Method to Quickly Map Multifocal Pupillary Response Fields (mPRF) Using Frequency Tagging.

We present a method for mapping multifocal Pupillary Response Fields in a short amount of time using a visual stimulus covering 40° of the visual angle divided into nine contiguous sectors simultaneously modulated in luminance at specific, incommensurate, temporal frequencies. We test this multifocal Pupillary Frequency Tagging (mPFT) approach with young healthy participants (N = 36) and show that the spectral power of the sustained pupillary response elicited by 45 s of fixation of this multipartite stimulus reflects the relative contribution of each sector/frequency to the overall pupillary response. We further analyze the phase lag for each temporal frequency as well as several global features related to pupil state. Test/retest performed on a subset of participants indicates good repeatability. We also investigate the existence of structural (RNFL)/functional (mPFT) relationships. We then summarize the results of clinical studies conducted with mPFT on patients with neuropathies and retinopathies and show that the features derived from pupillary signal analyses, the distribution of spectral power in particular, are homologous to disease characteristics and allow for sorting patients from healthy participants with excellent sensitivity and specificity. This method thus appears as a convenient, objective, and fast tool for assessing the integrity of retino-pupillary circuits as well as idiosyncrasies and permits to objectively assess and follow-up retinopathies or neuropathies in a short amount of time.

Cortical recurrence supports resilience to sensory variance in the primary visual cortex.

Our daily endeavors occur in a complex visual environment, whose intrinsic variability challenges the way we integrate information to make decisions. By processing myriads of parallel sensory inputs, our brain is theoretically able to compute the variance of its environment, a cue known to guide our behavior. Yet, the neurobiological and computational basis of such variance computations are still poorly understood. Here, we quantify the dynamics of sensory variance modulations of cat primary visual cortex neurons. We report two archetypal neuronal responses, one of which is resilient to changes in variance and co-encodes the sensory feature and its variance, improving the population encoding of orientation. The existence of these variance-specific responses can be accounted for by a model of intracortical recurrent connectivity. We thus propose that local recurrent circuits process uncertainty as a generic computation, advancing our understanding of how the brain handles naturalistic inputs.

High resolution, wide field optical imaging of macaque visual cortex with a curved detector.

Objective. Cortical activity can be recorded using a variety of tools, ranging in scale from the single neuron (microscopic) to the whole brain (macroscopic). There is usually a trade-off between scale and resolution; optical imaging techniques, with their high spatio-temporal resolution and wide field of view, are best suited to study brain activity at the mesoscale. Optical imaging of cortical areas is however in practice limited by the curvature of the brain, which causes the image quality to deteriorate significantly away from the center of the image.Approach. To address this issue and harness the full potential of optical cortical imaging techniques, we developed a new wide-field optical imaging system adapted to the macaque brain. Our system is composed of a curved detector, an aspherical lens and a ring composed of light emitting diodes providing uniform illumination at wavelengths relevant for the different optical imaging methods, including intrinsic and fluorescence imaging.Main results. The system was characterized and compared with the standard macroscope used for cortical imaging, and a three-fold increase of the area in focus was measured as well as a four-fold increase in the evenness of the optical qualityin vivo.Significance. This new instrument, which is to the best of our knowledge the first use of a curved detector for cortical imaging, should facilitate the observation of wide mesoscale phenomena such as dynamic propagating waves within and between cortical maps, which are otherwise difficult to observe due to technical limitations of the currently available recording tools.

Pooling strategies in V1 can account for the functional and structural diversity across species.

Neurons in the primary visual cortex are selective to orientation with various degrees of selectivity to the spatial phase, from high selectivity in simple cells to low selectivity in complex cells. Various computational models have suggested a possible link between the presence of phase invariant cells and the existence of orientation maps in higher mammals' V1. These models, however, do not explain the emergence of complex cells in animals that do not show orientation maps. In this study, we build a theoretical model based on a convolutional network called Sparse Deep Predictive Coding (SDPC) and show that a single computational mechanism, pooling, allows the SDPC model to account for the emergence in V1 of complex cells with or without that of orientation maps, as observed in distinct species of mammals. In particular, we observed that pooling in the feature space is directly related to the orientation map formation while pooling in the retinotopic space is responsible for the emergence of a complex cells population. Introducing different forms of pooling in a predictive model of early visual processing as implemented in SDPC can therefore be viewed as a theoretical framework that explains the diversity of structural and functional phenomena observed in V1.

Revisiting horizontal connectivity rules in V1: from like-to-like towards like-to-all.

Horizontal connections in the primary visual cortex of carnivores, ungulates and primates organize on a near-regular lattice. Given the similar length scale for the regularity found in cortical orientation maps, the currently accepted theoretical standpoint is that these maps are underpinned by a like-to-like connectivity rule: horizontal axons connect preferentially to neurons with similar preferred orientation. However, there is reason to doubt the rule's explanatory power, since a growing number of quantitative studies show that the like-to-like connectivity preference and bias mostly observed at short-range scale, are highly variable on a neuron-to-neuron level and depend on the origin of the presynaptic neuron. Despite the wide availability of published data, the accepted model of visual processing has never been revised. Here, we review three lines of independent evidence supporting a much-needed revision of the like-to-like connectivity rule, ranging from anatomy to population functional measures, computational models and to theoretical approaches. We advocate an alternative, distance-dependent connectivity rule that is consistent with new structural and functional evidence: from like-to-like bias at short horizontal distance to like-to-all at long horizontal distance. This generic rule accounts for the observed high heterogeneity in interactions between the orientation and retinotopic domains, that we argue is necessary to process non-trivial stimuli in a task-dependent manner.

Like a clock in the rabbit's visual cortex.

Three rules govern the connectivity between neurons in the thalamus and inhibitory neurons in the visual cortex of rabbits.

Sparse deep predictive coding captures contour integration capabilities of the early visual system.

Both neurophysiological and psychophysical experiments have pointed out the crucial role of recurrent and feedback connections to process context-dependent information in the early visual cortex. While numerous models have accounted for feedback effects at either neural or representational level, none of them were able to bind those two levels of analysis. Is it possible to describe feedback effects at both levels using the same model? We answer this question by combining Predictive Coding (PC) and Sparse Coding (SC) into a hierarchical and convolutional framework applied to realistic problems. In the Sparse Deep Predictive Coding (SDPC) model, the SC component models the internal recurrent processing within each layer, and the PC component describes the interactions between layers using feedforward and feedback connections. Here, we train a 2-layered SDPC on two different databases of images, and we interpret it as a model of the early visual system (V1 & V2). We first demonstrate that once the training has converged, SDPC exhibits oriented and localized receptive fields in V1 and more complex features in V2. Second, we analyze the effects of feedback on the neural organization beyond the classical receptive field of V1 neurons using interaction maps. These maps are similar to association fields and reflect the Gestalt principle of good continuation. We demonstrate that feedback signals reorganize interaction maps and modulate neural activity to promote contour integration. Third, we demonstrate at the representational level that the SDPC feedback connections are able to overcome noise in input images. Therefore, the SDPC captures the association field principle at the neural level which results in a better reconstruction of blurred images at the representational level.

Functional ultrasound imaging of deep visual cortex in awake nonhuman primates.

Deep regions of the brain are not easily accessible to investigation at the mesoscale level in awake animals or humans. We have recently developed a functional ultrasound (fUS) technique that enables imaging hemodynamic responses to visual tasks. Using fUS imaging on two awake nonhuman primates performing a passive fixation task, we constructed retinotopic maps at depth in the visual cortex (V1, V2, and V3) in the calcarine and lunate sulci. The maps could be acquired in a single-hour session with relatively few presentations of the stimuli. The spatial resolution of the technology is illustrated by mapping patterns similar to ocular dominance (OD) columns within superficial and deep layers of the primary visual cortex. These acquisitions using fUS suggested that OD selectivity is mostly present in layer IV but with extensions into layers II/III and V. This imaging technology provides a new mesoscale approach to the mapping of brain activity at high spatiotemporal resolution in awake subjects within the whole depth of the cortex.

Interocular suppressive interactions in amblyopia depend on spatial frequency.

In amblyopia, there is an interocular suppressive imbalance that results in the fixing eye dominating perception. In this study, we aimed to determine whether these suppressive interactions were narrowband and tuned for spatial frequency or broadband and independent of spatial frequency. We measured the contrast sensitivity and masking functions of fifteen amblyopic subjects and seventeen control subjects using the quick Contrast Sensitivity Function (qCSF) approach (Lesmes, Lu, Baek, & Albright, 2010). We first measured the monocular sensitivity functions of each participant and thereafter corrected for it. We then measured masking sensitivity functions for low, mid and high spatial frequency masks, normalized to their visibility. In the control group, we observed that the strength of dichoptic masking is equivalent between the two eyes. It is also tuned such that masking by low spatial frequencies in one eye mainly affects low spatial frequencies in the other eye and masking by high spatial frequencies mainly affects high spatial frequencies. In amblyopes, although the interocular masking is also tuned for spatial frequency, it is not equivalent between the two eyes: the masking effect from the amblyopic to fixing eye is weaker than the other way around. The asymmetry observed in the strength of masking between the two eyes in amblyopia is tuned for spatial frequency. It is not the consequence of the contrast sensitivity deficit of the amblyopic eye nor is it the consequence of abnormally strong masking from the fixing eye. Rather it is due to an abnormally weak masking strength by the amblyopic eye per se.

Morphologic Analysis of Peripapillary Retinal Arteriole Using Adaptive Optics in Primary Open-angle Glaucoma.

PURPOSE: The purpose of this study was to better understand the role of vascular risk factors in the pathogenesis of primary open-angle glaucoma (POAG), a detailed analysis of retinal arterial wall thickness is needed. The purpose of the present study was to make a morphologic analysis of peripapillary arteriole in POAG using adaptive optics (AO) technology. PATIENTS AND METHODS: We included otherwise healthy subjects with an isolated confirmed diagnosis of bilateral POAG. Patients' clinical characteristics were noted. AO imaging followed by a complete ophthalmic examination was performed. A single operator masked to clinical data performed 5 measurements at different locations of each analyzed vessel. For each location, lumen diameter and wall thickness were measured. Total diameter, wall-to-lumen ratio (WLR), and whole cross-sectional area were calculated. RESULTS: Lumen diameter and total diameter were significantly lower in the glaucoma group (n=31) than in the control group (n=29): [median (interquartile ranges)] 88.3 (82.6-99.2) versus 102.3 (87-113.1) (P=0.03) and 121.1 (109.3-130.5) versus 134.4 (112.7-144.4), respectively (P=0.015). Wall thickness, WLR, and whole cross-sectional area were not significantly different. Apart from a significantly higher WLR in subjects with reported high cholesterol levels, we did not observe any correlation between patients' clinical characteristics and any of the parameters. CONCLUSIONS: We observed in POAG a narrowing of the arteriolar lumen without modification of the vessel wall thickness. To date, it is the first time that these data are obtained using AO. This suggests that the vascular risk factor in POAG only reduces the vascular caliber without inducing any patent atherosclerosis of the retinal arterial wall.

Contribution of Short-Time Occlusion of the Amblyopic Eye to a Passive Dichoptic Video Treatment for Amblyopia beyond the Critical Period.

Dichoptic movie viewing has been shown to significantly improve visual acuity in amblyopia in children. Moreover, short-term occlusion of the amblyopic eye can transiently increase its contribution to binocular fusion in adults. In this study, we first asked whether dichoptic movie viewing could improve the visual function of amblyopic subjects beyond the critical period. Secondly, we tested if this effect could be enhanced by short-term monocular occlusion of the amblyopic eye. 17 subjects presenting stable functional amblyopia participated in this study. 10 subjects followed 6 sessions of 1.5 hour of dichoptic movie viewing (nonpatched group), and 7 subjects, prior to each of these sessions, had to wear an occluding patch over the amblyopic eye for two hours (patched group). Best-corrected visual acuity, monocular contrast sensitivity, interocular balance, and stereoacuity were measured before and after the training. For the nonpatched group, mean amblyopic eye visual acuity significantly improved from 0.54 to 0.46 logMAR (p < 0.05). For the patched group, mean amblyopic eye visual acuity significantly improved from 0.62 to 0.43 logMAR (p < 0.05). Stereoacuity improved significantly when the data of both groups were combined. No significant improvement was observed for the other visual functions tested. Our training procedure combines modern video technologies and recent fundamental findings in human plasticity: (i) long-term plasticity induced by dichoptic movie viewing and (ii) short-term adaptation induced by temporary monocular occlusion. This passive dichoptic movie training approach is shown to significantly improve visual acuity of subjects beyond the critical period. The addition of a short-term monocular occlusion to the dichoptic training shows promising trends but was not significant for the sample size used here. The passive movie approach combined with interocular contrast balancing even over such a short period as 2 weeks has potential as a clinical therapy to treat amblyopia in older children and adults.

Suppressive Traveling Waves Shape Representations of Illusory Motion in Primary Visual Cortex of Awake Primate.

How does the brain link visual stimuli across space and time? Visual illusions provide an experimental paradigm to study these processes. When two stationary dots are flashed in close spatial and temporal succession, human observers experience a percept of apparent motion. Large spatiotemporal separation challenges the visual system to keep track of object identity along the apparent motion path, the so-called "correspondence problem." Here, we use voltage-sensitive dye imaging in primary visual cortex (V1) of awake monkeys to show that intracortical connections within V1 can solve this issue by shaping cortical dynamics to represent the illusory motion. We find that the appearance of the second stimulus in V1 creates a systematic suppressive wave traveling toward the retinotopic representation of the first. Using a computational model, we show that the suppressive wave is the emergent property of a recurrent gain control fed by the intracortical network. This suppressive wave acts to explain away ambiguous correspondence problems and contributes to precisely encode the expected motion velocity at the surface of V1. Together, these results demonstrate that the nonlinear dynamics within retinotopic maps can shape cortical representations of illusory motion. Understanding these dynamics will shed light on how the brain links sensory stimuli across space and time, by preformatting population responses for a straightforward read-out by downstream areas.SIGNIFICANCE STATEMENT Traveling waves have recently been observed in different animal species, brain areas, and behavioral states. However, it is still unclear what are their functional roles. In the case of cortical visual processing, waves propagate across retinotopic maps and can hereby generate interactions between spatially and temporally separated instances of feedforward driven activity. Such interactions could participate in processing long-range apparent motion stimuli, an illusion for which no clear neuronal mechanisms have yet been proposed. Using this paradigm in awake monkeys, we show that suppressive traveling waves produce a spatiotemporal normalization of apparent motion stimuli. Our study suggests that cortical waves shape the representation of illusory moving stimulus within retinotopic maps for a straightforward read-out by downstream areas.

Cortical travelling waves: mechanisms and computational principles.

Multichannel recording technologies have revealed travelling waves of neural activity in multiple sensory, motor and cognitive systems. These waves can be spontaneously generated by recurrent circuits or evoked by external stimuli. They travel along brain networks at multiple scales, transiently modulating spiking and excitability as they pass. Here, we review recent experimental findings that have found evidence for travelling waves at single-area (mesoscopic) and whole-brain (macroscopic) scales. We place these findings in the context of the current theoretical understanding of wave generation and propagation in recurrent networks. During the large low-frequency rhythms of sleep or the relatively desynchronized state of the awake cortex, travelling waves may serve a variety of functions, from long-term memory consolidation to processing of dynamic visual stimuli. We explore new avenues for experimental and computational understanding of the role of spatiotemporal activity patterns in the cortex.

Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons.

Voltage-sensitive dye imaging (VSDi) has revealed fundamental properties of neocortical processing at macroscopic scales. Since for each pixel VSDi signals report the average membrane potential over hundreds of neurons, it seems natural to use a mean-field formalism to model such signals. Here, we present a mean-field model of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based synaptic interactions. We study a network of regular-spiking (RS) excitatory neurons and fast-spiking (FS) inhibitory neurons. We use a Master Equation formalism, together with a semi-analytic approach to the transfer function of AdEx neurons to describe the average dynamics of the coupled populations. We compare the predictions of this mean-field model to simulated networks of RS-FS cells, first at the level of the spontaneous activity of the network, which is well predicted by the analytical description. Second, we investigate the response of the network to time-varying external input, and show that the mean-field model predicts the response time course of the population. Finally, to model VSDi signals, we consider a one-dimensional ring model made of interconnected RS-FS mean-field units. We found that this model can reproduce the spatio-temporal patterns seen in VSDi of awake monkey visual cortex as a response to local and transient visual stimuli. Conversely, we show that the model allows one to infer physiological parameters from the experimentally-recorded spatio-temporal patterns.

Neural field model to reconcile structure with function in primary visual cortex.

Voltage-sensitive dye imaging experiments in primary visual cortex (V1) have shown that local, oriented visual stimuli elicit stable orientation-selective activation within the stimulus retinotopic footprint. The cortical activation dynamically extends far beyond the retinotopic footprint, but the peripheral spread stays non-selective-a surprising finding given a number of anatomo-functional studies showing the orientation specificity of long-range connections. Here we use a computational model to investigate this apparent discrepancy by studying the expected population response using known published anatomical constraints. The dynamics of input-driven localized states were simulated in a planar neural field model with multiple sub-populations encoding orientation. The realistic connectivity profile has parameters controlling the clustering of long-range connections and their orientation bias. We found substantial overlap between the anatomically relevant parameter range and a steep decay in orientation selective activation that is consistent with the imaging experiments. In this way our study reconciles the reported orientation bias of long-range connections with the functional expression of orientation selective neural activity. Our results demonstrate this sharp decay is contingent on three factors, that long-range connections are sufficiently diffuse, that the orientation bias of these connections is in an intermediate range (consistent with anatomy) and that excitation is sufficiently balanced by inhibition. Conversely, our modelling results predict that, for reduced inhibition strength, spurious orientation selective activation could be generated through long-range lateral connections. Furthermore, if the orientation bias of lateral connections is very strong, or if inhibition is particularly weak, the network operates close to an instability leading to unbounded cortical activation.

Improving voltage-sensitive dye imaging: with a little help from computational approaches.

Voltage-sensitive dye imaging (VSDI) is a key neurophysiological recording tool because it reaches brain scales that remain inaccessible to other techniques. The development of this technique from in vitro to the behaving nonhuman primate has only been made possible thanks to the long-lasting, visionary work of Amiram Grinvald. This work has opened new scientific perspectives to the great benefit to the neuroscience community. However, this unprecedented technique remains largely under-utilized, and many future possibilities await for VSDI to reveal new functional operations. One reason why this tool has not been used extensively is the inherent complexity of the signal. For instance, the signal reflects mainly the subthreshold neuronal population response and is not linked to spiking activity in a straightforward manner. Second, VSDI gives access to intracortical recurrent dynamics that are intrinsically complex and therefore nontrivial to process. Computational approaches are thus necessary to promote our understanding and optimal use of this powerful technique. Here, we review such approaches, from computational models to dissect the mechanisms and origin of the recorded signal, to advanced signal processing methods to unravel new neuronal interactions at mesoscopic scale. Only a stronger development of interdisciplinary approaches can bridge micro- to macroscales.

Spontaneous cortical activity is transiently poised close to criticality.

Brain activity displays a large repertoire of dynamics across the sleep-wake cycle and even during anesthesia. It was suggested that criticality could serve as a unifying principle underlying the diversity of dynamics. This view has been supported by the observation of spontaneous bursts of cortical activity with scale-invariant sizes and durations, known as neuronal avalanches, in recordings of mesoscopic cortical signals. However, the existence of neuronal avalanches in spiking activity has been equivocal with studies reporting both its presence and absence. Here, we show that signs of criticality in spiking activity can change between synchronized and desynchronized cortical states. We analyzed the spontaneous activity in the primary visual cortex of the anesthetized cat and the awake monkey, and found that neuronal avalanches and thermodynamic indicators of criticality strongly depend on collective synchrony among neurons, LFP fluctuations, and behavioral state. We found that synchronized states are associated to criticality, large dynamical repertoire and prolonged epochs of eye closure, while desynchronized states are associated to sub-criticality, reduced dynamical repertoire, and eyes open conditions. Our results show that criticality in cortical dynamics is not stationary, but fluctuates during anesthesia and between different vigilance states.

Probing the functional impact of sub-retinal prosthesis.

Retinal prostheses are promising tools for recovering visual functions in blind patients but, unfortunately, with still poor gains in visual acuity. Improving their resolution is thus a key challenge that warrants understanding its origin through appropriate animal models. Here, we provide a systematic comparison between visual and prosthetic activations of the rat primary visual cortex (V1). We established a precise V1 mapping as a functional benchmark to demonstrate that sub-retinal implants activate V1 at the appropriate position, scalable to a wide range of visual luminance, but with an aspect-ratio and an extent much larger than expected. Such distorted activation profile can be accounted for by the existence of two sources of diffusion, passive diffusion and activation of ganglion cells' axons en passant. Reverse-engineered electrical pulses based on impedance spectroscopy is the only solution we tested that decreases the extent and aspect-ratio, providing a promising solution for clinical applications.

Effects of GABAA kinetics on cortical population activity: computational studies and physiological confirmations.

Voltage-sensitive dye (VSD) imaging produces an unprecedented real-time and high-resolution mesoscopic signal to measure the cortical population activity. We have previously shown that the neuronal compartments contributions to the signal are dynamic and stimulus-dependent (Chemla S, Chavane F. Neuroimage 53: 420-438, 2010). Moreover, the VSD signal can also be strongly affected by the network state, such as in anesthetized vs. awake preparations. Here, we investigated the impact of the network state, through GABAA receptors modulation, on the VSD signal using a computational approach. We therefore systematically measured the effect of the GABAA-mediated inhibitory postsynaptic potentials (IPSPs) decay time constant (τG) on our modeled VSD response to an input stimulus of increasing strength. Our simulations suggest that τG strongly modulates the dynamics of the VSD signal, affecting the amplitude, input response function, and the transient balance of excitation and inhibition. We confirmed these predictions experimentally on awake and anesthetized monkeys, comparing VSD responses to drifting gratings stimuli of various contrasts. Lastly, one in vitro study has suggested that GABAA receptors may also be directly affected by the VSDs themselves (Mennerick S, Chisari M, Shu H, Taylor A, Vasek M, Eisenman L, Zorumski C. J Neurosci 30: 2871-2879, 2010). Our modeling approach suggests that the type of modulation described in this study would actually have a negligible influence on the population response. This study highlights that functional results acquired with different techniques and network states must be compared with caution. Biophysical models are proposed here as an adequate tool to delineate the domain of VSD data interpretation.