Çelik, Emin2022-01-192022-01-192021-122021-122022-01-14http://hdl.handle.net/11693/76750Cataloged from PDF version of article.Thesis (Ph.D.): Bilkent University, Department of Neuroscience, İhsan Doğramacı Bilkent University, 2021.Includes bibliographical references (leaves 92-116).Humans 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.xxiv, 116 leaves : illustrations, charts ; 30 cm.Englishinfo:eu-repo/semantics/openAccessfMRIVoxelwise modelingComputational neuroscienceSpatial regu-larizationCoherent representationDynamic scene category representationClus-ter analysisThesisB016172