Contrast is the differential luminance between one object and another. Contrast sensitivity (CS) quantifies the ability to detect this difference: estimating contrast threshold provides information about the quality of vision and helps diagnose and monitor eye diseases. High contrast visual acuity assessment is traditionally performed in the eye care practice, whereas the estimate of the discrimination of low contrast targets, an important complementary task for the perception of details, is far less employed. An example is driving when the contrast between vehicles, obstacles, pedestrians, and the background is reduced by fog. Many conditions can selectively degrade CS, while visual acuity remains intact. In addition to spatial CS, “temporal” CS is defined as the ability to discriminate luminance differences in the temporal domain, i.e., to discriminate information that reaches the visual cortex as a function of time. Likewise, temporal sensitivity of the visual system can be investigated in terms of critical fusion frequency (CFF), an indicator of the integrity of the magnocellular system that is responsible for the perception of transient stimulations. As a matter of fact, temporal resolution can be abnormal in neuro-ophthalmological clinical conditions. This paper aims at considering CS and its application to the clinical practice.

Perception is the ability to see, hear, or become aware of external stimuli through the senses. Visual stimuli are electromagnetic waves that interact with the eye and elicit a sensation. Sensations, indeed, imply the detection, resolution, and recognition of objects and images, and their accuracy depends on the integrity of the visual system. In clinical practice, evaluating the integrity of the visual system relies greatly on the assessment of visual acuity, that is to say on the capacity to identify a signal. Visual acuity, indeed, is of utmost importance for diagnosing and monitoring ophthalmological diseases. Visual acuity is a function that detects the presence of a stimulation (a signal) and resolves its detail(s). This is the case of a symbol like “E”: the stimulus is detected, then it is resolved as three horizontal bars and a vertical bar. In fact, within the clinical setting visual acuity is usually measured with alphanumeric symbols and is a three-step process that involves not only detection and resolution, but, due to the semantic content of letters and numbers, their recognition. Along with subjective (psychophysical) procedures, objective methods that do not require the active participation of the observer have been proposed to estimate visual acuity in non-collaborating subjects, malingerers, or toddlers. This paper aims to explain the psychophysical rationale underlying the measurement of visual acuity and revise the most common procedures used for its assessment.

Background: To compare objective electrophysiological contrast sensitivity function (CSF) in patients implanted with either multifocal intraocular lenses (MIOLs) or monofocal intraocular lenses (IOLs) by pattern reversal visual evoked potentials (prVEP) measurements.

Methods: Fourty-five cataract patients were randomly allocated to receive bilaterally: apodized diffractive-refractive Alcon Acrysof MIOL (A), full diffractive AMO Tecnis MIOL (B) or monofocal Alcon Acrysof IOL (C). Primary outcomes: 1-year differences in objective binocular CSF measured by prVEP with sinusoid grating stimuli of 6 decreasing contrast levels at 6 spatial frequencies. Secondary outcomes: psychophysical CSF measured with VCTS-6500, photopic uncorrected distance (UDVA), and mesopic and photopic uncorrected near and intermediate visual acuities (UNVA and UIVA respectively).

Results: Electrophysiological CSF curve had an inverted U-shaped morphology in all groups, with a biphasic pattern in Group B. Group A showed a lower CSF than group B at 4 and 8 cpd, and a lower value than group C at 8 cpd. Psychophysical CSF in group A exhibited a lower value at 12 cpd than group B. Mean photopic and mesopic UNVA and UIVA were worse in monofocal group compared to the multifocal groups. Mesopic UNVA and UIVA were better in group B.

Conclusions: Electrophysiological CSF behaves differently depending on the types of multifocal or monofocal IOLs. This may be related to the visual acuity under certain conditions or to IOL characteristics. This objective method might be a potential new tool to investigate on MIOL differences and on subjective device-related quality of vision.

Background: Understanding the neurophysiological mechanisms of Amblyopia, a neurodevelopmental disorder of the visual cortex, will bring us closer to full recovery. Past findings have been contradictory. Results have shown that despite having severe acuity impairment, amblyopes can nonetheless perceive sharp edges. In this study, we explore the representation of blur through a series of image blur-discrimination and matching tasks, to understand more about the amblyopes’ visual system.

Methods: Monocular image blur-discrimination thresholds were measured in a spatial two-alternative forced-choice procedure whereby subjects had to decide which image was the blurriest. Subjects also had to interocularly match pictures that were identical to those used for the image blur discrimination task. Ten amblyopes, as well as a group of ten controls were under study.

Results: Data on amblyopes and controls will be presented for both experiments. According to previous research that was done on blur-edge discrimination and matching, we predict that subjects’ performance will follow a dipper function, that is, all observers will be better at discriminating between both images when a small amount of blur is applied rather than when the image is either sharp or very blurry. We also predict that amblyopes’ blur discrimination will be noisier, but that they will paradoxically be able to match the sharpness of the images presented in the matching task.

Conclusions: This would confirm our hypothesis about amblyopes’ visual system, that they can represent blur levels defined by spatial frequencies that are beyond their resolution limit, and would also raise interesting questions about the visual system in general regarding the different perceptions driven by images versus edges.

Background: The guiding principle of functional brain mapping is that the cortex exhibits a spatial pattern of response reflecting its underlying functional organization. We know that large-scale patterns are common across individuals—everyone roughly has the same visual areas for example, but we do not know about small patterns, like the distribution of ocular dominance and orientation columns. Studies investigating the temporal aspect of brain-to-brain similarity have shown that a large portion of the brain is temporally synchronized across subjects (Hasson et al., 2004), but spatial pattern similarity has been scarcely studied, let alone at a fine scale. In the current study, we investigated fine-scale spatial pattern similarity between subjects during movie viewing and generated a map of prototypical patterns spanning the visual system. Characteristics of the map, such as spatial pattern size and distribution, reveal properties of the underlying structure and organisation of the visual cortex. These results will guide future brain mapping studies in decoding the informative spatial patterns of the visual cortex and increasing the resolution of current brain maps.

Methods: We had 56 subjects watch two movie clips from “Under the Sea 3D:IMAX” during an fMRI scan. Each clip was 5 minutes in length and was presented in 2D and 3D, in random order. We calculated the intersubject correlation of the spatial pattern inside predefined searchlights of diameter 3, 5, 7, 9 and 11 mm, covering the entire brain. A single threshold permutations test was used to test for significance: we generated 1,000 permutations made from scrambling the spatial patterns inside each searchlight of every subject, pooled these permutations together to generate a large distribution and used the 95th percentile to threshold the actual measurements. We compared these spatial pattern correlations to convexity variance between subjects to determine whether spatial pattern correlation could be explained by differing degrees of alignment across the cortex. We also compared spatial pattern correlation during 2D and 3D movie presentation.

Results: We found significant correlations in spatial pattern between subjects in the majority of early visual cortex, as well as higher visual areas. We found that mean spatial pattern similarity in a visual area tended to decrease as we move up the visual hierarchy. Spatial pattern correlation showed significant positive correlation with convexity variance for most visual areas, meaning that as anatomical misalignment increased, patterns became more similar. Spatial pattern correlation therefore cannot be explained by anatomical misalignment. Lastly, spatial pattern correlations tended to be higher for 3D movie presentation compared to 2D.

Conclusions: Our results suggest that many processes in early visual areas and even higher visual areas process visual information the same way in different individuals. Our results expand past studies by exploring spatial patterns instead of temporal patterns and studying at a fine-scale. This is the first study, to our knowledge, exploring fine-scale spatial patterns across the visual system. Our results show that fine-scale structures underlying activation patterns may be highly similar across subjects, pointing to a more ingrained organisation of the visual system than previously believed. This map we termed the “protoSPACE map”, may one day result in the detection of more subtle abnormalities that arise only during realistic vision in situations such as schizophrenia or mild traumatic brain injury, where traditional anatomical MRI scans report no changes.

Background: Visual cortex neurons often respond to stimuli very differently on repeated trials. This trial-by-trial variability is known to be correlated among nearby neurons. Our long-term goal is to quantitatively estimate neuronal response variability, using multi-channel local field potential (LFP) data from single trials.

Methods: Acute experiments were performed with anesthetized (Remifentanil, Propofol, nitrous oxide) and paralyzed (Gallamine Triethiodide) cats. Computer-controlled visual stimuli were displayed on a gamma-corrected CRT monitor. For the principal experiment, two kinds of visual stimuli were used: drifting sine-wave gratings, and a uniform mean-luminance gray screen. These two stimuli were each delivered monocularly for 100 sec in a random order, for 10 trials. Multi-unit activity (MUA) and LFP signals were extracted from broadband raw data acquired from Area 17 and 18 using A1X32 linear arrays (NeuroNexus) and the OpenEphys recording system. LFP signal processing was performed using Chronux, an open-source MATLAB toolbox. Current source density (CSD) analysis was performed on responses to briefly flashed full-field stimuli using the MATLAB toolbox, CSDplotter. The common response variability (global noise) of MUA was estimated using the model proposed by Scholvinck et al. [2015].

Results: On different trials, a given neuron responded with different firing to the same visual stimuli. Within one trial, a neuron’s firing rate also fluctuated across successive cycles of a drifting grating. When the animal was given extra anesthesia, neurons fired in a desynchronized pattern; with lighter levels of anesthesia, neuronal firing because more synchronized. By examining the cross-correlations of LFP signals recorded from different cortical layers, we found LFP signals could be divided to two groups: those recorded in layer IV and above, and those from layers V and VI. Within each group, LFP signals recorded by different channels are highly correlated. These two groups were observed in lighter and deeper anesthetized animals, also in sine-wave and uniform gray stimulus conditions. We also investigated correlations between LFP signals and global noise. Power in the LFP beta band was highly correlated with global noise, when animals were in deeper anesthesia.

Conclusions: Brain states contribute to variations in neuronal responses. Raw LFP correlation results suggest that we should analyze LFP data according to their laminar organization. Correlation of low-frequency LFP under deeper anesthesia with global noise gives us some insight to predict noise from single-trial data, and we hope to extend this analysis to lighter anesthesia in the future.

Background: The expression, localization, and function of the endocannabinoid system has been well characterized in recent years in the monkey retina and in the primary thalamic relay, the lateral geniculate nucleus (dLGN). Few data are available on cortical recipients’ structures of the dLGN, namely the primary visual cortex (V1). The goal of this study is to characterize the expression and localization of the metabotropic cannabinoid receptor type 1 (CB1R), the synthesizing enzyme N-acyl phosphatidyl-ethanolamine phospholipase D (NAPE-PLD), and the degradation enzyme fatty acid amide hydrolase (FAAH) in the vervet monkey area V1.

Methods: Using Western blots and immunohistochemistry, we investigated the expression patterns of CB1R, NAPE-PLD, and FAAH in the vervet monkey primary visual cortex.

Results: CB1R, NAPE-PLD, and FAAH were expressed in the primary visual cortex throughout the rostro-caudal axis. CB1R showed very low levels of staining in cortical layer 4, with higher expressions in all other cortical layers, especially layer 1. NAPE-PLD and FAAH expressions were highest in layers 1, 2 and 3, and lowest in layer 4.

Conclusions: Interestingly enough, CB1R was very low in layer 4 of V1 in comparison to the other cortical layers. The visual information coming from the dLGN and entering layer 4Calpha (magno cells) and 4Cbeta (parvo cells) may be therefore modulated by the higher expression levels of CB1R in cortical layers 2 and 3 on the way to the dorsal and ventral visual streams. This is further supported by the higher expression of NAPE-PLD and FAAH in the outer cortical layers. These data indicate that CB1R system can influence the network of activity patterns in the visual stream after the visual information has reached area V1. These novel results provide insights for understanding the role of the endocannabinoids in the modulation of cortical visual inputs, and hence, visual perception.

Background: Research suggests that the analysis of facial expressions by a healthy brain would take place approximately 170 ms after the presentation of a facial expression in the superior temporal sulcus and the fusiform gyrus, mostly in the right hemisphere. Some researchers argue that a fast pathway through the amygdala would allow automatic and early emotional treatment around 90 ms after stimulation. This treatment would be done subconsciously, even before this stimulus is perceived and could be approximated by presenting the stimuli quickly on the periphery of the fovea. The present study aimed to identify the neural correlates of a peripheral and simultaneous presentation of emotional expressions through a frequency tagging paradigm.

Methods: The presentation of emotional facial expressions at a specific frequency induces in the visual cortex a stable and precise response to the presentation frequency [i.e., a steady-state visual evoked potential (ssVEP)] that can be used as a frequency tag (i.e., a frequency-tag to follow the cortical treatment of this stimulus. Here, the use of different specific stimulation frequencies allowed us to label the different facial expressions presented simultaneously and to obtain a reliable cortical response being associated with (I) each of the emotions and (II) the different times of presentations repeated (1/0.170 ms =~5.8 Hz, 1/0.090 ms =~10.8 Hz). To identify the regions involved in emotional discrimination, we subtracted the brain activity induced by the rapid presentation of six emotional expressions of the activity induced by the presentation of the same emotion (reduced by neural adaptation). The results were compared to the hemisphere in which attention was sought, emotion and frequency of stimulation.

Results: The signal-to-noise ratio of the cerebral oscillations referring to the treatment of the expression of fear was stronger in the regions specific to the emotional treatment when they were presented in the subjects peripheral vision, unbeknownst to them. In addition, the peripheral emotional treatment of fear at 10.8 Hz was associated with greater activation within the Gamma 1 and 2 frequency bands in the expected regions (frontotemporal and T6), as well as desynchronization in the Alpha frequency bands for the temporal regions. This modulation of the spectral power is independent of the attentional request.

Conclusions: These results suggest that the emotional stimulation of fear presented in the peripheral vision and outside the attentional framework elicit an increase in brain activity, especially in the temporal lobe. The localization of this activity as well as the optimal stimulation frequency found for this facial expression suggests that it is treated by the fast pathway of the magnocellular layers.

Background: It is well known that the pulvinar establishes reciprocal connections with areas of the visual cortex, allowing the transfer of cortico-cortical signals through transthalamic pathways. However, the exact function of these signals in coordinating activity across the visual cortical hierarchy remains largely unknown. In anesthetized cats, we have explored whether pulvinar inactivation affects the dynamic of interactions between the primary visual cortex (a17) and area 21a, a higher visual cortical area, as well as between layers within each cortical area. We found that pulvinar inactivation modifies the local field potentials (LFPs) coherence between a17 and 21a during a visual stimulation. In addition, the Granger causality analysis showed that the functional connectivity changed across visual areas and between cortical layers during pulvinar inactivation, the effects being stronger in layers of the same area. We observed that the effects of pulvinar inactivation arise at two different epochs of the visual response, i.e., at the early and late components. The proportion of feedback and feedforward functional events was higher during the early and the late phases of the responses, respectively. We also found that pulvinar inactivation facilitates the feedback propagation of gamma oscillations from 21a to a17. This feedback transmission was predominant during the late response. At the temporal level, pulvinar inactivation also delayed the signals from a17 and 21a, depending on the source and the target of the cortical layer. Thus, the pulvinar can not only modify the functional connectivity between intra and inter cortical layers but may also control the temporal dynamics of neuronal activity across the visual cortical hierarchy.

Methods: In vivo electrophysiological recordings of visual cortical areas, area 17 and 21a, in anesthetized cats, were then explored with temporal serial analysis (i.e., Fourier analysis, Coherence, Cross-correlation and Granger causality) of the local field potential.

Results: Inactivation of the thalamic nucleus modifies the dynamics of areas 17 and 21a. The changes observed depends on the source and the target of the cortical layer. The pulvinar inactivation arise at two different epochs of visual response.

Conclusions: The pulvinar modifies the functional connectivity between intra and inter cortical layers and may also control the temporal dynamics of neuronal activity across the visual cortical hierarchy.