An active electronic bidirectional interface for high resolution interrogation of the spinal cord.

Epidural electrical stimulation (EES) has shown promise as both a clinical therapeutic tool and research aid in the study of nervous system function. However, available clinical paddles are limited to using a small number of contacts due to the burden of wires necessary to connect each contact to the therapeutic device. Here, we introduce for the first time the integration of a hermetic active electronic multiplexer onto the electrode paddle array itself, removing this interconnect limitation. We evaluated the chronic implantation of an active electronic 60-contact paddle (the HD64) on the lumbosacral spinal cord of two sheep. The HD64 was implanted for 13 months and 15 months, with no device-related malfunctions or adverse events. We identified increased selectivity in EES-evoked motor responses using dense stimulating bipoles. Further, we found that dense recording bipoles decreased the spatial correlation between channels during recordings. Finally, spatial electrode encoding enabled a neural network to accurately perform EES parameter inference for unseen stimulation electrodes, reducing training data requirements. A high-density EES paddle, containing active electronics safely integrated into neural interfaces, opens new avenues for the study of nervous system function and new therapies to treat neural injury and dysfunction.

Synergy Through Integration of Wearable EEG and Virtual Reality for Mild Cognitive Impairment and Mild Dementia Screening.

Virtual reality (VR) technologies have shown promising potential in the early diagnosis of dementia by enabling accessible and regular assessment. However, previous VR studies were restricted to the analysis of behavioral responses, so information about degenerated brain dynamics could not be directly acquired. To address this issue, we provide a cognitive impairment (CI) screening tool based on a wearable EEG device integrated into a VR platform. Subjects were asked to use a hardware setup consisting of a frontal six-channel EEG device mounted on a VR device and to perform four cognitive tasks in VR. Behavioral response profiles and EEG features were extracted during the tasks, and classifiers were trained on extracted features to differentiate subjects with CI from healthy controls (HCs). Notably, the performance of the patient classification consistently improved when EEG characteristics measured during cognitive tasks were additionally included in feature attributes than when only the task scores or resting-state EEG features were used, suggesting that our protocol provides discriminative information for screening. These results propose that the integration of EEG devices into a VR framework could emerge as a powerful and synergistic strategy for constructing an easily accessible EEG-based CI screening tool.

Face detection in untrained deep neural networks.

Face-selective neurons are observed in the primate visual pathway and are considered as the basis of face detection in the brain. However, it has been debated as to whether this neuronal selectivity can arise innately or whether it requires training from visual experience. Here, using a hierarchical deep neural network model of the ventral visual stream, we suggest a mechanism in which face-selectivity arises in the complete absence of training. We found that units selective to faces emerge robustly in randomly initialized networks and that these units reproduce many characteristics observed in monkeys. This innate selectivity also enables the untrained network to perform face-detection tasks. Intriguingly, we observed that units selective to various non-face objects can also arise innately in untrained networks. Our results imply that the random feedforward connections in early, untrained deep neural networks may be sufficient for initializing primitive visual selectivity.

Non-Cell Autonomous Epileptogenesis in Focal Cortical Dysplasia.

OBJECTIVE: Low-level somatic mosaicism in the brain has been shown to be a major genetic cause of intractable focal epilepsy. However, how a relatively few mutation-carrying neurons are able to induce epileptogenesis at the local network level remains poorly understood. METHODS: To probe the origin of epileptogenesis, we measured the excitability of neurons with MTOR mutation and nearby nonmutated neurons recorded by whole-cell patch-clamp and array-based electrodes comparing the topographic distribution of mutation. Computational simulation is used to understand neural network-level changes based on electrophysiological properties. To examine the underlying mechanism, we measured inhibitory and excitatory synaptic inputs in mutated neurons and nearby neurons by electrophysiological and histological methods using the mouse model and postoperative human brain tissue for cortical dysplasia. To explain non-cell-autonomous hyperexcitability, an inhibitor of adenosine kinase was injected into mice to enhance adenosine signaling and to mitigate hyperactivity of nearby nonmutated neurons. RESULTS: We generated mice with a low-level somatic mutation in MTOR presenting spontaneous seizures. The seizure-triggering hyperexcitability originated from nonmutated neurons near mutation-carrying neurons, which proved to be less excitable than nonmutated neurons. Interestingly, the net balance between excitatory and inhibitory synaptic inputs onto mutated neurons remained unchanged. Additionally, we found that inhibition of adenosine kinase, which affects adenosine metabolism and neuronal excitability, reduced the hyperexcitability of nonmutated neurons. INTERPRETATION: This study shows that neurons carrying somatic mutations in MTOR lead to focal epileptogenesis via non-cell-autonomous hyperexcitability of nearby nonmutated neurons. ANN NEUROL 2021;90:285-299.

Periodic clustering of simple and complex cells in visual cortex.

Neurons in the primary visual cortex (V1) are often classified as simple or complex cells, but it is debated whether they are discrete hierarchical classes of neurons or if they represent a continuum of variation within a single class of cells. Herein, we show that simple and complex cells may arise commonly from the feedforward projections from the retina. From analysis of the cortical receptive fields in cats, we show evidence that simple and complex cells originate from the periodic variation of ON-OFF segregation in the feedforward projection of retinal mosaics, by which they organize into periodic clusters in V1. From data in cats, we observed that clusters of simple and complex receptive fields correlate topographically with orientation maps, which supports our model prediction. Our results suggest that simple and complex cells are not two distinct neural populations but arise from common retinal afferents, simultaneous with orientation tuning.

Visual number sense in untrained deep neural networks.

Number sense, the ability to estimate numerosity, is observed in naïve animals, but how this cognitive function emerges in the brain remains unclear. Here, using an artificial deep neural network that models the ventral visual stream of the brain, we show that number-selective neurons can arise spontaneously, even in the complete absence of learning. We also show that the responses of these neurons can induce the abstract number sense, the ability to discriminate numerosity independent of low-level visual cues. We found number tuning in a randomly initialized network originating from a combination of monotonically decreasing and increasing neuronal activities, which emerges spontaneously from the statistical properties of bottom-up projections. We confirmed that the responses of these number-selective neurons show the single- and multineuron characteristics observed in the brain and enable the network to perform number comparison tasks. These findings provide insight into the origin of innate cognitive functions.

Projection of Orthogonal Tiling from the Retina to the Visual Cortex.

In higher mammals, the primary visual cortex (V1) is organized into diverse tuning maps of visual features. The topography of these maps intersects orthogonally, but it remains unclear how such a systematic relationship can develop. Here, we show that the orthogonal organization already exists in retinal ganglion cell (RGC) mosaics, providing a blueprint of the organization in V1. From analysis of the RGC mosaics data in monkeys and cats, we find that the ON-OFF RGC distance and ON-OFF angle of neighboring RGCs are organized into a topographic tiling across mosaics, analogous to the orthogonal intersection of cortical tuning maps. Our model simulation shows that the ON-OFF distance and angle in RGC mosaics correspondingly initiate ocular dominance/spatial frequency tuning and orientation tuning, resulting in the orthogonal intersection of cortical tuning maps. These findings suggest that the regularly structured ON-OFF patterns mirrored from the retina initiate the uniform representation of combinations of map features over the visual space.

Spontaneous Retinal Waves Can Generate Long-Range Horizontal Connectivity in Visual Cortex.

In the primary visual cortex (V1) of higher mammals, long-range horizontal connections (LHCs) are observed to develop, linking iso-orientation domains of cortical tuning. It is unknown how this feature-specific wiring of circuitry develops before eye-opening. Here, we suggest that LHCs in V1 may originate from spatiotemporally structured feedforward activities generated from spontaneous retinal waves. Using model simulations based on the anatomy and observed activity patterns of the retina, we show that waves propagating in retinal mosaics can initialize the wiring of LHCs by coactivating neurons of similar tuning, whereas equivalent random activities cannot induce such organizations. Simulations showed that emerged LHCs can produce the patterned activities observed in V1, matching the topography of the underlying orientation map. The model can also reproduce feature-specific microcircuits in the salt-and-pepper organizations found in rodents. Our results imply that early peripheral activities contribute significantly to cortical development of functional circuits.SIGNIFICANCE STATEMENT Long-range horizontal connections (LHCs) in the primary visual cortex (V1) are observed to emerge before the onset of visual experience, thereby selectively connecting iso-domains of orientation map. However, it is unknown how such feature-specific wirings develop before eye-opening. Here, we show that LHCs in V1 may originate from the feature-specific activation of cortical neurons by spontaneous retinal waves during early developmental stages. Our simulations of a visual cortex model show that feedforward activities from the retina initialize the spatial organization of activity patterns in V1, which induces visual feature-specific wirings in the V1 neurons. Our model also explains the origin of cortical microcircuits observed in rodents, suggesting that the proposed developmental mechanism is universally applicable to circuits of various mammalian species.

Retino-Cortical Mapping Ratio Predicts Columnar and Salt-and-Pepper Organization in Mammalian Visual Cortex.

In the mammalian primary visual cortex, neural tuning to stimulus orientation is organized in either columnar or salt-and-pepper patterns across species. For decades, this sharp contrast has spawned fundamental questions about the origin of functional architectures in visual cortex. However, it is unknown whether these patterns reflect disparate developmental mechanisms across mammalian taxa or simply originate from variation of biological parameters under a universal development process. In this work, after the analysis of data from eight mammalian species, we show that cortical organization is predictable by a single factor, the retino-cortical mapping ratio. Groups of species with or without columnar clustering are distinguished by the feedforward sampling ratio, and model simulations with controlled mapping conditions reproduce both types of organization. Prediction from the Nyquist theorem explains this parametric division of the patterns with high accuracy. Our results imply that evolutionary variation of physical parameters may induce development of distinct functional circuitry.

BRAF somatic mutation contributes to intrinsic epileptogenicity in pediatric brain tumors.

Pediatric brain tumors are highly associated with epileptic seizures1. However, their epileptogenic mechanisms remain unclear. Here, we show that the oncogenic BRAF somatic mutation p.Val600Glu (V600E) in developing neurons underlies intrinsic epileptogenicity in ganglioglioma, one of the leading causes of intractable epilepsy2. To do so, we developed a mouse model harboring the BRAFV600E somatic mutation during early brain development to reflect the most frequent mutation, as well as the origin and timing thereof. Therein, the BRAFV600E mutation arising in progenitor cells during brain development led to the acquisition of intrinsic epileptogenic properties in neuronal lineage cells, whereas tumorigenic properties were attributed to high proliferation of glial lineage cells. RNA sequencing analysis of patient brain tissues with the mutation revealed that BRAFV600E-induced epileptogenesis is mediated by RE1-silencing transcription factor (REST), which is a regulator of ion channels and neurotransmitter receptors associated with epilepsy. Moreover, we found that seizures in mice were significantly alleviated by an FDA-approved BRAFV600E inhibitor, vemurafenib, as well as various genetic inhibitions of Rest. Accordingly, this study provides direct evidence of a BRAF somatic mutation contributing to the intrinsic epileptogenicity in pediatric brain tumors and suggests that BRAF and REST could be treatment targets for intractable epilepsy.

Interlayer Repulsion of Retinal Ganglion Cell Mosaics Regulates Spatial Organization of Functional Maps in the Visual Cortex.

In higher mammals, orientation tuning of neurons is organized into a quasi-periodic pattern in the primary visual cortex. Our previous model studies suggested that the topography of cortical orientation maps may originate from moiré interference of ON and OFF retinal ganglion cell (RGC) mosaics, but did not account for how the consistent spatial period of maps could be achieved. Here we address this issue with two crucial findings on the development of RGC mosaics: first, homotypic local repulsion between RGCs can develop a long-range hexagonal periodicity. Second, heterotypic interaction restrains the alignment of ON and OFF mosaics, and generates a periodic interference pattern map with consistent spatial frequency. To validate our model, we quantitatively analyzed the RGC mosaics in cat data, and confirmed that the observed retinal mosaics showed evidence of heterotypic interactions, contrary to the previous view that ON and OFF mosaics are developed independently.SIGNIFICANCE STATEMENT Orientation map is one of the most studied functional maps in the brain, but it has remained unanswered how the consistent spatial periodicity of maps could be developed. In the current study, we address this issue with our developmental model for the retinal origin of orientation map. We showed that local repulsive interactions between retinal ganglion cells (RGCs) can develop a hexagonal periodicity in the RGC mosaics and restrict the alignment between ON and OFF mosaics, so that they generate a periodic pattern with consistent spatial frequency for both the RGC mosaics and the cortical orientation maps. Our results demonstrate that the organization of functional maps in visual cortex, including its structural consistency, may be constrained by a retinal blueprint.

Synaptic convergence regulates synchronization-dependent spike transfer in feedforward neural networks.

Correlated neural activities such as synchronizations can significantly alter the characteristics of spike transfer between neural layers. However, it is not clear how this synchronization-dependent spike transfer can be affected by the structure of convergent feedforward wiring. To address this question, we implemented computer simulations of model neural networks: a source and a target layer connected with different types of convergent wiring rules. In the Gaussian-Gaussian (GG) model, both the connection probability and the strength are given as Gaussian distribution as a function of spatial distance. In the Uniform-Constant (UC) and Uniform-Exponential (UE) models, the connection probability density is a uniform constant within a certain range, but the connection strength is set as a constant value or an exponentially decaying function, respectively. Then we examined how the spike transfer function is modulated under these conditions, while static or synchronized input patterns were introduced to simulate different levels of feedforward spike synchronization. We observed that the synchronization-dependent modulation of the transfer function appeared noticeably different for each convergence condition. The modulation of the spike transfer function was largest in the UC model, and smallest in the UE model. Our analysis showed that this difference was induced by the different spike weight distributions that was generated from convergent synapses in each model. Our results suggest that, the structure of the feedforward convergence is a crucial factor for correlation-dependent spike control, thus must be considered important to understand the mechanism of information transfer in the brain.