Modelling the molecular mechanisms of synaptic plasticity using systems biology approaches

Combined statistical-biophysical modeling links ion channel genes to physiology of cortical neuron types

Neural cell types have classically been characterized by their anatomy and electrophysiology. More recently, single-cell transcriptomics has enabled an increasingly fine genetically defined taxonomy of cortical cell types, but the link between the gene expression of individual cell types and their physiological and anatomical properties remains poorly understood. Here, we develop a hybrid modeling approach to bridge this gap. Our approach combines statistical and mechanistic models to predict cells' electrophysiological activity from their gene expression pattern. To this end, we fit biophysical Hodgkin-Huxley-based models for a wide variety of cortical cell types using simulation-based inference, while overcoming the challenge posed by the mismatch between the mathematical model and the data. Using multimodal Patch-seq data, we link the estimated model parameters to gene expression using an interpretable sparse linear regression model. Our approach recovers specific ion channel gene expressions as predictive of biophysical model parameters including ion channel densities, directly implicating their mechanistic role in determining neural firing.

Setting a double-capacitive neuron coupled with Josephson junction and piezoelectric source

Perception of voice means acoustic electric conversion in the auditory system, and changes of external magnetic field can affect the neural activities by taming the channel current via some field components including memristor and Josephson junction. Combination of two capacitors via an electric component is effective to describe the physical property of artificial cell membrane, which is often used to reproduce the characteristic of electric activities in cell membrane. Involvement of two capacitive variables for two capacitors in the neural circuit can discern the effect of field diversity in the media in two sides of the cell membrane in theoretical way. A Josephson junction is used to couple a piezoelectric neural circuit composed of two capacitors, one inductor and one nonlinear resistor. Field energy is mainly kept in the capacitive and inductive components, and it is obtained and converted into dimensionless energy function. The Hamilton energy function in an equivalent auditory neuron is verified by using the Helmholtz theorem. Noisy excitation on the neural circuit can be detected via the Josephson junction channel and similar stochastic resonance is detected by regulating the noise intensity, as a result, the average energy reaches a peak value under stochastic resonance. An adaptive law controls the bifurcation parameter, which is relative to the membrane property, and energy shift controls the mode selection during continuous growth of the bifurcation parameter. That is, external energy injection derived from acoustic wave or magnetic field will control the energy level, and then suitable firing patterns are controlled effectively.

Human Neural Organoid Microphysiological Systems Show the Building Blocks Necessary for Basic Learning and Memory

Brain Microphysiological Systems including neural organoids derived from human induced pluripotent stem cells offer a unique lens to study the intricate workings of the human brain. This paper investigates the foundational elements of learning and memory in neural organoids, also known as Organoid Intelligence by quantifying immediate early gene expression, synaptic plasticity, neuronal network dynamics, and criticality to demonstrate the utility of these organoids in basic science research. Neural organoids showed synapse formation, glutamatergic and GABAergic receptor expression, immediate early gene expression basally and evoked, functional connectivity, criticality, and synaptic plasticity in response to theta-burst stimulation. In addition, pharmacological interventions on GABAergic and glutamatergic receptors, and input specific theta-burst stimulation further shed light on the capacity of neural organoids to mirror synaptic modulation and short-term potentiation, demonstrating their potential as tools for studying neurophysiological and neurological processes and informing therapeutic strategies for diseases.

Biophysical Modeling of Synaptic Plasticity

Dendritic spines are small, bulbous compartments that function as postsynaptic sites and undergo intense biochemical and biophysical activity. The role of the myriad signaling pathways that are implicated in synaptic plasticity is well studied. A recent abundance of quantitative experimental data has made the events associated with synaptic plasticity amenable to quantitative biophysical modeling. Spines are also fascinating biophysical computational units because spine geometry, signal transduction, and mechanics work in a complex feedback loop to tune synaptic plasticity. In this sense, ideas from modeling cell motility can inspire us to develop multiscale approaches for predictive modeling of synaptic plasticity. In this article, we review the key steps in postsynaptic plasticity with a specific focus on the impact of spine geometry on signaling, cytoskeleton rearrangement, and membrane mechanics. We summarize the main experimental observations and highlight how theory and computation can aid our understanding of these complex processes.

Fast Hebbian plasticity and working memory

Theories and models of working memory (WM) were at least since the mid-1990s dominated by the persistent activity hypothesis. The past decade has seen rising concerns about the shortcomings of sustained activity as the mechanism for short-term maintenance of WM information in the light of accumulating experimental evidence for so-called activity-silent WM and the fundamental difficulty in explaining robust multi-item WM. In consequence, alternative theories are now explored mostly in the direction of fast synaptic plasticity as the underlying mechanism. The question of non-Hebbian vs Hebbian synaptic plasticity emerges naturally in this context. In this review, we focus on fast Hebbian plasticity and trace the origins of WM theories and models building on this form of associative learning.

Heterogeneous off-target impact of ion-channel deletion on intrinsic properties of hippocampal model neurons that self-regulate calcium

How do neurons that implement cell-autonomous self-regulation of calcium react to knockout of individual ion-channel conductances? To address this question, we used a heterogeneous population of 78 conductance-based models of hippocampal pyramidal neurons that maintained cell-autonomous calcium homeostasis while receiving theta-frequency inputs. At calcium steady-state, we individually deleted each of the 11 active ion-channel conductances from each model. We measured the acute impact of deleting each conductance (one at a time) by comparing intrinsic electrophysiological properties before and immediately after channel deletion. The acute impact of deleting individual conductances on physiological properties (including calcium homeostasis) was heterogeneous, depending on the property, the specific model, and the deleted channel. The underlying many-to-many mapping between ion channels and properties pointed to ion-channel degeneracy. Next, we allowed the other conductances (barring the deleted conductance) to evolve towards achieving calcium homeostasis during theta-frequency activity. When calcium homeostasis was perturbed by ion-channel deletion, post-knockout plasticity in other conductances ensured resilience of calcium homeostasis to ion-channel deletion. These results demonstrate degeneracy in calcium homeostasis, as calcium homeostasis in knockout models was implemented in the absence of a channel that was earlier involved in the homeostatic process. Importantly, in reacquiring homeostasis, ion-channel conductances and physiological properties underwent heterogenous plasticity (dependent on the model, the property, and the deleted channel), even introducing changes in properties that were not directly connected to the deleted channel. Together, post-knockout plasticity geared towards maintaining homeostasis introduced heterogenous off-target effects on several channels and properties, suggesting that extreme caution be exercised in interpreting experimental outcomes involving channel knockouts.

Spine apparatus modulates Ca 2+ in spines through spatial localization of sources and sinks

Dendritic spines are small protrusions on dendrites in neurons and serve as sites of postsynaptic activity. Some of these spines contain smooth endoplasmic reticulum (SER), and sometimes an even further specialized SER known as the spine apparatus (SA). In this work, we developed a stochastic spatial model to investigate the role of the SER and the SA in modulating Ca 2+ dynamics. Using this model, we investigated how ryanodine receptor (RyR) localization, spine membrane geometry, and SER geometry can impact Ca 2+ transients in the spine and in the dendrite. Our simulations found that RyR opening is dependent on where it is localized in the SER and on the SER geometry. In order to maximize Ca 2+ in the dendrites (for activating clusters of spines and spine-spine communication), a laminar SA was favorable with RyRs localized in the neck region, closer to the dendrite. We also found that the presence of the SER without the laminar structure, coupled with RyR localization at the head, leads to higher Ca 2+ presence in the spine. These predictions serve as design principles for understanding how spines with an ER can regulate Ca 2+ dynamics differently from spines without ER through a combination of geometry and receptor localization.

Highlights

1RyR opening in dendritic spine ER is location dependent and spine geometry dependent. Ca 2+ buffers and SERCA can buffer against runaway potentiation of spines even when CICR is activated. RyRs located towards the ER neck allow for more Ca 2+ to reach the dendrites. RyRs located towards the spine head are favorable for increased Ca 2+ in spines.

Efficient inference of synaptic plasticity rule with Gaussian process regression

Finding the form of synaptic plasticity is critical to understanding its functions underlying learning and memory. We investigated an efficient method to infer synaptic plasticity rules in various experimental settings. We considered biologically plausible models fitting a wide range of in-vitro studies and examined the recovery of their firing-rate dependence from sparse and noisy data. Among the methods assuming low-rankness or smoothness of plasticity rules, Gaussian process regression (GPR), a nonparametric Bayesian approach, performs the best. Under the conditions measuring changes in synaptic weights directly or measuring changes in neural activities as indirect observables of synaptic plasticity, which leads to different inference problems, GPR performs well. Also, GPR could simultaneously recover multiple plasticity rules and robustly perform under various plasticity rules and noise levels. Such flexibility and efficiency, particularly at the low sampling regime, make GPR suitable for recent experimental developments and inferring a broader class of plasticity models.

Crosstalk between biochemical signalling network architecture and trafficking governs AMPAR dynamics in synaptic plasticity

Synaptic plasticity involves modification of both biochemical and structural components of neurons. Many studies have revealed that the change in the number density of the glutamatergic receptor AMPAR at the synapse is proportional to synaptic weight update; an increase in AMPAR corresponds to strengthening of synapses while a decrease in AMPAR density weakens synaptic connections. The dynamics of AMPAR are thought to be regulated by upstream signalling, primarily the calcium-CaMKII pathway, trafficking to and from the synapse, and influx from extrasynaptic sources. Previous work in the field of deterministic modelling of CaMKII dynamics has assumed bistable kinetics, while experiments and rule-based modelling have revealed that CaMKII dynamics can be either monostable or ultrasensitive. This raises the following question: how does the choice of model assumptions involving CaMKII dynamics influence AMPAR dynamics at the synapse? To answer this question, we have developed a set of models using compartmental ordinary differential equations to systematically investigate contributions of different signalling and trafficking variations, along with their coupled effects, on AMPAR dynamics at the synaptic site. We find that the properties of the model including network architecture describing different stability features of CaMKII and parameters that capture the endocytosis and exocytosis of AMPAR significantly affect the integration of fast upstream species by slower downstream species. Furthermore, we predict that the model outcome, as determined by bound AMPAR at the synaptic site, depends on (1) the choice of signalling model (bistable CaMKII or monostable CaMKII dynamics), (2) trafficking versus influx contributions and (3) frequency of stimulus. KEY POINTS: The density of AMPA receptors (AMPARs) at the postsynaptic density of the synapse provides a readout of synaptic plasticity, which involves crosstalk between complex biochemical signalling networks including CaMKII dynamics and trafficking pathways including exocytosis and endocytosis. Here we build a model that integrates CaMKII dynamics and AMPAR trafficking to explore this crosstalk. We compare different models of CaMKII that result in monostable or bistable kinetics and their impact on AMPAR dynamics. Our results show that AMPAR density depends on the coupling between aspects of biochemical signalling and trafficking. Specifically, assumptions regarding CaMKII dynamics and its stability features can alter AMPAR density at the synapse. Our model also predicts that the kinetics of trafficking versus influx of AMPAR from the extrasynaptic space can further impact AMPAR density. Thus, the contributions of both signalling and trafficking should be considered in computational models.

Noise-induced collective dynamics in the small-world network of photosensitive neurons

Photosensitive neurons can capture and convert external optical signals, and then realize the encoding signal. It is confirmed that a variety of firing modes could be induced under optical stimuli. As a result, it is interesting to explore the mode transitions of collective dynamics in the photosensitive neuron network under external stimuli. In this work, the collective dynamics of photosensitive neurons in a small-world network with non-synaptic coupling will be discussed with spatial diversity of noise and uniform noise applied on, respectively. The results prove that a variety of different collective electrical activities could be induced under different conditions. Under spatial diversity of noise applied on, a chimera state could be observed in the evolution, and steady cluster synchronization could be detected in the end; even the nodes in each cluster depend on the degree of each node. Under uniform noise applied on, the complete synchronization window could be observed alternately in the transient process, and steady complete synchronization could be detected finally. The potential mechanism is that continuous energy is pumped in the phototubes, and energy exchange and balance between neurons to form the resonance synchronization in the network with different noise applied on. Furthermore, it is confirmed that the evolution of collective dynamical behaviors in the network depends on the external stimuli on each node. Moreover, the bifurcation analysis for the single neuron model is calculated, and the results confirm that the electrical activities of single neuron are sensitive to different kinds of noise.

Dendritic spine morphology regulates calcium-dependent synaptic weight change

Dendritic spines act as biochemical computational units and must adapt their responses according to their activation history. Calcium influx acts as the first signaling step during postsynaptic activation and is a determinant of synaptic weight change. Dendritic spines also come in a variety of sizes and shapes. To probe the relationship between calcium dynamics and spine morphology, we used a stochastic reaction-diffusion model of calcium dynamics in idealized and realistic geometries. We show that despite the stochastic nature of the various calcium channels, receptors, and pumps, spine size and shape can modulate calcium dynamics and subsequently synaptic weight updates in a deterministic manner. Through a series of exhaustive simulations and analyses, we found that the calcium dynamics and synaptic weight change depend on the volume-to-surface area of the spine. The relationships between calcium dynamics and spine morphology identified in idealized geometries also hold in realistic geometries, suggesting that there are geometrically determined deterministic relationships that may modulate synaptic weight change.

Computational analysis of memory consolidation following inhibitory avoidance (IA) training in adult and infant rats: Critical roles of CaMKIIα and MeCP2

Key features of long-term memory (LTM), such as its stability and persistence, are acquired during processes collectively referred to as consolidation. The dynamics of biological changes during consolidation are complex. In adult rodents, consolidation exhibits distinct periods during which the engram is more or less resistant to disruption. Moreover, the ability to consolidate memories differs during developmental periods. Although the molecular mechanisms underlying consolidation are poorly understood, the initial stages rely on interacting signaling pathways that regulate gene expression, including brain-derived neurotrophic factor (BDNF) and Ca2+/calmodulin-dependent protein kinase II α (CaMKIIα) dependent feedback loops. We investigated the ways in which these pathways may contribute to developmental and dynamical features of consolidation. A computational model of molecular processes underlying consolidation following inhibitory avoidance (IA) training in rats was developed. Differential equations described the actions of CaMKIIα, multiple feedback loops regulating BDNF expression, and several transcription factors including methyl-CpG binding protein 2 (MeCP2), histone deacetylase 2 (HDAC2), and SIN3 transcription regulator family member A (Sin3a). This model provides novel explanations for the (apparent) rapid forgetting of infantile memory and the temporal progression of memory consolidation in adults. Simulations predict that dual effects of MeCP2 on the expression of bdnf, and interaction between MeCP2 and CaMKIIα, play critical roles in the rapid forgetting of infantile memory and the progress of memory resistance to disruptions. These insights suggest new potential targets of therapy for memory impairment.

A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex

Pyramidal cells (PCs) form the backbone of the layered structure of the neocortex, and plasticity of their synapses is thought to underlie learning in the brain. However, such long-term synaptic changes have been experimentally characterized between only a few types of PCs, posing a significant barrier for studying neocortical learning mechanisms. Here we introduce a model of synaptic plasticity based on data-constrained postsynaptic calcium dynamics, and show in a neocortical microcircuit model that a single parameter set is sufficient to unify the available experimental findings on long-term potentiation (LTP) and long-term depression (LTD) of PC connections. In particular, we find that the diverse plasticity outcomes across the different PC types can be explained by cell-type-specific synaptic physiology, cell morphology and innervation patterns, without requiring type-specific plasticity. Generalizing the model to in vivo extracellular calcium concentrations, we predict qualitatively different plasticity dynamics from those observed in vitro. This work provides a first comprehensive null model for LTP/LTD between neocortical PC types in vivo, and an open framework for further developing models of cortical synaptic plasticity.

Autoregulation of switching behavior by cellular compartment size

Biochemical reactions often occur in small volumes within a cell, restricting the number of molecules to the hundreds or even tens. At this scale, reactions are discrete and stochastic, making reliable signaling difficult. This paper shows that the transition between discrete, stochastic reactions and macroscopic reactions can be exploited to make a self-regulating switch. This constitutes a previously unidentified kind of reaction network that may be present in small structures, such as synapses.

Many kinds of cellular compartments comprise decision-making mechanisms that control growth and shrinkage of the compartment in response to external signals. Key examples include synaptic plasticity mechanisms that regulate the size and strength of synapses in the nervous system. However, when synaptic compartments and postsynaptic densities are small, such mechanisms operate in a regime where chemical reactions are discrete and stochastic due to low copy numbers of the species involved. In this regime, fluctuations are large relative to mean concentrations, and inherent discreteness leads to breakdown of mass-action kinetics. Understanding how synapses and other small compartments achieve reliable switching in the low–copy number limit thus remains a key open problem. We propose a self-regulating signaling motif that exploits the breakdown of mass-action kinetics to generate a reliable size-regulated switch. We demonstrate this in simple two- and three-species chemical reaction systems and uncover a key role for inhibitory loops among species in generating switching behavior. This provides an elementary motif that could allow size-dependent regulation in more complex reaction pathways and may explain discrepant experimental results on well-studied biochemical pathways.

A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience

Neuroscience incorporates knowledge from a range of scales, from single molecules to brain wide neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a variety of different software tools in the model building and analysis process. Here we focus on the scale of biochemical pathways, which is one of the main objects of study in systems biology. While systems biology is among the more standardized fields, conversion between different model formats and interoperability between various tools is still somewhat problematic. To offer our take on tackling these shortcomings and by keeping in mind the FAIR (findability, accessibility, interoperability, reusability) data principles, we have developed a workflow for building and analyzing biochemical pathway models, using pre-existing tools that could be utilized for the storage and refinement of models in all phases of development. We have chosen the SBtab format which allows the storage of biochemical models and associated data in a single file and provides a human readable set of syntax rules. Next, we implemented custom-made MATLAB® scripts to perform parameter estimation and global sensitivity analysis used in model refinement. Additionally, we have developed a web-based application for biochemical models that allows simulations with either a network free solver or stochastic solvers and incorporating geometry. Finally, we illustrate convertibility and use of a biochemical model in a biophysically detailed single neuron model by running multiscale simulations in NEURON. Using this workflow, we can simulate the same model in three different simulators, with a smooth conversion between the different model formats, enhancing the characterization of different aspects of the model.

Personalized Medicine to Improve Treatment of Dopa-Responsive Dystonia-A Focus on Tyrosine Hydroxylase Deficiency

Dopa-responsive dystonia (DRD) is a rare movement disorder associated with defective dopamine synthesis. This impairment may be due to the fact of a deficiency in GTP cyclohydrolase I (GTPCHI, GCH1 gene), sepiapterin reductase (SR), tyrosine hydroxylase (TH), or 6-pyruvoyl tetrahydrobiopterin synthase (PTPS) enzyme functions. Mutations in GCH1 are most frequent, whereas fewer cases have been reported for individual SR-, PTP synthase-, and TH deficiencies. Although termed DRD, a subset of patients responds poorly to L-DOPA. As this is regularly observed in severe cases of TH deficiency (THD), there is an urgent demand for more adequate or personalized treatment options. TH is a key enzyme that catalyzes the rate-limiting step in catecholamine biosynthesis, and THD patients often present with complex and variable phenotypes, which results in frequent misdiagnosis and lack of appropriate treatment. In this expert opinion review, we focus on THD pathophysiology and ongoing efforts to develop novel therapeutics for this rare disorder. We also describe how different modeling approaches can be used to improve genotype to phenotype predictions and to develop in silico testing of treatment strategies. We further discuss the current status of mathematical modeling of catecholamine synthesis and how such models can be used together with biochemical data to improve treatment of DRD patients.

Microbes, metabolites and (synaptic) malleability, oh my! The effect of the microbiome on synaptic plasticity

The microbiome influences the emotional and cognitive phenotype of its host, as well as the neurodevelopment and pathophysiology of various brain processes and disorders, via the well‐established microbiome–gut–brain axis. Rapidly accumulating data link the microbiome to severe neuropsychiatric disorders in humans, including schizophrenia, Alzheimer's and Parkinson's. Moreover, preclinical work has shown that perturbation of the microbiome is closely associated with social, cognitive and behavioural deficits. The potential of the microbiome as a diagnostic and therapeutic tool is currently undercut by a lack of clear mechanistic understanding of the microbiome–gut–brain axis. This review establishes the hypothesis that the mechanism by which this influence is carried out is synaptic plasticity – long‐term changes to the physical and functional neuronal structures that enable the brain to undertake learning, memory formation, emotional regulation and more. By examining the different constituents of the microbiome–gut–brain axis through the lens of synaptic plasticity, this review explores the diverse aspects by which the microbiome shapes the behaviour and mental wellbeing of the host. Key elements of this complex bi‐directional relationship include neurotransmitters, neuronal electrophysiology, immune mediators that engage with both the central and enteric nervous systems and signalling cascades that trigger long‐term potentiation of synapses. The importance of establishing mechanistic correlations along the microbiome–gut–brain axis cannot be overstated as they hold the potential for furthering current understanding regarding the vast fields of neuroscience and neuropsychiatry. This review strives to elucidate the promising theory of microbiome‐driven synaptic plasticity in the hope of enlightening current researchers and inspiring future ones.

Stable continual learning through structured multiscale plasticity manifolds

Biological plasticity is ubiquitous. How does the brain navigate this complex plasticity space, where any component can seemingly change, in adapting to an ever-changing environment? We build a systematic case that stable continuous learning is achieved by structured rules that enforce multiple, but not all, components to change together in specific directions. This rule-based low-dimensional plasticity manifold of permitted plasticity combinations emerges from cell type-specific molecular signaling and triggers cascading impacts that span multiple scales. These multiscale plasticity manifolds form the basis for behavioral learning and are dynamic entities that are altered by neuromodulation, metaplasticity, and pathology. We explore the strong links between heterogeneities, degeneracy, and plasticity manifolds and emphasize the need to incorporate plasticity manifolds into learning-theoretical frameworks and experimental designs.

A computational model of dopaminergic modulation of hippocampal Schaffer collateral-CA1 long-term plasticity

Dopamine plays a critical role in modulating the long-term synaptic plasticity of the hippocampal Schaffer collateral-CA1 pyramidal neuron synapses (SC-CA1), a widely accepted cellular model of learning and memory. Limited results from hippocampal slice experiments over the last four decades have shown that the timing of the activation of dopamine D1/D5 receptors relative to a high/low-frequency stimulation (HFS/LFS) in SC-CA1 synapses regulates the modulation of HFS/LFS-induced long-term potentiation/depression (LTP/LTD) in these synapses. However, the existing literature lacks a complete picture of how various concentrations of D1/D5 agonists and the relative timing between the activation of D1/D5 receptors and LTP/LTD induction by HFS/LFS, affect the spatiotemporal modulation of SC-CA1 synaptic dynamics. In this paper, we have developed a computational model, a first of its kind, to make quantitative predictions of the temporal dose-dependent modulation of the HFS/LFS induced LTP/LTD in SC-CA1 synapses by various D1/D5 agonists. Our model combines the biochemical effects with the electrical effects at the electrophysiological level. We have estimated the model parameters from the published electrophysiological data, available from diverse hippocampal CA1 slice experiments, in a Bayesian framework. Our modeling results demonstrate the capability of our model in making quantitative predictions of the available experimental results under diverse HFS/LFS protocols. The predictions from our model show a strong nonlinear dependency of the modulated LTP/LTD by D1/D5 agonists on the relative timing between the activated D1/D5 receptors and the HFS/LFS protocol and the applied concentration of D1/D5 agonists.

From synapse to network: models of information storage and retrieval in neural circuits

The mechanisms of information storage and retrieval in brain circuits are still the subject of debate. It is widely believed that information is stored at least in part through changes in synaptic connectivity in networks that encode this information and that these changes lead in turn to modifications of network dynamics, such that the stored information can be retrieved at a later time. Here, we review recent progress in deriving synaptic plasticity rules from experimental data and in understanding how plasticity rules affect the dynamics of recurrent networks. We show that the dynamics generated by such networks exhibit a large degree of diversity, depending on parameters, similar to experimental observations in vivo during delayed response tasks.

Stochastic reaction-diffusion modeling of calcium dynamics in 3D dendritic spines of Purkinje cells

Calcium (Ca²⁺) is a second messenger assumed to control changes in synaptic strength in the form of both long-term depression and long-term potentiation at Purkinje cell dendritic spine synapses via inositol trisphosphate (IP₃)-induced Ca²⁺ release. These Ca²⁺ transients happen in response to stimuli from parallel fibers (PFs) from granule cells and climbing fibers (CFs) from the inferior olivary nucleus. These events occur at low numbers of free Ca²⁺, requiring stochastic single-particle methods when modeling them. We use the stochastic particle simulation program MCell to simulate Ca²⁺ transients within a three-dimensional Purkinje cell dendritic spine. The model spine includes the endoplasmic reticulum, several Ca²⁺ transporters, and endogenous buffer molecules. Our simulations successfully reproduce properties of Ca²⁺ transients in different dynamical situations. We test two different models of the IP₃ receptor (IP₃R). The model with nonlinear concentration response of binding of activating Ca²⁺ reproduces experimental results better than the model with linear response because of the filtering of noise. Our results also suggest that Ca²⁺-dependent inhibition of the IP₃R needs to be slow to reproduce experimental results. Simulations suggest the experimentally observed optimal timing window of CF stimuli arises from the relative timing of CF influx of Ca²⁺ and IP₃ production sensitizing IP₃R for Ca²⁺-induced Ca²⁺ release. We also model ataxia, a loss of fine motor control assumed to be the result of malfunctioning information transmission at the granule to Purkinje cell synapse, resulting in a decrease or loss of Ca²⁺ transients. Finally, we propose possible ways of recovering Ca²⁺ transients under ataxia.

The biophysical basis underlying the maintenance of early phase long-term potentiation

The maintenance of synaptic changes resulting from long-term potentiation (LTP) is essential for brain function such as memory and learning. Different LTP phases have been associated with diverse molecular processes and pathways, and the molecular underpinnings of LTP on the short, as well as long time scales, are well established. However, the principles on the intermediate time scale of 1-6 hours that mediate the early phase of LTP (E-LTP) remain elusive. We hypothesize that the interplay between specific features of postsynaptic receptor trafficking is responsible for sustaining synaptic changes during this LTP phase. We test this hypothesis by formalizing a biophysical model that integrates several experimentally-motivated mechanisms. The model captures a wide range of experimental findings and predicts that synaptic changes are preserved for hours when the receptor dynamics are shaped by the interplay of structural changes of the spine in conjunction with increased trafficking from recycling endosomes and the cooperative binding of receptors. Furthermore, our model provides several predictions to verify our findings experimentally.

Astrocyte-mediated spike-timing-dependent long-term depression modulates synaptic properties in the developing cortex

Astrocytes have been shown to modulate synaptic transmission and plasticity in specific cortical synapses, but our understanding of the underlying molecular and cellular mechanisms remains limited. Here we present a new biophysicochemical model of a somatosensory cortical layer 4 to layer 2/3 synapse to study the role of astrocytes in spike-timing-dependent long-term depression (t-LTD) in vivo. By applying the synapse model and electrophysiological data recorded from rodent somatosensory cortex, we show that a signal from a postsynaptic neuron, orchestrated by endocannabinoids, astrocytic calcium signaling, and presynaptic N-methyl-D-aspartate receptors coupled with calcineurin signaling, induces t-LTD which is sensitive to the temporal difference between post- and presynaptic firing. We predict for the first time the dynamics of astrocyte-mediated molecular mechanisms underlying t-LTD and link complex biochemical networks at presynaptic, postsynaptic, and astrocytic sites to the time window of t-LTD induction. During t-LTD a single astrocyte acts as a delay factor for fast neuronal activity and integrates fast neuronal sensory processing with slow non-neuronal processing to modulate synaptic properties in the brain. Our results suggest that astrocytes play a critical role in synaptic computation during postnatal development and are of paramount importance in guiding the development of brain circuit functions, learning and memory.

Significance and Translational Value of High-Frequency Cortico-Basal Ganglia Oscillations in Parkinson's Disease

The mechanisms and significance of basal ganglia oscillations is a fundamental research question engaging both clinical and basic investigators. In Parkinson’s disease (PD), neural activity in basal ganglia nuclei is characterized by oscillatory patterns that are believed to disrupt the dynamic processing of movement-related information and thus generate motor symptoms. Beta-band oscillations associated with hypokinetic states have been reviewed in several excellent previous articles. Here we focus on faster oscillatory phenomena that have been reported in association with a diverse range of motor states. We review the occurrence of different types of fast oscillations and the evidence supporting their pathophysiological role. We also provide a general discussion on the definition, possible mechanisms, and translational value of synchronized oscillations of different frequencies in cortico-basal ganglia structures. Revealing how oscillatory phenomena are caused and spread in cortico-basal ganglia-thalamocortical networks will offer a key to unlock the neural codes of both motor and non-motor symptoms in PD. In preclinical therapeutic research, recording of oscillatory neural activities holds the promise to unravel mechanisms of action of current and future treatments.

The microcircuits of striatum in silico

The basal ganglia play an important role in decision making and selection of action primarily based on input from cortex, thalamus, and the dopamine system. Their main input structure, striatum, is central to this process. It consists of two types of projection neurons, together representing 95% of the neurons, and 5% of interneurons, among which are the cholinergic, fast-spiking, and low threshold-spiking subtypes. The membrane properties, soma-dendritic shape, and intrastriatal and extrastriatal synaptic interactions of these neurons are quite well described in the mouse, and therefore they can be simulated in sufficient detail to capture their intrinsic properties, as well as the connectivity. We focus on simulation at the striatal cellular/microcircuit level, in which the molecular/subcellular and systems levels meet. We present a nearly full-scale model of the mouse striatum using available data on synaptic connectivity, cellular morphology, and electrophysiological properties to create a microcircuit mimicking the real network. A striatal volume is populated with reconstructed neuronal morphologies with appropriate cell densities, and then we connect neurons together based on appositions between neurites as possible synapses and constrain them further with available connectivity data. Moreover, we simulate a subset of the striatum involving 10,000 neurons, with input from cortex, thalamus, and the dopamine system, as a proof of principle. Simulation at this biological scale should serve as an invaluable tool to understand the mode of operation of this complex structure. This platform will be updated with new data and expanded to simulate the entire striatum.

Mathematical Models to Shed Light on Amyloid-Beta and Tau Protein Dependent Pathologies in Alzheimer's Disease

The number of patients suffering from dementia due to Alzheimer's disease (AD) is constantly rising worldwide. This has accordingly resulted in huge burdens on the health systems and involved families. Lack of profound understanding of neural networking in normal brain and their interruption in AD makes the treatment of this neurodegenerative multifaceted disease a challenging issue. In recent years, mathematical and computational methods have paved the way towards a better understanding of the brain functional connectivity. Thus, much attention has been paid to this matter from both basic science researchers and clinicians with an interdisciplinary approach to determine what is not functioning properly in AD patients and how this malfunctioning can be addressed. In this review, a number of AD-related articles and well-studied pathophysiologic topics (e.g., amyloid-beta, neurofibrillary tangles, Ca²⁺ dysregulation, and synaptic plasticity alterations) has been literally surveyed from a computational and systems biology point of view. The neural networks were discussed from biological and mathematical point of views and their alterations in recent findings were further highlighted. Application of the graph theoretical analysis in the brain imaging was reviewed, depicting the relations between brain structure and function, without diving into mathematical details. Moreover, differential rate equations were briefly articulated, emphasizing the potential use of these equations in simplifying complex processes in relevance to pathologies of AD. Comprehensive insights were given into the AD progression from neural networks perspective, which may lead us towards potential strategies for early diagnosis and effective treatment of AD.

Degeneracy in hippocampal physiology and plasticity

Degeneracy, defined as the ability of structurally disparate elements to perform analogous function, has largely been assessed from the perspective of maintaining robustness of physiology or plasticity. How does the framework of degeneracy assimilate into an encoding system where the ability to change is an essential ingredient for storing new incoming information? Could degeneracy maintain the balance between the apparently contradictory goals of the need to change for encoding and the need to resist change towards maintaining homeostasis? In this review, we explore these fundamental questions with the mammalian hippocampus as an example encoding system. We systematically catalog lines of evidence, spanning multiple scales of analysis that point to the expression of degeneracy in hippocampal physiology and plasticity. We assess the potential of degeneracy as a framework to achieve the conjoint goals of encoding and homeostasis without cross-interferences. We postulate that biological complexity, involving interactions among the numerous parameters spanning different scales of analysis, could establish disparate routes towards accomplishing these conjoint goals. These disparate routes then provide several degrees of freedom to the encoding-homeostasis system in accomplishing its tasks in an input- and state-dependent manner. Finally, the expression of degeneracy spanning multiple scales offers an ideal reconciliation to several outstanding controversies, through the recognition that the seemingly contradictory disparate observations are merely alternate routes that the system might recruit towards accomplishment of its goals.

Geometric principles of second messenger dynamics in dendritic spines

Dendritic spines are small, bulbous protrusions along dendrites in neurons and play a critical role in synaptic transmission. Dendritic spines come in a variety of shapes that depend on their developmental state. Additionally, roughly 14-19% of mature spines have a specialized endoplasmic reticulum called the spine apparatus. How does the shape of a postsynaptic spine and its internal organization affect the spatio-temporal dynamics of short timescale signaling? Answers to this question are central to our understanding the initiation of synaptic transmission, learning, and memory formation. In this work, we investigated the effect of spine and spine apparatus size and shape on the spatio-temporal dynamics of second messengers using mathematical modeling using reaction-diffusion equations in idealized geometries (ellipsoids, spheres, and mushroom-shaped). Our analyses and simulations showed that in the short timescale, spine size and shape coupled with the spine apparatus geometries govern the spatiotemporal dynamics of second messengers. We show that the curvature of the geometries gives rise to pseudo-harmonic functions, which predict the locations of maximum and minimum concentrations along the spine head. Furthermore, we showed that the lifetime of the concentration gradient can be fine-tuned by localization of fluxes on the spine head and varying the relative curvatures and distances between the spine apparatus and the spine head. Thus, we have identified several key geometric determinants of how the spine head and spine apparatus may regulate the short timescale chemical dynamics of small molecules that control synaptic plasticity.

Biophysical Psychiatry-How Computational Neuroscience Can Help to Understand the Complex Mechanisms of Mental Disorders

The brain is the most complex of human organs, and the pathophysiology underlying abnormal brain function in psychiatric disorders is largely unknown. Despite the rapid development of diagnostic tools and treatments in most areas of medicine, our understanding of mental disorders and their treatment has made limited progress during the last decades. While recent advances in genetics and neuroscience have a large potential, the complexity and multidimensionality of the brain processes hinder the discovery of disease mechanisms that would link genetic findings to clinical symptoms and behavior. This applies also to schizophrenia, for which genome-wide association studies have identified a large number of genetic risk loci, spanning hundreds of genes with diverse functionalities. Importantly, the multitude of the associated variants and their prevalence in the healthy population limit the potential of a reductionist functional genetics approach as a stand-alone solution to discover the disease pathology. In this review, we outline the key concepts of a "biophysical psychiatry," an approach that employs large-scale mechanistic, biophysics-founded computational modelling to increase transdisciplinary understanding of the pathophysiology and strive toward robust predictions. We discuss recent scientific advances that allow a synthesis of previously disparate fields of psychiatry, neurophysiology, functional genomics, and computational modelling to tackle open questions regarding the pathophysiology of heritable mental disorders. We argue that the complexity of the increasing amount of genetic data exceeds the capabilities of classical experimental assays and requires computational approaches. Biophysical psychiatry, based on modelling diseased brain networks using existing and future knowledge of basic genetic, biochemical, and functional properties on a single neuron to a microcircuit level, may allow a leap forward in deriving interpretable biomarkers and move the field toward novel treatment options.

Dendritic spine geometry and spine apparatus organization govern the spatiotemporal dynamics of calcium

Dendritic spines are small subcompartments that protrude from the dendrites of neurons and are important for signaling activity and synaptic communication. These subcompartments have been characterized to have different shapes. While it is known that these shapes are associated with spine function, the specific nature of these shape-function relationships is not well understood. In this work, we systematically investigated the relationship between the shape and size of both the spine head and spine apparatus, a specialized endoplasmic reticulum compartment within the spine head, in modulating rapid calcium dynamics using mathematical modeling. We developed a spatial multicompartment reaction-diffusion model of calcium dynamics in three dimensions with various flux sources, including N-methyl-D-aspartate receptors (NMDARs), voltage-sensitive calcium channels (VSCCs), and different ion pumps on the plasma membrane. Using this model, we make several important predictions. First, the volume to surface area ratio of the spine regulates calcium dynamics. Second, membrane fluxes impact calcium dynamics temporally and spatially in a nonlinear fashion. Finally, the spine apparatus can act as a physical buffer for calcium by acting as a sink and rescaling the calcium concentration. These predictions set the stage for future experimental investigations of calcium dynamics in dendritic spines.

Sensory mechanisms of natal stream imprinting and homing in Oncorhynchus spp

Juvenile Oncorhynchus spp. can memorise their natal stream during downstream migration; juveniles migrate to feed during their growth phase and then they migrate long distances from their feeding habitat to their natal stream to reproduce as adults. Two different sensory mechanisms, olfaction and navigation, are involved in the imprinting and homing processes during short-distance migration within the natal stream and long-distance migration in open water, respectively. Here, olfactory functions are reviewed from both neurophysiological studies on the olfactory discrimination ability of natal stream odours and neuroendocrinological studies on the hormonal controlling mechanisms of olfactory memory formation and retrieval in the brain. These studies revealed that the long-term stability of dissolved free amino-acid composition in the natal stream is crucial for olfactory imprinting and homing. Additionally, the brain-pituitary-thyroid and brain-pituitary-gonadal hormones play important roles in olfactory memory formation and retrieval, respectively. Navigation functions were reviewed from physiological biotelemetry techniques with sensory interference experiments during the homing migration of anadromous and lacustrine Oncorhynchus spp. The experiments demonstrated that Oncorhynchus spp. used compass navigation mechanisms in the open water. These findings are discussed in relation to the sensory mechanisms involved in natal stream imprinting and homing in Oncorhynchus spp.

Increased levels of synaptic proteins involved in synaptic plasticity after chronic intraocular pressure elevation and modulation by brain-derived neurotrophic factor in a glaucoma animal model

The dendrites of retinal ganglion cells (RGCs) synapse with the axon terminals of bipolar cells in the inner plexiform layer (IPL). Changes in the RGC dendrites and synapses between the bipolar cells in the inner retinal layer may critically alter the function of RGCs in glaucoma. The present study attempted to discover changes in the synapse using brain-derived neurotrophic factor (BDNF) after glaucoma induction by chronic intraocular pressure elevation in a rat model. Immunohistochemical staining revealed that the BDNF-injected group had a significant increase in the level of synaptophysin, which is a presynaptic vesicle protein, in the innermost IPL compared with the phosphate-buffered saline (PBS)-injected group. SMI-32, which is a marker of RGCs, was colocalized with synaptophysin in RGC dendrites, and this colocalization significantly increased in the BDNF-injected group. After the induction of glaucoma, the BDNF-injected group exhibited increases in the total number of ribbon synapses, as seen using electron microscopy. Expression of calcium/calmodulin-dependent protein kinase II (CaMKII), cAMP-response element binding protein (CREB) and F-actin, which are key molecules involved in synaptic changes were upregulated after BDNF injection. These initial findings show the capability of BDNF to induce beneficial synaptic changes in glaucoma.

Summary: Application of BDNF increased the expression of synaptic vesicle proteins in the inner retina via the p-Akt, CaMKII and CREB pathways, increasing F-actin in RGC dendrites.

Analog Signaling With the "Digital" Molecular Switch CaMKII

Molecular switches, such as the protein kinase CaMKII, play a fundamental role in cell signaling by decoding inputs into either high or low states of activity; because the high activation state can be turned on and persist after the input ceases, these switches have earned a reputation as "digital." Although this on/off, binary perspective has been valuable for understanding long timescale synaptic plasticity, accumulating experimental evidence suggests that the CaMKII switch can also control plasticity on short timescales. To investigate this idea further, a non-autonomous, nonlinear ordinary differential equation, representative of a general bistable molecular switch, is analyzed. The results suggest that switch activity in regions surrounding either the high- or low-stable states of activation could act as a reliable analog signal, whose short timescale fluctuations relative to equilibrium track instantaneous input frequency. The model makes intriguing predictions and is validated against previous work demonstrating its suitability as a minimal representation of switch dynamics; in combination with existing experimental evidence, the theory suggests a multiplexed encoding of instantaneous frequency information over short timescales, with integration of total activity over longer timescales.

Active dendrites regulate the spatiotemporal spread of signaling microdomains

Microdomains that emerge from spatially constricted spread of biochemical signaling components play a central role in several neuronal computations. Although dendrites, endowed with several voltage-gated ion channels, form a prominent structural substrate for microdomain physiology, it is not known if these channels regulate the spatiotemporal spread of signaling microdomains. Here, we employed a multiscale, morphologically realistic, conductance-based model of the hippocampal pyramidal neuron that accounted for experimental details of electrical and calcium-dependent biochemical signaling. We activated synaptic N-Methyl-d-Aspartate receptors through theta-burst stimulation (TBS) or pairing (TBP) and assessed microdomain propagation along a signaling pathway that included calmodulin, calcium/calmodulin-dependent protein kinase II (CaMKII) and protein phosphatase 1. We found that the spatiotemporal spread of the TBS-evoked microdomain in phosphorylated CaMKII (pCaMKII) was amplified in comparison to that of the corresponding calcium microdomain. Next, we assessed the role of two dendritically expressed inactivating channels, one restorative (A-type potassium) and another regenerative (T-type calcium), by systematically varying their conductances. Whereas A-type potassium channels suppressed the spread of pCaMKII microdomains by altering the voltage response to TBS, T-type calcium channels enhanced this spread by modulating TBS-induced calcium influx without changing the voltage. Finally, we explored cross-dependencies of these channels with other model components, and demonstrated the heavy mutual interdependence of several biophysical and biochemical properties in regulating microdomains and their spread. Our conclusions unveil a pivotal role for dendritic voltage-gated ion channels in actively amplifying or suppressing biochemical signals and their spatiotemporal spread, with critical implications for clustered synaptic plasticity, robust information transfer and efficient neural coding.

The spatiotemporal spread of biochemical signals in neurons and other cells regulate signaling specificity, tuning of signal propagation, along with specificity and clustering of adaptive plasticity. Theoretical and experimental studies have demonstrated a critical role for cellular morphology and the topology of signaling networks in regulating this spread. In this study, we add a significantly complex dimension to this narrative by demonstrating that voltage-gated ion channels on the plasma membrane could actively amplify or suppress the strength and spread of downstream signaling components. Given the expression of different ion channels with wide-ranging heterogeneity in gating kinetics, localization and density, our results point to an increase in complexity of and degeneracy in signaling spread, and unveil a powerful mechanism for regulating biochemical-signaling pathways across different cell types.

Computational models of memory consolidation and long-term synaptic plasticity during sleep

The brain stores memories by persistently changing the connectivity between neurons. Sleep is known to be critical for these changes to endure. Research on the neurobiology of sleep and the mechanisms of long-term synaptic plasticity has provided data in support of various theories of how brain activity during sleep affects long-term synaptic plasticity. The experimental findings - and therefore the theories - are apparently quite contradictory, with some evidence pointing to a role of sleep in the forgetting of irrelevant memories, whereas other results indicate that sleep supports the reinforcement of the most valuable recollections. A unified theoretical framework is in need. Computational modeling and simulation provide grounds for the quantitative testing and comparison of theoretical predictions and observed data, and might serve as a strategy to organize the rather complicated and diverse pool of data and methodologies used in sleep research. This review article outlines the emerging progress in the computational modeling and simulation of the main theories on the role of sleep in memory consolidation.

AMPA receptor trafficking and its role in heterosynaptic plasticity

Historically, long-term potentiation (LTP) and long-term depression (LTD), the best-characterized forms of long-term synaptic plasticity, are viewed as experience-dependent and input-specific processes. However, cumulative experimental and theoretical data have demonstrated that LTP and LTD can promote compensatory alterations in non-stimulated synapses. In this work, we have developed a computational model of a tridimensional spiny dendritic segment to investigate the role of AMPA receptor (AMPAR) trafficking during synaptic plasticity at specific synapses and its consequences for the populations of AMPAR at nearby synapses. Our results demonstrated that the mechanisms of AMPAR trafficking involved with LTP and LTD can promote heterosynaptic plasticity at non-stimulated synapses. These alterations are compensatory and arise from molecular competition. Moreover, the heterosynaptic changes observed in our model can modulate further activity-driven inductions of synaptic plasticity.

Challenges in Reproducibility, Replicability, and Comparability of Computational Models and Tools for Neuronal and Glial Networks, Cells, and Subcellular Structures

The possibility to replicate and reproduce published research results is one of the biggest challenges in all areas of science. In computational neuroscience, there are thousands of models available. However, it is rarely possible to reimplement the models based on the information in the original publication, let alone rerun the models just because the model implementations have not been made publicly available. We evaluate and discuss the comparability of a versatile choice of simulation tools: tools for biochemical reactions and spiking neuronal networks, and relatively new tools for growth in cell cultures. The replicability and reproducibility issues are considered for computational models that are equally diverse, including the models for intracellular signal transduction of neurons and glial cells, in addition to single glial cells, neuron-glia interactions, and selected examples of spiking neuronal networks. We also address the comparability of the simulation results with one another to comprehend if the studied models can be used to answer similar research questions. In addition to presenting the challenges in reproducibility and replicability of published results in computational neuroscience, we highlight the need for developing recommendations and good practices for publishing simulation tools and computational models. Model validation and flexible model description must be an integral part of the tool used to simulate and develop computational models. Constant improvement on experimental techniques and recording protocols leads to increasing knowledge about the biophysical mechanisms in neural systems. This poses new challenges for computational neuroscience: extended or completely new computational methods and models may be required. Careful evaluation and categorization of the existing models and tools provide a foundation for these future needs, for constructing multiscale models or extending the models to incorporate additional or more detailed biophysical mechanisms. Improving the quality of publications in computational neuroscience, enabling progressive building of advanced computational models and tools, can be achieved only through adopting publishing standards which underline replicability and reproducibility of research results.

Foxp1 in Forebrain Pyramidal Neurons Controls Gene Expression Required for Spatial Learning and Synaptic Plasticity

Genetic perturbations of the transcription factor Forkhead Box P1 (FOXP1) are causative for severe forms of autism spectrum disorder that are often comorbid with intellectual disability. Recent work has begun to reveal an important role for FoxP1 in brain development, but the brain-region-specific contributions of Foxp1 to autism and intellectual disability phenotypes have yet to be determined fully. Here, we describe Foxp1 conditional knock-out (Foxp1^cKO) male and female mice with loss of Foxp1 in the pyramidal neurons of the neocortex and the CA1/CA2 subfields of the hippocampus. Foxp1^cKO mice exhibit behavioral phenotypes that are of potential relevance to autism spectrum disorder, including hyperactivity, increased anxiety, communication impairments, and decreased sociability. In addition, Foxp1^cKO mice have gross deficits in learning and memory tasks of relevance to intellectual disability. Using a genome-wide approach, we identified differentially expressed genes in the hippocampus of Foxp1^cKO mice associated with synaptic function and development. Furthermore, using magnetic resonance imaging, we uncovered a significant reduction in the volumes of both the entire hippocampus as well as individual hippocampal subfields of Foxp1^cKO mice. Finally, we observed reduced maintenance of LTP in area CA1 of the hippocampus in these mutant mice. Together, these data suggest that proper expression of Foxp1 in the pyramidal neurons of the forebrain is important for regulating gene expression pathways that contribute to specific behaviors reminiscent of those seen in autism and intellectual disability. In particular, Foxp1 regulation of gene expression appears to be crucial for normal hippocampal development, CA1 plasticity, and spatial learning.SIGNIFICANCE STATEMENT Loss-of-function mutations in the transcription factor Forkhead Box P1 (FOXP1) lead to autism spectrum disorder and intellectual disability. Understanding the potential brain-region-specific contributions of FOXP1 to disease-relevant phenotypes could be a critical first step in the management of patients with these mutations. Here, we report that Foxp1 conditional knock-out (Foxp1^cKO) mice with loss of Foxp1 in the neocortex and hippocampus display autism and intellectual-disability-relevant behaviors. We also show that these phenotypes correlate with changes in both the genomic and physiological profiles of the hippocampus in Foxp1^cKO mice. Our work demonstrates that brain-region-specific FOXP1 expression may relate to distinct, clinically relevant phenotypes.

The Intellectual Disability and Schizophrenia Associated Transcription Factor TCF4 Is Regulated by Neuronal Activity and Protein Kinase A

Transcription factor 4 (TCF4 also known as ITF2 or E2-2) is a basic helix-loop-helix (bHLH) protein associated with Pitt-Hopkins syndrome, intellectual disability, and schizophrenia (SCZ). Here, we show that TCF4-dependent transcription in cortical neurons cultured from embryonic rats of both sexes is induced by neuronal activity via soluble adenylyl cyclase and protein kinase A (PKA) signaling. PKA phosphorylates TCF4 directly and a PKA phosphorylation site in TCF4 is necessary for its transcriptional activity in cultured neurons and in the developing brain in vivo We also demonstrate that Gadd45g (growth arrest and DNA damage inducible gamma) is a direct target of neuronal-activity-induced, TCF4-dependent transcriptional regulation and that TCF4 missense variations identified in SCZ patients alter the transcriptional activity of TCF4 in neurons. This study identifies a new role for TCF4 as a neuronal-activity-regulated transcription factor, offering a novel perspective on the association of TCF4 with cognitive disorders.SIGNIFICANCE STATEMENT The importance of the basic helix-loop-helix transcription factor transcription factor 4 (TCF4) in the nervous system is underlined by its association with common and rare cognitive disorders. In the current study, we show that TCF4-controlled transcription in primary cortical neurons is induced by neuronal activity and protein kinase A. Our results support the hypotheses that dysregulation of neuronal-activity-dependent signaling plays a significant part in the etiology of neuropsychiatric and neurodevelopmental disorders.

Gene networks show associations with seed region connectivity

Primary patterns in adult brain connectivity are established during development by coordinated networks of transiently expressed genes; however, neural networks remain malleable throughout life. The present study hypothesizes that structural connectivity from key seed regions may induce effects on their connected targets, which are reflected in gene expression at those targeted regions. To test this hypothesis, analyses were performed on data from two brains from the Allen Human Brain Atlas, for which both gene expression and DW-MRI were available. Structural connectivity was estimated from the DW-MRI data and an approach motivated by network topology, that is, weighted gene coexpression network analysis (WGCNA), was used to cluster genes with similar patterns of expression across the brain. Group exponential lasso models were then used to predict gene cluster expression summaries as a function of seed region structural connectivity patterns. In several gene clusters, brain regions located in the brain stem, diencephalon, and hippocampal formation were identified that have significant predictive power for these expression summaries. These connectivity-associated clusters are enriched in genes associated with synaptic signaling and brain plasticity. Furthermore, using seed region based connectivity provides a novel perspective in understanding relationships between gene expression and connectivity. Hum Brain Mapp 38:3126-3140, 2017. © 2017 Wiley Periodicals, Inc.

Contributions of polyunsaturated fatty acids (PUFA) on cerebral neurobiology: an integrated omics approach with epigenomic focus

The epigenetic landscape is vulnerable to diets. Here, we investigated the influence of different polyunsaturated fatty acids (PUFA) dietary supplements on rodents' nervous system development and functions and potential consequences to neurodegenerative disorders. Our previous nutrigenomics study showed significant impact of high n-3 PUFA-enriched diet (ERD) on synaptogenesis and various neuromodulators. The present study introduced a second equicaloric diet with n-6 PUFA balanced by n-3 PUFA (BLD). The typical lab diet with high n-6 PUFA was the baseline. Transcriptomic and epigenetic investigations, namely microRNA (miRNA) and DNA methylation assays, were carried out on the hemibrains of the C57BL/6j mice fed on any of these three diets from their neonatal age to midlife. Integrating the multiomics data, we focused on the genes encoding both hypermethylated CpG islands and suppressed transcripts. In addition, miRNA:mRNA pairs were screened to identify those overexpressed miRNAs that reduced transcriptional expressions. The majority of miRNAs overexpressed by BLD are associated with Alzheimer's and schizophrenia. BLD implicated long-term potentiation, memory, cognition and learning, primarily via hypermethylation of those genes that enrich the calcium-releasing neurotransmitters. ERD caused hypermethylation of those genes that enrich cytoskeletal development networks and promote the formation of neuronal precursors. We drew the present observations in light of our limited knowledge regarding the epigenetic influences on biofunctions. A more comprehensive study is essential to understand the broad influences of dietary supplements and to suggest optimal dietary solutions for neurological disorders.

Towards the Molecular Foundations of Glutamatergic-targeted Antidepressants

BACKGROUND

Depression affects over 120 million individuals of all ages and is the leading cause of disability worldwide. The lack of objective diagnostic criteria, together with the heterogeneity of the depressive disorder itself, makes it challenging to develop effective therapies. The accumulation of preclinical data over the past 20 years derived from a multitude of models using many divergent approaches, has fueled the resurgence of interest in targeting glutamatergic neurotransmission for the treatment of major depression.

OBJECTIVE

The emergence of mechanistic studies are advancing our understanding of the molecular underpinnings of depression. While clearly far from complete and conclusive, they offer the potential to lead to the rational design of more specific therapeutic strategies and the development of safer and more effective rapid acting, long lasting antidepressants.

METHODS

The development of comprehensive omics-based approaches to the dysregulation of synaptic transmission and plasticity that underlies the core pathophysiology of MDD are reviewed to illustrate the fundamental elements.

RESULTS

This review frames the rationale for the conceptualization of depression as a "pathway disease". As such, it culminates in the call for the development of novel state-of-the-art "-omics approaches" and neurosystems biological techniques necessary to advance our understanding of spatiotemporal interactions associated with targeting glutamatergic-triggered signaling in the CNS.

CONCLUSION

These technologies will enable the development of novel psychiatric medications specifically targeted to impact specific, critical intracellular networks in a more focused manner and have the potential to offer new dimensions in the area of translational neuropsychiatry.

Comparative electrostatic analysis of adenylyl cyclase for isoform dependent regulation properties

The enzyme adenylyl cyclase (AC) plays a pivotal role in a variety of signal transduction pathways inside the cell, where it catalyzes the cyclization of adenosine triphosphate (ATP) into the second-messenger cyclic adenosine monophosphate (cAMP). Among other roles, AC regulates processes involved in neural plasticity, innervation of smooth muscles of the heart and the endocrine system of the pancreas. The functional diversity of AC is manifested in its different isoforms, each having a specific regulation pattern. There is an increasing amount of data available concerning the regulatory properties of AC isoforms, however little is known about the interactions on a structural level. Here, we conducted a comparative electrostatic analysis of the catalytic domains of all nine transmembrane AC isoforms with the aim of detecting, verifying and predicting the binding sites of molecular regulators on AC. The results provide support for the positioning of the binding site of the inhibitory protein G_i α at a pseudo-symmetric position to the stimulatory G_s α binding site. They also provide a structural interpretation of the Gβγ interaction with ACs 2, 4, and 7 and suggest a new binding site for RGS2. Comparison of the small molecule binding sites on AC shows that overall they have high electrostatic similarity, but regions of electrostatic differences are identified. These could provide a basis for the development of novel compounds with isoform-specific modulatory effects on AC. Proteins 2016; 84:1844-1858. © 2016 Wiley Periodicals, Inc.

Temporal phases of long-term potentiation (LTP): myth or fact?

Long-term potentiation (LTP) remains the most widely accepted model for learning and memory. In accordance with this belief, the temporal differentiation of LTP into early and late phases is accepted as reflecting the differentiation of short-term and long-term memory. Moreover, during the past 30 years, protein synthesis inhibitors have been used to separate the early, protein synthesis-independent (E-LTP) phase and the late, protein synthesis-dependent (L-LTP) phase. However, the role of these proteins has not been formally identified. Additionally, several reports failed to show an effect of protein synthesis inhibitors on LTP. In this review, a detailed analysis of extensive behavioral and electrophysiological data reveals that the presumed correspondence of LTP temporal phases to memory phases is neither experimentally nor theoretically consistent. Moreover, an overview of the time courses of E-LTP in hippocampal slices reveals a wide variability ranging from <1 h to more than 5 h. The existence of all these conflictual findings should lead to a new vision of LTP. We believe that the E-LTP vs. L-LTP distinction, established with protein synthesis inhibitor studies, reflects a false dichotomy. We suggest that the duration of LTP and its dependency on protein synthesis are related to the availability of a set of proteins at synapses and not to the de novo synthesis of plasticity-related proteins. This availability is determined by protein turnover kinetics, which is regulated by previous and ongoing electrical activities and by energy store availability.

Stochastic Induction of Long-Term Potentiation and Long-Term Depression

Long-term depression (LTD) and long-term potentiation (LTP) of granule-Purkinje cell synapses are persistent synaptic alterations induced by high and low rises of the intracellular calcium ion concentration ([Ca²⁺]), respectively. The occurrence of LTD involves the activation of a positive feedback loop formed by protein kinase C, phospholipase A₂, and the extracellular signal-regulated protein kinase pathway, and its expression comprises the reduction of the population of synaptic AMPA receptors. Recently, a stochastic computational model of these signalling processes demonstrated that, in single synapses, LTD is probabilistic and bistable. Here, we expanded this model to simulate LTP, which requires protein phosphatases and the increase in the population of synaptic AMPA receptors. Our results indicated that, in single synapses, while LTD is bistable, LTP is gradual. Ca²⁺ induced both processes stochastically. The magnitudes of the Ca²⁺ signals and the states of the signalling network regulated the likelihood of LTP and LTD and defined dynamic macroscopic Ca²⁺ thresholds for the synaptic modifications in populations of synapses according to an inverse Bienenstock, Cooper and Munro (BCM) rule or a sigmoidal function. In conclusion, our model presents a unifying mechanism that explains the macroscopic properties of LTP and LTD from their dynamics in single synapses.

Understanding the role of synaptopodin and the spine apparatus in Hebbian synaptic plasticity - New perspectives and the need for computational modeling

Synaptopodin (SP) is a proline-rich actin-associated protein essential for the formation of a spine apparatus (SA) in dendritic spines. The SA consists of stacks of smooth endoplasmic reticulum (sER) contiguous with the meshwork of somatodendritic ER. Spines of SP-deficient mice contain sER but no SA, demonstrating that SP is necessary for the assembly of ER cisterns into the more complex SA organelle. Although the SA was described decades ago, its function was difficult to investigate and remained elusive, in part because reliable markers for the SA were missing. After SP was identified as an essential component and a reliable marker of the SA, a role of SP/SA in hippocampal synaptic plasticity could be firmly established using loss-of-function approaches. Further studies revealed that SP/SA participate in the regulation of Ca²⁺-dependent spine-specific Hebbian plasticity and in activity-dependent changes in the spine actin cytoskeleton. In this review we are summarizing recent progress made on SP/SA in Hebbian plasticity and discuss open questions such as causality, spatiotemporal dynamics and complementarity of SP/SA-dependent mechanisms. We are proposing that computational modeling of spine Ca²⁺-signaling and actin remodeling pathways could address some of these issues and could indicate future research directions. Moreover, reaction-diffusion simulations could help to identify key feedforward and feedback regulatory motifs regulating the switch between an LTP and an LTD signaling module in SP/SA-containing spines, thus helping to find a unified view of SP/SA action in Hebbian plasticity.