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Faces convey an assortment of emotional information via low and high spatial frequencies (LSFs and HSFs). However, there is no consensus on the role of particular spatial frequency (SF) information during facial fear processing. Comparison across studies is hampered by the high variability in cut-off values for demarcating the SF spectrum and by differences in task demands. We investigated which SF information is minimally required to rapidly detect briefly presented fearful faces in an implicit and automatic manner, by sweeping through an entire SF range without constraints of predefined cut-offs for LSFs and HSFs. We combined fast periodic visual stimulation with electroencephalography. We presented neutral faces at 6 ​Hz, periodically interleaved every 5th image with a fearful face, allowing us to quantify an objective neural index of fear discrimination at exactly 1.2 ​Hz. We started from a stimulus containing either only very low or very high SFs and gradually increased the SF content by adding higher or lower SF information, respectively, to reach the full SF spectrum over the course of 70 ​s. We found that faces require at least SF information higher than 5.93 cycles per image (cpi) to implicitly differentiate fearful from neutral faces. However, exclusive HSF faces, even in a restricted SF range between 94.82 and 189.63 cpi already carry the critical information to extract the emotional expression of the faces., Competing Interests: Declaration of competing interest None., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Several computational models explain how symmetry might be detected and represented in the human brain. However, while there is an abundance of psychophysical studies on symmetry detection and several neural studies showing where and when symmetry is detected in the brain, important questions remain about how this detection happens and how symmetric patterns are represented. We studied the representation of (vertical) symmetry in regions of the ventral visual stream, using multi-voxel pattern analyses (MVPA) and functional connectivity analyses. Our results suggest that neural representations gradually change throughout the ventral visual stream, from very similar part-based representations for symmetrical and asymmetrical stimuli in V1 and V2, over increasingly different representations for symmetrical and asymmetrical stimuli which are nevertheless still part-based in both V3 and V4, to a more holistic representation for symmetrical compared to asymmetrical stimuli in high-level LOC. This change in representations is accompanied by increased communication between left and right retinotopic areas, evidenced by higher interhemispheric functional connectivity during symmetry perception in areas V2 and V4., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Visual object perception is an important function in primates which can be fine-tuned by experience, even in adults. Which factors determine the regions and the neurons that are modified by learning is still unclear. Recently, it was proposed that the exact cortical focus and distribution of learning effects might depend upon the pre-learning mapping of relevant functional properties and how this mapping determines the informativeness of neural units for the stimuli and the task to be learned. From this hypothesis we would expect that visual experience would strengthen the pre-learning distributed functional map of the relevant distinctive object properties. Here we present a first test of this prediction in twelve human subjects who were trained in object categorization and differentiation, preceded and followed by a functional magnetic resonance imaging session. Specifically, training increased the distributed multi-voxel pattern information for trained object distinctions in object-selective cortex, resulting in a generalization from pre-training multi-voxel activity patterns to after-training activity patterns. Simulations show that the increased selectivity combined with the inter-session generalization is consistent with a training-induced strengthening of a pre-existing selectivity map. No training-related neural changes were detected in other regions. In sum, training to categorize or individuate objects strengthened pre-existing representations in human object-selective cortex, providing a first indication that the neuroanatomical distribution of learning effects depends upon the pre-learning mapping of visual object properties., (Copyright © 2015 Elsevier Inc. All rights reserved.)

To study the dynamics of contour integration in the human brain, we simultaneously acquired EEG and fMRI data while participants were engaged in a passive viewing task. The stimuli were Gabor arrays with some Gabor elements positioned on the contour of an embedded shape, in three conditions: with local and global structure (perfect contour alignment), with global structure only (orthogonal orientations interrupting the alignment), or without contour. By applying JointICA to the EEG and fMRI responses of the subjects, new insights could be obtained that cannot be derived from unimodal recordings. In particular, only in the global structure condition, an ERP peak around 300ms was identified that involved a loop from LOC to the early visual areas. This component can be interpreted as being related to the verification of the consistency of the different local elements with the globally defined shape, which is necessary when perfect local-to-global alignment is absent. By modifying JointICA, a quantitative comparison of brain regions and the time-course of their interplay were obtained between different conditions. More generally, we provide additional support for the presence of feedback loops from higher areas to lower level sensory regions., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Natural scenes typically contain multiple visual objects, often in interaction, such as when a bottle is used to fill a glass. Previous studies disagree about the representation of multiple objects and the role of object position herein, nor did they pinpoint the effect of potential interactions between the objects. In an fMRI study, we presented four single objects in two different positions and object pairs consisting of all possible combinations of the single objects. Objects pairs could form either a meaningful action configuration in which they interact with each other or a non-meaningful configuration. We found that for single objects and object pairs both identity and position were represented in multi-voxel activity patterns in LOC. The response patterns of object pairs were best predicted by a weighted average of the response patterns of the constituent objects, with the strongest single-object response (the max response) weighted more than the min response. The difference in weight between the max and the min object was larger for familiar action pairs than for other pairs when participants attended to the configuration. A weighted average thus relates the response patterns of object pairs to the response patterns of single objects, even when the objects interact., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Since several years, neuroscience research started to focus on multimodal approaches. One such multimodal approach is the combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). However, no standard integration procedure has been established so far. One promising data-driven approach consists of a joint decomposition of event-related potentials (ERPs) and fMRI maps derived from the response to a particular stimulus. Such an algorithm (joint independent component analysis or JointICA) has recently been proposed by Calhoun et al. (2006). This method provides sources with both a fine spatial and temporal resolution, and has shown to provide meaningful results. However, the algorithm's performance has not been fully characterized yet, and no procedure has been proposed to assess the quality of the decomposition. In this paper, we therefore try to answer why and how JointICA works. We show the performance of the algorithm on data obtained in a visual detection task, and compare the performance for EEG recorded simultaneously with fMRI data and for EEG recorded in a separate session (outside the scanner room). We perform several analyses in order to set the necessary conditions that lead to a sound decomposition, and to give additional insights for exploration in future studies. In that respect, we show how the algorithm behaves when different EEG electrodes are used and we test the robustness with respect to the number of subjects in the study. The performance of the algorithm in all the experiments is validated based on results from previous studies., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Efficient word reading depends on a left fusiform brain region, the Visual Word Form Area (VWFA). The internal organization of this brain area is currently unknown, as are the potential factors that might underlie this organization. Here we combine functional magnetic resonance imaging (fMRI) with multi-voxel pattern analyses (MVPA) to probe the internal organization of the VWFA. Our findings reveal distinct activation patterns in the VWFA associated with individual letter strings, as well as for pictures. This result demonstrates that the VWFA is organized in such a way that similar words (as well as pictures) activate neurons consistently, irrespective of their exact visual appearance or location. The activation patterns for letter strings were driven primarily by orthographic similarity, and we observed no effects of semantics or lexical status (words versus pseudowords). This effect of orthographic similarity was significant only in the VWFA and not in retinotopic areas and object-selective control regions. Given the relationships between the internal organization and how visual input is processed in a brain region, our findings provide important constraints for computational models of how visual word forms are represented in the brain., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Object knowledge is hierarchical. Several hypotheses have proposed that this property might be reflected in the spatial organization of ventral visual cortex. For example, all exemplars of a category might activate the same patches of cortex, but with a slightly different position of the peak of activation in each patch. According to this view, category selectivity would be organized at a larger spatial scale compared to exemplar selectivity. No empirical evidence for such proposals is available from experiments with human subjects. Here, we compare the relative scale of organization for category and exemplar selectivity in two datasets with two methods: (i) by investigating the previously reported beneficial effect of spatial smoothing of the fMRI data on the reliability of multi-voxel selectivity patterns; and (ii) by comparing the relative weight of lower and higher spatial frequencies in the spatial frequency spectrum of these selectivity patterns. The findings are consistent with the proposal that selectivity for stimulus properties that underlie finer distinctions between objects is organized at a finer scale than selectivity for stimulus properties that differentiate categories. This finding confirms the existence of multiple scales of organization in the ventral visual pathway., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Multimodal approaches are of growing interest in the study of neural processes. To this end much attention has been paid to the integration of electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data because of their complementary properties. However, the simultaneous acquisition of both types of data causes serious artifacts in the EEG, with amplitudes that may be much larger than those of EEG signals themselves. The most challenging of these artifacts is the ballistocardiogram (BCG) artifact, caused by pulse-related electrode movements inside the magnetic field. Despite numerous efforts to find a suitable approach to remove this artifact, still a considerable discrepancy exists between current EEG-fMRI studies. This paper attempts to clarify several methodological issues regarding the different approaches with an extensive validation based on event-related potentials (ERPs). More specifically, Optimal Basis Set (OBS) and Independent Component Analysis (ICA) based methods were investigated. Their validation was not only performed with measures known from previous studies on the average ERPs, but most attention was focused on task-related measures, including their use on trial-to-trial information. These more detailed validation criteria enabled us to find a clearer distinction between the most widely used cleaning methods. Both OBS and ICA proved to be able to yield equally good results. However, ICA methods needed more parameter tuning, thereby making OBS more robust and easy to use. Moreover, applying OBS prior to ICA can optimize the data quality even more, but caution is recommended since the effect of the additional ICA step may be strongly subject-dependent., (Copyright 2010 Elsevier Inc. All rights reserved.)

Previous studies have revealed regions in human visual cortex with a strong preference for faces, headless bodies, and buildings. We investigated whether the pattern of activity in these category-selective regions is related to more subordinate distinctions among objects. Our experiments included two types of faces (elderly faces and baby faces), body parts (hands and torsos), and buildings (rural buildings and skyscrapers). Multi-voxel pattern analyses revealed very clear differences in the activation pattern between hands and torsos, and smaller but significant differences in the activation pattern between the two face conditions and between the two building conditions. The subordinate specificity was very distributed, as all category-selective regions were most selective for the distinction between hands and torsos, independently from their preferred category. The selectivity for hands versus torsos was preserved across exemplars and image orientations in all category-selective regions, indicating that the distributed subordinate selectivity is related to relatively invariant and higher-order properties of the images., (Copyright 2009 Elsevier Inc. All rights reserved.)

To investigate the role of human fusiform gyrus in shape processing, we determined the effect of shape degradation on BOLD contrast in this region with fMRI during three tasks requiring subjects to determine either whether two successively presented nonsense shapes had the same global orientation (OR task); whether two successively presented meaningful objects belonged to the same basic level category (CAT task); or whether two successively presented objects represented the same exemplar of a category (EX task). On the behavioral level, shape degradation by locally shifting the pixels constituting the lines of stimuli had no effect on performance in the OR task, while it was detrimental to performance in the CAT and EX tasks. In comparison to the OR task, both the CAT and EX tasks were associated with activations in the occipitotemporal and parietal cortex. When shape degradation was applied, activation in the middle fusiform gyrus was reduced in all tasks. The occurrence of this effect in the OR task indicates that it is independent of memory representations. The persistence of the effect in both tasks that showed a behavioral effect of degradation suggests that it does not reflect the amount of shape processing performed on the stimuli, but rather the specificity of the final perceptual representation that can be built from the shape information that is available. Other studies have shown effects of stimulus familiarity and task requirements in the fusiform gyrus, suggesting that there is no need to assume different modules for perceptual representation and representation in memory., (Copyright 2000 Academic Press.)