Biology subjects

Mäki-Marttunen, T.

Publications and source records attributed to Mäki-Marttunen, T..

11 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

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↗

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↗

bioRxiv2022

Excitatory and inhibitory effects of HCN channel modulation on excitability of layer V pyramidal cells

Dendrites of cortical pyramidal cells are densely populated by hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, a.k.a. Ih channels. Ih channels are targeted by multiple neuromodulatory pathways, and thus are one of the key ion-channel populations regulating the pyramidal cell activity. Previous observations and theories attribute opposing effects of the Ih channels on neuronal excitability due to their mildly hyperpolarized reversal potential. These effects are difficult to measure experimentally due to the fine spatiotemporal landscape of the Ih activity in the dendrites, but computational models provide an efficient tool for studying this question in a reduced but generalizable setting. In this work, we build upon existing biophysically detailed models of thick-tufted layer V pyramidal cells and model the effects of over- and under-expression of Ih channels as well as their neuromodulation by dopamine (gain of Ih function) and acetylcholine (loss of Ih function). We show that Ih channels facilitate the action potentials of layer V pyramidal cells in response to proximal dendritic stimulus while they hinder the action potentials in response to distal dendritic stimulus at the apical dendrite. We also show that the inhibitory action of the Ih channels in layer V pyramidal cells is due to the interactions between Ih channels and a hot zone of low voltage-activated Ca2+ channels at the apical dendrite. Our simulations suggest that a combination of Ih-enhancing neuromodulation at the proximal apical dendrite and Ih-inhibiting modulation at the distal apical dendrite can increase the layer V pyramidal excitability more than any of the two neuromodulators alone. Our analyses uncover the effects of Ih-channel neuromodulation of layer V pyramidal cells at a single-cell level and shed light on how these neurons integrate information and enable higher-order functions of the brain.

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.

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bioRxiv2020

Evidence for reduced long-term potentiation-like visual cortical plasticity in schizophrenia and bipolar disorder

BackgroundSeveral lines of research suggest that impairments in long-term potentiation (LTP)-like synaptic plasticity might be a key pathophysiological mechanism in schizophrenia (SZ) and bipolar disorder type I (BDI) and II (BDII). Using modulations of visually evoked potentials (VEP) of the electroencephalogram, impaired LTP-like visual cortical plasticity has been implicated in patients with BDII, while there has been conflicting evidence in SZ, a lack of research in BDI, and mixed results regarding associations with symptom severity, mood states, and medication. MethodsWe measured the VEP of patients with SZ spectrum disorders (n=31), BDI (n=34), BDII (n=33), and other BD spectrum disorders (n=2), and age-matched healthy control participants (n=200) before and after prolonged visual stimulation. ResultsCompared to healthy controls, modulation of VEP component N1b, but not C1 or P1, was impaired both in patients within the SZ spectrum ({chi}2=35.1, p=3.1x10-9) and BD spectrum ({chi}2=7.0, p=8.2x10-3), including BDI ({chi}2=6.4, p=0.012), but not BDII ({chi}2=2.2, p=0.14). N1b modulation was also more severely impaired in SZ spectrum than BD spectrum patients ({chi}2=14.2, p=1.7x10-4). The reduction in N1b modulation was related to PANSS total scores ({chi}2=10.8, p=1.0x10-3), and nominally to number of psychotic episodes ({chi}2=4.9, p=0.027). Conclusions. These results suggest that LTP-like plasticity is impaired in SZ and BDI, but not BDII, and related to psychotic symptom severity. Adding to previous genetic, pharmacological, and anatomical evidence, these results implicate aberrant synaptic plasticity as a mechanism underlying SZ and BD.

neuroscience↗

bioRxiv2020

Experience-dependent modulation of the visual evoked potential: testing effect sizes, retention over time, and associations with age in 415 healthy individuals

Experience-dependent modulation of the visual evoked potential (VEP) is a promising proxy measure of synaptic plasticity in the cerebral cortex. However, existing studies are limited by small to moderate sample sizes as well as by considerable variability in how VEP modulation is quantified. In the present study, we used a large sample (n = 415) of healthy volunteers to compare different quantifications of VEP modulation with regards to effect sizes and retention of the modulation effect over time. We observed significant modulation for VEP components C1 (Cohens d = 0.53), P1 (d = 0.66), N1 (d = -0.27), N1b (d = -0.66), but not P2 (p = 0.1), and in one time-frequency cluster (~30 Hz and ~70 ms post-stimulus; d = -0.48), 2-4 minutes after 2 Hz prolonged visual stimulation. For components N1 (d = -0.21) and N1b (d = -0.38), as well for the time-frequency cluster (d = -0.33), this effect was retained after 54-56 minutes. Moderate to high correlations ({rho} = [0.39, 0.69]) between modulation at different postintervention blocks revealed a relatively high temporal stability in the modulation effect for each VEP component. However, different VEP components also showed markedly different temporal retention patterns. Finally, P1 modulation correlated positively with age (t = 5.26), and was larger for female participants (t = 3.91), with no effects of either age or sex on N1 and N1b potentiation. These results provide strong support for VEP modulation, and especially N1b modulation, as a robust measure of synaptic plasticity, but underscore the need to differentiate between components, and to control for demographic confounders.

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bioRxiv2020

A unified computational model for cortical post-synaptic plasticity

Cortical synapses possess a machinery of signalling pathways that leads to various modes of post-synaptic plasticity. Such pathways have been examined to a great detail separately in many types of experimental studies. However, a unified picture on how multiple biochemical pathways collectively shape the observed synaptic plasticity in the neocortex is missing. Here, we built a biochemically detailed model of post-synaptic plasticity that includes the major signalling cascades, namely, CaMKII, PKA, and PKC pathways which, upon activation by Ca2+, lead to synaptic potentiation or depression. We adjusted model components from existing models of intracellular signalling into a single-compartment simulation framework. Furthermore, we propose a statistical model for the prevalence of different types of membrane-bound AMPA-receptor tetramers consisting of GluR1 and GluR2 subunits in proportions suggested by the biochemical signalling model, which permits the estimation of the AMPA-receptor-mediated maximal synaptic conductance. We show that our model can reproduce neuromodulator-gated spike-timing-dependent plasticity as observed in the visual cortex. Moreover, we demonstrate that our model can be fit to data from many cortical areas and that the resulting model parameters reflect the involvement of the pathways pinpointed by the underlying experimental studies. Our model explains the dependence of different forms of plasticity on the availability of different proteins and can be used for the study of mental disorder-associated impairments of cortical plasticity. Significance statementNeocortical synaptic plasticity has been studied experimentally in a number of cortical areas, showing how interactions between neuromodulators and post-synaptic proteins shape the outcome of the plasticity. On the other hand, non-detailed computational models of long-term plasticity, such as Hebbian rules of synaptic potentiation and depression, have been widely used in modelling of neocortical circuits. In this work, we bridge the gap between these two branches of neuroscience by building a detailed model of post-synaptic plasticity that can reproduce observations on cortical plasticity and provide biochemical meaning to the simple rules of plasticity. Our model can be used for predicting the effects of chemical or genetic manipulations of various intracellular signalling proteins on induction of plasticity in health and disease.

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