Dynamics of normalization underlying masking in human visual cortex

Temporal dynamics of normalization reweighting

For decades, neural suppression in early visual cortex has been thought to be fixed. But recent work has challenged this assumption by showing that suppression can be reweighted based on recent history; when pairs of stimuli are repeatedly presented together, suppression between them strengthens. Here we investigate the temporal dynamics of this process using a steady-state visual evoked potential (SSVEP) paradigm that provides a time-resolved, direct index of suppression between pairs of stimuli flickering at different frequencies (5 and 7 Hz). Our initial analysis of an existing electroencephalography (EEG) dataset (N = 100) indicated that suppression increases substantially during the first 2-5 seconds of stimulus presentation (with some variation across stimulation frequency). We then collected new EEG data (N = 100) replicating this finding for both monocular and dichoptic mask arrangements in a preregistered study designed to measure reweighting. A third experiment (N = 20) used source-localized magnetoencephalography and found that these effects are apparent in primary visual cortex (V1), consistent with results from neurophysiological work. Because long-standing theories propose inhibition/excitation differences in autism, we also compared reweighting between individuals with high versus low autistic traits, and with and without an autism diagnosis, across our three datasets (total N = 220). We find no compelling differences in reweighting that are associated with autism. Our results support the normalization reweighting model and indicate that for prolonged stimulation, increases in suppression occur on the order of 2-5 seconds after stimulus onset.

Effects of normalized summation in the visual illusion of extent

In the present study, the features of summation of effects caused by contextual distracting dots in the length-matching task (a variant of the filled-space illusion) were investigated. In the first two series of psychophysical experiments, the illusion magnitude was measured as a function of the displacement of distractors (either single or double sets of dots) orthogonally to the main axis of the stimulus. It was demonstrated that with increasing displacement, the illusion smoothly decreases for a single set of distractors, while for two sets, the illusion first increases to a certain maximum value, and then gradually decreases. In the third and fourth series of experiments, magnitude of the illusion was measured as a function of the luminance of one set of distracting dots, while the luminance of the other set was fixed. It has been shown that increasing the luminance until the same value is reached for both sets leads to a monotonous growth in the illusion magnitude; after that, the illusion asymptotically decreases to an almost constant level. The theoretical interpretation of the established functional dependencies was performed using a quantitative model based on the assumption that the illusion may arise due to the weighted summation of the distractor-induced normalized neural activity, which leads to the perceptual mislocalization of terminators of stimulus spatial intervals.

Different rules for binocular combination of luminance flicker in cortical and subcortical pathways

How does the human brain combine information across the eyes? It has been known for many years that cortical normalization mechanisms implement 'ocularity invariance': equalizing neural responses to spatial patterns presented either monocularly or binocularly. Here, we used a novel combination of electrophysiology, psychophysics, pupillometry, and computational modeling to ask whether this invariance also holds for flickering luminance stimuli with no spatial contrast. We find dramatic violations of ocularity invariance for these stimuli, both in the cortex and also in the subcortical pathways that govern pupil diameter. Specifically, we find substantial binocular facilitation in both pathways with the effect being strongest in the cortex. Near-linear binocular additivity (instead of ocularity invariance) was also found using a perceptual luminance matching task. Ocularity invariance is, therefore, not a ubiquitous feature of visual processing, and the brain appears to repurpose a generic normalization algorithm for different visual functions by adjusting the amount of interocular suppression.

Effects of altered-reality training on interocular disinhibition in amblyopia

Training of viewing an altered-reality environment dichoptically has been found to reactivate human adult ocular dominance plasticity, allowing improvement of vision for amblyopia. One suspected mechanism for this training effect is ocular dominance rebalancing through interocular disinhibition. Here, we investigated whether the training modulated the neural responses reflecting interocular inhibition. Thirteen patients with amblyopia and 11 healthy controls participated in this study. Before and after six daily altered-reality training sessions, participants watched flickering video stimuli with their steady-state visually evoked potential (SSVEP) signals recorded simultaneously. We assessed the amplitude of SSVEP response at intermodulation frequencies, which was a potential neural indicator of interocular suppression. The results showed that training weakened the intermodulation response only in the amblyopic group, which was in agreement with the hypothesis that the training reduced interocular suppression specific to amblyopia. Moreover, even one month after the training ended, we could still observe this neural training effect. These findings provide preliminary neural evidence in support of the disinhibition account for treating amblyopia. We also explain these results with the ocular opponency model, which, to our knowledge, is the first time for this binocular rivalry model to be used in explaining long-term ocular dominance plasticity.

Decoding semantics from intermodulation responses in frequency-tagged stereotactic EEG

BACKGROUND

Humans perform object recognition using holistic processing, which is different from computers. Intermodulation responses in the steady-state visual evoked potential (SSVEP) of scalp electroencephalography (EEG) have recently been used as an objective label for holistic processing.

NEW METHOD

Using stereotactic EEG (sEEG) to record SSVEP directly from inside of the brain, we aimed to decode Chinese characters from non-characters with activation from multiple brain areas including occipital, parietal, temporal, and frontal cortices.

RESULTS

Semantic categories could be decoded from responses at the intermodulation frequency with high accuracy (80%-90%), but not the base frequency. Moreover, semantic categories could be decoded with activation from multiple areas including temporal, parietal, and frontal areas.

COMPARISON WITH EXISTING METHOD(S)

Previous studies investigated holistic processing in faces and words with frequency-tagged scalp EEGs. The current study extended the results to stereotactic EEG signals directly recorded from the brain.

CONCLUSIONS

The human brain applies holistic processing in recognizing objects like Chinese characters. Our findings could be extended to an add-on feature in the existing SSVEP BCI speller.

Reliable, Fast and Stable Contrast Response Function Estimation

A study was conducted to determine stable cortical contrast response functions (CRFs) accurately and repeatedly in the shortest possible experimentation time. The method consisted of searching for experimental temporal aspects (number and duration of trials and number and distribution of contrasts used) with a model based on inhomogeneous Poisson spike trains to varying contrast levels. The set of values providing both short experimental duration and maximizing fit of the CRFs were saved, and then tested on cats' visual cortical neurons. Our analysis revealed that 4 sets of parameters with less or equal to 6 experimental visual contrasts satisfied our premise of obtaining good CRFs' performance in a short recording period, in which the number of trials seems to be the experimental condition that stabilizes the fit.

Local Interactions between Steady-State Visually Evoked Potentials at Nearby Flickering Frequencies

Steady-state visually evoked potentials (SSVEPs) are widely used to index top-down cognitive processing in human electroencephalogram (EEG) studies. Typically, two stimuli flickering at different temporal frequencies (TFs) are presented, each producing a distinct response in the EEG at its flicker frequency. However, how SSVEP responses in EEGs are modulated in the presence of a competing flickering stimulus just because of sensory interactions is not well understood. We have previously shown in local field potentials (LFPs) recorded from awake monkeys that when two overlapping full-screen gratings are counterphased at different TFs, there is an asymmetric SSVEP response suppression, with greater suppression from lower TFs, which further depends on the relative orientations of the gratings (stronger suppression and asymmetry for parallel compared with orthogonal gratings). Here, we first confirmed these effects in both male and female human EEG recordings. Then, we mapped the response suppression of one stimulus (target) by a competing stimulus (mask) over a much wider range than the previous study. Surprisingly, we found that the suppression was not stronger at low frequencies in general, but systematically varied depending on the target TF, indicating local interactions between the two competing stimuli. These results were confirmed in both human EEG and monkey LFP and electrocorticogram (ECoG) data. Our results show that sensory interactions between multiple SSVEPs are more complex than shown previously and are influenced by both local and global factors, underscoring the need to cautiously interpret the results of studies involving SSVEP paradigms.SIGNIFICANCE STATEMENT Steady-state visually evoked potentials (SSVEPs) are extensively used in human cognitive studies and brain-computer interfacing applications where multiple stimuli flickering at distinct frequencies are concurrently presented in the visual field. We recently characterized interactions between competing flickering stimuli in animal recordings and found that stimuli flickering slowly produce larger suppression. Here, we confirmed these in human EEGs, and further characterized the interactions by using a much wider range of target and competing (mask) frequencies in both human EEGs and invasive animal recordings. These revealed a new "local" component, whereby the suppression increased when competing stimuli flickered at nearby frequencies. Our results highlight the complexity of sensory interactions among multiple SSVEPs and underscore the need to cautiously interpret studies involving SSVEP paradigms.

The specificity of orientation-tuned normalization within human early visual cortex

Normalization within visual cortex is modulated by contextual influences; stimuli sharing similar features suppress each other more than dissimilar stimuli. This feature-tuned component of suppression depends on multiple factors, including the orientation content of stimuli. Indeed, pairs of stimuli arranged in a center-surround configuration attenuate each other's response to a greater degree when oriented collinearly than when oriented orthogonally. Although numerous studies have examined the nature of surround suppression at these two extremes, far less is known about how the strength of tuned normalization varies as a function of continuous changes in orientation similarity, particularly in humans. In this study, we used functional magnetic resonance imaging (fMRI) to examine the bandwidth of orientation-tuned suppression within human visual cortex. Blood-oxygen-level-dependent (BOLD) responses were acquired as participants viewed a full-field circular stimulus composed of wedges of orientation-bandpass filtered noise. This stimulus configuration allowed us to parametrically vary orientation differences between neighboring wedges in gradual steps between collinear and orthogonal. We found the greatest suppression for collinearly arranged stimuli with a gradual increase in BOLD response as the orientation content became more dissimilar. We quantified the tuning width of orientation-tuned suppression, finding that the voxel-wise bandwidth of orientation tuned normalization was between 20° and 30°, and did not differ substantially between early visual areas. Voxel-wise analyses revealed that suppression width covaried with retinotopic preference, with the tightest bandwidths at outer eccentricities. Having an estimate of orientation-tuned suppression bandwidth can serve to constrain models of tuned normalization, establishing the precise degree to which suppression strength depends on similarity between visual stimulus components.NEW & NOTEWORTHY Neurons in the early visual cortex are subject to divisive normalization, but the feature-tuning aspect of this computation remains understudied, particularly in humans. We investigated orientation tuning of normalization in human early visual cortex using fMRI and estimated the bandwidth of the tuned normalization function across observers. Our findings provide a characterization of tuned normalization in early visual cortex that could help constrain models of divisive normalization in vision.

Excitatory Contribution to Binocular Interactions in Human Visual Cortex Is Reduced in Strabismic Amblyopia

Binocular summation in strabismic amblyopia is typically reported as being absent or greatly reduced in behavioral studies and is thought to be because of a preferential loss of excitatory interactions between the eyes. Here, we studied how excitatory and suppressive interactions contribute to binocular contrast interactions along the visual cortical hierarchy of humans with strabismic and anisometropic amblyopia in both sexes, using source-imaged steady-state visual evoked potentials (SSVEP) over a wide range of relative contrast between the two eyes. Dichoptic parallel grating stimuli modulated at unique temporal frequencies in each eye allowed us to quantify spectral response components associated with monocular inputs (self-terms) and the response components because of interaction of the inputs of the two eyes [intermodulation (IM) terms]. Although anisometropic amblyopes revealed a similar pattern of responses to normal-vision observers, strabismic amblyopes exhibited substantially reduced IM responses across cortical regions of interest (V1, V3a, hV4, hMT+ and lateral occipital cortex), indicating reduced interocular interactions in visual cortex. A contrast gain control model that simultaneously fits self- and IM-term responses within each cortical area revealed different patterns of binocular interactions between individuals with normal and disrupted binocularity. Our model fits show that in strabismic amblyopia, the excitatory contribution to binocular interactions is significantly reduced in both V1 and extra-striate cortex, whereas suppressive contributions remain intact. Our results provide robust electrophysiological evidence supporting the view that disruption of binocular interactions in strabismus or amblyopia is because of preferential loss of excitatory interactions between the eyes.SIGNIFICANCE STATEMENT We studied how excitatory and suppressive interactions contribute to binocular contrast interactions along the visual cortical hierarchy of humans with normal and amblyopic vision, using source-imaged SSVEP and frequency-domain analysis of dichoptic stimuli over a wide range of relative contrast between the two eyes. A dichoptic contrast gain control model was used to characterize these interactions in amblyopia and provided a quantitative comparison to normal vision. Our model fits revealed different patterns of binocular interactions between normal and amblyopic vision. Strabismic amblyopia significantly reduced excitatory contributions to binocular interactions, whereas suppressive contributions remained intact. Our results provide robust evidence supporting the view that the preferential loss of excitatory interactions disrupts binocular interactions in strabismic amblyopia.

Steady-state measures of visual suppression

In the early visual system, suppression occurs between neurons representing different stimulus properties. This includes features such as orientation (cross-orientation suppression), eye-of-origin (interocular suppression) and spatial location (surround suppression), which are thought to involve distinct anatomical pathways. We asked if these separate routes to suppression can be differentiated by their pattern of gain control on the contrast response function measured in human participants using steady-state electroencephalography. Changes in contrast gain shift the contrast response function laterally, whereas changes in response gain scale the function vertically. We used a Bayesian hierarchical model to summarise the evidence for each type of gain control. A computational meta-analysis of 16 previous studies found the most evidence for contrast gain effects with overlaid masks, but no clear evidence favouring either response gain or contrast gain for other mask types. We then conducted two new experiments, comparing suppression from four mask types (monocular and dichoptic overlay masks, and aligned and orthogonal surround masks) on responses to sine wave grating patches flickering at 5Hz. At the occipital pole, there was strong evidence for contrast gain effects in all four mask types at the first harmonic frequency (5Hz). Suppression generally became stronger at more lateral electrode sites, but there was little evidence of response gain effects. At the second harmonic frequency (10Hz) suppression was stronger overall, and involved both contrast and response gain effects. Although suppression from different mask types involves distinct anatomical pathways, gain control processes appear to serve a common purpose, which we suggest might be to suppress less reliable inputs.

Electrophysiological evidence for higher-level chromatic mechanisms in humans

Color vision in humans starts with three types of cones (short [S], medium [M], and long [L] wavelengths) in the retina and three retinal and subcortical cardinal mechanisms, which linearly combine cone signals into the luminance channel (L + M), the red-green channel (L - M), and the yellow-blue channel (S-(L + M)). Chromatic mechanisms at the cortical level, however, are less well characterized. The present study investigated such higher-order chromatic mechanisms by recording electroencephalograms (EEGs) on human observers in a noise masking paradigm. Observers viewed colored stimuli that consisted of a target embedded in noise. Color directions of the target and noise varied independently and systematically in an isoluminant plane of color space. The target was flickering on-off at 3 Hz, eliciting steady-state visual evoked potential (SSVEP) responses. As a result, the masking strength could be estimated from the SSVEP amplitude in the presence of 6 Hz noise. Masking was strongest (i.e. target eliciting smallest SSVEPs) when the target and noise were along the same color direction, and was weakest (i.e. target eliciting highest SSVEPs) when the target and noise were along orthogonal directions. This pattern of results was observed both when the target color varied along the cardinal and intermediate directions, which is evidence for higher-order chromatic mechanisms tuned to intermediate axes. The SSVEP result can be well predicted by a model with multiple broadly tuned chromatic mechanisms. In contrast, a model with only cardinal mechanisms failed to account for the data. These results provide strong electrophysiological evidence for multiple chromatic mechanisms in the early visual cortex of humans.

Effects of affective content and motivational context on neural gain functions during naturalistic scene perception

Visual scene processing is modulated by semantic, motivational, and emotional factors, in addition to physical scene statistics. An open question is to what extent those factors affect low-level visual processing. One index of low-level visual processing is the contrast response function (CRF), representing the change in neural or psychophysical gain with increasing stimulus contrast. Here we aimed to (a) establish the use of an electrophysiological technique for assessing CRFs with complex emotional scenes and (b) examine the effects of motivational context and emotional content on CRFs elicited by naturalistic stimuli, including faces and complex scenes (humans, animals). Motivational context varied by expectancy of threat (a noxious noise) versus safety. CRFs were measured in 18 participants by means of sweep steady-state visual evoked potentials. Results showed a facilitation in visuocortical sensitivity (contrast gain) under threat, compared with safe conditions, across all stimulus categories. Facial stimuli prompted heightened neural response gain, compared with scenes. Within the scenes, response gain was smaller for scenes high in emotional arousal, compared with low-arousing scenes, consistent with interference effects of emotional content. These findings support the notion that motivational context alters the contrast sensitivity of cortical tissue, differing from changes in response gain (activation) when visual cues themselves carry motivational/affective relevance.

Steady state evoked potential (SSEP) responses in the primary and secondary somatosensory cortices of anesthetized cats: Nonlinearity characterized by harmonic and intermodulation frequencies

When presented with an oscillatory sensory input at a particular frequency, F [Hz], neural systems respond with the corresponding frequency, f [Hz], and its multiples. When the input includes two frequencies (F1 and F2) and they are nonlinearly integrated in the system, responses at intermodulation frequencies (i.e., n1*f1+n2*f2 [Hz], where n1 and n2 are non-zero integers) emerge. Utilizing these properties, the steady state evoked potential (SSEP) paradigm allows us to characterize linear and nonlinear neural computation performed in cortical neurocircuitry. Here, we analyzed the steady state evoked local field potentials (LFPs) recorded from the primary (S1) and secondary (S2) somatosensory cortex of anesthetized cats (maintained with alfaxalone) while we presented slow (F1 = 23Hz) and fast (F2 = 200Hz) somatosensory vibration to the contralateral paw pads and digits. Over 9 experimental sessions, we recorded LFPs from N = 1620 and N = 1008 bipolar-referenced sites in S1 and S2 using electrode arrays. Power spectral analyses revealed strong responses at 1) the fundamental (f1, f2), 2) its harmonic, 3) the intermodulation frequencies, and 4) broadband frequencies (50-150Hz). To compare the computational architecture in S1 and S2, we employed simple computational modeling. Our modeling results necessitate nonlinear computation to explain SSEP in S2 more than S1. Combined with our current analysis of LFPs, our paradigm offers a rare opportunity to constrain the computational architecture of hierarchical organization of S1 and S2 and to reveal how a large-scale SSEP can emerge from local neural population activities.

Effect of Cross-Orientation Normalization on Different Neural Measures in Macaque Primary Visual Cortex

Divisive normalization is a canonical mechanism that can explain a variety of sensory phenomena. While normalization models have been used to explain spiking activity in response to different stimulus/behavioral conditions in multiple brain areas, it is unclear whether similar models can also explain modulation in population-level neural measures such as power at various frequencies in local field potentials (LFPs) or steady-state visually evoked potential (SSVEP) that is produced by flickering stimuli and popular in electroencephalogram studies. To address this, we manipulated normalization strength by presenting static as well as flickering orthogonal superimposed gratings (plaids) at varying contrasts to 2 female monkeys while recording multiunit activity (MUA) and LFP from the primary visual cortex and quantified the modulation in MUA, gamma (32-80 Hz), high-gamma (104-248 Hz) power, as well as SSVEP. Even under similar stimulus conditions, normalization strength was different for the 4 measures and increased as: spikes, high-gamma, SSVEP, and gamma. However, these results could be explained using a normalization model that was modified for population responses, by varying the tuned normalization parameter and semisaturation constant. Our results show that different neural measures can reflect the effect of stimulus normalization in different ways, which can be modeled by a simple normalization model.

Contrast-Dependence of Temporal Frequency Tuning in Mouse V1

The perception of speed is influenced by visual contrast. In primary visual cortex (V1), an early stage in the visual perception pathway, the neural tuning to speed is directly related to the neural tuning to temporal frequency of stimulus changes. The influence of contrast on speed perception can be caused by the joint dependency of neural responses in V1 on temporal frequency and contrast. Here, we investigated how tuning to contrast and temporal frequency in V1 of anesthetized mice are related. We found that temporal frequency tuning is contrast-dependent. V1 was more responsive at lower temporal frequencies than the dLGN, consistent with previous work at high contrast. The temporal frequency tuning moves toward higher temporal frequencies with increasing contrast. The low half-maximum temporal frequency does not change with contrast. The Heeger divisive normalization equation provides a good fit to many response characteristics in V1, but does not fit the dependency of temporal frequency and contrast with set of parameters for all temporal frequencies. Different mechanisms for normalization in the visual cortex may predict different relationships between temporal frequency and contrast non-linearity. Our data could help to make a model selection.

Nonlinear transduction of emotional facial expression

To create neural representations of external stimuli, the brain performs a number of processing steps that transform its inputs. For fundamental attributes, such as stimulus contrast, this involves one or more nonlinearities that are believed to optimise the neural code to represent features of the natural environment. Here we ask if the same is also true of more complex stimulus dimensions, such as emotional facial expression. We report the results of three experiments combining morphed facial stimuli with electrophysiological and psychophysical methods to measure the function mapping emotional expression intensity to internal response. The results converge on a nonlinearity that accelerates over weak expressions, and then becomes shallower for stronger expressions, similar to the situation for lower level stimulus properties. We further demonstrate that the nonlinearity is not attributable to the morphing procedure used in stimulus generation.

Contrast Normalization Accounts for Binocular Interactions in Human Striate and Extra-striate Visual Cortex

During binocular viewing, visual inputs from the two eyes interact at the level of visual cortex. Here we studied binocular interactions in human visual cortex, including both sexes, using source-imaged steady-state visual evoked potentials over a wide range of relative contrast between two eyes. The ROIs included areas V1, V3a, hV4, hMT⁺, and lateral occipital cortex. Dichoptic parallel grating stimuli in each eye modulated at distinct temporal frequencies allowed us to quantify spectral components associated with the individual stimuli from monocular inputs (self-terms) and responses due to interaction between the inputs from the two eyes (intermodulation [IM] terms). Data with self-terms revealed an interocular suppression effect, in which the responses to the stimulus in one eye were reduced when a stimulus was presented simultaneously to the other eye. The suppression magnitude varied depending on visual area, and the relative contrast between the two eyes. Suppression was strongest in V1 and V3a (50% reduction) and was least in lateral occipital cortex (20% reduction). Data with IM terms revealed another form of binocular interaction, compared with self-terms. IM response was strongest at V1 and was least in hV4. Fits of a family of divisive gain control models to both self- and IM-term responses within each cortical area indicated that both forms of binocular interaction shared a common gain control nonlinearity. However, our model fits revealed different patterns of binocular interaction along the cortical hierarchy, particularly in terms of excitatory and suppressive contributions.SIGNIFICANCE STATEMENT Using source-imaged steady-state visual evoked potentials and frequency-domain analysis of dichoptic stimuli, we measured two forms of binocular interactions: one is associated with the individual stimuli that represent interocular suppression from each eye, and the other is a direct measure of interocular interaction between inputs from the two eyes. We demonstrated that both forms of binocular interactions share a common gain control mechanism in striate and extra-striate cortex. Furthermore, our model fits revealed different patterns of binocular interaction along the visual cortical hierarchy, particularly in terms of excitatory and suppressive contributions.

Interaction between steady-state visually evoked potentials at nearby flicker frequencies

Steady-state visually evoked potential (SSVEP) studies routinely employ simultaneous presentation of two temporally modulated stimuli, with SSVEP amplitude modulations serving to index top-down cognitive processes. However, the nature of SSVEP amplitude modulations as a function of competing temporal frequency (TF) has not been systematically studied, especially in relation to the normalization framework which has been extensively used to explain visual responses to multiple stimuli. We recorded spikes and local field potential (LFP) from the primary visual cortex (V1) as well as EEG from two awake macaque monkeys while they passively fixated plaid stimuli with components counterphasing at different TFs. We observed asymmetric SSVEP response suppression by competing TFs (greater suppression for lower TFs), which further depended on the relative orientations of plaid components. A tuned normalization model, adapted to SSVEP responses, provided a good account of the suppression. Our results provide new insights into processing of temporally modulated visual stimuli.

EEGSourceSim: A framework for realistic simulation of EEG scalp data using MRI-based forward models and biologically plausible signals and noise

BACKGROUND

Electroencephalography (EEG) is widely used to investigate human brain function. Simulation studies are essential for assessing the validity of EEG analysis methods and the interpretability of results.

NEW METHOD

Here we present a simulation environment for generating EEG data by embedding biologically plausible signal and noise into MRI-based forward models that incorporate individual-subject variability in structure and function.

RESULTS

The package includes pipelines for the evaluation and validation of EEG analysis tools for source estimation, functional connectivity, and spatial filtering. EEG dynamics can be simulated using realistic noise and signal models with user specifiable signal-to-noise ratio (SNR). We also provide a set of quantitative metrics tailored to source estimation, connectivity and spatial filtering applications.

COMPARISON WITH EXISTING METHOD(S)

We provide a larger set of forward solutions for individual MRI-based head models than has been available previously. These head models are surface-based and include two sets of regions-of-interest (ROIs) that have been brought into registration with the brain of each individual using surface-based alignment - one from a whole brain and the other from a visual cortex atlas. We derive a realistic model of noise by fitting different model components to measured resting state EEG. We also provide a set of quantitative metrics for evaluating source-localization, functional connectivity and spatial filtering methods.

CONCLUSIONS

The inclusion of a larger number of individual head-models, combined with surface-atlas based labeling of ROIs and plausible models of signal and noise, allows for simulation of EEG data with greater realism than previous packages.

Predicting neuronal dynamics with a delayed gain control model

Visual neurons respond to static images with specific dynamics: neuronal responses sum sub-additively over time, reduce in amplitude with repeated or sustained stimuli (neuronal adaptation), and are slower at low stimulus contrast. Here, we propose a simple model that predicts these seemingly disparate response patterns observed in a diverse set of measurements-intracranial electrodes in patients, fMRI, and macaque single unit spiking. The model takes a time-varying contrast time course of a stimulus as input, and produces predicted neuronal dynamics as output. Model computation consists of linear filtering, expansive exponentiation, and a divisive gain control. The gain control signal relates to but is slower than the linear signal, and this delay is critical in giving rise to predictions matched to the observed dynamics. Our model is simpler than previously proposed related models, and fitting the model to intracranial EEG data uncovers two regularities across human visual field maps: estimated linear filters (temporal receptive fields) systematically differ across and within visual field maps, and later areas exhibit more rapid and substantial gain control. The model is further generalizable to account for dynamics of contrast-dependent spike rates in macaque V1, and amplitudes of fMRI BOLD in human V1.

From intermodulation components to visual perception and cognition-a review

Perception results from complex interactions among sensory and cognitive processes across hierarchical levels in the brain. Intermodulation (IM) components, used in frequency tagging neuroimaging designs, have emerged as a promising direct measure of such neural interactions. IMs have initially been used in electroencephalography (EEG) to investigate low-level visual processing. In a more recent trend, IMs in EEG and other neuroimaging methods are being used to shed light on mechanisms of mid- and high-level perceptual processes, including the involvement of cognitive functions such as attention and expectation. Here, we provide an account of various mechanisms that may give rise to IMs in neuroimaging data, and what these IMs may look like. We discuss methodologies that can be implemented for different uses of IMs and we demonstrate how IMs can provide insights into the existence, the degree and the type of neural integration mechanisms at hand. We then review a range of recent studies exploiting IMs in visual perception research, placing an emphasis on high-level vision and the influence of awareness and cognition on visual processing. We conclude by suggesting future directions that can enhance the benefits of IM-methodology in perception research.

Slow Temporal Dynamics of Motion-Induced Brightness Shift Reveals Impact of Adaptation

Brightness of an object is determined by various factors including ambient illumination, surface reflectance of the object, and spatial and temporal relation between the object and its surrounding context. Recently, it has been demonstrated that the motion of an object alters its own and nearby object’s appearance such as brightness and color. This study aims to unveil mechanisms of the motion-induced brightness shift by measuring its temporal dynamics. We found that the motion-induced brightness shift occurred instantaneously with the motion onset when the motion was introduced abruptly. However, the brightness of a stationary object was altered gradually by a nearby moving object in about 2 s time window when the stationary dot was introduced abruptly. Two distinct temporal dynamics (slow vs. fast) of the motion-induced brightness shift demonstrate that both slow neural adaptation and fast neural normalization processes determine the brightness shift induced by the object’s motion.

Inverted Encoding Models Reconstruct an Arbitrary Model Response, Not the Stimulus

Probing how large populations of neurons represent stimuli is key to understanding sensory representations as many stimulus characteristics can only be discerned from population activity and not from individual single-units. Recently, inverted encoding models have been used to produce channel response functions from large spatial-scale measurements of human brain activity that are reminiscent of single-unit tuning functions and have been proposed to assay "population-level stimulus representations" (Sprague et al., 2018a). However, these channel response functions do not assay population tuning. We show by derivation that the channel response function is only determined up to an invertible linear transform. Thus, these channel response functions are arbitrary, one of an infinite family and therefore not a unique description of population representation. Indeed, simulations demonstrate that bimodal, even random, channel basis functions can account perfectly well for population responses without any underlying neural response units that are so tuned. However, the approach can be salvaged by extending it to reconstruct the stimulus, not the assumed model. We show that when this is done, even using bimodal and random channel basis functions, a unimodal function peaking at the appropriate value of the stimulus is recovered which can be interpreted as a measure of population selectivity. More precisely, the recovered function signifies how likely any value of the stimulus is, given the observed population response. Whether an analysis is recovering the hypothetical responses of an arbitrary model rather than assessing the selectivity of population representations is not an issue unique to the inverted encoding model and human neuroscience, but a general problem that must be confronted as more complex analyses intervene between measurement of population activity and presentation of data.

Measuring Integration Processes in Visual Symmetry with Frequency-Tagged EEG

Symmetry is a highly salient feature of the natural world which requires integration of visual features over space. The aim of the current work is to isolate dynamic neural correlates of symmetry-specific integration processes. We measured steady-state visual evoked potentials (SSVEP) as participants viewed symmetric patterns comprised of distinct spatial regions presented at two different frequencies (f₁ and f₂). We measured intermodulation components, shown to reflect non-linear processing at the neural level, indicating integration of spatially separated parts of the pattern. We generated a wallpaper pattern containing two reflection symmetry axes by tiling the plane with a two-fold reflection symmetric unit-pattern and split each unit-pattern diagonally into separate parts which could be presented at different frequencies. We compared SSVEPs measured for wallpapers and control patterns for which both images were equal in terms of translation and rotation symmetry but reflection symmetry could only emerge for the wallpaper pattern through integration of the image-pairs. We found that low-frequency intermodulation components differed between the wallpaper and control stimuli, indicating the presence of integration mechanisms specific to reflection symmetry. These results showed that spatial integration specific to symmetry perception can be isolated through a combination of stimulus design and the frequency tagging approach.

Evidence for an Optimal Algorithm Underlying Signal Combination in Human Visual Cortex

How does the cortex combine information from multiple sources? We tested several computational models against data from steady-state electroencephalography (EEG) experiments in humans, using periodic visual stimuli combined across either retinal location or eye-of-presentation. A model in which signals are raised to an exponent before being summed in both the numerator and the denominator of a gain control nonlinearity gave the best account of the data. This model also predicted the pattern of responses in a range of additional conditions accurately and with no free parameters, as well as predicting responses at harmonic and intermodulation frequencies between 1 and 30 Hz. We speculate that this model implements the optimal algorithm for combining multiple noisy inputs, in which responses are proportional to the weighted sum of both inputs. This suggests a novel purpose for cortical gain control: implementing optimal signal combination via mutual inhibition, perhaps explaining its ubiquity as a neural computation.

Adaptive Value Normalization in the Prefrontal Cortex Is Reduced by Memory Load

Adaptation facilitates neural representation of a wide range of diverse inputs, including reward values. Adaptive value coding typically relies on contextual information either obtained from the environment or retrieved from and maintained in memory. However, it is unknown whether having to retrieve and maintain context information modulates the brain's capacity for value adaptation. To address this issue, we measured hemodynamic responses of the prefrontal cortex (PFC) in two studies on risky decision-making. In each trial, healthy human subjects chose between a risky and a safe alternative; half of the participants had to remember the risky alternatives, whereas for the other half they were presented visually. The value of safe alternatives varied across trials. PFC responses adapted to contextual risk information, with steeper coding of safe alternative value in lower-risk contexts. Importantly, this adaptation depended on working memory load, such that response functions relating PFC activity to safe values were steeper with presented versus remembered risk. An independent second study replicated the findings of the first study and showed that similar slope reductions also arose when memory maintenance demands were increased with a secondary working memory task. Formal model comparison showed that a divisive normalization model fitted effects of both risk context and working memory demands on PFC activity better than alternative models of value adaptation, and revealed that reduced suppression of background activity was the critical parameter impairing normalization with increased memory maintenance demand. Our findings suggest that mnemonic processes can constrain normalization of neural value representations.

Attention to Multiple Objects Facilitates Their Integration in Prefrontal and Parietal Cortex

Selective attention is known to interact with perceptual organization. In visual scenes, individual objects that are distinct and discriminable may occur on their own, or in groups such as a stack of books. The main objective of this study is to probe the neural interaction that occurs between individual objects when attention is directed toward one or more objects. Here we record steady-state visual evoked potentials via electrocorticography to directly assess the responses to individual stimuli and to their interaction. When human participants attend to two adjacent stimuli, prefrontal and parietal cortex shows a selective enhancement of only the neural interaction between stimuli, but not the responses to individual stimuli. When only one stimulus is attended, the neural response to that stimulus is selectively enhanced in prefrontal and parietal cortex. In contrast, early visual areas generally manifest responses to individual stimuli and to their interaction regardless of attentional task, although a subset of the responses is modulated similarly to prefrontal and parietal cortex. Thus, the neural representation of the visual scene as one progresses up the cortical hierarchy becomes more highly task-specific and represents either individual stimuli or their interaction, depending on the behavioral goal. Attention to multiple objects facilitates an integration of objects akin to perceptual grouping.SIGNIFICANCE STATEMENT Individual objects in a visual scene are seen as distinct entities or as parts of a whole. Here we examine how attention to multiple objects affects their neural representation. Previous studies measured single-cell or fMRI responses and obtained only aggregate measures that combined the activity to individual stimuli as well as their potential interaction. Here, we directly measure electrocorticographic steady-state responses corresponding to individual objects and to their interaction using a frequency-tagging technique. Attention to two stimuli increases the interaction component that is a hallmark for perceptual integration of stimuli. Furthermore, this stimulus-specific interaction is represented in prefrontal and parietal cortex in a task-dependent manner.

Development of contrast normalization mechanisms during childhood and adolescence

Contrast sensitivity is regulated by neural mechanisms that flexibly adjust responsiveness to optimize stimulus encoding across different environments. Here we studied the developmental status of gain control mechanisms in school-age children (5-17years) and adults using a visual masking paradigm. A variable contrast, spatially random 2-D noise test pattern was masked by the presence of a superimposed independent noise pattern presented at 0, 12 and 40% contrast. Frequency-tagged steady state visual evoked potentials were used to separately record responses to the test (5.14Hz) and the mask (7.2Hz). By incrementally increasing the test contrast we measured contrast response functions for each mask contrast. The unmasked contrast response functions were largely similar in shape across age, but peak amplitude was higher in the children. Masking shifted the contrast response function rightward on the contrast axis in both the adults and older children, elevating contrast thresholds by a similar factor across age. However, in younger children, masking resulted in a change in the slope of the contrast response function. These findings suggest that immaturity in the contrast normalization process persists until approximately 11years of age.

Altered visual contrast gain control is sensitive for idiopathic generalized epilepsies

OBJECTIVE

Visual hyperexcitability in the form of abnormal contrast gain control has been shown in photosensitive epilepsy and idiopathic generalized epilepsies. We assessed the accuracy and reliability of measures of visual contrast gain control in discerning individuals with idiopathic generalized epilepsies from healthy controls.

METHODS

Twenty-four adult patients with idiopathic generalized epilepsy and 32 neurotypical control subjects from two study sites participated in a prospective, cross-sectional study. We recorded steady-state visual evoked potentials to a wide range of contrasts of a flickering grating stimulus. The resultant response magnitude vs. contrast curves were fitted to a standard model of contrast response function, and the model parameters were used as input features to a linear classifier to separate patients from controls. Additionally we compared the relative contribution of model parameters towards the classification using a sparse feature-selection approach.

RESULTS

Classification accuracy was 80% or better. Sensitivity and specificity both were 80-85%. Cross validation confirmed robust classifier performance generalizable across the data from the two samples. Patients' relative lack of gain control at high contrasts was the most important information distinguishing patients from controls.

CONCLUSIONS

Individuals with idiopathic generalized epilepsy were distinguishable from the neurotypical with a high degree of accuracy and reliability by a reduction in gain control at high contrasts.

SIGNIFICANCE

Gain control is an essential neural operation that regulates neuronal sensitivity to stimuli and may represent a novel biomarker of hyperexcitability.

Short-term monocular patching boosts the patched eye's response in visual cortex

Purpose: Several recent studies have demonstrated that following short-term monocular deprivation in normal adults, the patched eye, rather than the unpatched eye, becomes stronger in subsequent binocular viewing. However, little is known about the site and nature of the underlying processes. In this study, we examine the underlying mechanisms by measuring steady-state visual evoked potentials (SSVEPs) as an index of the neural contrast response in early visual areas.

Methods: The experiment consisted of three consecutive stages: a pre-patching EEG recording (14 minutes), a monocular patching stage (2.5 hours) and a post-patching EEG recording (14 minutes; started immediately after the removal of the patch). During the patching stage, a diffuser (transmits light but not pattern) was placed in front of one randomly selected eye. During the EEG recording stage, contrast response functions for each eye were measured.

Results: The neural responses from the patched eye increased after the removal of the patch, whilst the responses from the unpatched eye remained the same. Such phenomena occurred under both monocular and dichoptic viewing conditions.

Conclusions: We interpret this eye dominance plasticity in adult human visual cortex as homeostatic intrinsic plasticity regulated by an increase of contrast-gain in the patched eye.

Semantic Wavelet-Induced Frequency-Tagging (SWIFT) Periodically Activates Category Selective Areas While Steadily Activating Early Visual Areas

Primate visual systems process natural images in a hierarchical manner: at the early stage, neurons are tuned to local image features, while neurons in high-level areas are tuned to abstract object categories. Standard models of visual processing assume that the transition of tuning from image features to object categories emerges gradually along the visual hierarchy. Direct tests of such models remain difficult due to confounding alteration in low-level image properties when contrasting distinct object categories. When such contrast is performed in a classic functional localizer method, the desired activation in high-level visual areas is typically accompanied with activation in early visual areas. Here we used a novel image-modulation method called SWIFT (semantic wavelet-induced frequency-tagging), a variant of frequency-tagging techniques. Natural images modulated by SWIFT reveal object semantics periodically while keeping low-level properties constant. Using functional magnetic resonance imaging (fMRI), we indeed found that faces and scenes modulated with SWIFT periodically activated the prototypical category-selective areas while they elicited sustained and constant responses in early visual areas. SWIFT and the localizer were selective and specific to a similar extent in activating category-selective areas. Only SWIFT progressively activated the visual pathway from low- to high-level areas, consistent with predictions from standard hierarchical models. We confirmed these results with criterion-free methods, generalizing the validity of our approach and show that it is possible to dissociate neural activation in early and category-selective areas. Our results provide direct evidence for the hierarchical nature of the representation of visual objects along the visual stream and open up future applications of frequency-tagging methods in fMRI.

Targets but not flankers are suppressed in crowding as revealed by EEG frequency tagging

Perception of a visual target can strongly deteriorate in the presence of flanking elements (crowding). For example, adding lines next to a vernier makes vernier offset discrimination difficult. Crowding is often considered a bottleneck of low-level vision, determined by the unavoidable limitations of the early visual system. In accordance with this proposal, neural processing of the flankers should be impaired in crowding as much as that of the target. To test this prediction, we used steady-state visually evoked potentials (ssVEPs) to separate target responses from flanker responses. We presented a vernier target either alone or flanked by lines, which had the same color as the vernier or a different color. Crowding by same-color flankers was stronger than by different-color flankers. Mirroring the behavioral results, ssVEP amplitudes corresponding to the target were higher for different-color flankers than for same-color flankers. Flanker related ssVEPs, however, did not depend on crowding strength. It seems that target, but not flanker processing, is susceptible to crowding. In line with previous results, we suggest that crowding is not caused by low-level interferences but is linked to target-flanker grouping instead.

Value-based attentional capture influences context-dependent decision-making

Normative theories posit that value-based decision-making is context independent. However, decisions between two high-value options can be suboptimally biased by the introduction of a third low-value option. This context-dependent modulation is consistent with the divisive normalization of the value of each stimulus by the total value of all stimuli. In addition, an independent line of research demonstrates that pairing a stimulus with a high-value outcome can lead to attentional capture that can mediate the efficiency of visual information processing. Here we tested the hypothesis that value-based attentional capture interacts with value-based normalization to influence the optimality of decision-making. We used a binary-choice paradigm in which observers selected between two targets and the color of each target indicated the magnitude of their reward potential. Observers also had to simultaneously ignore a task-irrelevant distractor rendered in a color that was previously associated with a specific reward magnitude. When the color of the task-irrelevant distractor was previously associated with a high reward, observers responded more slowly and less optimally. Moreover, as the learned value of the distractor increased, electrophysiological data revealed an attenuation of the lateralized N1 and N2Pc responses evoked by the relevant choice stimuli and an attenuation of the late positive deflection (LPD). Collectively, these behavioral and electrophysiological data suggest that value-based attentional capture and value-based normalization jointly mediate the influence of context on free-choice decision-making.

Attention alters orientation processing in the human lateral geniculate nucleus

Orientation selectivity is a cornerstone property of vision, commonly believed to emerge in the primary visual cortex. We found that reliable orientation information could be detected even earlier, in the human lateral geniculate nucleus, and that attentional feedback selectively altered these orientation responses. This attentional modulation may allow the visual system to modify incoming feature-specific signals at the earliest possible processing site.

Steady-state contrast response functions provide a sensitive and objective index of amblyopic deficits

PURPOSE

Visual deficits in amblyopia are neural in origin, yet are difficult to characterize with functional magnetic resonance imagery (fMRI). Our aim was to develop an objective electroencephalography (EEG) paradigm that can be used to provide a clinically useful index of amblyopic deficits.

METHODS

We used steady-state visual evoked potentials (SSVEPs) to measure full contrast response functions in both amblyopic (n = 10, strabismic or mixed amblyopia, mean age: 44 years) and control (n = 5, mean age: 31 years) observers, both with and without a dichoptic mask.

RESULTS

At the highest target contrast, the ratio of amplitudes across the weaker and stronger eyes was highly correlated (r = 0.76) with the acuity ratio between the eyes. We also found that the contrast response function in the amblyopic eye had both a greatly reduced amplitude and a shallower slope, but that surprisingly dichoptic masking was weaker than in controls. The results were compared with the predictions of a computational model of amblyopia and suggest a modification to the model whereby excitatory (but not suppressive) signals are attenuated in the amblyopic eye.

CONCLUSIONS

We suggest that SSVEPs offer a sensitive and objective measure of the ocular imbalance in amblyopia and could be used to assess the efficacy of amblyopia therapies currently under development.

The steady-state visual evoked potential in vision research: A review

Periodic visual stimulation and analysis of the resulting steady-state visual evoked potentials were first introduced over 80 years ago as a means to study visual sensation and perception. From the first single-channel recording of responses to modulated light to the present use of sophisticated digital displays composed of complex visual stimuli and high-density recording arrays, steady-state methods have been applied in a broad range of scientific and applied settings.The purpose of this article is to describe the fundamental stimulation paradigms for steady-state visual evoked potentials and to illustrate these principles through research findings across a range of applications in vision science.

Developmental self-construction and -configuration of functional neocortical neuronal networks

The prenatal development of neural circuits must provide sufficient configuration to support at least a set of core postnatal behaviors. Although knowledge of various genetic and cellular aspects of development is accumulating rapidly, there is less systematic understanding of how these various processes play together in order to construct such functional networks. Here we make some steps toward such understanding by demonstrating through detailed simulations how a competitive co-operative (‘winner-take-all’, WTA) network architecture can arise by development from a single precursor cell. This precursor is granted a simplified gene regulatory network that directs cell mitosis, differentiation, migration, neurite outgrowth and synaptogenesis. Once initial axonal connection patterns are established, their synaptic weights undergo homeostatic unsupervised learning that is shaped by wave-like input patterns. We demonstrate how this autonomous genetically directed developmental sequence can give rise to self-calibrated WTA networks, and compare our simulation results with biological data.

Models of learning in artificial neural networks generally assume that the neurons and approximate network are given, and then learning tunes the synaptic weights. By contrast, we address the question of how an entire functional neuronal network containing many differentiated neurons and connections can develop from only a single progenitor cell. We chose a winner-take-all network as the developmental target, because it is a computationally powerful circuit, and a candidate motif of neocortical networks. The key aspect of this challenge is that the developmental mechanisms must be locally autonomous as in Biology: They cannot depend on global knowledge or supervision. We have explored this developmental process by simulating in physical detail the fundamental biological behaviors, such as cell proliferation, neurite growth and synapse formation that give rise to the structural connectivity observed in the superficial layers of the neocortex. These differentiated, approximately connected neurons then adapt their synaptic weights homeostatically to obtain a uniform electrical signaling activity before going on to organize themselves according to the fundamental correlations embedded in a noisy wave-like input signal. In this way the precursor expands itself through development and unsupervised learning into winner-take-all functionality and orientation selectivity in a biologically plausible manner.

How to use fMRI functional localizers to improve EEG/MEG source estimation

EEG and MEG have excellent temporal resolution, but the estimation of the neural sources that generate the signals recorded by the sensors is a difficult, ill-posed problem. The high spatial resolution of functional MRI makes it an ideal tool to improve the localization of the EEG/MEG sources using data fusion. However, the combination of the two techniques remains challenging, as the neural generators of the EEG/MEG and BOLD signals might in some cases be very different. Here we describe a data fusion approach that was developed by our team over the last decade in which fMRI is used to provide source constraints that are based on functional areas defined individually for each subject. This mini-review describes the different steps that are necessary to perform source estimation using this approach. It also provides a list of pitfalls that should be avoided when doing fMRI-informed EEG/MEG source imaging. Finally, it describes the advantages of using a ROI-based approach for group-level analysis and for the study of sensory systems.

Broadband noise masks suppress neural responses to narrowband stimuli

White pixel noise is widely used to estimate the level of internal noise in a system by injecting external variance into the detecting mechanism. Recent work (Baker and Meese, 2012) has provided psychophysical evidence that such noise masks might also cause suppression that could invalidate estimates of internal noise. Here we measure neural population responses directly, using steady-state visual evoked potentials, elicited by target stimuli embedded in different mask types. Sinusoidal target gratings of 1 c/deg flickered at 5 Hz, and were shown in isolation, or with superimposed orthogonal grating masks or 2D white noise masks, flickering at 7 Hz. Compared with responses to a blank screen, the Fourier amplitude at the target frequency increased monotonically as a function of target contrast when no mask was present. Both orthogonal and white noise masks caused rightward shifts of the contrast response function, providing evidence of contrast gain control suppression. We also calculated within-observer amplitude variance across trials. This increased in proportion to the target response, implying signal-dependent (i.e., multiplicative) noise at the system level, the implications of which we discuss for behavioral tasks. This measure of variance was reduced by both mask types, consistent with the changes in mean target response. An alternative variety of noise, which we term zero-dimensional noise, involves trial-by-trial jittering of the target contrast. This type of noise produced no gain control suppression, and increased the amplitude variance across trials.

Dissociation of part-based and integrated neural responses to faces by means of electroencephalographic frequency tagging

In order to isolate the repetition suppression effects for each part of a whole-face stimulus, the left and right halves of face stimuli were flickered at different frequency rates (5.88 or 7.14 Hz), changing or not changing identity at every stimulation cycle. The human electrophysiological (electroencephalographic) responses to each face half increased in amplitude when different rather than repeated face half identities were presented at every stimulation cycle. Contrary to the repetition suppression effects for whole faces, which are usually found over the right occipito-temporal cortex, these part-based repetition suppression effects were found on all posterior electrode sites and were unchanged when the two face halves were manipulated by separation, lateral misalignment, or inversion. In contrast, intermodulation components (e.g. 7.14-5.88 = 1.26 Hz) were found mainly over the right occipito-temporal cortex and were significantly reduced following the aforementioned manipulations. In addition, the intermodulation components decreased substantially for face halves belonging to different identities, which form a less coherent face than when they belong to the same face identity. These observations provide objective evidence for dissociation between part-based and integrated (i.e. holistic/configural) responses to faces in the human brain, suggesting that only responses to integrated face parts reflect high-level, possibly face-specific, representations.

Abnormal visual gain control in a Parkinson's disease model

Our understanding of Parkinson's disease (PD) has been revolutionized by the discovery of disease-causing genetic mutations. The most common of these is the G2019S mutation in the LRRK2 kinase gene, which leads to increased kinase activity. However, the link between increased kinase activity and PD is unclear. Previously, we showed that dopaminergic expression of the human LRRK2-G2019S transgene in flies led to an activity-dependent loss of vision in older animals and we hypothesized that this may have been preceded by a failure to regulate neuronal activity correctly in younger animals. To test this hypothesis, we used a sensitive measure of visual function based on frequency-tagged steady-state visually evoked potentials. Spectral analysis allowed us to identify signals from multiple levels of the fly visual system and wild-type visual response curves were qualitatively similar to those from human cortex. Dopaminergic expression of hLRRK2-G2019S increased contrast sensitivity throughout the retinal network. To test whether this was due to increased kinase activity, we fed Drosophila with kinase inhibitors targeted at LRRK2. Contrast sensitivity in both day 1 and day 14 flies was normalized by a novel LRRK2 kinase inhibitor ‘BMPPB-32’. Biochemical and cellular assays suggested that BMPPB-32 would be a more specific kinase inhibitor than LRRK2-IN-1. We confirmed this in vivo, finding that dLRRK⁻ null flies show large off-target effects with LRRK2-IN-1 but not BMPPB-32. Our data link the increased Kinase activity of the G2019S-LRRK2 mutation to neuronal dysfunction and demonstrate the power of the Drosophila visual system in assaying the neurological effects of genetic diseases and therapies.

Perceptual consequence of normalization revealed by a novel brightness induction

The human brain is renowned for its dynamic regulation of sensory inputs, which enables our brain to operate under an enormous range of physical energy with sensory neurons whose processing range is limited. Here we present a novel and strong brightness induction that reflects neural mechanisms underlying this dynamic regulation of sensory inputs. When physically identical, stationary and moving objects are viewed simultaneously, the stationary and moving objects appear largely different. Experiments reveal that normalization at multiple stages of visual processing provides a plausible account for the large shifts in perceptual experiences, observed in both the stationary and the moving objects. This novel brightness induction suggests that brightness of an object is influenced not only by variations in surrounding light (i.e. simultaneous contrast) but also by dynamically changing neural responses associated with stimulus motion.

Dynamics of local input normalization result from balanced short- and long-range intracortical interactions in area V1

To efficiently drive many behaviors, sensory systems have to integrate the activity of large neuronal populations within a limited time window. These populations need to rapidly achieve a robust representation of the input image, probably through canonical computations such as divisive normalization. However, little is known about the dynamics of the corticocortical interactions implementing these rapid and robust computations. Here, we measured the real-time activity of a large neuronal population in V1 using voltage-sensitive dye imaging in behaving monkeys. We found that contrast gain of the population increases over time with a time constant of ~30 ms and propagates laterally over the cortical surface. This dynamic is well accounted for by a divisive normalization achieved through a recurrent network that transiently increases in size after response onset with a slow swelling speed of 0.007-0.014 m/s, suggesting a polysynaptic intracortical origin. In the presence of a surround, this normalization pool is gradually balanced by lateral inputs propagating from distant cortical locations. This results in a centripetal propagation of surround suppression at a speed of 0.1-0.3 m/s, congruent with horizontal intracortical axons speed. We propose that a simple generalized normalization scheme can account for both the dynamical contrast response function through recurrent polysynaptic intracortical loops and for the surround suppression through long-range monosynaptic horizontal spread. Our results demonstrate that V1 achieves a rapid and robust context-dependent input normalization through a timely push-pull between local and lateral networks. We suggest that divisive normalization, a fundamental canonical computation, should be considered as a dynamic process.

More is not always better: adaptive gain control explains dissociation between perception and action

Moving objects generate motion information at different scales, which are processed in the visual system with a bank of spatiotemporal frequency channels. It is not known how the brain pools this information to reconstruct object speed and whether this pooling is generic or adaptive; that is, dependent on the behavioral task. We used rich textured motion stimuli of varying bandwidths to decipher how the human visual motion system computes object speed in different behavioral contexts. We found that, although a simple visuomotor behavior such as short-latency ocular following responses takes advantage of the full distribution of motion signals, perceptual speed discrimination is impaired for stimuli with large bandwidths. Such opposite dependencies can be explained by an adaptive gain control mechanism in which the divisive normalization pool is adjusted to meet the different constraints of perception and action.