in two different datasets. Our results show that the R1-GLM model significantly outperforms competing methods in both encoding and decoding settings, positioning it as an attractive method both from the points of view of accuracy and computational efficiency.
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