Sustained Extracellular Electrical Stimulation Modulates the Permeability of Gap Junctions in rd1 Mouse Retina with Photoreceptor Degeneration

Neurons build vast gap junction-coupled networks (GJ-nets) that are permeable to ions or small molecules, enabling lateral signaling. Herein, we investigate (1) the effect of blinding diseases on GJ-nets in mouse retinas and (2) the impact of electrical stimulation on GJ permeability. GJ permeability was traced in the acute retinal explants of blind retinal degeneration 1 (rd1) mice using the GJ tracer neurobiotin. The tracer was introduced via the edge cut method into the GJ-net, and its spread was visualized in histological preparations (fluorescent tagged) using microscopy. Sustained stimulation was applied to modulate GJ permeability using a single large electrode. Our findings are: (1) The blind rd1 retinas displayed extensive intercellular coupling via open GJs. Three GJ-nets were identified: horizontal, amacrine, and ganglion cell networks. (2) Sustained stimulation significantly diminished the tracer spread through the GJs in all the cell layers, as occurs with pharmaceutical inhibition with carbenoxolone. We concluded that the GJ-nets of rd1 retinas remain coupled and functional after blinding disease and that their permeability is regulatable by sustained stimulation. These findings are essential for understanding molecular signaling in diseases over coupled networks and therapeutic approaches using electrical implants, such as eliciting visual sensations or suppressing cortical seizures.

Unexplained Causes of Glioma-Associated Epilepsies: A Review of Theories and an Area for Research

Approximately 30% of glioma patients are able to survive beyond one year postdiagnosis. And this short time is often overshadowed by glioma-associated epilepsy. This condition severely impairs the patient's quality of life and causes great suffering. The genetic, molecular and cellular mechanisms underlying tumour development and epileptogenesis remain incompletely understood, leading to numerous unanswered questions. The various types of gliomas, namely glioblastoma, astrocytoma and oligodendroglioma, demonstrate distinct seizure susceptibility and disease progression patterns. Patterns have been identified in the presence of IDH mutations and epilepsy, with tumour location in cortical regions, particularly the frontal lobe, showing a more frequent association with seizures. Altered expression of TP53, MGMT and VIM is frequently detected in tumour cells from individuals with epilepsy associated with glioma. However, understanding the pathogenesis of these modifications poses a challenge. Moreover, hypoxic effects induced by glioma and associated with the HIF-1a factor may have a significant impact on epileptogenesis, potentially resulting in epileptiform activity within neuronal networks. We additionally hypothesise about how the tumour may affect the functioning of neuronal ion channels and contribute to disruptions in the blood-brain barrier resulting in spontaneous depolarisations.

3D Functional Neuronal Networks in Free-Standing Bioprinted Hydrogel Constructs

The composition, elasticity, and organization of the extracellular matrix within the central nervous system contribute to the architecture and function of the brain. From an in vitro modeling perspective, soft biomaterials are needed to mimic the 3D neural microenvironments. While many studies have investigated 3D culture and neural network formation in bulk hydrogel systems, these approaches have limited ability to position cells to mimic sophisticated brain architectures. In this study, cortical neurons and astrocytes acutely isolated from the brains of rats are bioprinted in a hydrogel to form 3D neuronal constructs. Successful bioprinting of cellular and acellular strands in a multi-bioink approach allows the subsequent formation of gray- and white-matter tracts reminiscent of cortical structures. Immunohistochemistry shows the formation of dense, 3D axon networks. Calcium signaling and extracellular electrophysiology in these 3D neuronal networks confirm spontaneous activity in addition to evoked activities under pharmacological and electrical stimulation. The system and bioprinting approaches are capable of fabricating soft, free-standing neuronal structures of different bioink and cell types with high resolution and throughput, which provide a promising platform for understanding fundamental questions of neural networks, engineering neuromorphic circuits, and for in vitro drug screening.

Sialic acid biosynthesis pathway blockade disturbs neuronal network formation in human iPSC-derived excitatory neurons

N-acetylneuraminic acid (sialic acid) is present in large quantities in the brain and plays a crucial role in brain development, learning, and memory formation. How sialic acid contributes to brain development is not fully understood. The purpose of this study was to determine the effects of reduced sialylation on network formation in human iPSC-derived neurons (iNeurons). Using targeted mass spectrometry and antibody binding, we observed an increase in free sialic acid and polysialic acid during neuronal development, which was disrupted by treatment of iNeurons with a synthetic inhibitor of sialic acid biosynthesis. Sialic acid inhibition disturbed synapse formation and network formation on microelectrode array (MEA), showing short but frequent (network) bursts and an overall lower firing rate, and higher percentage of random spikes. This study shows that sialic acid is necessary for neuronal network formation during human neuronal development and provides a physiologically relevant model to study the role of sialic acid in patient-derived iNeurons.

In vitro exposure of neuronal networks to the 5G-3.5 GHz signal

Introduction

The current deployment of the fifth generation (5G) of wireless communications raises new questions about the potential health effects of exposure to radiofrequency (RF) fields. So far, most of the established biological effects of RF have been known to be caused by heating. We previously reported inhibition of the spontaneous electrical activity of neuronal networks in vitro when exposed to 1.8 GHz signals at specific absorption rates (SAR) well above the guidelines. The present study aimed to assess the effects of RF fields at 3.5 GHz, one of the frequencies related to 5G, on neuronal activity in-vitro. Potential differences in the effects elicited by continuous-wave (CW) and 5G-modulated signals were also investigated.

Methods

Spontaneous activity of neuronal cultures from embryonic cortices was recorded using 60-electrode multi-electrode arrays (MEAs) between 17 and 27 days in vitro. The neuronal cultures were subjected to 15 min RF exposures at SAR of 1, 3, and 28 W/kg.

Results

At SAR close to the guidelines (1 and 3 W/kg), we found no conclusive evidence that 3.5 GHz RF exposure impacts the activity of neurons in vitro. On the contrary, CW and 5G-modulated signals elicited a clear decrease in bursting and total firing rates during RF exposure at high SAR levels (28 W/kg). Our experimental findings extend our previous results, showing that RF, at 1.8 to 3.5 GHz, inhibits the electrical activity of neurons in vitro at levels above environmental standards.

Intrinsic neural diversity quenches the dynamic volatility of neural networks

Heterogeneity is the norm in biology. The brain is no different: Neuronal cell types are myriad, reflected through their cellular morphology, type, excitability, connectivity motifs, and ion channel distributions. While this biophysical diversity enriches neural systems' dynamical repertoire, it remains challenging to reconcile with the robustness and persistence of brain function over time (resilience). To better understand the relationship between excitability heterogeneity (variability in excitability within a population of neurons) and resilience, we analyzed both analytically and numerically a nonlinear sparse neural network with balanced excitatory and inhibitory connections evolving over long time scales. Homogeneous networks demonstrated increases in excitability, and strong firing rate correlations-signs of instability-in response to a slowly varying modulatory fluctuation. Excitability heterogeneity tuned network stability in a context-dependent way by restraining responses to modulatory challenges and limiting firing rate correlations, while enriching dynamics during states of low modulatory drive. Excitability heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in population size, connection probability, strength and variability of synaptic weights, by quenching the volatility (i.e., its susceptibility to critical transitions) of its dynamics. Together, these results highlight the fundamental role played by cell-to-cell heterogeneity in the robustness of brain function in the face of change.

A simple method for poly-D-lysine coating to enhance adhesion and maturation of primary cortical neuron cultures in vitro

Introduction

Glass coverslips are used as a substrate since Harrison's initial nerve cell culture experiments in 1910. In 1974, the first study of brain cells seeded onto polylysine (PL) coated substrate was published. Usually, neurons adhere quickly to PL coating. However, maintaining cortical neurons in culture on PL coating for a prolonged time is challenging.

Methods

A collaborative study between chemical engineers and neurobiologists was conducted to find a simple method to enhance neuronal maturation on poly-D-lysine (PDL). In this work, a simple protocol to coat PDL efficiently on coverslips is presented, characterized, and compared to a conventional adsorption method. We studied the adhesion and maturation of primary cortical neurons with various morphological and functional approaches, including phase contrast microscopy, immunocytochemistry, scanning electron microscopy, patch clamp recordings, and calcium imaging.

Results

We observed that several parameters of neuronal maturation are influenced by the substrate: neurons develop more dense and extended networks and synaptic activity is enhanced, when seeded on covalently bound PDL compared to adsorbed PDL.

Discussion

Hence, we established reproducible and optimal conditions enhancing maturation of primary cortical neurons in vitro. Our method allows higher reliability and yield of results and could also be profitable for laboratories using PL with other cell types.

Biological neurons act as generalization filters in reservoir computing

Reservoir computing is a machine learning paradigm that transforms the transient dynamics of high-dimensional nonlinear systems for processing time-series data. Although the paradigm was initially proposed to model information processing in the mammalian cortex, it remains unclear how the nonrandom network architecture, such as the modular architecture, in the cortex integrates with the biophysics of living neurons to characterize the function of biological neuronal networks (BNNs). Here, we used optogenetics and calcium imaging to record the multicellular responses of cultured BNNs and employed the reservoir computing framework to decode their computational capabilities. Micropatterned substrates were used to embed the modular architecture in the BNNs. We first show that the dynamics of modular BNNs in response to static inputs can be classified with a linear decoder and that the modularity of the BNNs positively correlates with the classification accuracy. We then used a timer task to verify that BNNs possess a short-term memory of several 100 ms and finally show that this property can be exploited for spoken digit classification. Interestingly, BNN-based reservoirs allow categorical learning, wherein a network trained on one dataset can be used to classify separate datasets of the same category. Such classification was not possible when the inputs were directly decoded by a linear decoder, suggesting that BNNs act as a generalization filter to improve reservoir computing performance. Our findings pave the way toward a mechanistic understanding of information representation within BNNs and build future expectations toward the realization of physical reservoir computing systems based on BNNs.

Deep Learning for Fully Automated Radiographic Measurements of the Pelvis and Hip

The morphometry of the hip and pelvis can be evaluated in native radiographs. Artificial-intelligence-assisted analyses provide objective, accurate, and reproducible results. This study investigates the performance of an artificial intelligence (AI)-based software using deep learning algorithms to measure radiological parameters that identify femoroacetabular impingement and hip dysplasia. Sixty-two radiographs (124 hips) were manually evaluated by three observers and fully automated analyses were performed by an AI-driven software (HIPPO™, ImageBiopsy Lab, Vienna, Austria). We compared the performance of the three human readers with the HIPPO™ using a Bayesian mixed model. For this purpose, we used the absolute deviation from the median ratings of all readers and HIPPO™. Our results indicate a high probability that the AI-driven software ranks better than at least one manual reader for the majority of outcome measures. Hence, fully automated analyses could provide reproducible results and facilitate identifying radiographic signs of hip disorders.

How Functional Connectivity Measures Affect the Outcomes of Global Neuronal Network Characteristics in Patients with Schizophrenia Compared to Healthy Controls

Modern computational solutions used in the reconstruction of the global neuronal network arrangement seem to be particularly valuable for research on neuronal disconnection in schizophrenia. However, the vast number of algorithms used in these analyses may be an uncontrolled source of result inconsistency. Our study aimed to verify to what extent the characteristics of the global network organization in schizophrenia depend on the inclusion of a given type of functional connectivity measure. Resting-state EEG recordings from schizophrenia patients and healthy controls were collected. Based on these data, two identical procedures of graph-theory-based network arrangements were computed twice using two different functional connectivity measures (phase lag index, PLI, and phase locking value, PLV). Two series of between-group comparisons regarding global network parameters calculated on the basis of PLI or PLV gave contradictory results. In many cases, the values of a given network index based on PLI were higher in the patients, and the results based on PLV were lower in the patients than in the controls. Additionally, selected network measures were significantly different within the patient group when calculated from PLI or PLV. Our analysis shows that the selection of FC measures significantly affects the parameters of graph-theory-based neuronal network organization and might be an important source of disagreement in network studies on schizophrenia.

New insights into binocular rivalry from the reconstruction of evolving percepts using model network dynamics

When the two eyes are presented with highly distinct stimuli, the resulting visual percept generally switches every few seconds between the two monocular images in an irregular fashion, giving rise to a phenomenon known as binocular rivalry. While a host of theoretical studies have explored potential mechanisms for binocular rivalry in the context of evoked model dynamics in response to simple stimuli, here we investigate binocular rivalry directly through complex stimulus reconstructions based on the activity of a two-layer neuronal network model with competing downstream pools driven by disparate monocular stimuli composed of image pixels. To estimate the dynamic percept, we derive a linear input-output mapping rooted in the non-linear network dynamics and iteratively apply compressive sensing techniques for signal recovery. Utilizing a dominance metric, we are able to identify when percept alternations occur and use data collected during each dominance period to generate a sequence of percept reconstructions. We show that despite the approximate nature of the input-output mapping and the significant reduction in neurons downstream relative to stimulus pixels, the dominant monocular image is well-encoded in the network dynamics and improvements are garnered when realistic spatial receptive field structure is incorporated into the feedforward connectivity. Our model demonstrates gamma-distributed dominance durations and well obeys Levelt's four laws for how dominance durations change with stimulus strength, agreeing with key recurring experimental observations often used to benchmark rivalry models. In light of evidence that individuals with autism exhibit relatively slow percept switching in binocular rivalry, we corroborate the ubiquitous hypothesis that autism manifests from reduced inhibition in the brain by systematically probing our model alternation rate across choices of inhibition strength. We exhibit sufficient conditions for producing binocular rivalry in the context of natural scene stimuli, opening a clearer window into the dynamic brain computations that vary with the generated percept and a potential path toward further understanding neurological disorders.

Long-term calcium imaging reveals functional development in hiPSC-derived cultures comparable to human but not rat primary cultures

Models for human brain-oriented research are often established on primary cultures from rodents, which fails to recapitulate cellular specificity and molecular cues of the human brain. Here we investigated whether neuronal cultures derived from human induced pluripotent stem cells (hiPSCs) feature key advantages compared with rodent primary cultures. Using calcium fluorescence imaging, we tracked spontaneous neuronal activity in hiPSC-derived, human, and rat primary cultures and compared their dynamic and functional behavior as they matured. We observed that hiPSC-derived cultures progressively changed upon development, exhibiting gradually richer activity patterns and functional traits. By contrast, rat primary cultures were locked in the same dynamic state since activity onset. Human primary cultures exhibited features in between hiPSC-derived and rat primary cultures, although traits from the former predominated. Our study demonstrates that hiPSC-derived cultures are excellent models to investigate development in neuronal assemblies, a hallmark for applications that monitor alterations caused by damage or neurodegeneration.

Flexibility of in vitro cortical circuits influences resilience from microtrauma

Background

Small clusters comprising hundreds to thousands of neurons are an important level of brain architecture that correlates single neuronal properties to fulfill brain function, but the specific mechanisms through which this scaling occurs are not well understood. In this study, we developed an in vitro experimental platform of small neuronal circuits (islands) to probe the importance of structural properties for their development, physiology, and response to microtrauma.

Methods

Primary cortical neurons were plated on a substrate patterned to promote attachment in clusters of hundreds of cells (islands), transduced with GCaMP6f, allowed to mature until 10-13 days in vitro (DIV), and monitored with Ca²⁺ as a non-invasive proxy for electrical activity. We adjusted two structural factors-island size and cellular density-to evaluate their role in guiding spontaneous activity and network formation in neuronal islands.

Results

We found cellular density, but not island size, regulates of circuit activity and network function in this system. Low cellular density islands can achieve many states of activity, while high cellular density biases islands towards a limited regime characterized by low rates of activity and high synchronization, a property we summarized as "flexibility." The injury severity required for an island to lose activity in 50% of its population was significantly higher in low-density, high flexibility islands.

Conclusion

Together, these studies demonstrate flexible living cortical circuits are more resilient to microtrauma, providing the first evidence that initial circuit state may be a key factor to consider when evaluating the consequences of trauma to the cortex.

Human iPSC-Derived Cortical Neurons Display Homeostatic Plasticity

Maintaining the excitability of neurons and circuits is fundamental for healthy brain functions. The global compensatory increase in excitatory synaptic strength, in response to decreased activity, is one of the main homeostatic mechanisms responsible for such regulation. This type of plasticity has been extensively characterized in rodents in vivo and in vitro, but few data exist on human neurons maturation. We have generated an in vitro cortical model system, based on differentiated human-induced pluripotent stem cells, chronically treated with tetrodotoxin, to investigate homeostatic plasticity at different developmental stages. Our findings highlight the presence of homeostatic plasticity in human cortical networks and show that the changes in synaptic strength are due to both pre- and post-synaptic mechanisms. Pre-synaptic plasticity involves the potentiation of neurotransmitter release machinery, associated to an increase in synaptic vesicle proteins expression. At the post-synaptic level, we report an increase in the expression of post-synaptic density proteins, involved in glutamatergic receptor anchoring. These results extend our understanding of neuronal homeostasis and reveal the developmental regulation of its expression in human cortical networks. Since induced pluripotent stem cell-derived neurons can be obtained from patients with neurodevelopmental and neurodegenerative diseases, our platform offers a versatile model for assessing human neural plasticity under physiological and pathological conditions.

Spontaneous Cell Cluster Formation in Human iPSC-Derived Neuronal Spheroid Networks Influences Network Activity

Three-dimensional neuronal culture systems such as spheroids, organoids, and assembloids constitute a branch of neuronal tissue engineering that has improved our ability to model the human brain in the laboratory. However, the more elaborate the brain model, the more difficult it becomes to study functional properties such as electrical activity at the neuronal level, similar to the challenges of studying neurophysiology in vivo We describe a simple approach to generate self-assembled three-dimensional neuronal spheroid networks with defined human cell composition on microelectrode arrays. Such spheroid networks develop a highly three-dimensional morphology with cell clusters up to 60 µm in thickness and are interconnected by pronounced bundles of neuronal fibers and glial processes. We could reliably record from up to hundreds of neurons simultaneously per culture for ≤90 d. By quantifying the formation of these three-dimensional structures over time, while regularly monitoring electrical activity, we were able to establish a strong link between spheroid morphology and network activity. In particular, the formation of cell clusters accelerates formation and maturation of correlated network activity. Astrocytes both influence electrophysiological network activity as well as accelerate the transition from single cell layers to cluster formation. Higher concentrations of astrocytes also have a strong effect of modulating synchronized network activity. This approach thus represents a practical alternative to often complex and heterogeneous organoids, providing easy access to activity within a brain-like 3D environment.Significance StatementNeuronal "organoid" cultures with multiple cell types grown on elaborate three-dimensional scaffolds have become popular tools to generate brain-like properties in vitro but bring with them similar problems concerning access to physiological function as real brain tissue. Here, we developed a new approach to form simple brain-like spheroid networks from human neurons, but using the normal supporting cells of the brain, astrocytes, as the scaffold. By growing these cultures on conventional microelectrode arrays, we were able to observe development of complex patterns of electrical activity for months. Our results highlight how formation of three-dimensional structures accelerated the formation of synchronized neuronal network activity and provide a promising new simple model system for studying interactions between known human cell types in vitro.

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON's ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON's reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.

Self-organization of in vitro neuronal assemblies drives to complex network topology

Activity-dependent self-organization plays an important role in the formation of specific and stereotyped connectivity patterns in neural circuits. By combining neuronal cultures, and tools with approaches from network neuroscience and information theory, we can study how complex network topology emerges from local neuronal interactions. We constructed effective connectivity networks using a transfer entropy analysis of spike trains recorded from rat embryo dissociated hippocampal neuron cultures between 6 and 35 days in vitro to investigate how the topology evolves during maturation. The methodology for constructing the networks considered the synapse delay and addressed the influence of firing rate and population bursts as well as spurious effects on the inference of connections. We found that the number of links in the networks grew over the course of development, shifting from a segregated to a more integrated architecture. As part of this progression, three significant aspects of complex network topology emerged. In agreement with previous in silico and in vitro studies, a small-world architecture was detected, largely due to strong clustering among neurons. Additionally, the networks developed in a modular topology, with most modules comprising nearby neurons. Finally, highly active neurons acquired topological characteristics that made them important nodes to the network and integrators of modules. These findings leverage new insights into how neuronal effective network topology relates to neuronal assembly self-organization mechanisms.

Recent Advances in the Understanding of Specific Efferent Pathways Emerging From the Cerebellum

The cerebellum has a long history in terms of research on its network structures and motor functions, yet our understanding of them has further advanced in recent years owing to technical developments, such as viral tracers, optogenetic and chemogenetic manipulation, and single cell gene expression analyses. Specifically, it is now widely accepted that the cerebellum is also involved in non-motor functions, such as cognitive and psychological functions, mainly from studies that have clarified neuronal pathways from the cerebellum to other brain regions that are relevant to these functions. The techniques to manipulate specific neuronal pathways were effectively utilized to demonstrate the involvement of the cerebellum and its pathways in specific brain functions, without altering motor activity. In particular, the cerebellar efferent pathways that have recently gained attention are not only monosynaptic connections to other brain regions, including the periaqueductal gray and ventral tegmental area, but also polysynaptic connections to other brain regions, including the non-primary motor cortex and hippocampus. Besides these efferent pathways associated with non-motor functions, recent studies using sophisticated experimental techniques further characterized the historically studied efferent pathways that are primarily associated with motor functions. Nevertheless, to our knowledge, there are no articles that comprehensively describe various cerebellar efferent pathways, although there are many interesting review articles focusing on specific functions or pathways. Here, we summarize the recent findings on neuronal networks projecting from the cerebellum to several brain regions. We also introduce various techniques that have enabled us to advance our understanding of the cerebellar efferent pathways, and further discuss possible directions for future research regarding these efferent pathways and their functions.

The importance of fasciculation and elongation protein zeta-1 in neural circuit establishment and neurological disorders

The human brain contains an estimated 100 billion neurons that must be systematically organized into functional neural circuits for it to function properly. These circuits range from short-range local signaling networks between neighboring neurons to long-range networks formed between various brain regions. Compelling converging evidence indicates that alterations in neural circuits arising from abnormalities during early neuronal development or neurodegeneration contribute significantly to the etiology of neurological disorders. Supporting this notion, efforts to identify genetic causes of these disorders have uncovered an over-representation of genes encoding proteins involved in the processes of neuronal differentiation, maturation, synaptogenesis and synaptic function. Fasciculation and elongation protein zeta-1, a Kinesin-1 adapter, has emerged as a key central player involved in many of these processes. Fasciculation and elongation protein zeta-1-dependent transport of synaptic cargoes and mitochondria is essential for neuronal development and synapse establishment. Furthermore, it acts downstream of guidance cue pathways to regulate axo-dendritic development. Significantly, perturbing its function causes abnormalities in neuronal development and synapse formation both in the brain as well as the peripheral nervous system. Mutations and deletions of the fasciculation and elongation protein zeta-1 gene are linked to neurodevelopmental disorders. Moreover, altered phosphorylation of the protein contributes to neurodegenerative disorders. Together, these findings strongly implicate the importance of fasciculation and elongation protein zeta-1 in the establishment of neuronal circuits and its maintenance.

A Compartmentalized Neuronal Cell-Culture Platform Compatible With Cryo-Fixation by High-Pressure Freezing for Ultrastructural Imaging

The human brain contains a wide array of billions of neurons and interconnections, which are often simplified for analysis in vitro using compartmentalized microfluidic devices for neuronal cell culturing, to better understand neuronal development and disease. However, such devices are traditionally incompatible for high-pressure freezing and high-resolution nanoscale imaging and analysis of their sub-cellular processes by methods including electron microscopy. Here we develop a novel compartmentalized neuronal co-culture platform allowing reconstruction of neuronal networks with high variable spatial control, which is uniquely compatible for high-pressure freezing. This cryo-fixation method is well-established to enable high-fidelity preservation of the reconstructed neuronal networks and their sub-cellular processes in a near-native vitreous state without requiring chemical fixatives. To direct the outgrowth of neurites originating from two distinct groups of neurons growing in the two different compartments, polymer microstructures akin to microchannels are fabricated atop of sapphire disks. Two populations of neurons expressing either enhanced green fluorescent protein (EGFP) or mCherry were grown in either compartment, facilitating the analysis of the specific interactions between the two separate groups of cells. Neuronally differentiated PC12 cells, murine hippocampal and striatal neurons were successfully used in this context. The design of this device permits direct observation of entire neuritic processes within microchannels by optical microscopy with high spatial and temporal resolution, prior to processing for high-pressure freezing and electron microscopy. Following freeze substitution, we demonstrate that it is possible to process the neuronal networks for ultrastructural imaging by electron microscopy. Several key features of the embedded neuronal networks, including mitochondria, synaptic vesicles, axonal terminals, microtubules, with well-preserved ultrastructures were observed at high resolution using focused ion beam - scanning electron microscopy (FIB-SEM) and serial sectioning - transmission electron microscopy (TEM). These results demonstrate the compatibility of the platform with optical microscopy, high-pressure freezing and electron microscopy. The platform can be extended to neuronal models of brain disease or development in future studies, enabling the investigation of subcellular processes at the nanoscale within two distinct groups of neurons in a functional neuronal pathway, as well as pharmacological testing and drug screening.

Mind Causality: A Computational Neuroscience Approach

A neuroscience-based approach has recently been proposed for the relation between the mind and the brain. The proposal is that events at the sub-neuronal, neuronal, and neuronal network levels take place simultaneously to perform a computation that can be described at a high level as a mental state, with content about the world. It is argued that as the processes at the different levels of explanation take place at the same time, they are linked by a non-causal supervenient relationship: causality can best be described in brains as operating within but not between levels. This mind-brain theory allows mental events to be different in kind from the mechanistic events that underlie them; but does not lead one to argue that mental events cause brain events, or vice versa: they are different levels of explanation of the operation of the computational system. Here, some implications are developed. It is proposed that causality, at least as it applies to the brain, should satisfy three conditions. First, interventionist tests for causality must be satisfied. Second, the causally related events should be at the same level of explanation. Third, a temporal order condition must be satisfied, with a suitable time scale in the order of 10 ms (to exclude application to quantum physics; and a cause cannot follow an effect). Next, although it may be useful for different purposes to describe causality involving the mind and brain at the mental level, or at the brain level, it is argued that the brain level may sometimes be more accurate, for sometimes causal accounts at the mental level may arise from confabulation by the mentalee, whereas understanding exactly what computations have occurred in the brain that result in a choice or action will provide the correct causal account for why a choice or action was made. Next, it is argued that possible cases of "downward causation" can be accounted for by a within-levels-of-explanation account of causality. This computational neuroscience approach provides an opportunity to proceed beyond Cartesian dualism and physical reductionism in considering the relations between the mind and the brain.

Neuronal Graphs: A Graph Theory Primer for Microscopic, Functional Networks of Neurons Recorded by Calcium Imaging

Connected networks are a fundamental structure of neurobiology. Understanding these networks will help us elucidate the neural mechanisms of computation. Mathematically speaking these networks are "graphs"-structures containing objects that are connected. In neuroscience, the objects could be regions of the brain, e.g., fMRI data, or be individual neurons, e.g., calcium imaging with fluorescence microscopy. The formal study of graphs, graph theory, can provide neuroscientists with a large bank of algorithms for exploring networks. Graph theory has already been applied in a variety of ways to fMRI data but, more recently, has begun to be applied at the scales of neurons, e.g., from functional calcium imaging. In this primer we explain the basics of graph theory and relate them to features of microscopic functional networks of neurons from calcium imaging-neuronal graphs. We explore recent examples of graph theory applied to calcium imaging and we highlight some areas where researchers new to the field could go awry.

Neuronal Cytoskeleton in Intellectual Disability: From Systems Biology and Modeling to Therapeutic Opportunities

Intellectual disability (ID) is a pathological condition characterized by limited intellectual functioning and adaptive behaviors. It affects 1–3% of the worldwide population, and no pharmacological therapies are currently available. More than 1000 genes have been found mutated in ID patients pointing out that, despite the common phenotype, the genetic bases are highly heterogeneous and apparently unrelated. Bibliomic analysis reveals that ID genes converge onto a few biological modules, including cytoskeleton dynamics, whose regulation depends on Rho GTPases transduction. Genetic variants exert their effects at different levels in a hierarchical arrangement, starting from the molecular level and moving toward higher levels of organization, i.e., cell compartment and functions, circuits, cognition, and behavior. Thus, cytoskeleton alterations that have an impact on cell processes such as neuronal migration, neuritogenesis, and synaptic plasticity rebound on the overall establishment of an effective network and consequently on the cognitive phenotype. Systems biology (SB) approaches are more focused on the overall interconnected network rather than on individual genes, thus encouraging the design of therapies that aim to correct common dysregulated biological processes. This review summarizes current knowledge about cytoskeleton control in neurons and its relevance for the ID pathogenesis, exploiting in silico modeling and translating the implications of those findings into biomedical research.

Guiding the Patterned Growth of Neuronal Axons and Dendrites Using Anisotropic Micropillar Scaffolds

The patterning of axonal and dendritic growth is an important topic in neural tissue engineering and critical for generating directed neuronal networks in vitro. Evidence shows that artificial micro/nanotopography can better mimic the environment for neuronal growth in vivo. However, the potential mechanisms by which neurons interact with true three dimensional (3D) topographical cues and form directional networks are unclear. Herein, 3D micropillar scaffolds are designed to guide the growth of neural stem cells and hippocampal neurons in vitro. Discontinuous and anisotropic micropillars are fabricated by femtosecond direct laser writing to form patterned scaffolds with various spacings and heights, which are found to affect the branching and orientation of axons and dendrites. Interestingly, axons and dendrites tend to grow on an array of 3D micropillar scaffolds of the same height and form functionally connected neuronal networks, as reflected by synchronous neuronal activity visualized by calcium imaging. This method may represent a promising tool for studying neuron behavior and directed neuronal networks in a 3D environment.

Human Cerebrospinal Fluid Promotes Neuronal Circuit Maturation of Human Induced Pluripotent Stem Cell-Derived 3D Neural Aggregates

Human induced pluripotent stem cell (hiPSC)-derived in vitro neural and organoid models resemble fetal, rather than adult brain properties, indicating that currently applied cultivation media and supplements are insufficient to achieve neural maturation beyond the fetal stage. In vivo, cerebrospinal fluid molecules are regulating the transition of the immature fetal human brain into a mature adult brain. By culturing hiPSC-3D neural aggregates in human cerebrospinal fluid (hCSF) obtained from healthy adult individuals, we demonstrate that hCSF rapidly triggers neurogenesis, gliogenesis, synapse formation, neurite outgrowth, suppresses proliferation of residing neural stem cells, and results in the formation of synchronously active neuronal circuits in vitro within 3 days. Thus, a physiologically relevant and adult brain-like milieu triggers maturation of hiPSC-3D neural aggregates into highly functional neuronal circuits in vitro. The approach presented here opens a new avenue to identify novel physiological factors for the improvement of hiPSC neural in vitro models.

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Human CSF triggers rapidly multiple maturation processes in human 3D neural models

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Human CSF triggers human neurogenesis and suppresses neural stem cell proliferation

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Human CSF triggers human astrocyte development, neurite growth, and synapse formation

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Human CSF triggers the maturation of neurons into highly functional neuronal circuits

Structure-Activity Relationship of Neuroactive Steroids, Midazolam, and Perampanel Toward Mitigating Tetramine-Triggered Activity in Murine Hippocampal Neuronal Networks

Tetramethylenedisulfotetramine (tetramine or TETS), a potent convulsant, triggers abnormal electrical spike activity (ESA) and synchronous Ca2+ oscillation (SCO) patterns in cultured neuronal networks by blocking gamma-aminobutyric acid (GABAA) receptors. Murine hippocampal neuronal/glial cocultures develop extensive dendritic connectivity between glutamatergic and GABAergic inputs and display two distinct SCO patterns when imaged with the Ca2+ indicator Fluo-4: Low amplitude SCO events (LASE) and High amplitude SCO events (HASE) that are dependent on TTX-sensitive network electrical spike activity (ESA). Acute TETS (3.0 µM) increased overall network SCO amplitude and decreased SCO frequency by stabilizing HASE and suppressing LASE while increasing ESA. In multielectrode arrays, TETS also increased burst frequency and synchronicity. In the presence of TETS (3.0 µM), the clinically used anticonvulsive perampanel (0.1-3.0 µM), a noncompetitive AMPAR antagonist, suppressed all SCO activity, whereas the GABAA receptor potentiator midazolam (1.0-30 µM), the current standard of care, reciprocally suppressed HASE and stabilized LASE. The neuroactive steroid (NAS) allopregnanolone (0.1-3.0 µM) normalized TETS-triggered patterns by selectively suppressing HASE and increasing LASE, a pharmacological pattern distinct from its epimeric form eltanolone, ganaxolone, alphaxolone, and XJ-42, which significantly potentiated TETS-triggered HASE in a biphasic manner. Cortisol failed to mitigate TETS-triggered patterns and at >1 µM augmented them. Combinations of allopregnanolone and midazolam were significantly more effective at normalizing TETS-triggered SCO patterns, ESA patterns, and more potently enhanced GABA-activated Cl- current, than either drug alone.

A Neuroscience Levels of Explanation Approach to the Mind and the Brain

The relation between mental states and brain states is important in computational neuroscience, and in psychiatry in which interventions with medication are made on brain states to alter mental states. The relation between the brain and the mind has puzzled philosophers for centuries. Here a neuroscience approach is proposed in which events at the sub-neuronal, neuronal, and neuronal network levels take place simultaneously to perform a computation that can be described at a high level as a mental state, with content about the world. It is argued that as the processes at the different levels of explanation take place at the same time, they are linked by a non-causal supervenient relationship: causality can best be described in brains as operating within but not between levels. This allows the supervenient (e.g., mental) properties to be emergent, though once understood at the mechanistic levels they may seem less emergent, and expected. This mind-brain theory allows mental events to be different in kind from the mechanistic events that underlie them; but does not lead one to argue that mental events cause brain events, or vice versa: they are different levels of explanation of the operation of the computational system. This approach may provide a way of thinking about brains and minds that is different from dualism and from reductive physicalism, and which is rooted in the computational processes that are fundamental to understanding brain and mental events, and that mean that the mental and mechanistic levels are linked by the computational process being performed. Explanations at the different levels of operation may be useful in different ways. For example, if we wish to understand how arithmetic is performed in the brain, description at the mental level of the algorithm being computed will be useful. But if the brain operates to result in mental disorders, then understanding the mechanism at the neural processing level may be more useful, in for example, the treatment of psychiatric disorders.

Color-Patterns to Architecture Conversion through Conditional Generative Adversarial Networks

Often an apparent complex reality can be extrapolated into certain patterns that in turn are evidenced in natural behaviors (whether biological, chemical or physical). The Architecture Design field has manifested these patterns as a conscious (inspired designs) or unconscious manner (emerging organizations). If such patterns exist and can be recognized, can we therefore use them as genotypic DNA? Can we be capable of generating a phenotypic architecture that is manifestly more complex than the original pattern? Recent developments in the field of Evo-Devo around gene regulators patterns or the explosive development of Machine Learning tools could be combined to set the basis for developing new, disruptive workflows for both design and analysis. This study will test the feasibility of using conditional Generative Adversarial Networks (cGANs) as a tool for coding architecture into color pattern-based images and translating them into 2D architectural representations. A series of scaled tests are performed to check the feasibility of the hypothesis. A second test assesses the flexibility of the trained neural networks against cases outside the database.

A spiking model of basal ganglia dynamics in stopping behavior supported by arkypallidal neurons

The common view that stopping action plans by the basal ganglia is achieved mainly by the subthalamic nucleus alone due to its direct excitatory projection onto the output nuclei of the basal ganglia has been challenged by recent findings. The proposed "pause-then-cancel" model suggests that the subthalamic nucleus provides a rapid stimulus-unspecific "pause" signal, followed by a stop-cue-specific "cancel" signal from striatum-projecting arkypallidal neurons. To determine more precisely the relative contribution of the different basal ganglia nuclei in stopping, we simulated a stop-signal task with a spiking neuron model of the basal ganglia, considering recently discovered connections from the arkypallidal neurons, and cortex-projecting GPe neurons. For the arkypallidal and prototypical GPe neurons, we obtained neuron model parameters by fitting their neuronal responses to published experimental data. Our model replicates findings of stop-signal tasks at neuronal and behavioral levels. We provide evidence for the existence of a stop-related cortical input to the arkypallidal and cortex-projecting GPe neurons such that the stop responses of the subthalamic nucleus, the arkypallidal neurons, and the cortex-projecting GPe neurons complement each other to achieve functional stopping behavior. Particularly, the cortex-projecting GPe neurons may complement the stopping within the basal ganglia caused by the arkypallidal and STN neurons by diminishing cortical go-related processes. Furthermore, we predict effects of lesions on stopping performance and propose that arkypallidal neurons mainly participate in stopping by inhibiting striatal neurons of the indirect rather than the direct pathway.

Cannabinoids in Audiogenic Seizures: From Neuronal Networks to Future Perspectives for Epilepsy Treatment

Cannabinoids and Cannabis-derived compounds have been receiving especial attention in the epilepsy research scenario. Pharmacological modulation of endocannabinoid system's components, like cannabinoid type 1 receptors (CB1R) and their bindings, are associated with seizures in preclinical models. CB1R expression and functionality were altered in humans and preclinical models of seizures. Additionally, Cannabis-derived compounds, like cannabidiol (CBD), present anticonvulsant activity in humans and in a great variety of animal models. Audiogenic seizures (AS) are induced in genetically susceptible animals by high-intensity sound stimulation. Audiogenic strains, like the Genetically Epilepsy Prone Rats, Wistar Audiogenic Rats, and Krushinsky-Molodkina, are useful tools to study epilepsy. In audiogenic susceptible animals, acute acoustic stimulation induces brainstem-dependent wild running and tonic-clonic seizures. However, during the chronic protocol of AS, the audiogenic kindling (AuK), limbic and cortical structures are recruited, and the initially brainstem-dependent seizures give rise to limbic seizures. The present study reviewed the effects of pharmacological modulation of the endocannabinoid system in audiogenic seizure susceptibility and expression. The effects of Cannabis-derived compounds in audiogenic seizures were also reviewed, with especial attention to CBD. CB1R activation, as well Cannabis-derived compounds, induced anticonvulsant effects against audiogenic seizures, but the effects of cannabinoids modulation and Cannabis-derived compounds still need to be verified in chronic audiogenic seizures. The effects of cannabinoids and Cannabis-derived compounds should be further investigated not only in audiogenic seizures, but also in epilepsy related comorbidities present in audiogenic strains, like anxiety, and depression.

Dynamics of Structured Networks of Winfree Oscillators

Winfree oscillators are phase oscillator models of neurons, characterized by their phase response curve and pulsatile interaction function. We use the Ott/Antonsen ansatz to study large heterogeneous networks of Winfree oscillators, deriving low-dimensional differential equations which describe the evolution of the expected state of networks of oscillators. We consider the effects of correlations between an oscillator's in-degree and out-degree, and between the in- and out-degrees of an “upstream” and a “downstream” oscillator (degree assortativity). We also consider correlated heterogeneity, where some property of an oscillator is correlated with a structural property such as degree. We finally consider networks with parameter assortativity, coupling oscillators according to their intrinsic frequencies. The results show how different types of network structure influence its overall dynamics.

The Logic of Developing Neocortical Circuits in Health and Disease

The sensory and cognitive abilities of the mammalian neocortex are underpinned by intricate columnar and laminar circuits formed from an array of diverse neuronal populations. One approach to determining how interactions between these circuit components give rise to complex behavior is to investigate the rules by which cortical circuits are formed and acquire functionality during development. This review summarizes recent research on the development of the neocortex, from genetic determination in neural stem cells through to the dynamic role that specific neuronal populations play in the earliest circuits of neocortex, and how they contribute to emergent function and cognition. While many of these endeavors take advantage of model systems, consideration will also be given to advances in our understanding of activity in nascent human circuits. Such cross-species perspective is imperative when investigating the mechanisms underlying the dysfunction of early neocortical circuits in neurodevelopmental disorders, so that one can identify targets amenable to therapeutic intervention.

Stimulating the parietal cortex by transcranial direct current stimulation (tDCS): no effects on attention and memory

Selective attention is relevant for goal directed behavior as it allows people to attend to task-relevant target stimuli and to ignore task-irrelevant distractors. Attentional focus at encoding affects subsequent memory for target and distractor stimuli. Remembering selectively more targets than distractors represents memory selectivity. Brain imaging studies suggest that the superior parietal cortex is associated with the dorsal attentional network supporting top-down control of selective attention while the inferior parietal cortex is associated with the ventral attentional network supporting bottom-up attentional orienting. To investigate the roles of the dorsal and ventral networks in the effect of selective attention during encoding on long-term memory, we stimulated the left superior and the right inferior parietal cortex. Building on previous work, we applied transcranial direct current stimulation (tDCS) during a study phase where pictures and words were presented simultaneously and participants had to switch between a picture and a word decision. A subsequent recognition test assessed memory for target and distractor pictures and words. We hypothesized that a relative increase in activity in the dorsal network would boost selective attention while increased activity in the ventral network would impair selective attention. We also expected to find corresponding effects on memory. Enhanced selective attention should lead to higher memory selectivity, while impaired selective attention should lead to lower memory selectivity. Our results replicated that task switching reduced memory selectivity. However, we found no significant effects of tDCS. Thus, the present study questions the effectiveness of the present tDCS protocol for modulating attention during task switching and subsequent memory.

Distinct Pathogenic Genes Causing Intellectual Disability and Autism Exhibit a Common Neuronal Network Hyperactivity Phenotype

Pathogenic mutations in either one of the epigenetic modifiers EHMT1, MBD5, MLL3, or SMARCB1 have been identified to be causative for Kleefstra syndrome spectrum (KSS), a neurodevelopmental disorder with clinical features of both intellectual disability (ID) and autism spectrum disorder (ASD). To understand how these variants lead to the phenotypic convergence in KSS, we employ a loss-of-function approach to assess neuronal network development at the molecular, single-cell, and network activity level. KSS-gene-deficient neuronal networks all develop into hyperactive networks with altered network organization and excitatory-inhibitory balance. Interestingly, even though transcriptional data reveal distinct regulatory mechanisms, KSS target genes share similar functions in regulating neuronal excitability and synaptic function, several of which are associated with ID and ASD. Our results show that KSS genes mainly converge at the level of neuronal network communication, providing insights into the pathophysiology of KSS and phenotypically congruent disorders.

Toward Brain-on-a-Chip: Human Induced Pluripotent Stem Cell-Derived Guided Neuronal Networks in Tailor-Made 3D Nanoprinted Microscaffolds

Brain-on-a-chip (BoC) concepts should consider three-dimensional (3D) scaffolds to mimic the 3D nature of the human brain not accessible by conventional planar cell culturing. Furthermore, the essential key to adequately address drug development for human pathophysiological diseases of the nervous system, such as Parkinson's or Alzheimer's, is to employ human induced pluripotent stem cell (iPSC)-derived neurons instead of neurons from animal models. To address both issues, we present electrophysiologically mature human iPSC-derived neurons cultured in BoC applicable microscaffolds prepared by direct laser writing. 3D nanoprinted tailor-made elevated cavities interconnected by freestanding microchannels were used to create defined neuronal networks-as a proof of concept-with two-dimensional topology. The neuronal outgrowth in these nonplanar structures was investigated, among others, in terms of neurite length, size of continuous networks, and branching behavior using z-stacks prepared by confocal microscopy and cross-sectional scanning electron microscopy images prepared by focused ion beam milling. Functionality of the human iPSC-derived neurons was demonstrated with patch clamp measurements in both current- and voltage-clamp mode. Action potentials and spontaneous excitatory postsynaptic currents-fundamental prerequisites for proper network signaling-prove full integrity of these artificial neuronal networks. Considering the network formation occurring within only a few days and the versatile nature of direct laser writing to create even more complex scaffolds for 3D network topologies, we believe that our study offers additional approaches in human disease research to mimic the complex interconnectivity of the human brain in BoC studies.

Poly I:C-induced maternal immune challenge reduces perineuronal net area and raises spontaneous network activity of hippocampal neurons in vitro

Activation of the maternal immune system (MIA) during gestation is linked to neuropsychiatric diseases like schizophrenia. While many studies address behavioural aspects, less is known about underlying cellular mechanisms. In the following study, BALB/c mice received intraperitoneal injections of polyinosinic-polycytidylic acid (Poly I:C) (20 µg/ml) or saline (0.9%) at gestation day (GD) 9.5 before hippocampal neurons were isolated and cultured from embryonic mice for further analysis. Interestingly, strongest effects were observed when the perineuronal net (PNN) wearing subpopulation of neurons was analysed. Here, a significant reduction of aggrecan staining intensity, area and soma size could be detected. Alterations of PNNs are often linked to neuropsychiatric diseases, changes in synaptic plasticity and in electrophysiology. Utilizing multielectrode array analysis (MEA), we observed a remarkable increase of the spontaneous network activity in neuronal networks after 21 days in vitro (DIV) when mother mice suffered a prenatal immune challenge. As PNNs are associated with GABAergic interneurons, our data indicate that this neuronal subtype might be stronger affected by a prenatal MIA. Degradation or damage of this subtype might cause the hyperexcitability observed in the whole network. In addition, embryonic neurons of the Poly I:C condition developed significantly shorter axons after five days in culture, while dendritic parameters and apoptosis rate remained unchanged. Structural analysis of synapse numbers revealed an increase of postsynaptic density 95 (PSD-95) puncta after 14 DIV and an increase of presynaptic vesicular glutamate transporter (vGlut) puncta after 21 DIV, while inhibitory synaptic proteins were not altered.

Deep brain stimulation for aggressiveness: long-term follow-up and tractography study of the stimulated brain areas

OBJECTIVE

Initial studies applying deep brain stimulation (DBS) of the posteromedial hypothalamus (PMH) to patients with pathological aggressiveness have yielded encouraging results. However, the anatomical structures involved in its therapeutic effect have not been precisely identified. The authors' objective was to describe the long-term outcome in their 7-patient series, and the tractography analysis of the volumes of tissue activated in 2 of the responders.

METHODS

This was a retrospective study of 7 subjects with pathological aggressiveness. The findings on MRI with diffusion tensor imaging (DTI) in 2 of the responders were analyzed. The authors generated volumes of tissue activated according to the parameters used, and selected those volumes as regions of interest to delineate the tracts affected by stimulation.

RESULTS

The series consisted of 5 men and 2 women. Of the 7 patients, 5 significantly improved with stimulation. The PMH, ventral tegmental area, dorsal longitudinal fasciculus, and medial forebrain bundle seem to be involved in the stimulation field.

CONCLUSIONS

In this series, 5 of 7 medication-resistant patients with severe aggressiveness who were treated with bilateral PMH DBS showed a significant long-lasting improvement. The PMH, ventral tegmental area, dorsal longitudinal fasciculus, and medial forebrain bundle seem to be in the stimulation field and might be responsible for the therapeutic effect of DBS.

Mechanisms underlying the response of mouse cortical networks to optogenetic manipulation

GABAergic interneurons can be subdivided into three subclasses: parvalbumin positive (PV), somatostatin positive (SOM) and serotonin positive neurons. With principal cells (PCs) they form complex networks. We examine PCs and PV responses in mouse anterior lateral motor cortex (ALM) and barrel cortex (S1) upon PV photostimulation in vivo. In ALM layer five and S1, the PV response is paradoxical: photoexcitation reduces their activity. This is not the case in ALM layer 2/3. We combine analytical calculations and numerical simulations to investigate how these results constrain the architecture. Two-population models cannot explain the results. Four-population networks with V1-like architecture account for the data in ALM layer 2/3 and layer 5. Our data in S1 can be explained if SOM neurons receive inputs only from PCs and PV neurons. In both four-population models, the paradoxical effect implies not too strong recurrent excitation. It is not evidence for stabilization by inhibition.

Compressive Sensing Inference of Neuronal Network Connectivity in Balanced Neuronal Dynamics

Determining the structure of a network is of central importance to understanding its function in both neuroscience and applied mathematics. However, recovering the structural connectivity of neuronal networks remains a fundamental challenge both theoretically and experimentally. While neuronal networks operate in certain dynamical regimes, which may influence their connectivity reconstruction, there is widespread experimental evidence of a balanced neuronal operating state in which strong excitatory and inhibitory inputs are dynamically adjusted such that neuronal voltages primarily remain near resting potential. Utilizing the dynamics of model neurons in such a balanced regime in conjunction with the ubiquitous sparse connectivity structure of neuronal networks, we develop a compressive sensing theoretical framework for efficiently reconstructing network connections by measuring individual neuronal activity in response to a relatively small ensemble of random stimuli injected over a short time scale. By tuning the network dynamical regime, we determine that the highest fidelity reconstructions are achievable in the balanced state. We hypothesize the balanced dynamics observed in vivo may therefore be a result of evolutionary selection for optimal information encoding and expect the methodology developed to be generalizable for alternative model networks as well as experimental paradigms.

CoreNEURON : An Optimized Compute Engine for the NEURON Simulator

The NEURON simulator has been developed over the past three decades and is widely used by neuroscientists to model the electrical activity of neuronal networks. Large network simulation projects using NEURON have supercomputer allocations that individually measure in the millions of core hours. Supercomputer centers are transitioning to next generation architectures and the work accomplished per core hour for these simulations could be improved by an order of magnitude if NEURON was able to better utilize those new hardware capabilities. In order to adapt NEURON to evolving computer architectures, the compute engine of the NEURON simulator has been extracted and has been optimized as a library called CoreNEURON. This paper presents the design, implementation, and optimizations of CoreNEURON. We describe how CoreNEURON can be used as a library with NEURON and then compare performance of different network models on multiple architectures including IBM BlueGene/Q, Intel Skylake, Intel MIC and NVIDIA GPU. We show how CoreNEURON can simulate existing NEURON network models with 4-7x less memory usage and 2-7x less execution time while maintaining binary result compatibility with NEURON.

The Energy Homeostasis Principle: Neuronal Energy Regulation Drives Local Network Dynamics Generating Behavior

A major goal of neuroscience is understanding how neurons arrange themselves into neural networks that result in behavior. Most theoretical and experimental efforts have focused on a top-down approach which seeks to identify neuronal correlates of behaviors. This has been accomplished by effectively mapping specific behaviors to distinct neural patterns, or by creating computational models that produce a desired behavioral outcome. Nonetheless, these approaches have only implicitly considered the fact that neural tissue, like any other physical system, is subjected to several restrictions and boundaries of operations. Here, we proposed a new, bottom-up conceptual paradigm: The Energy Homeostasis Principle, where the balance between energy income, expenditure, and availability are the key parameters in determining the dynamics of neuronal phenomena found from molecular to behavioral levels. Neurons display high energy consumption relative to other cells, with metabolic consumption of the brain representing 20% of the whole-body oxygen uptake, contrasting with this organ representing only 2% of the body weight. Also, neurons have specialized surrounding tissue providing the necessary energy which, in the case of the brain, is provided by astrocytes. Moreover, and unlike other cell types with high energy demands such as muscle cells, neurons have strict aerobic metabolism. These facts indicate that neurons are highly sensitive to energy limitations, with Gibb's free energy dictating the direction of all cellular metabolic processes. From this activity, the largest energy, by far, is expended by action potentials and post-synaptic potentials; therefore, plasticity can be reinterpreted in terms of their energy context. Consequently, neurons, through their synapses, impose energy demands over post-synaptic neurons in a close loop-manner, modulating the dynamics of local circuits. Subsequently, the energy dynamics end up impacting the homeostatic mechanisms of neuronal networks. Furthermore, local energy management also emerges as a neural population property, where most of the energy expenses are triggered by sensory or other modulatory inputs. Local energy management in neurons may be sufficient to explain the emergence of behavior, enabling the assessment of which properties arise in neural circuits and how. Essentially, the proposal of the Energy Homeostasis Principle is also readily testable for simple neuronal networks.

Molecular and Cellular Networks in The Suprachiasmatic Nuclei

Why do we experience the ailments of jetlag when we travel across time zones? Why is working night-shifts so detrimental to our health? In other words, why can’t we readily choose and stick to non-24 h rhythms? Actually, our daily behavior and physiology do not simply result from the passive reaction of our organism to the external cycle of days and nights. Instead, an internal clock drives the variations in our bodily functions with a period close to 24 h, which is supposed to enhance fitness to regular and predictable changes of our natural environment. This so-called circadian clock relies on a molecular mechanism that generates rhythmicity in virtually all of our cells. However, the robustness of the circadian clock and its resilience to phase shifts emerge from the interaction between cell-autonomous oscillators within the suprachiasmatic nuclei (SCN) of the hypothalamus. Thus, managing jetlag and other circadian disorders will undoubtedly require extensive knowledge of the functional organization of SCN cell networks. Here, we review the molecular and cellular principles of circadian timekeeping, and their integration in the multi-cellular complexity of the SCN. We propose that new, in vivo imaging techniques now enable to address these questions directly in freely moving animals.

Robust Generation of Person-Specific, Synchronously Active Neuronal Networks Using Purely Isogenic Human iPSC-3D Neural Aggregate Cultures

Reproducibly generating human induced pluripotent stem cell-based functional neuronal circuits, solely obtained from single individuals, poses particular challenges to achieve personalized and patient specific functional neuronal in vitro models. A hallmark of functional neuronal assemblies, synchronous neuronal activity, can be non-invasively studied by microelectrode array (MEA) technology, reliably capturing physiological and pathophysiological aspects of human brain function. In our here presented manuscript, we demonstrate a procedure to generate 3D neural aggregates comprising astrocytes, oligodendroglial cells, and neurons obtained from the same human tissue sample. Moreover, we demonstrate the robust ability of those neurons to create a highly synchronously active neuronal network within 3 weeks in vitro, without additionally applied astrocytes. The fusion of MEA-technology with functional neuronal circuits solely obtained from one individual's cells represent isogenic person-specific human neuronal sensor chips that pave the way for specific personalized in vitro neuronal networks as well as neurological and neuropsychiatric disease modeling.

N3-MEA Probes: Scooping Neuronal Networks

In the current work, we introduce a brand new line of versatile, flexible, and multifunctional MEA probes, the so-called Nano Neuro Net, or N³-MEAs. Material choice, dimensions, and room for further upgrade, were carefully considered when designing such probes in order to cover the widest application range possible. Proof of the operation principle of these novel probes is shown in the manuscript via the recording of extracellular signals, such as action potentials and local field potentials from cardiac cells and retinal ganglion cells of the heart tissue and eye respectively. Reasonably large signal to noise ratio (SNR) combined with effortless operation of the devices, mechanical and chemical stability, multifunctionality provide, in our opinion, an unprecedented blend. We show successful recordings of (1) action potentials from heart tissue with a SNR up to 13.2; (2) spontaneous activity of retinal ganglion cells with a SNR up to 12.8; and (3) local field potentials with an ERG-like waveform, as well as spiking responses of the retina to light stimulation. The results reveal not only the multi-functionality of these N³-MEAs, but high quality recordings of electrogenic tissues.

Network Properties Revealed during Multi-Scale Calcium Imaging of Seizure Activity in Zebrafish

Seizures are characterized by hypersynchronization of neuronal networks. Understanding these networks could provide a critical window for therapeutic control of recurrent seizure activity, i.e., epilepsy. However, imaging seizure networks has largely been limited to microcircuits in vitro or small “windows” in vivo. Here, we combine fast confocal imaging of genetically encoded calcium indicator (GCaMP)-expressing larval zebrafish with local field potential (LFP) recordings to study epileptiform events at whole-brain and single-neuron levels in vivo. Using an acute seizure model (pentylenetetrazole, PTZ), we reliably observed recurrent electrographic ictal-like events associated with generalized activation of all major brain regions and uncovered a well-preserved anterior-to-posterior seizure propagation pattern. We also examined brain-wide network synchronization and spatiotemporal patterns of neuronal activity in the optic tectum microcircuit. Brain-wide and single-neuronal level analysis of PTZ-exposed and 4-aminopyridine (4-AP)-exposed zebrafish revealed distinct network dynamics associated with seizure and non-seizure hyperexcitable states, respectively. Neuronal ensembles, comprised of coactive neurons, were also uncovered during interictal-like periods. Taken together, these results demonstrate that macro- and micro-network calcium motifs in zebrafish may provide a greater understanding of epilepsy.

A Fully 3D Interconnected Graphene-Carbon Nanotube Web Allows the Study of Glioma Infiltration in Bioengineered 3D Cortex-Like Networks

Currently available 3D assemblies based on carbon nanotubes (CNTs) lag far behind their 2D CNT-based bricks and require major improvements for biological applications. By using Fe nanoparticles confined to the interlamination of graphite as catalyst, a fully 3D interconnected CNT web is obtained through the pores of graphene foam (GCNT web) by in situ chemical vapor deposition. This 3D GCNT web has a thickness up to 1.5 mm and a completely geometric, mechanical and electrical interconnectivity. Dissociated cortical cells cultured inside the GCNT web form a functional 3D cortex-like network exhibiting a spontaneous electrical activity that is closer to what is observed in vivo. By coculturing and fluorescently labeling glioma and healthy cortical cells with different colors, a new in vitro model is obtained to investigate malignant glioma infiltration. This model allows the 3D trajectories and velocity distribution of individual infiltrating glioma to be reconstructed with an unprecedented precision. The model is cost effective and allows a quantitative and rigorous screening of anticancer drugs. The fully 3D interconnected GCNT web is biocompatible and is an ideal tool to study 3D biological processes in vitro representing a pivotal step toward precise and personalized medicine.

Constraints of Metabolic Energy on the Number of Synaptic Connections of Neurons and the Density of Neuronal Networks

Neuronal networks in the brain are the structural basis of human cognitive function, and the plasticity of neuronal networks is thought to be the principal neural mechanism underlying learning and memory. Dominated by the Hebbian theory, researchers have devoted extensive effort to studying the changes in synaptic connections between neurons. However, understanding the network topology of all synaptic connections has been neglected over the past decades. Furthermore, increasing studies indicate that synaptic activities are tightly coupled with metabolic energy, and metabolic energy is a unifying principle governing neuronal activities. Therefore, the network topology of all synaptic connections may also be governed by metabolic energy. Here, by implementing a computational model, we investigate the general synaptic organization rules for neurons and neuronal networks from the perspective of energy metabolism. We find that to maintain the energy balance of individual neurons in the proposed model, the number of synaptic connections is inversely proportional to the average of the synaptic weights. This strategy may be adopted by neurons to ensure that the ability of neurons to transmit signals matches their own energy metabolism. In addition, we find that the density of neuronal networks is also an important factor in the energy balance of neuronal networks. An abnormal increase or decrease in the network density could lead to failure of energy metabolism in the neuronal network. These rules may change our view of neuronal networks in the brain and have guiding significance for the design of neuronal network models.

Code Generation in Computational Neuroscience: A Review of Tools and Techniques

Advances in experimental techniques and computational power allowing researchers to gather anatomical and electrophysiological data at unprecedented levels of detail have fostered the development of increasingly complex models in computational neuroscience. Large-scale, biophysically detailed cell models pose a particular set of computational challenges, and this has led to the development of a number of domain-specific simulators. At the other level of detail, the ever growing variety of point neuron models increases the implementation barrier even for those based on the relatively simple integrate-and-fire neuron model. Independently of the model complexity, all modeling methods crucially depend on an efficient and accurate transformation of mathematical model descriptions into efficiently executable code. Neuroscientists usually publish model descriptions in terms of the mathematical equations underlying them. However, actually simulating them requires they be translated into code. This can cause problems because errors may be introduced if this process is carried out by hand, and code written by neuroscientists may not be very computationally efficient. Furthermore, the translated code might be generated for different hardware platforms, operating system variants or even written in different languages and thus cannot easily be combined or even compared. Two main approaches to addressing this issues have been followed. The first is to limit users to a fixed set of optimized models, which limits flexibility. The second is to allow model definitions in a high level interpreted language, although this may limit performance. Recently, a third approach has become increasingly popular: using code generation to automatically translate high level descriptions into efficient low level code to combine the best of previous approaches. This approach also greatly enriches efforts to standardize simulator-independent model description languages. In the past few years, a number of code generation pipelines have been developed in the computational neuroscience community, which differ considerably in aim, scope and functionality. This article provides an overview of existing pipelines currently used within the community and contrasts their capabilities and the technologies and concepts behind them.

Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network of Caenorhabditis elegans

We propose an approach to represent neuronal network dynamics as a probabilistic graphical model (PGM). To construct the PGM, we collect time series of neuronal responses produced by the neuronal network and use singular value decomposition to obtain a low-dimensional projection of the time-series data. We then extract dominant patterns from the projections to get pairwise dependency information and create a graphical model for the full network. The outcome model is a functional connectome that captures how stimuli propagate through the network and thus represents causal dependencies between neurons and stimuli. We apply our methodology to a model of the Caenorhabditis elegans somatic nervous system to validate and show an example of our approach. The structure and dynamics of the C. elegans nervous system are well studied and a model that generates neuronal responses is available. The resulting PGM enables us to obtain and verify underlying neuronal pathways for known behavioural scenarios and detect possible pathways for novel scenarios.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling C. elegans at cellular resolution'.