Biology subjects

Meijer, H. G. E.

Publications and source records attributed to Meijer, H. G. E..

7 recordsLinked to original sources

bioRxiv2026

Weak electric fields of deep brain stimulation can entrain spiking in multi-compartment cortical neuron models

Background: Deep brain stimulation (DBS) is widely used to treat neurological disorders, but how it exerts its therapeutic effects remains an open question. Although DBS primarily acts through stimulated subcortical structures and their networks, recent studies have demonstrated that cortical electric field (E-field) strengths generated during DBS are comparable to, and can exceed, those shown to modulate neuronal activity with transcranial alternating current stimulation. We therefore investigated whether and how these weak DBS fields can directly modulate cortical spike timing. Methods: We used multi-compartment computational models of five neuron types across all cortical layers and exposed them to E-fields modeled as DBS pulses. E-field amplitudes spanned the typical range of cortical E-field strengths during DBS, while frequency and orientation were varied. Entrainment was assessed using peri-stimulus time histograms and quantified by the phase locking value (PLV). Results: Weak DBS fields modulated spike timing of some neurons by either increasing or decreasing the likelihood of firing immediately following the stimulation pulse. This modulation reflected entrainment to the stimulation, with the PLV increasing with E-field amplitude and frequency. The magnitude and direction of spike-timing modulation varied across neuron types and depended on the orientation of the field. Conclusion: These findings suggest that cortical E-fields of DBS may directly influence activity of some cortical neurons, alongside the established indirect cortical effects mediated by subcortical targets and their networks. This provides a new perspective on how DBS may influence cortical activity and offers insights into its potential mechanisms of therapeutic and/or side effects.

neuroscience↗

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

DANSE: a pipeline for dynamic modelling of time-series multi-omics data

BackgroundUnderstanding time-dependent intracellular processes, such as cell differentiation, is key to developing new therapies for a wide range of diseases. Models that connect transcription factor activity to dynamic expression patterns are rare, despite the increased availability of time-series data. ResultsTo identify key regulators of time-dependent biological processes, we present the pipeline DANSE: Dynamics inference Algorithm on Networks Specified by Enhancers. Starting from multi-omics data, our pipeline constructs a data-driven mechanistic transcription factor (TF) network and subsequently defines a dynamic model based on this TF network. The combination of a TF network and a mechanistic model allows for the identification of a small set of key transcription factors predicted to drive the modelled biological process. We showcase the result of our pipeline by applying DANSE to two different datasets that describe iPSC differentiation. ConclusionsModels constructed using DANSE suggest testable hypotheses for the perturbation of gene expression, for example, knockdown or overexpression, that influence cell fate. In this way, DANSE is a powerful tool for generating novel hypotheses in a data-driven manner that take into account the dynamic nature of multi-omics time series data.

systems biology↗

bioRxiv2025

Atypical plume-like events drive glutamate accumulation in metabolic stress conditions

Neural glutamate homeostasis plays a key role in health and disease. In ischemic conditions, such as stroke, this homeostasis is severely disrupted since energy depletion and ion imbalances lead to more glutamate release and less uptake. We here used the fluorescent glutamate sensor SF-iGluSnFR(A184V) to probe the effects of chemical ischemia on extracellular glutamate dynamics in situ, using organotypic slice cultures from mouse cortex. SF-iGluSnFR imaging reported spontaneous glutamate release events, which indicate synchronous network activity, similar to calcium signals detected with GCaMP6f. In addition, glutamate imaging revealed local, asynchronous release events, which were atypically large and long-lasting and showed plume-like characteristics. Under baseline conditions plumes occurred with low frequency, were independent of network activity, and persisted in the presence of TTX. Plume induction was strongly favored by blocking glutamate uptake with TFB-TBOA, whereas blocking ionotropic glutamate receptors (iGluRs) suppressed plumes. Upon inducing chemical ischemia plumes became more pronounced and overly abundant, which resulted in large-scale accumulation of extracellular glutamate. Similar plumes were recently also observed in models of cortical spreading depression and migraine. We therefore propose that plumes represent a more general phenomenon induced by glutamate uptake dysfunction, which may contribute to glutamate-related excitotoxicity in various neurodegenerative and neurological disorders.

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↗

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↗

bioRxiv2021

On the role of arkypallidal and prototypical neurons for phase transitions in the external pallidum

The external pallidum (GPe) plays a central role for basal ganglia functions and dynamics and, consequently, has been included in most computational studies of the basal ganglia. These studies considered the GPe as a homogeneous neural population. However, experimental studies have shown that the GPe contains at least two distinct cell types (prototypical and arkypallidal cells). In this work, we provide in silico insight into how pallidal heterogeneity modulates dynamic regimes inside the GPe and how they affect the GPe response to oscillatory input. We derive a mean-field model of the GPe system from a microscopic spiking neural network of recurrently coupled prototypical and arkypallidal neurons. Using bifurcation analysis, we examine the influence of the intra-pallidal connectivity on the GPe dynamics. We find that under healthy conditions, the inhibitory coupling determines whether the GPe is close to either a bi-stable or an oscillatory regime. Furthermore, we show that oscillatory input to the GPe, arriving from subthalamic nucleus or striatum, leads to characteristic patterns of cross-frequency coupling observed at the GPe. Based on these findings, we propose two different hypotheses of how dopamine depletion at the GPe may lead to phase-amplitude coupling between the parkinsonian beta rhythm and a GPe-intrinsic gamma rhythm. Finally, we show that these findings generalize to realistic spiking neural networks of sparsely coupled type-I excitable GPe neurons. Significant StatementOur work provides (a) insight into the theoretical implications of a dichotomous GPe organization for its macroscopic dynamic regimes, and (b) an exact mean-field model that allows for future investigations of the relationship between GPe spiking activity and local field potential fluctuations. We identify the major phase transitions that the GPe can undergo when subject to static or periodic input and link these phase transitions to the emergence of synchronized oscillations and cross-frequency coupling in the basal ganglia. Due to the close links between our model and experimental findings on the structure and dynamics of prototypical and arkypallidal cells, our results can be used to guide both experimental and computational studies on the role of the GPe for basal ganglia dynamics in health and disease.

neuroscience↗