Knowing with Which Eye We See: Utrocular Discrimination and Eye-Specific Signals in Human Visual Cortex

Neurophysiological and behavioral reports converge to suggest that monocular neurons in the primary visual cortex are biased toward low spatial frequencies, while binocular neurons favor high spatial frequencies. Here we tested this hypothesis with functional magnetic resonance imaging (fMRI). Human participants viewed flickering gratings at one of two spatial frequencies presented to either the left or the right eye, and judged which of the two eyes was being stimulated (utrocular discrimination). Using multivoxel pattern analysis we found that local spatial patterns of signals in primary visual cortex (V1) allowed successful decoding of the eye-of-origin. Decoding was above chance for low but not high spatial frequencies, confirming the presence of a bias reported by animal studies in human visual cortex. Behaviorally, we found that reliable judgment of the eye-of-origin did not depend on spatial frequency. We further analyzed the mean response in visual cortex to our stimuli and revealed a weak difference between left and right eye stimulation. Our results are thus consistent with the interpretation that participants use overall levels of neural activity in visual cortex, perhaps arising due to local luminance differences, to judge the eye-of-origin. Taken together, we show that it is possible to decode eye-specific voxel pattern information in visual cortex but, at least in healthy participants with normal binocular vision, these patterns are unrelated to awareness of which eye is being stimulated

The Brightness of Colour

Background: The perception of brightness depends on spatial context: the same stimulus can appear light or dark depending on what surrounds it. A less well-known but equally important contextual phenomenon is that the colour of a stimulus can also alter its brightness. Specifically, stimuli that are more saturated (i.e. purer in colour) appear brighter than stimuli that are less saturated at the same luminance. Similarly, stimuli that are red or blue appear brighter than equiluminant yellow and green stimuli. This non-linear relationship between stimulus intensity and brightness, called the Helmholtz-Kohlrausch (HK) effect, was first described in the nineteenth century but has never been explained. Here, we take advantage of the relative simplicity of this 'illusion' to explain it and contextual effects more generally, by using a simple Bayesian ideal observer model of the human visual ecology. We also use fMRI brain scans to identify the neural correlates of brightness without changing the spatial context of the stimulus, which has complicated the interpretation of related fMRI studies.Results: Rather than modelling human vision directly, we use a Bayesian ideal observer to model human visual ecology. We show that the HK effect is a result of encoding the non-linear statistical relationship between retinal images and natural scenes that would have been experienced by the human visual system in the past. We further show that the complexity of this relationship is due to the response functions of the cone photoreceptors, which themselves are thought to represent an efficient solution to encoding the statistics of images. Finally, we show that the locus of the response to the relationship between images and scenes lies in the primary visual cortex (V1), if not earlier in the visual system, since the brightness of colours (as opposed to their luminance) accords with activity in V1 as measured with fMRI.Conclusions: The data suggest that perceptions of brightness represent a robust visual response to the likely sources of stimuli, as determined, in this instance, by the known statistical relationship between scenes and their retinal responses. While the responses of the early visual system (receptors in this case) may represent specifically the statistics of images, post receptor responses are more likely represent the statistical relationship between images and scenes. A corollary of this suggestion is that the visual cortex is adapted to relate the retinal image to behaviour given the statistics of its past interactions with the sources of retinal images: the visual cortex is adapted to the signals it receives from the eyes, and not directly to the world beyond

Predominant pathogen competition and core microbiota divergence in chronic airway infection

© 2015 International Society for Microbial Ecology All rights reserved. Chronic bacterial lung infections associated with non-cystic fibrosis bronchiectasis represent a substantial and growing health-care burden. Where Pseudomonas aeruginosa is the numerically dominant species within these infections, prognosis is significantly worse. However, in many individuals, Haemophilus influenzae predominates, a scenario associated with less severe disease. The mechanisms that determine which pathogen is most abundant are not known. We hypothesised that the distribution of H. influenzae and P. aeruginosa would be consistent with strong interspecific competition effects. Further, we hypothesised that where P. aeruginosa is predominant, it is associated with a distinct 'accessory microbiota' that reflects a significant interaction between this pathogen and the wider bacterial community. To test these hypotheses, we analysed 16S rRNA gene pyrosequencing data generated previously from 60 adult bronchiectasis patients, whose airway microbiota was dominated by either P. aeruginosa or H. influenzae. The relative abundances of the two dominant species in their respective groups were not significantly different, and when present in the opposite pathogen group the two species were found to be in very low abundance, if at all. These findings are consistent with strong competition effects, moving towards competitive exclusion. Ordination analysis indicated that the distribution of the core microbiota associated with each pathogen, readjusted after removal of the dominant species, was significantly divergent (analysis of similarity (ANOSIM), R=0.07, P=0.019). Taken together, these findings suggest that both interspecific competition and also direct and/or indirect interactions between the predominant species and the wider bacterial community may contribute to the predominance of P. aeruginosa in a subset of bronchiectasis lung infections

Clinically applicable deep learning for diagnosis and referral in retinal disease

The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting