Zebrafish Differentially Process Color across Visual Space to Match Natural Scenes

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Dissociation between Dorsal and Ventral Posterior Parietal Cortical Responses to Incidental Changes in Natural Scenes

Background

The posterior parietal cortex (PPC) is thought to interact with the medial temporal lobe (MTL) to support spatial cognition and topographical memory. While the response of medial temporal lobe regions to topographical stimuli has been intensively studied, much less research has focused on the role of PPC and its functional connectivity with the medial temporal lobe.

Methodology/Principle Findings

Here we report a dissociation between dorsal and ventral regions of PPC in response to different types of change in natural scenes using an fMRI adaptation paradigm. During scanning subjects performed an incidental target detection task whilst viewing trial unique sequentially presented pairs of natural scenes, each containing a single prominent object. We observed a dissociation between the superior parietal gyrus and the angular gyrus, with the former showing greater sensitivity to spatial change, and the latter showing greater sensitivity to scene novelty. In addition, we observed that the parahippocampal cortex has increased functional connectivity with the angular gyrus, but not superior parietal gyrus, when subjects view change to the scene content.

Conclusions/Significance

Our findings provide support for proposed dissociations between dorsal and ventral regions of PPC and suggest that the dorsal PPC may support the spatial coding of the visual environment even when this information is incidental to the task at hand. Further, through revealing the differential functional interactions of the SPG and AG with the MTL our results help advance our understanding of how the MTL and PPC cooperate to update representations of the world around us.     

Timing Precision in Population Coding of Natural Scenes in the Early Visual System

The timing of spiking activity across neurons is a fundamental aspect of the neural population code. Individual neurons in the retina, thalamus, and cortex can have very precise and repeatable responses but exhibit degraded temporal precision in response to suboptimal stimuli. To investigate the functional implications for neural populations in natural conditions, we recorded in vivo the simultaneous responses, to movies of natural scenes, of multiple thalamic neurons likely converging to a common neuronal target in primary visual cortex. We show that the response of individual neurons is less precise at lower contrast, but that spike timing precision across neurons is relatively insensitive to global changes in visual contrast. Overall, spike timing precision within and across cells is on the order of 10 ms. Since closely timed spikes are more efficient in inducing a spike in downstream cortical neurons, and since fine temporal precision is necessary to represent the more slowly varying natural environment, we argue that preserving relative spike timing at a ~10-ms resolution is a crucial property of the neural code entering cortex.     

Two-Variance-Component Model Improves Genetic Prediction in Family Datasets

Genetic prediction based on either identity by state (IBS) sharing or pedigree information has been investigated extensively with best linear unbiased prediction (BLUP) methods. Such methods were pioneered in plant and animal-breeding literature and have since been applied to predict human traits, with the aim of eventual clinical utility. However, methods to combine IBS sharing and pedigree information for genetic prediction in humans have not been explored. We introduce a two-variance-component model for genetic prediction: one component for IBS sharing and one for approximate pedigree structure, both estimated with genetic markers. In simulations using real genotypes from the Candidate-gene Association Resource (CARe) and Framingham Heart Study (FHS) family cohorts, we demonstrate that the two-variance-component model achieves gains in prediction r² over standard BLUP at current sample sizes, and we project, based on simulations, that these gains will continue to hold at larger sample sizes. Accordingly, in analyses of four quantitative phenotypes from CARe and two quantitative phenotypes from FHS, the two-variance-component model significantly improves prediction r² in each case, with up to a 20% relative improvement. We also find that standard mixed-model association tests can produce inflated test statistics in datasets with related individuals, whereas the two-variance-component model corrects for inflation.     

Unsupervised Feature Learning Improves Prediction of Human Brain Activity in Response to Natural Images

Encoding and decoding in functional magnetic resonance imaging has recently emerged as an area of research to noninvasively characterize the relationship between stimulus features and human brain activity. To overcome the challenge of formalizing what stimulus features should modulate single voxel responses, we introduce a general approach for making directly testable predictions of single voxel responses to statistically adapted representations of ecologically valid stimuli. These representations are learned from unlabeled data without supervision. Our approach is validated using a parsimonious computational model of (i) how early visual cortical representations are adapted to statistical regularities in natural images and (ii) how populations of these representations are pooled by single voxels. This computational model is used to predict single voxel responses to natural images and identify natural images from stimulus-evoked multiple voxel responses. We show that statistically adapted low-level sparse and invariant representations of natural images better span the space of early visual cortical representations and can be more effectively exploited in stimulus identification than hand-designed Gabor wavelets. Our results demonstrate the potential of our approach to better probe unknown cortical representations.     

Neural Responses in Parietal and Occipital Areas in Response to Visual Events Are Modulated by Prior Multisensory Stimuli

The effect of multi-modal vs uni-modal prior stimuli on the subsequent processing of a simple flash stimulus was studied in the context of the audio-visual ‘flash-beep’ illusion, in which the number of flashes a person sees is influenced by accompanying beep stimuli. EEG recordings were made while combinations of simple visual and audio-visual stimuli were presented. The experiments found that the electric field strength related to a flash stimulus was stronger when it was preceded by a multi-modal flash/beep stimulus, compared to when it was preceded by another uni-modal flash stimulus. This difference was found to be significant in two distinct timeframes – an early timeframe, from 130–160 ms, and a late timeframe, from 300–320 ms. Source localisation analysis found that the increased activity in the early interval was localised to an area centred on the inferior and superior parietal lobes, whereas the later increase was associated with stronger activity in an area centred on primary and secondary visual cortex, in the occipital lobe. The results suggest that processing of a visual stimulus can be affected by the presence of an immediately prior multisensory event. Relatively long-lasting interactions generated by the initial auditory and visual stimuli altered the processing of a subsequent visual stimulus.     

Categorization of Natural Dynamic Audiovisual Scenes

This work analyzed the perceptual attributes of natural dynamic audiovisual scenes. We presented thirty participants with 19 natural scenes in a similarity categorization task, followed by a semi-structured interview. The scenes were reproduced with an immersive audiovisual display. Natural scene perception has been studied mainly with unimodal settings, which have identified motion as one of the most salient attributes related to visual scenes, and sound intensity along with pitch trajectories related to auditory scenes. However, controlled laboratory experiments with natural multimodal stimuli are still scarce. Our results show that humans pay attention to similar perceptual attributes in natural scenes, and a two-dimensional perceptual map of the stimulus scenes and perceptual attributes was obtained in this work. The exploratory results show the amount of movement, perceived noisiness, and eventfulness of the scene to be the most important perceptual attributes in naturalistically reproduced real-world urban environments. We found the scene gist properties openness and expansion to remain as important factors in scenes with no salient auditory or visual events. We propose that the study of scene perception should move forward to understand better the processes behind multimodal scene processing in real-world environments. We publish our stimulus scenes as spherical video recordings and sound field recordings in a publicly available database.     

A Model of Self-Organizing Head-Centered Visual Responses in Primate Parietal Areas

We present a hypothesis for how head-centered visual representations in primate parietal areas could self-organize through visually-guided learning, and test this hypothesis using a neural network model. The model consists of a competitive output layer of neurons that receives afferent synaptic connections from a population of input neurons with eye position gain modulated retinal receptive fields. The synaptic connections in the model are trained with an associative trace learning rule which has the effect of encouraging output neurons to learn to respond to subsets of input patterns that tend to occur close together in time. This network architecture and synaptic learning rule is hypothesized to promote the development of head-centered output neurons during periods of time when the head remains fixed while the eyes move. This hypothesis is demonstrated to be feasible, and each of the core model components described is tested and found to be individually necessary for successful self-organization.     

Cerebral Asymmetry of fMRI-BOLD Responses to Visual Stimulation

Hemispheric asymmetry of a wide range of functions is a hallmark of the human brain. The visual system has traditionally been thought of as symmetrically distributed in the brain, but a growing body of evidence has challenged this view. Some highly specific visual tasks have been shown to depend on hemispheric specialization. However, the possible lateralization of cerebral responses to a simple checkerboard visual stimulation has not been a focus of previous studies. To investigate this, we performed two sessions of blood-oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) in 54 healthy subjects during stimulation with a black and white checkerboard visual stimulus. While carefully excluding possible non-physiological causes of left-to-right bias, we compared the activation of the left and the right cerebral hemispheres and related this to grey matter volume, handedness, age, gender, ocular dominance, interocular difference in visual acuity, as well as line-bisection performance. We found a general lateralization of cerebral activation towards the right hemisphere of early visual cortical areas and areas of higher-level visual processing, involved in visuospatial attention, especially in top-down (i.e., goal-oriented) attentional processing. This right hemisphere lateralization was partly, but not completely, explained by an increased grey matter volume in the right hemisphere of the early visual areas. Difference in activation of the superior parietal lobule was correlated with subject age, suggesting a shift towards the left hemisphere with increasing age. Our findings suggest a right-hemispheric dominance of these areas, which could lend support to the generally observed leftward visual attentional bias and to the left hemifield advantage for some visual perception tasks.     

A Model of Temporal Encoding of Stimulus Orientation by Neuronal Responses in the Primary Visual Cortex

It has been shown experimentally that the stimulus orientation that elicits the optimal response in an orientation column in the primary visual cortex (area V1) undergoes rapid systemic changes that last 10–100 ms. These changes allow different orientation columns to encode information from multiple items in the visual space (the so-called temporal encoding). However, the mechanism of these changes is still unknown. In addition, most of the modern biophysical models are unable to reproduce these changes; the peak orientation of their responses is constant over time. In this paper, we suggest a method to improve the firing-rate ring model of the orientation hypercolumn by replacing the spatial symmetric distribution of local connections with a spatial anti-symmetric distribution. As a result, we obtained a more perfect model that is capable of reproducing such changes. Moreover, their amplitude is proportional to the extent of asymmetry in the spatial distribution of local connections.     

Parametric Modeling of Visual Search Efficiency in Real Scenes

How should the efficiency of searching for real objects in real scenes be measured? Traditionally, when searching for artificial targets, e.g., letters or rectangles, among distractors, efficiency is measured by a reaction time (RT) × Set Size function. However, it is not clear whether the set size of real scenes is as effective a parameter for measuring search efficiency as the set size of artificial scenes. The present study investigated search efficiency in real scenes based on a combination of low-level features, e.g., visible size and target-flanker separation factors, and high-level features, e.g., category effect and target template. Visible size refers to the pixel number of visible parts of an object in a scene, whereas separation is defined as the sum of the flank distances from a target to the nearest distractors. During the experiment, observers searched for targets in various urban scenes, using pictures as the target templates. The results indicated that the effect of the set size in real scenes decreased according to the variances of other factors, e.g., visible size and separation. Increasing visible size and separation factors increased search efficiency. Based on these results, an RT × Visible Size × Separation function was proposed. These results suggest that the proposed function is a practicable predictor of search efficiency in real scenes.     

A Model for Responses to Activation by Axodendritic Synapses

A simple mathematical model of synaptic activation shows that the response to synaptic activation depends inversely on the size of the subsynaptic process. This provides a theoretical foundation for: the relationship between excitability and cell size; a possible source of plasticity in nerve cell behavior; and the hypothesis that postsynaptic responses to activation at axodendritic synapses are of large amplitude. The last-mentioned idea provides for flexible nonlinear interaction in dendritic regions because the diminution of postsynaptic potentials (PSPs) by prior potential becomes significant at high levels of depolarization. Digital-computer simulations of nerve cell input-output behavior for axodendritic activation based on these ideas reveal: frequency-transfer curves for axodendritic activation saturate; activations combined on different dendritic branches sum approximately linearly while those on the same branch occlude; simultaneous activation of several synapses on a previously inactive dendritic branch results in a large “peak” response at the onset of stimulation; and such an initial peak may be markedly mitigated by a prior depolarization of the branch. The third-mentioned finding may represent a widespread mode of hypersensitivity to stimulus onset in neural systems and in particular may contribute to the “on” responses of sensory channels, and the fourth suggests that depolarizing synapses at extreme peripheries of dendritic fibers might in some cases serve an inhibitory function.     

On the Contribution of Binocular Disparity to the Long-Term Memory for Natural Scenes

Binocular disparity is a fundamental dimension defining the input we receive from the visual world, along with luminance and chromaticity. In a memory task involving images of natural scenes we investigate whether binocular disparity enhances long-term visual memory. We found that forest images studied in the presence of disparity for relatively long times (7s) were remembered better as compared to 2D presentation. This enhancement was not evident for other categories of pictures, such as images containing cars and houses, which are mostly identified by the presence of distinctive artifacts rather than by their spatial layout. Evidence from a further experiment indicates that observers do not retain a trace of stereo presentation in long-term memory.     

Magnifying the Scale of Visual Biofeedback Improves Posture

Biofeedback has been shown to minimize body sway during quiet standing. However, limited research has reported the optimal sensitivity parameters of visual biofeedback related to the center of pressure (COP) sway. Accordingly, 19 young adults (6 males; 13 females; aged 21.3 ± 2.5) stood with feet together and performed three visual biofeedback intensities [unmodified biofeedback (UMBF), BF magnified by 5 (BF5), BF magnified by 10 (BF10)], along with control trials with no biofeedback (NBF). The participants were instructed to stand as still as possible while minimizing the movements of the visual target. The findings revealed that UMBF produced significantly greater COP displacement in both the anterior–posterior (AP) and medial–lateral directions, as well as greater standard deviation of the COP in the AP direction (p < 0.05). Additionally, NBF showed significantly greater 95 % area ellipse than the UMBF, BF5, and BF10 intensities (p < 0.001). Therefore, the most sensitive COP scales generated the least amount of postural sway. However, there were no significant differences on any of the COP measures between BF5 and BF10. This research provides insight with respect to the proper scale on which biofeedback should be given in order to improve postural control (i.e., BF5 or BF10).     

Racial Bias in Neural Empathic Responses to Pain

Recent studies have shown that perceiving the pain of others activates brain regions in the observer associated with both somatosensory and affective-motivational aspects of pain, principally involving regions of the anterior cingulate and anterior insula cortex. The degree of these empathic neural responses is modulated by racial bias, such that stronger neural activation is elicited by observing pain in people of the same racial group compared with people of another racial group. The aim of the present study was to examine whether a more general social group category, other than race, could similarly modulate neural empathic responses and perhaps account for the apparent racial bias reported in previous studies. Using a minimal group paradigm, we assigned participants to one of two mixed-race teams. We use the term race to refer to the Chinese or Caucasian appearance of faces and whether the ethnic group represented was the same or different from the appearance of the participant'' own face. Using fMRI, we measured neural empathic responses as participants observed members of their own group or other group, and members of their own race or other race, receiving either painful or non-painful touch. Participants showed clear group biases, with no significant effect of race, on behavioral measures of implicit (affective priming) and explicit group identification. Neural responses to observed pain in the anterior cingulate cortex, insula cortex, and somatosensory areas showed significantly greater activation when observing pain in own-race compared with other-race individuals, with no significant effect of minimal groups. These results suggest that racial bias in neural empathic responses is not influenced by minimal forms of group categorization, despite the clear association participants showed with in-group more than out-group members. We suggest that race may be an automatic and unconscious mechanism that drives the initial neural responses to observed pain in others.