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Informed feature regularization in voxelwise modeling for naturalistic fMRI experiments.
European Journal of Neroscience ( IF 3.4 ) Pub Date : 2020-04-28 , DOI: 10.1111/ejn.14760
Özgür Yılmaz 1, 2 , Emin Çelik 1, 3 , Tolga Çukur 1, 2, 3
Affiliation  

Voxelwise modeling is a powerful framework to predict single-voxel functional selectivity for the stimulus features that exist in complex natural stimuli. Yet, because VM disregards potential correlations across stimulus features or neighboring voxels, it may yield suboptimal sensitivity in measuring functional selectivity in the presence of high levels of measurement noise. Here, we introduce a novel voxelwise modeling approach that simultaneously utilizes stimulus correlations in model features and response correlations among voxel neighborhoods. The proposed method performs feature and spatial regularization while still generating single-voxel response predictions. We demonstrated the performance of our approach on a functional magnetic resonance imaging dataset from a natural vision experiment. Compared to VM, the proposed method yields clear improvements in prediction performance, together with increased feature coherence and spatial coherence of voxelwise models. Overall, the proposed method can offer improved sensitivity in modeling of single voxels in naturalistic functional magnetic resonance imaging experiments.

中文翻译:

自然 fMRI 实验的体素建模中的知情特征正则化。

体素建模是一个强大的框架,用于预测复杂自然刺激中存在的刺激特征的单体素功能选择性。然而,由于 VM 忽略了刺激特征或相邻体素之间的潜在相关性,因此在存在高水平测量噪声的情况下,它在测量功能选择性时可能会产生次优灵敏度。在这里,我们介绍了一种新颖的体素建模方法,该方法同时利用模型特征中的刺激相关性和体素邻域之间的响应相关性。所提出的方法执行特征和空间正则化,同时仍然生成单体素响应预测。我们通过自然视觉实验展示了我们的方法在功能性磁共振成像数据集上的性能。与虚拟机相比,所提出的方法在预测性能方面产生了明显的改进,同时增加了体素模型的特征相干性和空间相干性。总体而言,所提出的方法可以提高自然功能磁共振成像实验中单个体素建模的灵敏度。
更新日期:2020-04-28
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