Spatially informed voxelwise modeling and dynamic scene category representation in the human brain

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buir.advisorÇukur, Tolga
dc.contributor.authorÇelik, Emin
dc.date.accessioned2022-01-19T05:39:34Z
dc.date.available2022-01-19T05:39:34Z
dc.date.copyright2021-12
dc.date.issued2021-12
dc.date.submitted2022-01-14
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Ph.D.): Bilkent University, Department of Neuroscience, İhsan Doğramacı Bilkent University, 2021.en_US
dc.descriptionIncludes bibliographical references (leaves 92-116).en_US
dc.description.abstractHumans have an impressive ability to rapidly process global information in natural scenes to infer their category. Yet, it remains unclear whether and how scene categories observed dynamically in the natural world are represented in cerebral cortex beyond few canonical scene-selective areas. To address this question, here we examined the representation of dynamic visual scenes by recording whole-brain blood oxygenation level-dependent (BOLD) responses while subjects viewed natural movies. We fit voxelwise encoding models to estimate tuning for scene categories that reflect statistical ensembles of objects and actions in the natural world. Voxelwise modeling (VM) is a powerful framework to predict single voxel responses evoked by a rich set of stimulus features present in complex natural stimuli. However, because VM disregards correlations across neighboring voxels, its sensitivity in detecting functional selectivity can be diminished in the presence of high levels of measurement noise. Here, we introduce spatially-informed voxelwise modeling (SPIN-VM) to take advantage of response correlations in spa-tial neighborhoods of voxels. To optimally utilize shared information, SPIN-VM performs regularization across spatial neighborhoods in addition to model fea-tures, while still generating single-voxel response predictions. Compared to VM, SPIN-VM yields higher prediction accuracies and better capture locally congruent information representations across cortex. We find that this scene-category model explains a significant portion of the response variance broadly across cerebral cortex. Cluster analysis of scene-category tuning profiles across cortex reveals nine spatially-segregated networks of brain regions consistently across subjects. These networks show heterogeneous tuning for a diverse set of dynamic scene categories related to navigation, human activity, social interaction, civilization, natural environment, non-human animals, motion-energy, and texture, suggesting that the organization of scene category representation is quite complex.en_US
dc.description.provenanceSubmitted by Betül Özen (ozen@bilkent.edu.tr) on 2022-01-19T05:39:34Z No. of bitstreams: 1 Spatially informed voxelwise.pdf: 27997245 bytes, checksum: 15b87da8e14027143977deb7fbaf42b9 (MD5)en
dc.description.provenanceMade available in DSpace on 2022-01-19T05:39:34Z (GMT). No. of bitstreams: 1 Spatially informed voxelwise.pdf: 27997245 bytes, checksum: 15b87da8e14027143977deb7fbaf42b9 (MD5) Previous issue date: 2021-12en
dc.description.statementofresponsibilityby Emin Çeliken_US
dc.format.extentxxiv, 116 leaves : illustrations, charts ; 30 cm.en_US
dc.identifier.itemidB016172
dc.identifier.urihttp://hdl.handle.net/11693/76750
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectfMRIen_US
dc.subjectVoxelwise modelingen_US
dc.subjectComputational neuroscienceen_US
dc.subjectSpatial regu-larizationen_US
dc.subjectCoherent representationen_US
dc.subjectDynamic scene category representationen_US
dc.subjectClus-ter analysisen_US
dc.titleSpatially informed voxelwise modeling and dynamic scene category representation in the human brainen_US
dc.title.alternativeUzaysal destekli voksel-bazlı modelleme ve insan beyninde dinamik sahne kategorisi temsilien_US
dc.typeThesisen_US
thesis.degree.disciplineNeuroscience
thesis.degree.grantorBilkent University
thesis.degree.levelDoctoral
thesis.degree.namePh.D. (Doctor of Philosophy)

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