Biology subjects

van Putten, M. J. A. M.

Publications and source records attributed to van Putten, M. J. A. M..

10 recordsLinked to original sources

bioRxiv2025

The role of the glutamate-glutamine cycle in synaptic transmission during ischemia and recovery

Cerebral ischemia impairs neuronal and glial function, ranging from synaptic failure to irreversible damage. The effects of ischemia on excitatory synaptic transmission remain incompletely understood. Here, we present a detailed biophysical model including the first full implementation of the glutamate-glutamine cycle (GG-cycle), which is essential for proper functioning of glutamatergic synapses. We simulate a presynaptic neuron and an astrocyte in a finite extracellular space (ECS), surrounded by an oxygen bath, as a proxy for energy supply. The model includes ionic currents with corresponding channels and transporters such as the sodium-potassium AT-Pase. To model synaptic transmission, we combine calcium-dependent glutamate release, its uptake by the sodium-dependent excitatory amino acid transporters (EAATs), and the GG-cycle, including glutamine synthesis. We simulate ischemia by blocking energy supply completely. The neuron enters a depolarization block, ion concentrations reach pathological values, and glutamate accumulates in the ECS while glutamate release is disrupted. Surprisingly, we found that synaptic transmission failure was not primarily caused by excessive glutamate release nor by failure of glutamine synthetase. Instead, it mainly resulted from EAAT dysfunction, driven by the collapse of the sodium gradient. Enhancing glutamate clearance alone was insufficient for recovery of synaptic transmission. However, inhibition of the voltage-gated Na+ channels restored ion gradients, recovered glutamate uptake, and re-enabled glutamate release. Taken together, our study highlights the critical role of ion homeostasis, in particular the sodium gradient, in maintaining synaptic function during metabolic stress. Moreover, the model provides a better understanding of synaptic transmission failure and potential recovery strategies during ischemia.

neuroscience↗

bioRxiv2025

Burst suppression: a default brain state associated with loss of network complexity

Burst suppression (BS) is a highly stereotyped EEG pattern observed across a wide range of clinical contexts, from general anesthesia and postanoxic coma to neonatal encephalopathy. Despite its consistent appearance, BS comprises two distinct forms with markedly different implications. BS with identical bursts (IBS) is almost exclusively seen in patients with severe, irreversible encephalopathy and is consistently associated with poor neurological outcome. In contrast, heterogeneous BS (HBS) can appear in reversible conditions such as anesthesia. The mechanisms that give rise to these divergent forms remain elusive. Existing theories impose disease-specific processes on otherwise healthy networks, but such models fail to explain why BS emerges across diverse etiologies and disregard the clinically critical distinction between IBS and HBS. We combined clinical, experimental, and computational approaches to identify shared mechanisms underlying BS. We analyzed EEG recordings from patients with a severe postanoxic encephalopathy and from patients undergoing general anesthesia. These clinical observations were compared with activity recordings from human induced pluripotent stem cell-derived neuronal networks and rodent cortical cultures, and simulations of biophysically grounded neuronal network models. Purely excitatory, low-complexity networks, both in vitro and in silico, spontaneously generated activity virtually indistinguishable from pathological IBS. Introducing inhibitory neurons, modular network structure, or diverse external inputs progressively increased signal complexity and produced HBS-like or continuous activity resembling physiological EEG. Our findings suggest that BS, and particularly IBS, reflects a default dynamic state of simplified excitatory networks that emerges when biological complexity is lost. Different clinical conditions may compromise distinct mechanisms--inhibition, connectivity, or afferent input--yet converge on the same underlying activity pattern. While IBS reflects near-complete loss of complexity, HBS indicates partial preservation. This unified framework explains how diverse etiologies converge on BS and highlights identical forms as signatures of severely reduced network complexity.

neuroscience↗

bioRxiv2024

Does sex matter in neurons response to hypoxic stress?

BackgroundStroke exhibits significant sex differences in incidence, response to treatment and outcome. Preclinical studies suggest that hormones, particularly estrogens, are key to differential sensitivity, as female neurons demonstrate enhanced resilience compared to males in both in vivo and in vitro models. This study investigates whether these sex-specific differences in neuronal vulnerability extend to the ischemic penumbra and explores the effects of estrogens under such conditions. MethodsPrimary cortical neuronal networks were generated from male and female new-born Wistar rats and cultured on micro-electrode arrays or glass coverslips. Male and female networks were subjected to hypoxic conditions, followed by a recovery phase, with or without exogenous estrogen treatment. Electrophysiological activity, including spikes and bursts, was monitored and analyzed. Apoptosis was assessed through immunocytochemistry, focusing on caspase-dependent and apoptosis inducing factor (AIF)-dependent pathways. ResultsUnder hypoxic conditions, male and female neuronal networks exhibited a similar decrease in firing and network burst rates, with an associated increase in network burst durations. Estrogen treatment altered these dynamics, leading to increased network burst rates and decreased network burst duration for both sexes. During recovery, no significant differences were observed between estrogen-treated and untreated networks. Immunocyto-chemistry revealed that estrogen significantly influenced caspase-dependent apoptosis, and to a lesser extent AIF-dependent apoptosis. ConclusionsIn our model of the ischemic penumbra, sex-dependent differences in neuronal responses to hypoxic injury are primarily driven by estrogen, rather than intrinsic neuronal characteristics. Although our electrophysiological data demonstrated that estrogen influenced network activity, it did not offer long-term neuroprotection after hypoxia.

neuroscience↗

bioRxiv2024

Automated inference of disease mechanisms in patient-hiPSC-derived neuronal networks

Human induced pluripotent stem cells (hiPSCs)-derived neurons offer a valuable platform for studying neurological disorders in a patient-specific manner. These neurons can be rapidly differentiated into excitatory neuronal networks, whose activity is measurable using multi-electrode arrays (MEAs). Neuronal networks derived from patients exhibit distinct characteristics, reflecting underlying pathological molecular mechanisms. However, elucidating these mechanisms traditionally requires extensive and hypothesis-driven additional experiments. Computational models can link observable network activity to underlying molecular mechanisms by identifying biophysical model parameters that simulate the activity, but this identification process is challenging. Here, we address this challenge by using simulation-based inference (SBI), a machine-learning approach, to automatically identify the full range of model parameters able to explain the patient-derived MEA activity. Our study demonstrates that SBI can accurately identify ground-truth parameters in simulated data, and successfully estimate the parameters that replicate the network activity of healthy hiPSC-derived neuronal networks. Furthermore, we show that SBI can pinpoint molecular mechanisms affected by pharmacological agents and identify key disease mechanisms in neuronal networks derived from patients. These findings underscore the potential of SBI to automate and enhance the identification of disease mechanisms from MEA measurements, offering a robust and scalable method for advancing research with hiPSC-derived neuronal networks.

neuroscience↗

bioRxiv2024

Construction of invariant features for time-domain EEG/MEG signals using Grassmann manifolds

A challenge in interpreting features derived from source-space electroencephalography (EEG) and magnetoencephalography (MEG) signals is residual mixing of the true source signals. A common approach is to use features that are invariant under linear and instantaneous mixing. In the context of this approach, it is of interest to know which invariant features can be constructed from a given set of source-projected EEG/MEG signals. We address this question by exploiting the fact that invariant features can be viewed as functions on the Grassmann manifold. By embedding the Grassmann manifold in a vector space, coordinates are obtained that serve as building blocks for invariant features, in the sense that all invariant features can be constructed from them. We illustrate this approach by constructing several new bivariate, higher-order, and multidimensional functional connectivity measures for static and time-resolved analysis of time-domain EEG/MEG signals. Lastly, we apply such an invariant feature derived from the Grassmann manifold to EEG data from comatose survivors of cardiac arrest and show its superior sensitivity to identify changes in functional connectivity. Author SummaryElectroencephalography (EEG) and magnetoencephalography (MEG) are techniques to non-invasively measure brain activity in human subjects. This works by measuring the electric potentials on the scalp (EEG) or the magnetic fluxes surrounding the head (MEG) that are induced by currents flowing in the brains grey matter (the "brain activity"). However, reconstruction of brain activity from EEG/MEG sensor signals is an ill-posed inverse problem and, consequently, the reconstructed brain signals are linear superpositions of the true brain signals. This fact complicates the interpretation of the reconstructed brain activity. A common approach is to only use features of the reconstructed activity that are invariant under linear superpositions. In this study we show that all invariant features of reconstructed brain signals can be obtained by taking combinations of a finite set of fundamental features. The fundamental features are parametrized by a high-dimensional space known as the Grass-mann manifold, which has a rich geometric structure that can be exploited to construct new invariant features. Our study advances the systematic study of invariant properties of EEG/MEG data and can be used as a framework to systematize and interrelate existing results. We use the theory to construct a new invariant connectivity measure and apply it to EEG data from comatose survivors of cardiac arrest. We find that this measure enables superior identification of affected brain regions.

neuroscience↗

bioRxiv2024

Breaking the Burst: Unveiling Mechanisms Behind Fragmented Network Bursts in Patient-derived Neurons

Fragmented network bursts (NBs) are observed as a phenotypic driver in many patient-derived neuronal networks on multi-electrode arrays (MEAs), but the pathophysiological mechanisms underlying this phenomenon are unknown. Here, we used our previously developed biophysically detailed in silico model to investigate these mechanisms. Fragmentation of NBs in our model simulations occurred only when the level of short-term synaptic depression (STD) was enhanced, suggesting that STD is a key player. Experimental validation with Dynasore, an STD enhancer, induced fragmented NBs in healthy neuronal networks in vitro. Additionally, we showed that strong asynchronous neurotransmitter release, NMDA currents, or short-term facilitation (STF) can support the emergence of multiple fragments in NBs by producing excitation that persists after high-frequency firing stops. Our results provide important insights into disease mechanisms and potential pharmaceutical targets for neurological disorders modeled using hiPSC-derived neurons.

neuroscience↗

bioRxiv2024

The role of NMDA receptors in memory and prediction incultured neural networks

Memory has been extensively studied at the behavioural as well as the cellular level. Spike timing dependent plasticity (STDP) is associated with N-methyl-D-aspartate (NMDA) receptor activation and is widely accepted to be essential for long-term memory. However, experimental evidence remains sparse, probably due to the required complex combination of cellular and functional readouts. Recent work showed that in-vitro cortical networks memorize and predict inputs. The initial dependency of prediction on short-term memory decreased during the formation of long-term memory traces. Here, we stimulated in-vitro networks to investigate memory and prediction under control conditions, or under NMDA block. The NMDA anatagonist 2-amino-5-phosphonovaleric acid (APV) at a concentration that did not significantly reduce network excitability, but did impede long-term memory trace formation. In APV-treated cultures short-term memory of stimuli persisted, and they were still able to predict. In contrast to control cultures, prediction remained fully dependent on short-term memory. This confirms that NMDA receptor activation is essential for the formation of long-term memory traces and supports the notion that, as control cultures learn to memorize the stimulus, long-term memory starts to contribute to their predictive capability.

neuroscience↗

bioRxiv2023

A neural mass model for the EEG in ischemia

Normal brain function depends on continuous cerebral blood flow for the supply of oxygen and glucose, and is quickly compromised in conditions where the metabolic demand cannot be met. Insufficient cerebral perfusion can result in ischemic stroke, with symptoms ranging from loss of motor or language function to coma, depending on the brain areas affected. Cerebral ischemia also results in changes in the electroencephalogram. Initially, a reduction of the frequency of the rhythms occurs. Depending on the depth and duration of energy deprivation, this eventually leads to the disappearance of all rhythmic activity. Here, we study the relationship between electroencephalogram (EEG) phenomenology and cellular biophysical principles using a model of interacting thalamic and cortical neural masses coupled with energy-dependent synaptic transmission. Our model faithfully reproduces the characteristic EEG phenomenology during acute cerebral ischemia and shows that synaptic arrest occurs before cell swelling and irreversible neuronal depolarization. The early synaptic arrest is attributed to ion homeostatic failure due to dysfunctional Na+/K+-ATPase. Moreover, we show that the excitatory input from relay cells to the cortex controls rhythmic behavior. In particular, weak relay-interneuron interaction manifests in burst-like EEG behavior immediately prior to synaptic arrest. We corroborate our observations with human EEG data from patients undergoing carotid endarterectomy and patients after cardiac arrest with a postanoxic encephalopathy. The model thus reconciles the implications of stroke on a cellular, synaptic and circuit level and provides a basis for exploring other multi-scale therapeutic interventions. Significance statementReliable synaptic transmission and preservation of ion gradients across cellular membranes are essential for physiological brain function and consume significant energy. During cerebral ischemia, synaptic arrest occurs early due to energy deprivation (ED), which is characterized clinically by the loss of physiological electroencephalographic (EEG) rhythms. In this work, we explore connections between cellular and network behavior during ED by means of a novel computational model that describes ion dynamics in the cortex and thalamus, and resulting EEG. We reproduce characteristic EEG behavior during ED and show that synaptic arrest occurs before other pathologies like swelling and depolarization. Moreover, we predict that low excitatory thalamocortical projections cause burst-like EEG patterns before synaptic arrest, which may explain observations regarding post-stroke synaptic reorganization.

neuroscience↗

bioRxiv2022

Preservation of thalamocortical circuitry is essential for good recovery in comatose survivors of cardiac arrest

Continuous EEG monitoring contributes to prediction of neurological outcome in comatose cardiac arrest survivors. While the phenomenology of EEG abnormalities in postanoxic encephalopathy is well-known, the pathophysiology, especially the presumed role of selective synaptic failure is less understood. To further this understanding, we estimate biophysical model parameters from the EEG power spectra from individual patients with a good or poor recovery from a postanoxic encephalopathy. This biophysical model includes intracortical, intrathalamic, and corticothalamic synaptic strengths, as well as synaptic time constants and axonal conduction delays. We used continuous EEG measurements from hundred comatose patients recorded during the first 48 hours post-cardiac arrest, fifty with a poor neurological outcome (Cerebral Performance Category (CPC=5)) and fifty with a good neurological outcome (CPC=1). We only included patients that developed (dis-) continuous EEG activity within 48 hours post-cardiac arrest. For patients with a good outcome, we observed an initial relative excitation in the corticothalamic loop and corticothalamic propagation that subsequently evolved towards values observed in healthy controls. For patients with a poor outcome, we observed an initial increase in the cortical excitation-inhibition ratio, increased relative inhibition in the corticothalamic loop, delayed corticothalamic propagation of neuronal activity, and severely prolonged synaptic time constants, that did not return to physiological values. We conclude that the abnormal EEG evolution in patients with a poor neurological recovery after cardiac arrest may result from persistent and selective synaptic failure that includes corticothalamic circuitry, but also delayed corticothalamic propagation.

neuroscience↗

bioRxiv2021

Ion dynamics at the energy-deprived tripartite synapse

The anatomical and functional organization of neurons and astrocytes at tripartite synapses is essential for reliable neurotransmission, which critically depends on ATP. In low energy conditions, synaptic transmission fails, accompanied by a breakdown of ion gradients, changes in membrane potentials and cell swelling. The resulting cellular damage and cell death are causal to the often devastating consequences of an ischemic stroke. The severity of ischemic damage depends on the age and the brain region in which a stroke occurs, but the reasons for this differential vulnerability are far from understood. In the present study, we address this question by developing a comprehensive biophysical model of a glutamatergic synapse to identify key determinants of synaptic failure during energy deprivation. Our model is based on fundamental biophysical principles, includes dynamics of the most relevant ions, i.e., Na+, K+, Ca2+, Cl- and glutamate, and is calibrated with experimental data. It confirms the critical role of the Na+/K+-ATPase in maintaining ion gradients, membrane potentials and cell volumes. Our simulations demonstrate that the system exhibits two stable states, one physiological and one pathological. During energy deprivation, the physiological state may disappear, forcing a transit to the pathological state, which can be reverted when blocking voltage-gated Na+ and K+ channels. Our model predicts that the transition to the pathological state is favoured if the extracellular space fraction is small. A reduction in the extracellular space volume fraction, as, e.g. observed with ageing, will thus promote the brains susceptibility to ischemic damage. Our work provides new insights into the brains ability to recover from energy deprivation, with translational relevance for diagnosis and treatment of ischemic strokes. Author summaryThe brain consumes energy to keep ion concentrations at normal working conditions. In the case of energy deprivation (ED), e.g., during a stroke, synaptic communication fails first. Inspired by our recent experimental work on ED, we formulated a novel computational model to explore initial events during ED. Our model reproduces time courses for several ions from different experimental data. In some cases, the system returns to baseline upon restoring energy supply. In others, we observe that neurons and astrocytes cannot recover accompanied by cell swelling. There is a threshold depending on the depth and duration of ATP depletion differentiating these cases. Also, smaller extracellular spaces hamper recovery more. This result may explain clinical observations of increased vulnerability to stroke as the size of the extracellular space shrinks with ageing.

biophysics↗