Biology subjects

Linne, M.-L.

Publications and source records attributed to Linne, M.-L..

10 recordsLinked to original sources

bioRxiv2025

Integration of gene expression with computational modeling reveals region-specific brain energy metabolism dysregulation in schizophrenia

Schizophrenia is a highly heritable psychiatric disorder with suggested disturbances in multiple neurobiological pathways including brain energy metabolism, which is essential for sustaining precise neuronal signaling. The relationship between gene expression alterations and energy metabolism defects, however, is poorly understood. Here we combined differential expression (DE) analysis with computational modeling to investigate how the expression of cytosolic energy metabolism-associated genes influences the concentrations of key energy metabolites. The DE analysis used human post-mortem RNA sequencing data from the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (DLPFC) from the CommonMind Consortium. Metabolic simulations were carried out using a biophysical model of brain energy metabolism (Winter et al. 2018). We conducted both population-average and subject-specific simulations and assessed the effects of DE genes individually and collectively. The DE analysis identified nine genes in the ACC and 24 in the DLPFC. Approximately two thirds of the DE genes were downregulated in schizophrenia in both brain areas. Decreased expression of neuronal LDHB in the ACC caused a decrease of pyruvate and an increase of NAD+/NADH ratio. Furthermore, decreased expression of neuronal PFKM in the DLPFC caused an increase of glucose and NAD+/NADH ratio and a decrease of pyruvate, lactate, and ATP. These findings provide evidence for region-specific dysregulation of brain energy metabolism in schizophrenia and suggest mechanistic links between gene expression changes and altered levels of key energy metabolites. AUTHOR SUMMARYSchizophrenia is a mental disorder that affects perception, thought, and behavior, but its disease mechanisms are still not fully understood. One important aspect of brain function is energy metabolism, which powers nearly all cellular processes and supports communication between neurons. In this study, we explored how changes in the expression of specific genes involved in energy production influence the levels of key molecules in the brain. We combined human gene expression data from two brain regions with computer simulations of neuronal function. We found that certain genes had a strong impact on molecules such as glucose, pyruvate, lactate, ATP, and NAD+/NADH balance. Importantly, the effects were specific to particular brain regions. Still, the majority of gene expression changes had non-significant effects. By connecting gene expression with brain energy metabolism simulations, this work provides new insights into the biological mechanisms of schizophrenia and may guide future research aimed at understanding and improving brain function in the disorder.

neuroscience↗

bioRxiv2025

A unified model of short- and long-term plasticity: Effects on network connectivity and information capacity

Activity-dependent synaptic plasticity is a fundamental learning mechanism that shapes the connectivity and activity of neural circuits. Existing computational models of Spike-Timing-Dependent Plasticity (STDP) capture long-term synaptic changes with varying degrees of biological detail. A common approach is to neglect the influence of short-term dynamics on long-term plasticity, which may be an oversimplification for certain neuron types. Thus, there is a need for new models to investigate how short-term dynamics influence long-term plasticity. To address this gap, we introduce a novel phenomenological model, the Short-Long-Term STDP (SL-STDP) rule, which directly integrates the Tsodyks-Markram model of short-term dynamics with postsynaptic long-term plasticity. We fit the new model to recordings from layer 5 of the visual cortex and study how short-term plasticity affects the firing rate frequency dependence of long-term plasticity in a single synapse. Our analysis revealed that the pre- and postsynaptic frequency dependence of long-term plasticity plays a crucial role in shaping the self-organization of recurrent neural networks (RNNs) and their information processing through the emergence of sinks and source nodes. We applied the SL-STDP rule to RNNs and found that neurons in the SL-STDP network self-organize into distinct firing rate clusters, stabilizing the dynamics. We extended the experiments by including homeostatic balancing, namely weight normalization and excitatory-to-inhibitory plasticity, and observed differences in degree correlations between the SL-STDP network and a network without direct coupling between short-term and long-term plasticity. Finally, we evaluated how the modified connectivity affects the networks information capacity in reservoir computing tasks. The SL-STDP rule outperformed the uncoupled system in the majority of tasks, and including excitatory-to-inhibitory facilitating synapses further improved information capacity. Our study demonstrates that short-term dynamics-induced changes in the frequency dependence of long-term plasticity play a pivotal role in shaping network dynamics and link synaptic mechanisms to information processing in RNNs. Author summaryThe brain is a complex organ capable of developing, adapting and learning throughout life. Learning and development of the brain is facilitated by several different plasticity mechanisms that act on different brain areas and timescales. Computational modeling of these plasticity mechanisms not only help us to understand the working principles of the brain but can provide us useful algorithms for future computing devices. In this work, we develop a new activity-dependent plasticity model that acts locally in a synapse and combines two timescales of plasticity into one set of equations. We investigate the effects of this new synapse model on recurrently connected neural networks and observe changes in neural activity and connectivity compared to traditional approaches. We further evaluate the model by performing information capacity tests that are related to the working memory of the circuit. We find improved information capacity, indicating enhanced computational performance of the proposed model. Our study emphasizes how combining the two timescales of plasticity may be important for the development of neural circuits and working memory.

neuroscience↗

bioRxiv2025

Computational modelling of schizophrenia-associated alterations of ion-channel-encoding gene expression predicts a decrease in delta power

Schizophrenia presents with a wide range of phenotypes that can help to understand the the mechanisms of the disease. Among these, alterations in delta oscillations are especially amenable to experimental investigation, yet the mechanisms underlying these changes remain insufficiently understood. Biophysically detailed computational modeling offers a powerful approach to investigate these phenomena, as it enables multi-scale integration of genetic and electrophysiological data. In this study, we developed a minimal network model composed of biophysically detailed, multicompartmental neurons to replicate experimental data on the effects of pharmacological blockage of gabaergic neurotransmission on delta-band power. We inserted post-mortem RNA expression data from the anterior cingulate and prefrontal cortices of individuals with schizophrenia and matched controls into the model to study the effects of schizophrenia-associated alterations of ion-channel expression on delta-oscillation power. Our simulations revealed a significant reduction in delta-band power in schizophrenia, driven by altered expression of calcium channel genes in pyramidal neurons. These results provide insights into the genetic contributions to oscillatory disruptions observed in schizophrenia, and our modelling framework can help to develop stratification strategies that bridge genetics and in vivo electrophysiology.

neuroscience↗

bioRxiv2025

Computational modelling of novelty detection in the mismatch negativity protocols and its impairments in schizophrenia

The human auditory system rapidly distinguishes between novel and familiar sounds, a process reflected in mismatch negativity (MMN), an EEG-based biomarker of auditory novelty detection. MMN is impaired in psychiatric conditions, most notably schizophrenia (SCZ), yet the neuronal mechanisms underlying this deficit remain unclear. Here, we combined computational modelling and genetic analyses to investigate how SCZ-associated cellular abnormalities affect auditory novelty detection. We developed an integrate-and-fire spiking network model capable of detecting four types of auditory novelty, including stimulus omission. Based on assumptions of short-term depressing synapses between the subpopulations of the network and the existence of neuronal inputs that are phase-locked to the rhythm of the recently experienced stimulus sequence, the model reliably reproduced MMN-like novelty detection and allowed systematic testing of SCZ-related cellular alterations. Simulations revealed that both reduced pyramidal cell excitability, linked to ion-channel dysfunction, and decreased spine density impaired novelty detection, with the latter producing stronger deficits. Our work provides a flexible spiking network model of auditory novelty detection that can link cellular-level abnormalities to measurable MMN deficits, improving their mechanistic interpretation and helping to explain the heterogeneity of SCZ.

neuroscience↗

bioRxiv2025

CACNA1C TS-II variants alter single-cell dynamics in computational models of cortical pyramidal cells

Timothy Syndrome (TS) is a rare multi-system disorder and a monogenic calcium channelopathy. Previous computational work on this disorder has focused on the myocardium, ignoring the effects of TS on neural development and its strong association with autism spectrum disorder. Variation in the TS-causative gene, CACNA1C, is indeed also associated with a variety of complex neurodevelopmental and neuropsychiatric disorders. We apply computational methods, drawing on experimental data, to understand the mechanisms of calcium dysregulation in TS, and validate our findings in four well-established multi-compartmental neuronal models. CACNA1C encodes the L-type voltage-gated calcium channel, Cav1.2, which modulates neuronal excitability and several activity-dependent pathways. We investigate two mutations in CACNA1C causative of TS type II, Cav1.2G406R and Cav1.2G402S. Both variants show a loss of voltage-dependent inactivation and changes in their voltage dependence of activation. We incorporate the altered steady-state activity of these variants with additional morphological data indicating a significant increase in activity-dependent dendritic retraction of layer II/III pyramidal cells in TS mutant neurones. Our findings replicate experimental work suggesting that increased Ca2+ flux reduces firing frequency but does not affect the rheobase current for action potential initiation. Furthermore, models expressing dendritic Cav1.2 current show altered apical-somatic signal integration in mutant neurones. All models with shortened dendrite morphology show hyperexcitability, denoted by reduced rheobase current and increased responsiveness to current injection compared to their full-length counterparts. Importantly, our approach identifies robust and testable predictions on the impacts of TS on single-cell dynamics. We also discuss the broader implications of our findings for other Ca2+-related neurodevelopmental and neuropsychiatric disorders.

neuroscience↗

bioRxiv2025

Pre- and postsynaptic mechanisms of neuronal inhibition assessed through biochemically detailed modelling of GABAB receptor signalling

GABAB receptors (GABABRs) are an important building block in neural activity. Despite their widely hypothesized role in many basic neuronal functions and mental disorder symptomatology, there is a lack of biophysically and biochemically detailed models of these receptors and the way they mediate neuronal inhibition. Here, we developed a computational model for the activation of GABABRs and its effects on the activation of G protein-coupled inwardly rectifying potassium (GIRK) channels as well as inhibition of voltage-gated Ca2+ channels. To ensure the generality of our modelling framework, we fit our model to electrophysiological data including patch-clamp and intracellular recordings that described both pre- and postsynaptic effects of the receptor activation. We validated our model using data on postsynaptic effects of GABABRs on layer V pyramidal cell firing activity ex vivo and in vivo and confirmed the strong impact of dendritic GIRK channel activation on the neuron output. Finally, we reproduced and dissected the effects of a knockout of RGS7 (a G protein signalling protein) on CA1 pyramidal cell electrophysiological properties, which shows the potential of our model in generating insights on genetic manipulations of the GABABR system and related genetic variants. Our model thus provides a flexible tool for biochemically and biophysically detailed simulations of different aspects of GABABR activation that can reveal both foundational principles of neuronal dynamics and brain disorder-associated traits and treatment options.

neuroscience↗

bioRxiv2024

Modeling neuron-astrocyte interactions in neural networks using distributed simulation

Astrocytes engage in local interactions with neurons, synapses, other glial cell types, and the vasculature through intricate cellular and molecular processes, playing an important role in brain information processing, plasticity, cognition, and behavior. This study advances understanding of local interactions and self-organization of neuron-astrocyte networks and contributes to the broader investigation of their potential relationship with global activity regimes and overall brain function. We present six new contributions: (1) the development of a new model-building framework for neuron-astrocyte networks, (2) the introduction of connectivity concepts for tripartite neuron-astrocyte interactions in biological neural networks, (3) the design of a scalable architecture capable of simulating networks with up to a million cells, (4) a formalized description of neuron-astrocyte modeling that facilitates reproducibility, (5) the integration of experimental data to a greater extent than existing studies, and (6) simulation results demonstrating how neuron-astrocyte interactions drive the emergence of synchronization in local neuronal groups. Specifically, we develop a new technology for representing astrocytes and their interactions with neurons in distributed simulation code for large-scale spiking neuronal networks. This includes an astrocyte model with calcium dynamics, an extended neuron model receiving calcium-dependent signals from astrocytes, and a parallelized connectivity generation scheme for tripartite interactions between pre- and postsynaptic neurons and astrocytes. We verify the efficiency of our reference implementation through benchmarks varying in computing resources and network sizes. Our in silico experiments reproduce experimental data on astrocytic effects on neuronal synchronization, demonstrating that astrocytes consistently induce local synchronization in groups of neurons across various connectivity schemes and global activity regimes. By adjusting the strength of neuron-astrocyte interactions, we can switch the global activity regime from asynchronous to network-wide synchronization. This work represents an advancement in neuron-astrocyte modeling, introducing a novel framework that enables large-scale simulations of astrocytic influence on neuronal networks. Author summaryAstrocytes play an important role in regulating synapses, neuronal networks, and cognitive functions. However, models that include both neurons and astrocytes are underutilized compared to models with only neurons in theoretical and computational studies. We address this issue by developing theoretical concepts for representing astrocytic connectivity and interactions and provide a reference implementation supporting distributed parallel computing in the spiking neural network simulator NEST. Using these capabilities, we show how astrocytes help to synchronize neural networks under various connection patterns and activity levels. The new technology makes it easier to include astrocytes in simulations of neural systems, promoting the construction of more realistic, relevant, and reproducible models.

neuroscience↗

bioRxiv2023

Genetic mechanisms for impaired synaptic plasticity in schizophrenia revealed by computational modelling

Schizophrenia phenotypes are suggestive of impaired cortical plasticity in the disease, but the mechanisms of these deficits are unknown. Genomic association studies have implicated a large number of genes that regulate neuromodulation and plasticity, indicating that the plasticity deficits have a genetic origin. Here, we used biochemically detailed computational modelling of post-synaptic plasticity to investigate how schizophrenia-associated genes regulate long-term potentiation (LTP) and depression (LTD). We combined our model with data from post-mortem mRNA expression studies (CommonMind gene-expression datasets) to assess the consequences of altered expression of plasticity-regulating genes for the amplitude of LTP and LTD. Our results show that the expression alterations observed post mortem, especially those in anterior cingulate cortex, lead to impaired PKA-pathway-mediated LTP in synapses containing GluR1 receptors. We validated these findings using a genotyped EEG dataset where polygenic risk scores for synaptic and ion channel-encoding genes as well as modulation of visual evoked potentials (VEP) were determined for 286 healthy controls. Our results provide a possible genetic mechanism for plasticity impairments in schizophrenia, which can lead to improved understanding and, ultimately, treatment of the disorder.

neuroscience↗

bioRxiv2021

Analysis of cellular and synaptic mechanisms behind spontaneous cortical activity in vitro: Insights from optimization of spiking neuronal network models

Spontaneous network bursts, the intervals of intense network-wide activity interleaved with longer periods of sparse activity, are a hallmark phenomenon observed in cortical networks at postnatal developmental stages. Generation, propagation and termination of network bursts depend on a combination of synaptic, cellular and network mechanisms; however, the interplay between these mechanisms is not fully understood. We study this interplay in silico, using a new data-driven framework for generating spiking neuronal networks fitted to the microelectrode array recordings. We recorded the network bursting activity from rat postnatal cortical networks under several pharmacological conditions. In each condition, the function of specific excitatory and inhibitory synaptic receptors was reduced in order to examine their impact on global network dynamics. The obtained data was used to develop two complementary model fitting protocols for automatic model generation. These protocols allowed us to disentangle systematically the modeled cellular and synaptic mechanisms that affect the observed network bursts. We confirmed that the change in excitatory and inhibitory synaptic transmission in silico, consistent with pharmacological conditions, can account for the changes in network bursts relative to the control data. Reproducing the exact recorded network bursts statistics required adapting both the synaptic transmission and the cellular excitability separately for each pharmacological condition. Our results bring new understanding of the complex interplay between cellular, synaptic and network mechanisms supporting the burst dynamics. While here we focused on analysis of in vitro data, our approach can be applied ex vivo and in vivo given that the appropriate experimental data is available. New & NoteworthyWe studied the role of synaptic mechanisms in shaping the neural population activity by proposing a new method to combine experimental data and data-driven computational modeling based on spiking neuronal networks. We analyze a dataset recorded from postnatal rat cortical cultures in vitro under the pharmacological influence of excitatory and inhibitory synaptic receptor antagonists. Our computational model identifies neurobiological mechanisms necessary to reproduce the changes in population activity seen across pharmacological conditions.

neuroscience↗

bioRxiv2021

A key role of astrocytic calcium dynamics to link neuronal activity with the BOLD signal

Functional magnetic resonance imaging (fMRI) relies on the coupling between neuronal and vascular activity, but the mechanisms behind this coupling are still under discussion. Recent experimental evidence suggests that calcium signaling may play a significant role in neurovascular coupling. However, it is still controversial where this calcium signal is located (in neurons or elsewhere), how it operates and how relevant is its role. In this paper we introduce a biologically plausible model of the neurovascular coupling and we show that calcium signaling in astrocytes can explain the main aspects of the dynamics of the coupling. We find that calcium signaling can explain so-far unrelated features such as the linear and non-linear regimes, the negative vascular response (undershoot) and the emergence of a (calcium-driven) Hemodynamic Response Function. These features are reproduced here for the first time by a single model of the detailed neuronal-astrocyte-vascular pathway. Furthermore, we analyze how information is coded and transmitted from the neuronal to the vascular system and we find that frequency modulation of astrocytic calcium dynamics plays a key role in this process. Finally, our work provides a framework to link neuronal activity to the BOLD signal, and vice-versa, where neuronal activity can be inferred from the BOLD signal. This opens new ways to link known alterations of astrocytic calcium signaling in neurodegenerative diseases (e.g. Alzheimers and Parkinsons diseases) with detectable changes in neurovascular coupling.

neuroscience↗