Informed feature regularization in voxelwise modeling for naturalistic fMRI experiments.
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| Title: | Informed feature regularization in voxelwise modeling for naturalistic fMRI experiments. |
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| 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] |
| Copyright of European Journal of Neuroscience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 147889657 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Informed feature regularization in voxelwise modeling for naturalistic fMRI experiments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yılmaz%2C+Özgür%22">Yılmaz, Özgür</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Çelik%2C+Emin%22">Çelik, Emin</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Çukur%2C+Tolga%22">Çukur, Tolga</searchLink> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Neuroscience%22">European Journal of Neuroscience</searchLink>. Sep2020, Vol. 52 Issue 5, p3394-3410. 17p. 1 Color Photograph, 7 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Functional+magnetic+resonance+imaging%22">Functional magnetic resonance imaging</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of European Journal of Neuroscience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=147889657 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/ejn.14760 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 3394 Subjects: – SubjectFull: Functional magnetic resonance imaging Type: general Titles: – TitleFull: Informed feature regularization in voxelwise modeling for naturalistic fMRI experiments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yılmaz, Özgür – PersonEntity: Name: NameFull: Çelik, Emin – PersonEntity: Name: NameFull: Çukur, Tolga IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 09 Text: Sep2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 0953816X Numbering: – Type: volume Value: 52 – Type: issue Value: 5 Titles: – TitleFull: European Journal of Neuroscience Type: main |
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