用于fMRI数据分析的信息辅助词典学习
Information Assisted Dictionary Learning for fMRI data analysis
摘要：
在本文中，任务相关的功能磁共振成像问题的矩阵分解制定，重点是字典学习（DL）的方法。新方法允许将与试验设计以及与可用的脑损伤相关的先验知识结合起来。此外，所提出的方法可以有效地处理与HRF建模的不确定性。此外，所提出的方法绕过了与DL方法相关的主要缺点之一，即稀疏相关正则化参数的选择。在我们的配方中，另一种稀疏促进约束，承担的空间地图中的体素的数量有直接关系。因此，可以使用从大脑图谱中获得的信息来调整相关参数。所提出的方法进行评估对其他几个流行的技术，包括GLM。通过一个新的现实的合成功能磁共振成像数据集，以及真实的数据，是一个具有挑战性的试验设计报告所获得的性能增益。
英文摘要：
In this paper, the task-related fMRI problem is treated in its matrix factorization formulation, focused on the Dictionary Learning (DL) approach. The new method allows the incorporation of a priori knowledge associated both with the experimental design as well as with available brain Atlases. Moreover, the proposed method can efficiently cope with uncertainties related to the HRF modeling. In addition, the proposed method bypasses one of the major drawbacks that are associated with DL methods; that is, the selection of the sparsity-related regularization parameters. In our formulation, an alternative sparsity promoting constraint is employed, that bears a direct relation to the number of voxels in the spatial maps. Hence, the related parameters can be tuned using information that is available from brain atlases. The proposed method is evaluated against several other popular techniques, including GLM. The obtained performance gains are reported via a novel realistic synthetic fMRI dataset as well as real data that are related to a challenging experimental design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
