Informed feature regularization in voxelwise modeling for naturalistic fMRI experiments

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buir.contributor.authorYılmaz, Özgür
buir.contributor.authorÇelik, Emin
buir.contributor.authorÇukur, Tolga
dc.citation.epage3410en_US
dc.citation.issueNumber5en_US
dc.citation.spage3394en_US
dc.citation.volumeNumber52en_US
dc.contributor.authorYılmaz, Özgür
dc.contributor.authorÇelik, Emin
dc.contributor.authorÇukur, Tolga
dc.date.accessioned2021-02-18T11:07:15Z
dc.date.available2021-02-18T11:07:15Z
dc.date.issued2020-04-21
dc.departmentAysel Sabuncu Brain Research Center (BAM)en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.departmentNational Magnetic Resonance Research Center (UMRAM)en_US
dc.description.abstractVoxelwise 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.en_US
dc.embargo.release2021-04-21
dc.identifier.doi10.1111/ejn.14760en_US
dc.identifier.issn0953-816X
dc.identifier.urihttp://hdl.handle.net/11693/75450
dc.language.isoEnglishen_US
dc.publisherWileyen_US
dc.relation.isversionofhttps://doi.org/10.1111/ejn.14760en_US
dc.source.titleEuropean Journal of Neuroscienceen_US
dc.subjectComputational neuroscienceen_US
dc.subjectFeature regularizationen_US
dc.subjectModelingen_US
dc.subjectStimulus correlationen_US
dc.titleInformed feature regularization in voxelwise modeling for naturalistic fMRI experimentsen_US
dc.typeArticleen_US
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National Magnetic Resonance Research Center (UMRAM)
Aysel Sabuncu Brain Research Center (BAM)
Department of Electrical and Electronics Engineering