Browsing by Subject "Computational neuroscience"
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Item Open Access Effects of auditory attention on language representation across the human brain(Bilkent University, 2019-09) Yılmaz, ÖzgürHumans can effortlessly identify target auditory objects during natural listening and shift their focus between different targets. Unique allocation of brain resources would be inefficient for semantic search task. Here, we hypothesize that auditory attention shifts tuning of cortical voxels toward target category and that attention expands the representation of target words while compressing the representation of behaviorally irrelevant words across cortex. To test, we designed an fMRI experiment with a semantic search task. Subjects listened to natural stories twice while searching for words that are semantically related to either `humans' or `places'. Fit voxelwise models for two attention tasks were compared to identify semantic tuning shifts in single voxels. Results indicate that attention shifts semantic tuning of single voxels broadly across cortex and attention warps language representation in favor of target words across cortex. We also introduced a novel feature regularization in voxelwise modeling for a naturalistic movie experiment. Feature regularization simply enforces similar model weights over semantically related stimulus features. We tested the proposed method on an fMRI experiment with naturalistic movies. Results suggest that the proposed method offer improved sensitivity in modeling of single voxels. Moreover, we proposed a novel method to improve the sensitivity of phase-sensitive fatwater separation in balanced steady-state free precession (bSSFP) acquisitions. In bSSFP applications using phased-array coils, reconstructed images suffer a lot from spatial sensitivity variations within individual coils. To improve, we first performed region-growing phase correction in individual coil images, then used a linear combination of phase-corrected images. Tests on SSFP angiograms of the thigh, lower leg, and foot suggest that the proposed method enhances fat{water separation in phased-array acquisitions with improved phase estimates.Item Open Access Informed feature regularization in voxelwise modeling for naturalistic fMRI experiments(Wiley, 2020-04-21) Yılmaz, Özgür; Çelik, Emin; Çukur, TolgaVoxelwise 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.Item Open Access Spatially informed voxelwise modeling and dynamic scene category representation in the human brain(Bilkent University, 2021-12) Çelik, EminHumans 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.Item Open Access Spatially informed voxelwise modeling for naturalistic fMRI experiments(Elsevier, 2019) Çelik, Emin; Dar, Salman Ul Hassan; Yılmaz, Özgür; Keleş, Ümit; Çukur, TolgaVoxelwise 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 spatial neighborhoods of voxels. To optimally utilize shared information, SPIN-VM performs regularization across spatial neighborhoods in addition to model features, while still generating single-voxel response predictions. We demonstrated the performance of SPIN-VM on a rich dataset from a natural vision experiment. Compared to VM, SPIN-VM yields higher prediction accuracies and better capture locally congruent information representations across cortex. These results suggest that SPIN-VM offers improved performance in predicting single-voxel responses and recovering coherent information representations.