SCN1A-deficient excitatory neuronal networks display mutation-specific phenotypes.

Dravet syndrome is a severe epileptic encephalopathy, characterized by (febrile) seizures, behavioural problems and developmental delay. Eighty per cent of patients with Dravet syndrome have a mutation in SCN1A, encoding Nav1.1. Milder clinical phenotypes, such as GEFS+ (generalized epilepsy with febrile seizures plus), can also arise from SCN1A mutations. Predicting the clinical phenotypic outcome based on the type of mutation remains challenging, even when the same mutation is inherited within one family. This clinical and genetic heterogeneity adds to the difficulties of predicting disease progression and tailoring the prescription of anti-seizure medication. Understanding the neuropathology of different SCN1A mutations may help to predict the expected clinical phenotypes and inform the selection of best-fit treatments. Initially, the loss of Na+-current in inhibitory neurons was recognized specifically to result in disinhibition and consequently seizure generation. However, the extent to which excitatory neurons contribute to the pathophysiology is currently debated and might depend on the patient clinical phenotype or the specific SCN1A mutation. To examine the genotype-phenotype correlations of SCN1A mutations in relation to excitatory neurons, we investigated a panel of patient-derived excitatory neuronal networks differentiated on multi-electrode arrays. We included patients with different clinical phenotypes, harbouring various SCN1A mutations, along with a family in which the same mutation led to febrile seizures, GEFS+ or Dravet syndrome. We hitherto describe a previously unidentified functional excitatory neuronal network phenotype in the context of epilepsy, which corresponds to seizurogenic network prediction patterns elicited by proconvulsive compounds. We found that excitatory neuronal networks were affected differently, depending on the type of SCN1A mutation, but did not segregate according to clinical severity. Specifically, loss-of-function mutations could be distinguished from missense mutations, and mutations in the pore domain could be distinguished from mutations in the voltage sensing domain. Furthermore, all patients showed aggravated neuronal network responses at febrile temperatures compared with controls. Finally, retrospective drug screening revealed that anti-seizure medication affected GEFS+ patient- but not Dravet patient-derived neuronal networks in a patient-specific and clinically relevant manner. In conclusion, our results indicate a mutation-specific excitatory neuronal network phenotype, which recapitulates the foremost clinically relevant features, providing future opportunities for precision therapies.

Loss-of-function variants in the schizophrenia risk gene SETD1A alter neuronal network activity in human neurons through the cAMP/PKA pathway.

Heterozygous loss-of-function (LoF) mutations in SETD1A, which encodes a subunit of histone H3 lysine 4 methyltransferase, cause a neurodevelopmental syndrome and increase the risk for schizophrenia. Using CRISPR-Cas9, we generate excitatory/inhibitory neuronal networks from human induced pluripotent stem cells with a SETD1A heterozygous LoF mutation (SETD1A+/-). Our data show that SETD1A haploinsufficiency results in morphologically increased dendritic complexity and functionally increased bursting activity. This network phenotype is primarily driven by SETD1A haploinsufficiency in glutamatergic neurons. In accordance with the functional changes, transcriptomic profiling reveals perturbations in gene sets associated with glutamatergic synaptic function. At the molecular level, we identify specific changes in the cyclic AMP (cAMP)/Protein Kinase A pathway pointing toward a hyperactive cAMP pathway in SETD1A+/- neurons. Finally, by pharmacologically targeting the cAMP pathway, we are able to rescue the network deficits in SETD1A+/- cultures. Our results demonstrate a link between SETD1A and the cAMP-dependent pathway in human neurons.

Emergence of border-ownership by large-scale consistency and long-range interactions: Neuro-computational model to reflect global configurations.

The visual system performs remarkably well to perceive depth order of surfaces without stereo disparity, indicating the importance of figure-ground organization based on pictorial cues. To understand how figure-ground organization emerges, it is essential to investigate how the global configuration of an image is reflected. In the past, many neuro-computational models developed to reproduce figure-ground organization implemented algorithms to give a bias to convex areas. However, in certain conditions, a convex area can be perceived as a hole and a nonconvex area as figural. This occurs when the surface properties of the convex area are consistent with the background and, hence, are grouped together in our perception. We argue that large-scale consistency of surface properties is reflected in the border-ownership computation. We developed a model, called DISC2, that first analyzes relationships between two border-ownership signals of all possible combinations in the image. It then enhances signals if they satisfy the following conditions: (a) the two signals fit to a convex configuration and (b) the surface properties at the locations of the two signals are consistent. The strength of the enhancement decays with distance between the signals. The model gives extremely robust responses to various images with complexities both in shape and depth order. Furthermore, we developed an advanced version of the model ("augmented model") where the global computation above interacts with local computation of curvilinearity, which further enhanced the robust nature of the model. The results suggest the involvement of similar computational processes in the brain for figure-ground organization. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Dynamics of a Mutual Inhibition Circuit between Pyramidal Neurons Compared to Human Perceptual Competition.

Neural competition plays an essential role in active selection processes of noisy and ambiguous input signals, and it is assumed to underlie emergent properties of brain functioning, such as perceptual organization and decision-making. Despite ample theoretical research on neural competition, experimental tools to allow neurophysiological investigation of competing neurons have not been available. We developed a "hybrid" system where real-life neurons and a computer-simulated neural circuit interacted. It enabled us to construct a mutual inhibition circuit between two real-life pyramidal neurons. We then asked what dynamics this minimal unit of neural competition exhibits and compared them with the known behavioral-level dynamics of neural competition. We found that the pair of neurons shows bistability when activated simultaneously by current injections. The addition of modeled synaptic noise and changes in the activation strength showed that the dynamics of the circuit are strikingly similar to the known properties of bistable visual perception: The distribution of dominance durations showed a right-skewed shape, and the changes of the activation strengths caused changes in dominance, dominance durations, and reversal rates as stated in the well-known empirical laws of bistable perception known as Levelt's propositions.SIGNIFICANCE STATEMENT Visual perception emerges as the result of neural systems actively organizing visual signals that involves selection processes of competing neurons. While the neural competition, realized by a "mutual inhibition" circuit has been examined in many theoretical studies, its properties have not been investigated in real neurons. We have developed a "hybrid" system where two real-life pyramidal neurons in a mouse brain slice interact through a computer-simulated mutual inhibition circuit. We found that simultaneous activation of the neurons leads to bistable activity. We investigated the effect of noise and the effect of changes in the activation strength on the dynamics. We observed that the pair of neurons exhibit dynamics strikingly similar to the known properties of bistable visual perception.

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.

EEG frequency tagging reveals higher order intermodulation components as neural markers of learned holistic shape representations.

Shape perception is intrinsically holistic: combinations of features give rise to configurations with emergent properties that are different from the sum of the parts. The current study investigated neural markers of holistic shape representations learned by means of categorization training. We used the EEG frequency tagging technique, where two parts of a shape stimulus were 'tagged' by modifying their contrast at different temporal frequencies. Signals from both parts are integrated and, as a result, emergent frequency components (so-called, intermodulation responses, IMs), caused by nonlinear interaction of two frequency signals, are observed in the EEG spectrum. First, participants were trained in 4 sessions to discriminate highly similar, unfamiliar shapes into two categories, defined based on the combination of features. After training, EEG was recorded while frequency-tagged shapes from either the trained or the untrained shape family were presented. For all IMs combined, no learning effects were detected, but post hoc analyses of higher-order IMs revealed stronger occipital and occipito-temporal IMs for both trained and untrained exemplars of the trained shape family as compared to the untrained shape family. In line with recent findings, we suggest that the higher-order IMs may reflect high-level visual computations, like holistic shape categorization, resulting from a cascade of non-linear operations. Higher order frequency responses are relatively low in power, hence results should be interpreted cautiously and future research is needed to confirm these effects. In general, these findings are, to our knowledge, the first to show IMs as a neural correlate of perceptual learning.

EEG frequency tagging dissociates between neural processing of motion synchrony and human quality of multiple point-light dancers.

Do we perceive a group of dancers moving in synchrony differently from a group of drones flying in-sync? The brain has dedicated networks for perception of coherent motion and interacting human bodies. However, it is unclear to what extent the underlying neural mechanisms overlap. Here we delineate these mechanisms by independently manipulating the degree of motion synchrony and the humanoid quality of multiple point-light displays (PLDs). Four PLDs moving within a group were changing contrast in cycles of fixed frequencies, which permits the identification of the neural processes that are tagged by these frequencies. In the frequency spectrum of the steady-state EEG we found two emergent frequency components, which signified distinct levels of interactions between PLDs. The first component was associated with motion synchrony, the second with the human quality of the moving items. These findings indicate that visual processing of synchronously moving dancers involves two distinct neural mechanisms: one for the perception of a group of items moving in synchrony and one for the perception of a group of moving items with human quality. We propose that these mechanisms underlie high-level perception of social interactions.

Frequency tagging yields an objective neural signature of Gestalt formation.

The human visual system integrates separate visual inputs into coherently organized percepts, going beyond the information given. A striking example is the perception of an illusory square when physically separated inducers are positioned and oriented in a square-like configuration (illusory condition). This illusory square disappears when the specific configuration is broken, for instance, by rotating each inducer (non-illusory condition). Here we used frequency tagging and electroencephalography (EEG) to identify an objective neural signature of the global integration required for illusory surface perception. Two diagonal inducers were contrast-modulated at different frequency rates f1 and f2, leading to EEG responses exactly at these frequencies over the occipital cortex. Most importantly, nonlinear intermodulation (IM) components (e.g., f1+f2) appeared in the frequency spectrum, and were much larger in response to the illusory square figure than the non-illusory control condition. Since the IMs reflect long-range interactions between the signals from the inducers, these data provide an objective (i.e., at a precise and predicted EEG frequency) signature of neural processes involved in the emergence of illusory surface perception. More generally, these findings help to establish EEG frequency-tagging as a highly valuable approach to investigate the underlying neural mechanisms of subjective Gestalt phenomena in an objective and quantitative manner, at the system level in humans.

Temporal dynamics of different cases of bi-stable figure-ground perception.

Segmentation of a visual scene in "figure" and "ground" is essential for perception of the three-dimensional layout of a scene. In cases of bi-stable perception, two distinct figure-ground interpretations alternate over time. We were interested in the temporal dynamics of these alternations, in particular when the same image is presented repeatedly, with short blank periods in-between. Surprisingly, we found that the intermittent presentation of Rubin's classical "face-or-vase" figure, which is frequently taken as a standard case of bi-stable figure-ground perception, often evoked perceptual switches during the short presentations and stabilization was not prominent. Interestingly, bi-stable perception of Kanizsa's anomalous transparency figure did strongly stabilize across blanks. We also found stabilization for the Necker cube, which we used for comparison. The degree of stabilization (and the lack of it) varied across stimuli and across individuals. Our results indicate, against common expectation, that the stabilization phenomenon cannot be generally evoked by intermittent presentation. We argue that top-down feedback factors such as familiarity, semantics, expectation, and perceptual bias contribute to the complex processes underlying the temporal dynamics of bi-stable figure-ground perception.

Depth perception of illusory surfaces.

The perception of an illusory surface, a subjectively perceived surface that is not given in the image, is one of the most intriguing phenomena in vision. It strongly influences the perception of some fundamental properties, namely, depth, lightness and contours. Recently, we suggested (1) that the context-sensitive mechanism of depth computation plays a key role in creating the illusion, (2) that the illusory lightness perception can be explained by an influence of depth perception on the lightness computation, and (3) that the perception of variations of the Kanizsa figure can be well-reproduced by implementing these principles in a model (Kogo, Strecha, et al., 2010). However, depth perception, lightness perception, contour perception, and their interactions can be influenced by various factors. It is essential to measure the differences between the variation figures in these aspects separately to further understand the mechanisms. As a first step, we report here the results of a new experimental paradigm to compare the depth perception of the Kanizsa figure and its variations. One of the illusory figures was presented side-by-side with a non-illusory variation whose stereo disparities were varied. Participants had to decide in which of these two figures the central region appeared closer. The results indicate that the depth perception of the illusory surface was indeed different in the variation figures. Furthermore, there was a non-linear interaction between the occlusion cues and stereo disparity cues. Implications of the results for the neuro-computational mechanisms are discussed.

The "side" matters: how configurality is reflected in completion.

The perception of figure-ground organization is a highly context-sensitive phenomenon. Accumulating evidence suggests that the so-called completion phenomenon is tightly linked to this figure-ground organization. While many computational models have applied borderline completion algorithms based on the detection of boundary alignments, we point out the problems of this approach. We hypothesize that completion is a result of computing the figure-ground organization. Specifically, the global interactions in the neural network activate the "border-ownership" sensitive neurons at the location where no luminance contrast is given and this activation corresponds to the perception of illusory contours. The implications of this result to the general property of emerging Gestalt percepts are discussed.

The emergent property of border-ownership and the perception of illusory surfaces in a dynamic hierarchical system.

We argued that borderline completion does not explain the completion, that the computation of border-ownership (BOWN) causes illusory signals, and that neurons activated at illusory contours represent BOWN. Although most commentaries show support to our view, they further emphasized the importance of feedback and also pointed out some examples challenging our view. The signal processing in the hierarchy and the classification of neurons are also discussed. In this reply, we explain our position on a dynamic feedback system reflecting the global configuration, and clarify our view on completion, by examining the example figures and neurophysiological data indicated in the commentaries.

Switching dynamics of border ownership: a stochastic model for bi-stable perception.

A model that computes the border-ownership (BOWN) and the depth map of the image (DISC model, Kogo et al., 2010) is applied to investigate the stochastic nature of perception in "face or vase" stimuli. The 2-D integration of BOWN signals determines the figure-ground relationships in the image. In the present application of the model, BOWN signals are made stochastic and feedback connections are established between the higher level depth perception and the lower level BOWN signals. This feedback modifies the BOWN signals to further enhance the figure-ground segregation. Adaptation results in a decay of the response which leads to alternation. This triggers recovery of the adaptation. The alternation rate decreases in response to the increased levels of disambiguation. A detailed examination of the model parameters shows a prolongation of the alternation when the stimulus is presented intermittently in some conditions. In this framework, the adaptation and the recovery processes need to be perception dependent (not physical input dependent) to reproduce human perception.

What is required for a signal to be qualified as a 'grouping' tag?

In our commentary paper (Dry, Kogo, Putzeys, & Wagemans, 2010) on 'The utility of image descriptions in the initial stages of vision: a case study of printed text' (Watt & Dakin, 2010a), we raised a few concerns about the approach to perceptual grouping proposed by Watt and Dakin. Specifically, we argued that the 'overlap tokens' resulting from convolution with Gabor filters do not reflect the global configuration of the image and hence that they would not be able to reproduce cases of context-sensitive perception such as illusory contours in the Kanizsa image. In their reply to our commentary, Watt and Dakin (2010b) showed that the responses of their model do reflect the illusory contours. In the present brief commentary paper, we explain why their data are problematic. The crucial problem is that illusory contours are not mere borderlines drawn in the gap between the pacmen. Instead, the perception of the illusory contours corresponds to the existence of border-ownership signals, which reflect the global configuration of the image.

Surface construction by a 2-D differentiation-integration process: a neurocomputational model for perceived border ownership, depth, and lightness in Kanizsa figures.

Human visual perception is a fundamentally relational process: Lightness perception depends on luminance ratios, and depth perception depends on occlusion (difference of depth) cues. Neurons in low-level visual cortex are sensitive to the difference (but not the value itself) of signals, and these differences have to be used to reconstruct the input. This process can be regarded as a 2-dimensional differentiation and integration process: First, differentiated signals for depth and lightness are created at an earlier stage of visual processing and then 2-dimensionally integrated at a later stage to construct surfaces. The subjective filling in of physically missing parts of input images (completion) can be explained as a property that emerges from this surface construction process. This approach is implemented in a computational model, called DISC (Differentiation-Integration for Surface Completion). In the DISC model, border ownership (the depth order at borderlines) is computed based on local occlusion cues (L- and T-junctions) and the distribution of borderlines. Two-dimensional integration is then applied to construct surfaces in the depth domain, and lightness values are in turn modified by these depth measurements. Illusory percepts emerge through the surface-construction process with the development of illusory border ownership and through the interaction between depth and lightness perception. The DISC model not only produces a central surface with the correctly modified lightness values of the original Kanizsa figure but also responds to variations of this figure such that it can distinguish between illusory and nonillusory configurations in a manner that is consistent with human perception.

Linking depth to lightness and anchoring within the differentiation-integration formalism.

Recently we developed a model that reproduces the Kanizsa square illusion based on two principles: (1) a spatial 2-D integration of luminance ratio and differentiated depth signals creates a "primary" lightness map and a depth map, respectively, which is then followed by (2) a modification of the primary lightness values under influence of the perceived depth (Kogo, Strecha, Van Gool, & Wagemans, 2010). Within this model, the process of the spatial integration inevitably introduced an arbitrary offset. In order to obtain absolute values of depth and lightness, the offset values needed to be determined by other constraints. This is the anchoring problem of the depth and lightness measurements. Here we report the anchoring rules that were established by investigating the model's responses to the Kanizsa square and its wide range of variations. For the primary lightness map, the highest value rule was applied, while the area rule appeared most plausible for the depth map. By applying the same principles to simple figures consisting of black and white areas of different size ratios, the model succeeded in reproducing published empirical results on lightness anchoring (Li & Gilchrist, 1999).

Shunting inhibition in accessory optic system neurons.

The interaction of excitatory and inhibitory inputs to the accessory optic system was studied with whole cell recordings in the turtle basal optic nucleus. Previous studies have shown that visual patterns, drifting in the same preferred direction, evoke excitatory and inhibitory postsynaptic events simultaneously. Analysis of the reversal potentials for these events and their pharmacological profile suggest that they are mediated by AMPA and GABA(A) receptors, respectively. Here, neurons were recorded to study nonlinear interaction between excitatory and inhibitory responses evoked by electrical microstimulation of the retina and pretectum, respectively. The responses to coincident activation of excitatory and inhibitory inputs exhibited membrane shunting in that the excitatory response amplitude, adjusted for changes in driving force, was attenuated during the onset of the inhibitory response. This nonlinear interaction was seen in many but not all stimulus pairings. In some cases, attenuation was followed by an augmentation of the excitatory response. For comparison, the size of the excitatory response was evaluated during a hyperpolarizing current pulse that directly modulated voltage-sensitive channels of a slow rectifying I(h) current. Injection of hyperpolarizing current did not cause the attenuation of the excitatory synaptic responses. We conclude that there is a nonlinear interaction between these excitatory and inhibitory synaptic currents that is not due to hyperpolarization itself, but probably is a result of their own synaptic conductance changes, i.e., shunting. Since these events are evoked by identical visual stimuli, this interaction may play a role in visual processing.

Depression of GABAergic input to identified hippocampal neurons by group III metabotropic glutamate receptors in the rat.

The release of GABA in synapses is modulated by presynaptic metabotropic glutamate receptors (mGluRs). We tested whether GABA release to identified hippocampal neurons is influenced by group III mGluR activation using the agonist L-(+)-2-amino-4-phosphonobutyric acid (L-AP4) on inhibitory postsynaptic currents (IPSCs) evoked in CA1 interneurons and pyramidal cells. In interneurons, characterized with biocytin and immunolabelling for somatostatin, evoked IPSCs were depressed by 50 micro m L-AP4 (activating mGluR4 and 8) to 68 +/- 6% of control, but they were rarely depressed in pyramidal cells (96 +/- 4% of control). At 300-500 micro m concentration (activating mGluR4, 7 and 8), L-AP4 depressed IPSCs in both interneurons (to 70 +/- 6%) and pyramidal cells (to 67 +/- 4%). The change in trial-to-trial variability and in paired-pulse depression indicated a presynaptic action. In interneurons, the degree of IPSC depression was variable (to 9-87%), and a third of IPSCs were not affected by L-AP4. The L-AP4-evoked IPSC depression was blocked by LY341495. The depression of IPSCs was similar in O-LM cells and other interneurons. The lack of cell-type selectivity and the similar efficacy of different concentrations of L-AP4 suggest that several group III mGluRs are involved in the depression of IPSCs. Electron microscopic immunocytochemistry confirmed that mGluR4, mGluR7a and mGluR8a occur in the presynaptic active zone of GABAergic terminals on interneurons, but not on those innervating pyramidal cells. The high variability of L-AP4-evoked IPSC suppression is in line with the selective expression of presynaptic mGluRs by several distinct types of GABAergic neuron innervating each interneuron type.

Morphology of the turtle accessory optic system.

Neural signals of the moving visual world are detected by a subclass of retinal ganglion cells that project to the accessory optic system in the vertebrate brainstem. We studied the dendritic morphologies and direction tuning of these brainstem neurons in turtle (Pseudemys scripta elegans) to understand their role in visual processing. Full-field checkerboard patterns were drifted on the contralateral retina while whole-cell recordings were made in the basal optic nucleus in an intact brainstem preparation in vitro. Neurobiotin diffused into the neurons during the recording and was subsequently localized in brain sections. Neuronal morphologies were traced using appropriate computer software to analyze their position in the brainstem. Most labeled neurons were fusiform in shape and had numerous varicosities along their processes. The majority of dendritic trees spread out in a transverse plane perpendicular to the rostrocaudal axis of the nucleus. Neurons near the brainstem surface were often oriented tangential to that surface, whereas more cells at the dorsal side of the nucleus were oriented radial to the brainstem surface. Further analysis of Nissl-stained neurons revealed the largest neurons are located in the rostral and medial portions of the nucleus although neurons are most densely packed in the middle of the nucleus. The preferred directions of the visual responses of the neurons in this sample did not correlate with their morphology and position in the nucleus. Therefore, the morphology of the cells in the turtle accessory optic system appears dependent on its position within the nucleus while its visual responses may depend on the synaptic inputs that contact each cell.