Browsing by Subject "fMRI"

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Open Access
A large video set of natural human actions for visual and cognitive neuroscience studies and its validation with fMRI
(MDPI, 2022-12-29) Ürgen, Burcu Ayşen; Nizamoğlu, Hilal; Eroğlu, Aslı; Orban, G. A.
The investigation of the perception of others’ actions and underlying neural mechanisms has been hampered by the lack of a comprehensive stimulus set covering the human behavioral repertoire. To fill this void, we present a video set showing 100 human actions recorded in natural settings, covering the human repertoire except for emotion-driven (e.g., sexual) actions and those involving implements (e.g., tools). We validated the set using fMRI and showed that observation of the 100 actions activated the well-established action observation network. We also quantified the videos’ low-level visual features (luminance, optic flow, and edges). Thus, this comprehensive video set is a valuable resource for perceptual and neuronal studies.
Open Access
Arithmetic and temporal transformations of working memory activation in subjects prone to psychosis
(2018-09) Baş, Timuçin
Working memory (WM) deficit is a well-studied cognitive impairment in psychosis which is stemming from various developmental abnormalities containing neurobiological heterogeneity. Recently, many studies have concluded that the WM impairment is a symptom which manifests itself before the onset of the disorder, but these studies mostly focused on the individuals at clinically high risk. The mild proneness to psychosis which develops during the adolescent period is not well understood and how the working memory is affected due to mild proneness to psychosis has not been elucidated heretofore. In this research, we aimed to examine the association between the mild proneness to psychosis and working memory processing. Thirty-two individuals were split in half as mildly prone to psychosis and not prone to psychosis based on the Structured Interview for Schizotypy (SIS-R). Each participant performed a robust working memory task which consists of computational and temporally varying information loads. The data were collected via a magnetic resonance imaging (MRI) scanner and analysed by applying a general linear model to detect altered working memory activations due to proneness to psychosis. We have observed that the processes requiring manipulation and rapid updating of the information are associated with a large network of prefrontal cortex and superior parietal lobule. The finding of this study suggests that the mild proneness to psychosis has affected the working memory weakly and that the alterations demonstrated in the prefrontal cortex and parietal lobules may be clinically relevant to psychosis.
Open Access
Behavioral and neural investigation on the effect of spatial attention on surround suppression
(2023-09) Kınıklıoğlu, Merve
When a visual stimulus is presented together with other stimuli surrounding it, behavioral sensitivity and neural responses may change, often reduce, compared to when the same stimulus is presented alone. This is commonly referred to as center-surround interaction or surround suppression, and it is one of the most fundamental mechanisms in biological vision. It is well documented that in motion perception, center-surround interaction is affected by the size and contrast of the stimulus. As the size of a drifting grating increases, motion direction discrimination performance, as well as neural activity in one of the main cortical motion processing areas, medial temporal complex (MT+), decreases if the grating has high contrast (surround suppression). Whereas, when the size increases within certain limits, both the discrimination performance and the neural activity in MT+ may increase if the grating has low contrast (surround facilitation). On the other hand, spatial attention is known to modulate surround suppression both in humans and non-human animals with static stimuli. No previous study, how-ever, has directly and systematically investigated the effect of the spatial extent of attention on surround suppression in human motion perception. The studies presented in this dissertation aim to investigate the effect of the extent of spatial attention on center-surround interaction in visual motion processing. In our experiments, we used two attention conditions and a novel stimulus design, where a ‘center’ and a ‘surround’ drifting grating were presented to the participants. Under one of the attention conditions, which we call the ‘narrow-attention’ condition, participants performed a task that limited their attention to the central part of the stimulus. Under the other attention condition, which we call the ‘wide-attention’ condition, participants performed tasks that required them to extend their attention to both the center and surround gratings. Using this experimental paradigm, we measured motion direction discrimination thresholds behaviorally and cortical activity with fMRI. Behaviorally, we found increased thresholds, that is, stronger surround suppression, under the wide attention condition. In the hu-man homolog of MT+ (hMT+), we found that increasing the spatial extent of attention leads to reduced cortical responses, that is, to stronger neural suppression. This was not the case for the activity in the primary visual cortex (V1). Finally, we show that a parsimonious computational model that incorporates spatial attention and response normalization can successfully predict the response patterns in hMT+ and V1. Furthermore, the model could provide a link between cortical responses and behavioral thresholds. Overall, our findings and analyses showed that the behavioral effect can be successfully predicted by hMT+ activity. These results reveal the critical role of spatial attention on surround suppression, namely that surround suppression in motion perception becomes stronger with a wider attention field, and reveal possible cortical mechanisms underpinning the effect.
Open Access
Biased competition in semantic representation during natural visual search
(Elsevier, 2020) Shahdloo, Mohammad; Çelik, Emin; Çukur, Tolga
Humans divide their attention among multiple visual targets in daily life, and visual search can get more difficult as the number of targets increases. The biased competition hypothesis (BC) has been put forth as an explanation for this phenomenon. BC suggests that brain responses during divided attention are a weighted linear combination of the responses during search for each target individually. This combination is assumed to be biased by the intrinsic selectivity of cortical regions. Yet, it is unknown whether attentional modulation of semantic representations are consistent with this hypothesis when viewing cluttered, dynamic natural scenes. Here, we investigated whether BC accounts for semantic representation during natural category-based visual search. Subjects viewed natural movies, and their whole-brain BOLD responses were recorded while they attended to “humans”, “vehicles” (i.e. single-target attention tasks), or “both humans and vehicles” (i.e. divided attention) in separate runs. We computed a voxelwise linearity index to assess whether semantic representation during divided attention can be modeled as a weighted combination of representations during the two single-target attention tasks. We then examined the bias in weights of this linear combination across cortical ROIs. We find that semantic representations of both target and nontarget categories during divided attention are linear to a substantial degree, and that they are biased toward the preferred target in category-selective areas across ventral temporal cortex. Taken together, these results suggest that the biased competition hypothesis is a compelling account for attentional modulation of semantic representations.
Open Access
Border ownership selectivity in human early visual cortex and its modulation by attention
(Society for Neuroscience, 2009) Fang, F.; Boyacı, Hüseyin; Kersten, D.
Natural images are usually cluttered because objects occlude one another. A critical aspect of recognizing these visual objects is to identify the borders between image regions that belong to different objects. However, the neural coding of border ownership in human visual cortex is largely unknown. In this study, we designed two simple but compelling stimuli in which a slight change of contextual information could induce a dramatic change of border ownership. Using functional MRI adaptation, we found that border ownership selectivity in V2 was robust and reliable across subjects, and it was largely dependent on attention. Our study provides the first human evidence that V2 is a critical area for the processing of border ownership and that this processing depends on the modulation from higher-level cortical areas.
Open Access
Cortical networks of dynamic scene category representation in the human brain
(Elsevier, 2021-07-24) Keleş, Ümit; Kiremitçi, İbrahim; Gallant, J. L.; Çukur, Tolga; Çelik, Emin
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. 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.
Open Access
Dynamic dot displays reveal material motion network in the human brain
(Elsevier BV, 2021-03) Schmid, A. C.; Boyacı, Hüseyin; Doerschner,Katja
There is growing research interest in the neural mechanisms underlying the recognition of material categories and properties. This research field, however, is relatively more recent and limited compared to investigations of the neural mechanisms underlying object and scene category recognition. Motion is particularly important for the perception of non-rigid materials, but the neural basis of non-rigid material motion remains unexplored. Using fMRI, we investigated which brain regions respond preferentially to material motion versus other types of motion. We introduce a new database of stimuli – dynamic dot materials – that are animations of moving dots that induce vivid percepts of various materials in motion, e.g. flapping cloth, liquid waves, wobbling jelly. Control stimuli were scrambled versions of these same animations and rigid three-dimensional rotating dots. Results showed that isolating material motion properties with dynamic dots (in contrast with other kinds of motion) activates a network of cortical regions in both ventral and dorsal visual pathways, including areas normally associated with the processing of surface properties and shape, and extending to somatosensory and premotor cortices. We suggest that such a widespread preference for material motion is due to strong associations between stimulus properties. For example viewing dots moving in a specific pattern not only elicits percepts of material motion; one perceives a flexible, non-rigid shape, identifies the object as a cloth flapping in the wind, infers the object's weight under gravity, and anticipates how it would feel to reach out and touch the material. These results are a first important step in mapping out the cortical architecture and dynamics in material-related motion processing.
Open Access
Effect of visual stimuli with fearful emotional cue on population receptive field estimates
(2019-07) Yılmaz, Cemre
Previous studies showed that the content of stimulus might affect the results of population receptive field (pRF) estimation method [1, 2, 3]. In addition, emotion might modulate visual processing in humans by increasing BOLD activity [4, 5]. Taken together, the stimulus with emotional cue might affect the pRF parameters. To investigate the effect of emotion on visual processing, we used the population receptive field (pRF) method [6], with simultaneous wedge and ring stimuli rendered with scrambled, neutral or emotional images. Results showed that the pRF estimations were affected by the stimulus content in visual areas hV4 and V3A, as well as lower retinotopic regions: V1, V2 and V3. Moreover, we showed the emotional content of stimulus might lead to the increased pRF sizes as well as a shift in pRF centers toward the eccentric side. We argue that the increased pRF size and the pRF shift might be a result of emotional modulation of visual processing.
Embargo
Effective connectivity in cortical regions during bottom-up perception of biological motion under attentional load: an FMRI-DCM study
(2024-07) Mert, Sezan
The ability to detect biological motion holds an evolutionarily important role in vital and social functions. However, in our daily lives, we perceive biological motion while we are at a task most of the time. In other words, it is perceived when our attention is directed at another thing. In this aspect, understanding the dynamics of its bottom-up perception is of high importance. Meanwhile, the attentional mechanisms and where their effects occur are a matter of debate in the literature, sparking off various theories, such as early selection, late selection, and attentional load theory. Dynamic causal modeling (DCM) is a suitable tool for investigating the dynamics of attentional effects on the network, enabling the bottom-up perception of biological motion, and comparing the existing theories in the literature with Bayesian graph models. To this end, we utilized the DCM approach with fMRI data collected using an attentional load paradigm and biological motion peripheral distractors [1]. In our model space, we modeled the theories of selective attention along with two complementary models. The Bayesian Model Selection (BMS) showed that the model that explained the data the best was the model where both attentional load conditions modulated all top-down connections rather than the models of existing theories. This showed that attentional effects take part in the bottom-up perception, not in a focused location, such as early or late, but in a more distributed manner throughout the processing pipeline. Further statistical tests on the model parameters yielded no difference between load conditions and between biological motion and scrambled motion in their modulation strengths. Yet, the strengths of biological motion on different connections were different from each other. A similar observation is also made for the low load condition but not for the scrambled motion and high load conditions. The former can be accepted as evidence for the differential processing of biological and scrambled motion. The latter may be explained by a spillover of perceptual resources on biological motion and causing competition in low-load conditions.
Open Access
Effects of auditory attention on language representation across the human brain
(2019-09) Yılmaz, Özgür
Humans 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.
Open Access
Employing transformer encoders for enhanced functional connectivity mapping
(IEEE - Institute of Electrical and Electronics Engineers, 2023-08-28) Bedel, Hasan Atakan; Çukur, Tolga
Functional magnetic resonance imaging (fMRI) provides a way to spatially and temporally map brain activity, making it a crucial tool in many advanced psychology and neuroscience studies. A variety of techniques are suggested to analyze the four-dimensional data produced by fMRI scans. When it comes to classification tasks, the most prevalent method involves examining functional connectivity. This process involves dividing the brain volume into separate regions and determining the correlation between the series of events occurring over time in these regions. While deep graph models and deep convolutional models are frequently employed to process functional connectivity, these methods can sometimes overcomplicate the procedure. In contrast, we present a straightforward approach that utilizes transformer encoders to map functional connectivity to labels. Our method demonstrates superior performance in gender classification tasks when compared to existing deep graph and convolution models. We've validated this on two publicly accessible datasets.
Open Access
Examining the effects of audiovisual associations on motion perception through task-based fMRI
(Yerkure Tanitim ve Yayincilik Hizmetleri A.S., 2018) Kafalıgönül, Hulusi
Examining the effects of audiovisual associations on motion perception through task-based fMRI Objective: Previous studies showed that associative learning can lead to drastic changes in perceptual experience and unexpected levels of sensory plasticity in the adult brain. However, how associative learning is involved in shaping perception and the underlying neural mechanisms are quite poorly understood. In the current study, by taking advantage of well-studied visual motion-processing hierarchy, the roles of different brain areas in audiovisual association-induced changes in motion perception are investigated. Method: Using a previously developed audiovisual associative paradigm, behavioral and Blood Oxygen Level Dependent (BOLD) data were collected from adult human participants (n=13) before and after the association phase. Behavioral data were collected through reports on visual motion direction. Functional magnetic resonance imaging (fMRI) was based on block design and the functional data were analyzed according to a general linear model. Results: Audiovisual associations, acquired within a short time and without any feedback, significantly affected the perception of motion direction. This effect was much more salient when the physical direction of visual motion was ambiguous. Moreover, fMRI findings pointed out that the BOLD activities across different cortical regions changed after the associative phase. Conclusion: Taken together, these findings indicate that low-level sensory, multisensory and high-level cognitive areas play a role in the effects of audiovisual associations on motion perception. In general, this suggests that our prior experiences acquired through associations may affect perceptual processing at different hierarchical levels and over different cortical areas.
Open Access
Full‐brain coverage and high‐resolution imaging capabilities of passband b‐SSFP fMRI at 3T
(Wiley‐Liss, Inc., 2008) Lee, J. H.; Dumoulin, S.; Sarıtaş, Emine Ülkü; Glover, G.; Wandell, B.; Nishimura, D.; Pauly, J.
Passband balanced-steady-state free precession (b-SSFP)fMRI is a recently developed method that utilizes the passband(ﬂat portion) of the b-SSFP off-resonance response to measureMR signal changes elicited by changes in tissue oxygenationfollowing increases in neuronal activity. Rapid refocusing andshort readout durations of b-SSFP, combined with the relativelylarge ﬂat portion of the b-SSFP off-resonance spectrum allowsdistortion-free full-brain coverage with only two acquisitions.This allows for high-resolution functional imaging, without thespatial distortion frequently encountered in conventional high-resolution functional images. Finally, the 3D imaging compati-bility of the b-SSFP acquisitions permits isotropic-voxel-sizehigh-resolution acquisitions. In this study we address some ofthe major technical issues involved in obtaining passband b-SSFP-based functional brain images with practical imaging pa-rameters and demonstrate the advantages through breath-holding and visual ﬁeld mapping experiments. Magn ResonMed 59:1099 –1110, 2008.
Open Access
Functional subdomains within scene-selective cortex: parahippocampal place area, retrosplenial complex, and occipital place area
(Society for Neuroscience, 2016) Çukur, Tolga; Huth, A. G.; Nishimoto, S.; Gallant, J. L.
Functional MRI studies suggest that at least three brain regions in human visual cortex-the parahippocampal place area (PPA), retrosplenial complex (RSC), and occipital place area (OPA; often called the transverse occipital sulcus)-represent large-scale information in natural scenes. Tuning of voxels within each region is often assumed to be functionally homogeneous. To test this assumption, we recorded blood oxygenation level-dependent responses during passive viewing of complex natural movies. We then used a voxelwise modeling framework to estimate voxelwise category tuning profiles within each scene-selective region. In all three regions, cluster analysis of the voxelwise tuning profiles reveals two functional subdomains that differ primarily in their responses to animals, man-made objects, social communication, and movement. Thus, the conventional functional definitions of the PPA, RSC, and OPA appear to be too coarse. One attractive hypothesis is that this consistent functional subdivision of scene-selective regions is a reflection of an underlying anatomical organization into two separate processing streams, one selectively biased toward static stimuli and one biased toward dynamic stimuli.
Open Access
A graphical network layer for lagged analysis of FMRI data
(IEEE, 2022-08-29) Bedel, Hasan Atakan; Şıvgın, Irmak; Çukur, Tolga
Functional magnetic resonance imaging (fMRI) enables recording the brain’s neural activity spatiotemporally and is the center of much cutting-edge psychology and neuroscience research. Many methods are proposed to process the 4-dimensional data the fMRI scans provide. The most common approach for classification tasks is to analyze functional connectivity, where brain volume is parcelled to regions, and the correlation between their time series is calculated. Such an approach is very suitable for graphical neural networks, a popular deep learning method for graphical data analysis. A graph is constructed by formulating the parcelled brain regions as the graph nodes, while their features and edges are constructed from the correlations. However, in many studies, the correlations are calculated from simple methods that do not take account of the lagged relations between the node time-series. This paper addresses this issue by proposing a new graphical neural network layer. This layer accounts for lagged relationships between the nodes and learns reacher features rather than simple zero-lag correlations. We show that our graphical layer can be used in front of a known graphical model and increase its performance for two different downstream tasks in a large fMRI dataset.
Open Access
Neural underpinnings of biological motion perception under attentional load
(2022-06) Çalışkan, Hilal Nizamoğlu
Humans can detect and differentiate biological motion from non-biological motion stimuli effortlessly, even if the stimuli were shown as simplistic as a composition of moving dots (i.e. point-light displays [PLD]). Considering its survival and social significance, BM perception is assumed to occur automatically. Indeed, Thorn-ton and Vuong [1] showed that task-irrelevant BM in the periphery interfered with task performance at the fovea. However, the neural underpinnings of this bottom-up processing of BM lacks thorough examination in the field. Under selec-tive attention, BM perception is supported by a network of regions including the occipito-temporal, parietal, and premotor cortices. A retinotopy mapping study on BM showed distinct maps for its processing under and away from selective attention [2]. Based on these findings, we investigated how bottom-up percep-tion of BM would be processed under attentional load when it was shown away from the focus of attention as a task-irrelevant stimulus. Participants (N=31) underwent an fMRI study in which they performed an attentionally demand-ing visual detection task at the fovea while intact or scrambled PLDs of BM were shown at the periphery. Our results showed the main effect of attentional load in fronto-parietal regions; as well as, the main effect of peripheral stimuli in occipito-temporal cortex. Both univariate and multivariate pattern analysis results support the attentional load modulation on BM. Lastly, ROI results on each core node of BM processing network expanded these findings by showing that the attentional load modulation on both intact and scrambled BM stimuli were the strongest in bilateral occipito-temporal regions as compared to parietal and premotor cortices. In conclusion, BM was processed within the motion sensi-tive regions in the occipito-temporal cortex when shown away from the selective attention, and was modulated by attentional load.
Open Access
Perception of built environments and its neural modulation by the behavioral goals of the perceiver
(2023-07) Koç, Aysu Nur
A scene is a view of an environment with a spatial layout one can act within. Scene perception has been studied extensively in the neuroscience literature, examining changes in neural activity across the brain and scene-selective regions (PPA, RSC, OPA), in response to various low and high-level features and tasks. The focus of the field has been mostly on outdoor scenes based on broad categorical differences (e.g. natural/man-made) or basic differences between otherwise similar indoor environments (e.g. ceiling height) and behavioral components regarding scene perception have been overlooked. Interactions with fields such as environ-mental psychology or neuroarchitecture, which could inspire a more ecologically valid study of scenes, are rare. Hereby, we investigated the perception of built environments where we spend most of our time, drew our categorization method from the architecture literature, and employed multiple tasks. The categories were elements that (i) allow our access to and circulation within environments (entrances, exits, corridors, stairs); and that (ii) do not directly aid locomotion but rather serve human needs (restrooms, eating and seating areas). FMRI scans were obtained from 23 participants as they viewed scenes from built environments and performed two tasks: a categorization task based on the main afforded action differences between the defined categories, and an approach-avoidance task where participants decided to enter the scene or not, measuring the initial action regarding an environment. Scene-selective ROIs were defined with a localizer session. Univariate analyses did not reveal strong differences between the tasks. Searchlight MVPA revealed categories, but not tasks, are classified at the whole-brain level, at the lingual and parahippocampal gyri, the SMA, and the occipital cortex. Model-based RSA at the ROI level revealed that tasks modulate activation patterns to built environments in all three ROIs, but do not entirely explain them, whereas categorical and visual models did not correlate with the activation patterns in any of these regions. We utilize an interdisciplinary perspective to scene perception to expand the ecological validity of the stimuli and task con-tent, showing that neural responses to built environments are modulated by the behavioral goals of the observer at the ROI level, and stimulus category at the whole-brain level.
Open Access
Predictive processing account of action perception: evidence from effective connectivity in the actionobservation network
(Elsevier, 2020) Ürgen, Burcu A.; Saygın, A. P.
Visual perception of actions is supported by a network of brain regions in the occipito-temporal, parietal, and premotor cortex in the primate brain, known as the Action Observation Network (AON). Although there is a growing body of research that characterizes the functional properties of each node of this network, the communication and direction of information flow between the nodes is unclear. According to the predictive coding account of action perception (Kilner, Friston, & Frith, 2007a; 2007b), this network is not a purely feedforward system but has backward connections through which prediction error signals are communicated between the regions of the AON. In the present study, we investigated the effective connectivity of the AON in an experimental setting where the human subjects' predictions about the observed agent were violated, using fMRI and Dynamical Causal Modeling (DCM). We specifically examined the influence of the lowest and highest nodes in the AON hierarchy, pSTS and ventral premotor cortex, respectively, on the middle node, inferior parietal cortex during prediction violation. Our DCM results suggest that the influence on the inferior parietal node is through a feedback connection from ventral premotor cortex during perception of actions that violate people's predictions.
Open Access
Preparing fMRI data for postprocessing: conversion modalities, preprocessing pipeline, and parametric and nonparametric approaches
(Institute of Electrical and Electronics Engineers Inc., 2019) Jaber, Hussain A.; Aljobouri, H. K.; Çankaya, İ.; Koçak, O. M.; Algın, Oktay
The complexity of raw functional magnetic resonance imaging (fMRI) data with artifacts leads to significant challenges in multioperations with these data. FMRI data analysis is extensively used in neuroimaging fields, but the tools for processing fMRI data are lacking. A novel APP DESIGNER conversion, preprocessing, and postprocessing of fMRI (CPREPP fMRI) tool is proposed and developed in this work. This toolbox is intended for pipeline fMRI data analysis, including full analysis of fMRI data, starting from DICOM conversion, then checking the quality of data at each step, and ending in postprocessing analysis. The CPREPP fMRI tool includes 12 conversions of scientific processes that reflect all conversion possibilities among them. In addition, specific preprocessing order steps are proposed on the basis of data acquisition mode (interleaved and sequential modes). A severe and crucial comparison between statistical parametric and nonparametric mapping approaches of second-level analysis is presented in the same tool. The CPREPP fMRI tool can provide reports to exclude subjects with the extreme movement of the head during the scan, and a range of fMRI images are generated to verify the normalization effect easily. Real fMRI data are used in this work to prepare fMRI data tests. The experiment stimuli are chewing and biting, and the data are acquired from the National Magnetic Resonance Research (UMRAM) Center in Ankara, Turkey. A free dataset is used to compare the methods for postprocessing fMRI tests.
Open Access
Spatially informed voxelwise modeling and dynamic scene category representation in the human brain
(2021-12) Çelik, Emin
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.