Informed feature regularization in voxelwise modeling for naturalistic fMRI experiments.

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Bibliographic Details
Title:	Informed feature regularization in voxelwise modeling for naturalistic fMRI experiments.
Authors:	Yılmaz, Özgür (AUTHOR), Çelik, Emin (AUTHOR), Çukur, Tolga (AUTHOR)
Source:	European Journal of Neuroscience. Sep2020, Vol. 52 Issue 5, p3394-3410. 17p. 1 Color Photograph, 7 Graphs.
Subjects:	Functional magnetic resonance imaging
Abstract:	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. [ABSTRACT FROM AUTHOR]
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Database:	Psychology and Behavioral Sciences Collection

Description
Abstract:	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. [ABSTRACT FROM AUTHOR]
ISSN:	0953816X
DOI:	10.1111/ejn.14760