Cortical Neuronal Networks Research Articles

Reverse engineering of feedforward cortical-Hippocampal microcircuits for modelling neural network function and dysfunction

Engineered biological neural networks are indispensable models for investigation of neural function and dysfunction from the subcellular to the network level. Notably, advanced neuroengineering approaches are of significant interest for their potential to replicate the topological and functional organization of brain networks. In this study, we reverse engineered feedforward neural networks of primary cortical and hippocampal neurons, using a custom-designed multinodal microfluidic device with Tesla valve inspired microtunnels. By interfacing this device with nanoporous microelectrodes, we show that the reverse engineered multinodal neural networks exhibit capacity for both segregated and integrated functional activity, mimicking brain network dynamics. To advocate the broader applicability of our model system, we induced localized perturbations with amyloid beta to study the impact of pathology on network functionality. Additionally, we demonstrate long-term culturing of subregion- and layer specific neurons extracted from the entorhinal cortex and hippocampus of adult Alzheimer’s-model mice and rats. Our results thus highlight the potential of our approach for reverse engineering of anatomically relevant multinodal neural networks to study dynamic structure-function relationships in both healthy and pathological conditions.

Parallelized Mechanical Stimulation of Neuronal Calcium Through Cell-Internal Nanomagnetic Forces Provokes Lasting Shifts in the Network Activity State.

Neurons differentiate mechanical stimuli force and rate to elicit unique functional responses, driving the need for further tools to generate various mechanical stimuli. Here, cell-internal nanomagnetic forces (iNMF) are introduced by manipulating internalized magnetic nanoparticles with an external magnetic field across cortical neuron networks in vitro. Under iNMF, cortical neurons exhibit calcium (Ca2+) influx, leading to modulation of activity observed through Ca2+ event rates. Inhibiting particle uptake or altering nanoparticle exposure time reduced the neuronal response to nanomagnetic forces, exposing the requirement of nanoparticle uptake to induce the Ca2+ response. In highly active cortical networks, iNMF robustly modulates synchronous network activity, which is lasting and repeatable. Using pharmacological blockers, it is shown that iNMF activates mechanosensitive ion channels to induce the Ca2+ influx. Then, in contrast to transient mechanically evoked neuronal activity, iNMFactivates Ca2+-activated potassium (KCa) channelsto stabilize the neuronal membrane potential and induce network activity shifts. The findings reveal the potential of magnetic nanoparticle-mediated mechanical stimulation to modulate neuronal circuit dynamics, providing insights into the biophysics of neuronal computation.

Nanomagnetic Guidance Shapes the Structure-Function Relationship of Developing Cortical Networks.

In this study, we implement large-scale nanomagnetic guidance on cortical neurons to guide dissociated neuronal networks during development. Cortical networks cultured over microelectrode arrays were exposed to functionalized magnetic nanoparticles, followed by magnetic field exposure to guide neurites over 14 days in vitro. Immunofluorescence of the axonal protein Tau revealed a greater number of neurites that were longer and aligned with the nanomagnetic force relative to nonguided networks. This was further confirmed through brightfield imaging on the microelectrode arrays during development. Spontaneous electrophysiological recordings revealed that the guided networks exhibited increased firing rates and frequency in force-aligned connectivity identified through Granger Causality. Applying this methodology across networks with nonuniform force directions increased local activity in target regions, identified as regions in the direction of the nanomagnetic force. Altogether, these results demonstrate that nanomagnetic forces guide the structure and function of dissociated cortical neuron networks at the millimeter scale.

Impact of background input on memory consolidation

Memory consolidation involves repeated replay of new information by the hippocampus, which transfers memories to the neocortex for long-term storage. This occurs mainly during slow wave sleep, a phase characterized in the cortex by low cholinergic tone and low afferent input. High cholinergic tone has been shown to hamper memory consolidation, probably mediated by reduced network excitability (the ease of activity propagation in a network). We used cortical neuronal networks on multi electrode arrays to investigate whether low background input contributes to memory consolidation. Networks received focal electrical stimuli to memorize, with or without background afferent input (global optogenetic stimulation). Background stimulation hampered memory formation and consolidation, confirming the importance of low background input. Moreover, it lowered network excitability, similar to high cholinergic tone. These findings suggest that high network excitability is a critical feature of slow wave sleep that facilitates memory consolidation.

Altered development and network connectivity in a human neuronal model of 15q11.2 deletion-related neurodevelopmental disorders.

The chromosome 15q11.2 locus is deleted in 1.5% of patients with genetic epilepsy and confers a risk for intellectual disability and schizophrenia. Individuals with this deletion demonstrate increased cortical thickness, decreased cortical surface area and white matter abnormalities. Human induced pluripotent stem cell (iPSC)-derived neural progenitor cells (NPC) from 15q11.2 deletion individuals exhibit early adhesion junction and migration abnormalities, but later neuronal development and function have not been fully assessed. Imaging studies indicating altered structure and network connectivity in the anterior brain regions and the cingulum suggest that in addition to alterations in progenitor dynamics, there may also be structural and functional changes within discrete networks of mature neurons. To explore this, we generated human forebrain cortical neurons from iPSCs derived from individuals with or without 15q11.2 deletion and used longitudinal imaging and multielectrode array analysis to evaluate neuronal development over time. 15q11.2 deleted neurons exhibited fewer connections and an increase in inhibitory neurons. Individual neurons had decreased neurite complexity and overall decreased neurite length. These structural changes were associated with a reduction in multiunit action potential generation, bursting and synchronization. The 15q11.2 deleted neurons also demonstrated specific functional deficits in glutamate and GABA mediated network activity and synchronization with a delay in the maturation of the inhibitory response to GABA. These data indicate that deletion of the 15q11.2 region is sufficient to impair the structural and functional maturation of cortical neuron networks which likely underlies the pathologic changes in humans with the 15q11.2 deletion.

Criticality and universality in neuronal cultures during "up" and "down" states.

The brain can be seen as a self-organized dynamical system that optimizes information processing and storage capabilities. This is supported by studies across scales, from small neuronal assemblies to the whole brain, where neuronal activity exhibits features typically associated with phase transitions in statistical physics. Such a critical state is characterized by the emergence of scale-free statistics as captured, for example, by the sizes and durations of activity avalanches corresponding to a cascading process of information flow. Another phenomenon observed during sleep, under anesthesia, and in in vitro cultures, is that cortical and hippocampal neuronal networks alternate between "up" and "down" states characterized by very distinct firing rates. Previous theoretical work has been able to relate these two concepts and proposed that only up states are critical whereas down states are subcritical, also indicating that the brain spontaneously transitions between the two. Using high-speed high-resolution calcium imaging recordings of neuronal cultures, we test this hypothesis here by analyzing the neuronal avalanche statistics in populations of thousands of neurons during "up" and "down" states separately. We find that both "up" and "down" states can exhibit scale-free behavior when taking into account their intrinsic time scales. In particular, the statistical signature of "down" states is indistinguishable from those observed previously in cultures without "up" states. We show that such behavior can not be explained by network models of non-conservative leaky integrate-and-fire neurons with short-term synaptic depression, even when realistic noise levels, spatial network embeddings, and heterogeneous populations are taken into account, which instead exhibits behavior consistent with previous theoretical models. Similar differences were also observed when taking into consideration finite-size scaling effects, suggesting that the intrinsic dynamics and self-organization mechanisms of these cultures might be more complex than previously thought. In particular, our findings point to the existence of different mechanisms of neuronal communication, with different time scales, acting during either high-activity or low-activity states, potentially requiring different plasticity mechanisms.

Huntingtin lowering impairs the maturation and synchronized synaptic activity of human cortical neuronal networks derived from induced pluripotent stem cells

Despite growing descriptions of wild-type Huntingtin (wt-HTT) roles in both adult brain function and, more recently, development, several clinical trials are exploring HTT-lowering approaches that target both wt-HTT and the mutant isoform (mut-HTT) responsible for Huntington's disease (HD). This non-selective targeting is based on the autosomal dominant inheritance of HD, supporting the idea that mut-HTT exerts its harmful effects through a toxic gain-of-function or a dominant-negative mechanism. However, the precise amount of wt-HTT needed for healthy neurons in adults and during development remains unclear. In this study, we address this question by examining how wt-HTT loss affects human neuronal network formation, synaptic maturation, and homeostasis in vitro. Our findings establish a role of wt-HTT in the maturation of dendritic arborization and the acquisition of network-wide synchronized activity by human cortical neuronal networks modeled in vitro. Interestingly, the network synchronization defects only became apparent when more than two-thirds of the wt-HTT protein was depleted. Our study underscores the critical need to precisely understand wt-HTT role in neuronal health. It also emphasizes the potential risks of excessive wt-HTT loss associated with non-selective therapeutic approaches targeting both wt- and mut-HTT isoforms in HD patients.

Physiological Biomarkers of Upper Motor Neuron Dysfunction in ALS.

Upper motor neuron (UMN) dysfunction is an important feature of amyotrophic lateral sclerosis (ALS) for the diagnosis and understanding of pathogenesis. The identification of UMN signs forms the basis of ALS diagnosis, although may be difficult to discern, especially in the setting of severe muscle weakness. Transcranial magnetic stimulation (TMS) techniques have yielded objective physiological biomarkers of UMN dysfunction in ALS, enabling the interrogation of cortical and subcortical neuronal networks with diagnostic, pathophysiological, and prognostic implications. Transcranial magnetic stimulation techniques have provided pertinent pathogenic insights and yielded novel diagnostic and prognostic biomarkers. Cortical hyperexcitability, as heralded by a reduction in short interval intracortical inhibition (SICI) and an increase in short interval intracortical facilitation (SICF), has been associated with lower motor neuron degeneration, patterns of disease evolution, as well as the development of specific ALS clinical features including the split hand phenomenon. Reduction in SICI has also emerged as a potential diagnostic aid in ALS. More recently, physiological distinct inhibitory and facilitatory cortical interneuronal circuits have been identified, which have been shown to contribute to ALS pathogenesis. The triple stimulation technique (TST) was shown to enhance the diagnostic utility of conventional TMS measures in detecting UMN dysfunction. Resting-state EEG is a novel neurophysiological technique developed for directly interrogating cortical neuronal networks in ALS, that have yielded potentially useful physiological biomarkers of UMN dysfunction. The present review discusses physiological biomarkers of UMN dysfunction in ALS, encompassing conventional and novel TMS techniques developed to interrogate the functional integrity of the corticomotoneuronal system, focusing on pathogenic, diagnostic, and prognostic utility.

Advances in pharmacogenomics: optimizing antiepileptic drug therapy for drug-resistant epilepsy

Epilepsy, a complex neurological disorder, is influenced by intricate interactions within cortical, hippocampal, or thalamocortical neuronal networks, presenting a genetically complex condition with non-Mendelian inheritance patterns. This complexity is underscored by the involvement of numerous “susceptibilities” or “modifier” genes, complicating the assessment of risk and therapy outcomes. A critical inquiry in epilepsy treatment involves understanding how genetic diversity impacts treatment strategies and efficacy. Pharmacogenomic advancements have elaborated the connection between genetic variants and antiseizure medication (ASM) safety and response, marking a shift towards precision medicine in epilepsy care. Notably, genetic screening for variants such as HLA-B*1502 and HLA-A*3101 has demonstrated significant efficacy in preventing severe hypersensitivity reactions, including toxic epidermal necrolysis (TEN) and Stevens-Johnson syndrome (SJS), particularly among specific ethnic populations. However, putting pharmacogenomic discoveries into clinical practice faces numerous challenges, including educational, legal, and economic barriers, emphasizing the need for broader acceptance and integration of pharmacogenomic data. This review synthesizes recent studies on pharmacogenomics in epilepsy, highlighting the current advances and prospects for personalizing epilepsy treatment through genetic insights, aiming to enhance ASM safety, reduce adverse effects, and improve treatment outcomes. Through a comprehensive examination of the genetic basis of epilepsy and its influence on pharmacotherapy, this review endeavors to contribute to the evolving landscape of precision medicine in epilepsy care, advocating for a more individualized and effective treatment approach.

Reelin Regulates Developmental Desynchronization Transition of Neocortical Network Activity.

During the first and second stages of postnatal development, neocortical neurons exhibit a wide range of spontaneous synchronous activity (SSA). Towards the end of the second postnatal week, the SSA is replaced by a more sparse and desynchronized firing pattern. The developmental desynchronization of neocortical spontaneous neuronal activity is thought to be intrinsically generated, since sensory deprivation from the periphery does not affect the time course of this transition. The extracellular protein reelin controls various aspects of neuronal development through multimodular signaling. However, so far it is unclear whether reelin contributes to the developmental desynchronization transition of neocortical neurons. The present study aims to investigate the role of reelin in postnatal cortical developmental desynchronization using a conditional reelin knockout (RelncKO) mouse model. Conditional reelin deficiency was induced during early postnatal development, and Ca2+ recordings were conducted from organotypic cultures (OTCs) of the somatosensory cortex. Our results show that both wild type (wt) and RelncKO exhibited an SSA pattern during the early postnatal week. However, at the end of the second postnatal week, wt OTCs underwent a transition to a desynchronized network activity pattern, while RelncKO activity remained synchronous. This changing activity pattern suggests that reelin is involved in regulating the developmental desynchronization of cortical neuronal network activity. Moreover, the developmental desynchronization impairment observed in RelncKO was rescued when RelncKO OTCs were co-cultured with wt OTCs. Finally, we show that the developmental transition to a desynchronized state at the end of the second postnatal week is not dependent on glutamatergic signaling. Instead, the transition is dependent on GABAAR and GABABR signaling. The results suggest that reelin controls developmental desynchronization through GABAAR and GABABR signaling.

Chimera-like state in the bistable excitatory-inhibitory cortical neuronal network

In recent years, the coexistence of different states in the neural system has attracted widespread interest. Researchers have found a coexisting state of spiking and resting in homogeneous networks, which is known as the chimera-like state. The real cortical network is a much more complex and heterogeneous network. Therefore, the excitatory-inhibitory cortical neuronal network is constructed based on Hodgkin-Huxley neuronal model in this paper, and the chimera-like state is further investigated in the heterogeneous network. It is found that the chimera-like state is related to the balance between excitatory and inhibitory synaptic currents. The excitatory coupling current can counteract the initial condition effect and promote synchronized firing of neurons in the network. The inhibitory coupling current desynchronizes the network and thus induces synaptic noise, resulting in an inverse bell-shaped dependence of the change in the number of spiking neurons. We analyzed the underlying mechanisms of synaptic noise in the phase plane diagram and found it has asymmetry for the neuronal state transition. In addition, neurons with low degrees have a higher probability of undergoing state transitions. Finally, we verified that the chimera-like state is robust to network topology and initial conditions. The results provide a new insight into neuronal interactions in heterogeneous networks and might help to reveal the mechanisms of coexistence of different states in the cortical network.

Stretch-Induced Injury Affects Cortical Neuronal Networks in a Time- and Severity-Dependent Manner.

Traumatic brain injury (TBI) is the leading cause of accident-related death and disability in the world and can lead to long-term neuropsychiatric symptoms, such as a decline in cognitive function and neurodegeneration. TBI includes primary and secondary injury, with head trauma and deformation of the brain caused by the physical force of the impact as primary injury, and cellular and molecular cascades that lead to cell death as secondary injury. Currently, there is no treatment for TBI-induced cell damage and neural circuit dysfunction in the brain, and thus, it is important to understand the underlying cellular mechanisms that lead to cell damage. In the current study, we use stretchable microelectrode arrays (sMEAs) to model the primary injury of TBI to study the electrophysiological effects of physically injuring cortical cells. We recorded electrophysiological activity before injury and then stretched the flexible membrane of the sMEAs to injure the cells to varying degrees. At 1, 24, and 72h post-stretch, we recorded activity to analyze differences in spike rate, Fano factor, burstlet rate, burstlet width, synchrony of firing, local network efficiency, and Q statistic. Our results demonstrate that mechanical injury changes the firing properties of cortical neuron networks in culture in a time- and severity-dependent manner. Our results suggest that changes to electrophysiological properties after stretch are dependent on the strength of synchronization between neurons prior to injury.

Revealing and reshaping attractor dynamics in large networks of cortical neurons.

Attractors play a key role in a wide range of processes including learning and memory. Due to recent innovations in recording methods, there is increasing evidence for the existence of attractor dynamics in the brain. Yet, our understanding of how these attractors emerge or disappear in a biological system is lacking. By following the spontaneous network bursts of cultured cortical networks, we are able to define a vocabulary of spatiotemporal patterns and show that they function as discrete attractors in the network dynamics. We show that electrically stimulating specific attractors eliminates them from the spontaneous vocabulary, while they are still robustly evoked by the electrical stimulation. This seemingly paradoxical finding can be explained by a Hebbian-like strengthening of specific pathways into the attractors, at the expense of weakening non-evoked pathways into the same attractors. We verify this hypothesis and provide a mechanistic explanation for the underlying changes supporting this effect.

An interpretable mechanism for grating-induced cross-inhibition and gamma oscillation based on a visual cortical neuronal network model

<abstract><p>Biological experiments targeting the mammalian primary visual cortex have shown that neuronal response to a preferred orientation grating is cross-inhibited by an orthogonal orientation mask grating. The plaid formed by the overlap of the two gratings not only causes a decrease in the neuronal firing rate but also shifts the gamma oscillation to a weaker oscillation at a higher frequency. The mechanism for the above phenomena is unclarified. In this paper, a large-scale cortical neuronal network model with biological details is constructed. In this model, two modes of connectivity that may contribute to cross-inhibition are considered: the thalamo-cortical feedforward pathway and the push-pull organization of cortical layer 4. Based on this model, the modulation of firing rate and gamma oscillation by a plaid stimulation are successfully reproduced, which is consistent with biological experiments and suggests that it is the thalamo-cortical feedforward pathway that leads to cross-inhibition. Furthermore, our analysis of the neuronal spike clusters and current fluctuations suggests that the push-pull organization leads to an increase in gamma frequency during the transition of visual stimuli from grating to plaid by modulating the source of synaptic inhibition in local neuronal populations. Such results will help to understand the visual processing under multi-input integration.</p></abstract>

Signal transmission and energy consumption in excitatory–inhibitory cortical neuronal network

Signal transmission and energy consumption in excitatory–inhibitory cortical neuronal network

The Cerebellum and the Motor Cortex: Multiple Networks Controlling Multiple Aspects of Behavior.

The cerebellum and its thalamic projections to the primary motor cortex (M1) are well known to play an essential role in executing daily actions. Anatomic investigations in animals and postmortem humans have established the reciprocal connections between these regions; however, how these pathways can shape cortical activity in behavioral contexts and help promote recovery in neuropathological conditions remains not well understood. The present review aims to provide a comprehensive description of these pathways in animals and humans and discuss how novel noninvasive brain stimulation (NIBS) methods can be used to gain a deeper understanding of the cerebellar-M1 connections. In the first section, we focus on recent animal literature that details how information sent from the cerebellum and thalamus is integrated into an broad network of cortical motor neurons. We then discuss how NIBS approaches in humans can be used to reliably assess the connectivity between the cerebellum and M1. Moreover, we provide the latest perspectives on using advanced NIBS approaches to investigate and modulate multiple cerebellar-cortical networks involved in movement behavior and plasticity. Finally, we discuss how these emerging methods have been used in translation research to produce long-lasting modifications of cerebellar-thalamic-M1 to restore cortical activity and motor function in neurologic patients.

Critical dynamics arise during structured information presentation within embodied in vitro neuronal networks

Understanding how brains process information is an incredibly difficult task. Amongst the metrics characterising information processing in the brain, observations of dynamic near-critical states have generated significant interest. However, theoretical and experimental limitations associated with human and animal models have precluded a definite answer about when and why neural criticality arises with links from attention, to cognition, and even to consciousness. To explore this topic, we used an in vitro neural network of cortical neurons that was trained to play a simplified game of ‘Pong’ to demonstrate Synthetic Biological Intelligence (SBI). We demonstrate that critical dynamics emerge when neural networks receive task-related structured sensory input, reorganizing the system to a near-critical state. Additionally, better task performance correlated with proximity to critical dynamics. However, criticality alone is insufficient for a neuronal network to demonstrate learning in the absence of additional information regarding the consequences of previous actions. These findings offer compelling support that neural criticality arises as a base feature of incoming structured information processing without the need for higher order cognition.

Modular architecture facilitates noise-driven control of synchrony in neuronal networks.

High-level information processing in the mammalian cortex requires both segregated processing in specialized circuits and integration across multiple circuits. One possible way to implement these seemingly opposing demands is by flexibly switching between states with different levels of synchrony. However, the mechanisms behind the control of complex synchronization patterns in neuronal networks remain elusive. Here, we use precision neuroengineering to manipulate and stimulate networks of cortical neurons in vitro, in combination with an in silico model of spiking neurons and a mesoscopic model of stochastically coupled modules to show that (i) a modular architecture enhances the sensitivity of the network to noise delivered as external asynchronous stimulation and that (ii) the persistent depletion of synaptic resources in stimulated neurons is the underlying mechanism for this effect. Together, our results demonstrate that the inherent dynamical state in structured networks of excitable units is determined by both its modular architecture and the properties of the external inputs.

Experimental validation of the free-energy principle with in vitro neural networks

Empirical applications of the free-energy principle are not straightforward because they entail a commitment to a particular process theory, especially at the cellular and synaptic levels. Using a recently established reverse engineering technique, we confirm the quantitative predictions of the free-energy principle using in vitro networks of rat cortical neurons that perform causal inference. Upon receiving electrical stimuli—generated by mixing two hidden sources—neurons self-organised to selectively encode the two sources. Pharmacological up- and downregulation of network excitability disrupted the ensuing inference, consistent with changes in prior beliefs about hidden sources. As predicted, changes in effective synaptic connectivity reduced variational free energy, where the connection strengths encoded parameters of the generative model. In short, we show that variational free energy minimisation can quantitatively predict the self-organisation of neuronal networks, in terms of their responses and plasticity. These results demonstrate the applicability of the free-energy principle to in vitro neural networks and establish its predictive validity in this setting.

Comparative assessment of Ca2+ oscillations in 2- and 3-dimensional hiPSC derived and isolated cortical neuronal networks

Human induced Pluripotent Stem Cell (hiPSC) derived neural cells offer great potential for modelling neurological diseases and toxicities and have found application in drug discovery and toxicology. As part of the European Innovative Medicines Initiative (IMI2) NeuroDeRisk (Neurotoxicity De-Risking in Preclinical Drug Discovery), we here explore the Ca2+ oscillation responses of 2D and 3D hiPSC derived neuronal networks of mixed Glutamatergic/GABAergic activity with a compound set encompassing both clinically as well as experimentally determined seizurogenic compounds. Both types of networks are scored against Ca2+ responses of a primary mouse cortical neuronal 2D network model serving as an established comparator assay. Parameters of frequency and amplitude of spontaneous global network Ca2+ oscillations and the drug-dependent directional changes to these were assessed, and predictivity of seizurogenicity scored using contingency table analysis. In addition, responses between models were compared between both 2D models as well as between 2D and 3D models. Concordance of parameter responses was best between the hiPSC neurospheroid and the mouse primary cortical neuron model (77% for frequency and 65% for amplitude). Decreases in spontaneous Ca2+ oscillation frequency and amplitude were found to be the most basic shared determinants of risk of seizurogenicity between the mouse and the neurospheroid model based on testing of clinical compounds with documented seizurogenic activity. Increases in spontaneous Ca2+ oscillation frequency were primarily observed with the 2D hIPSC model, though the specificity of this effect to seizurogenic clinical compounds was low (33%), while decreases to spike amplitude in this model were more predictive of seizurogenicity. Overall predictivities of the models were similar, with sensitivity of the assays typically exceeding specificity due to high false positive rates. Higher concordance of the hiPSC 3D model over the 2D model when compared to mouse cortical 2D responses may be the result of both a longer maturation time of the neurospheroid (84–87 days for 3D vs. 22–24 days for 2D maturation) as well as the 3-dimensional nature of network connections established. The simplicity and reproducibility of spontaneous Ca2+ oscillation readouts support further investigation of hiPSC derived neuronal sources and their 2- and 3-dimensional networks for neuropharmacological safety screening.