NMDA/AMPA ratio impacts state transitions and entrainment to oscillations in a computational model of the nucleus accumbens medium spiny projection neuron

Neural energy computations based on Hodgkin-Huxley models bridge abnormal neuronal activities and energy consumption patterns of major depressive disorder

Limited by the current experimental techniques and neurodynamical models, the dysregulation mechanisms of decision-making related neural circuits in major depressive disorder (MDD) are still not clear. In this paper, we proposed a neural coding methodology using energy to further investigate it, which has been proven to strongly complement the neurodynamical methodology. We augmented the previous neural energy calculation method, and applied it to our VTA-NAc-mPFC neurodynamical H-H models. We particularly focused on the peak power and energy consumption of abnormal ion channel (ionic) currents under different concentrations of dopamine input, and investigated the abnormal energy consumption patterns for the MDD group. The results revealed that the energy consumption of medium spiny neurons (MSNs) in the NAc region were lower in the MDD group than that of the normal control group despite having the same firing frequencies, peak action potentials, and average membrane potentials in both groups. Dopamine concentration was also positively correlated with the energy consumption of the pyramidal neurons, but the patterns of different interneuron types were distinct. Additionally, the ratio of mPFC's energy consumption to total energy consumption of the whole network in MDD group was lower than that in normal control group, revealing that the mPFC region in MDD group encoded less neural information, which matched the energy consumption patterns of BOLD-fMRI results. It was also in line with the behavioral characteristics that MDD patients demonstrated in the form of reward insensitivity during decision-making tasks. In conclusion, the model in this paper was the first neural network energy computational model for MDD, which showed success in explaining its dynamical mechanisms with an energy consumption perspective. To build on this, we demonstrated that energy consumption levels can be used as a potential indicator for MDD, which also showed a promising pipeline to use an energy methodology for studying other neuropsychiatric disorders.

Diverse role of NMDA receptors for dendritic integration of neural dynamics

Neurons, represented as a tree structure of morphology, have various distinguished branches of dendrites. Different types of synaptic receptors distributed over dendrites are responsible for receiving inputs from other neurons. NMDA receptors (NMDARs) are expressed as excitatory units, and play a key physiological role in synaptic function. Although NMDARs are widely expressed in most types of neurons, they play a different role in the cerebellar Purkinje cells (PCs). Utilizing a computational PC model with detailed dendritic morphology, we explored the role of NMDARs at different parts of dendritic branches and regions. We found somatic responses can switch from silent, to simple spikes and complex spikes, depending on specific dendritic branches. Detailed examination of the dendrites regarding their diameters and distance to soma revealed diverse response patterns, yet explain two firing modes, simple and complex spike. Taken together, these results suggest that NMDARs play an important role in controlling excitability sensitivity while taking into account the factor of dendritic properties. Given the complexity of neural morphology varying in cell types, our work suggests that the functional role of NMDARs is not stereotyped but highly interwoven with local properties of neuronal structure.

The Role of Glutamate Receptors in Epilepsy

Glutamate is an essential excitatory neurotransmitter in the central nervous system, playing an indispensable role in neuronal development and memory formation. The dysregulation of glutamate receptors and the glutamatergic system is involved in numerous neurological and psychiatric disorders, especially epilepsy. There are two main classes of glutamate receptor, namely ionotropic and metabotropic (mGluRs) receptors. The former stimulate fast excitatory neurotransmission, are N-methyl-d-aspartate (NMDA), α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA), and kainate; while the latter are G-protein-coupled receptors that mediate glutamatergic activity via intracellular messenger systems. Glutamate, glutamate receptors, and regulation of astrocytes are significantly involved in the pathogenesis of acute seizure and chronic epilepsy. Some glutamate receptor antagonists have been shown to be effective for the treatment of epilepsy, and research and clinical trials are ongoing.

Basal ganglia network dynamics and function: Role of direct, indirect and hyper-direct pathways in action selection

Basal ganglia (BG) are a widely recognized neural basis for action selection, but its decision-making mechanism is still a difficult problem for researchers. Therefore, we constructed a spiking neural network inspired by the BG anatomical data. Simulation experiments were based on the principle of dis-inhibition and our functional hypothesis within the BG: the direct pathway, the indirect pathway, and the hyper-direct pathway of the BG jointly implement the initiation execution and termination of motor programs. Firstly, we studied the dynamic process of action selection with the network, which contained intra-group competition and inter-group competition. Secondly, we focused on the effects of the stimulus intensity and the proportion of excitation and inhibition on the GPi/SNr. The results suggested that inhibition and excitation shape action selection. They also explained why the firing rate of GPi/SNr did not continue to increase in the action-selection experiment. Finally, we discussed the experimental results with the functional hypothesis. Uniquely, this paper summarized the decision-making neural mechanism of action selection based on the direct pathway, the indirect pathway, and the hyper-direct pathway within BG.

Dopamine-Mediated Major Depressive Disorder in the Neural Circuit of Ventral Tegmental Area-Nucleus Accumbens-Medial Prefrontal Cortex: From Biological Evidence to Computational Models

Major depressive disorder (MDD) is a serious psychiatric disorder, with an increasing incidence in recent years. The abnormal dopaminergic pathways of the midbrain cortical and limbic system are the key pathological regions of MDD, particularly the ventral tegmental area- nucleus accumbens- medial prefrontal cortex (VTA-NAc-mPFC) neural circuit. MDD usually occurs with the dysfunction of dopaminergic neurons in VTA, which decreases the dopamine concentration and metabolic rate in NAc/mPFC brain regions. However, it has not been fully explained how abnormal dopamine concentration levels affect this neural circuit dynamically through the modulations of ion channels and synaptic activities. We used Hodgkin-Huxley and dynamical receptor binding model to establish this network, which can quantitatively explain neural activity patterns observed in MDD with different dopamine concentrations by changing the kinetics of some ion channels. The simulation replicated some important pathological patterns of MDD at the level of neurons and circuits with low dopamine concentration, such as the decreased action potential frequency in pyramidal neurons of mPFC with significantly reduced burst firing frequency. The calculation results also revealed that NaP and KS channels of mPFC pyramidal neurons played key roles in the functional regulation of this neural circuit. In addition, we analyzed the synaptic currents and local field potentials to explain the mechanism of MDD from the perspective of dysfunction of excitation-inhibition balance, especially the disinhibition effect in the network. The significance of this article is that we built the first computational model to illuminate the effect of dopamine concentrations for the NAc-mPFC-VTA circuit between MDD and normal groups, which can be used to quantitatively explain the results of existing physiological experiments, predict the results for unperformed experiments and screen possible drug targets.

Paired Feed-Forward Excitation With Delayed Inhibition Allows High Frequency Computations Across Brain Regions

The transmission of high frequency temporal information across brain regions is critical to perception, but the mechanisms underlying such transmission remain unclear. Long-range projection patterns across brain areas are often comprised of paired feed-forward excitation followed closely by delayed inhibition, including the thalamic triad synapse, thalamic projections to cortex, and projections within the hippocampus. Previous studies have shown that these joint projections produce a shortened period of depolarization, sharpening the timing window over which the postsynaptic neuron can fire. Here we show that these projections can facilitate the transmission of high frequency computations even at frequencies that are highly filtered by neuronal membranes. This temporal facilitation occurred over a range of synaptic parameter values, including variations in synaptic strength, synaptic time constants, short-term synaptic depression, and the delay between excitation and inhibition. Further, these projections can coordinate computations across multiple network levels, even amid ongoing local activity. We suggest that paired feed-forward excitation and inhibition provide a hybrid signal-carrying both a value and a clock-like trigger-to allow circuits to be responsive to input whenever it arrives.

Neuronal population models reveal specific linear conductance controllers sufficient to rescue preclinical disease phenotypes

Preclinical drug candidates are screened for their ability to ameliorate in vitro neuronal electrophysiology, and go/no-go decisions progress drugs to clinical trials based on population means across cells and animals. However, these measures do not mitigate clinical endpoint risk. Population-based modeling captures variability across multiple electrophysiological measures from healthy, disease, and drug phenotypes. We pursued optimizing therapeutic targets by identifying coherent sets of ion channel target modulations for recovering heterogeneous wild-type (WT) population excitability profiles from a heterogeneous Huntington's disease (HD) population. Our approach combines mechanistic simulations with population modeling of striatal neurons using evolutionary optimization algorithms to design 'virtual drugs'. We introduce efficacy metrics to score populations and rank virtual drug candidates. We found virtual drugs using heuristic approaches that performed better than single target modulators and standard classification methods. We compare a real drug to virtual candidates and demonstrate a novel in silico triaging method.

Biophysical mechanism of the interaction between default mode network and working memory network

Default mode network (DMN) is a functional brain network with a unique neural activity pattern that shows high activity in resting states but low activity in task states. This unique pattern has been proved to relate with higher cognitions such as learning, memory and decision-making. But neural mechanisms of interactions between the default network and the task-related network are still poorly understood. In this paper, a theoretical model of coupling the DMN and working memory network (WMN) is proposed. The WMN and DMN both consist of excitatory and inhibitory neurons connected by AMPA, NMDA, GABA synapses, and are coupled with each other only by excitatory synapses. This model is implemented to demonstrate dynamical processes in a working memory task containing encoding, maintenance and retrieval phases. Simulated results have shown that: (1) AMPA channels could produce significant synchronous oscillations in population neurons, which is beneficial to change oscillation patterns in the WMN and DMN. (2) Different NMDA conductance between the networks could generate multiple neural activity modes in the whole network, which may be an important mechanism to switch states of the networks between three different phases of working memory. (3) The number of sequentially memorized stimuli was related to the energy consumption determined by the network's internal parameters, and the DMN contributed to a more stable working memory process. (4) Finally, this model demonstrated that, in three phases of working memory, different memory phases corresponded to different functional connections between the DMN and WMN. Coupling strengths that measured these functional connections differed in terms of phase synchronization. Phase synchronization characteristics of the contained energy were consistent with the observations of negative and positive correlations between the WMN and DMN reported in referenced fMRI experiments. The results suggested that the coupled interaction between the WMN and DMN played important roles in working memory.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-021-09674-1.

Modeling nucleus accumbens : A Computational Model from Single Cell to Circuit Level

Nucleus accumbens is part of the neural structures required for reward based learning and cognitive processing of motivation. Understanding its cellular dynamics and its role in basal ganglia circuits is important not only in diagnosing behavioral disorders and psychiatric problems as addiction and depression but also for developing therapeutic treatments for them. Building a computational model would expand our comprehension of nucleus accumbens. In this work, we are focusing on establishing a model of nucleus accumbens which has not been considered as much as dorsal striatum in computational neuroscience. We will begin by modeling the behavior of single cells and then build a holistic model of nucleus accumbens considering the effect of synaptic currents. We will verify the validity of the model by showing the consistency of simulation results with the empirical data. Furthermore, the simulation results reveal the joint effect of cortical stimulation and dopaminergic modulation on the activity of medium spiny neurons. This effect differentiates with the type of dopamine receptors.

Transient, Consequential Increases in Extracellular Potassium Ions Accompany Channelrhodopsin2 Excitation

Channelrhodopsin2 (ChR2) optogenetic excitation is widely used to study neurons, astrocytes, and circuits. Using complementary approaches in situ and in vivo, we found that ChR2 stimulation leads to significant transient elevation of extracellular potassium ions by ~5 mM. Such elevations were detected in ChR2-expressing mice, following local in vivo expression of ChR2(H134R) with adeno-associated viruses (AAVs), in different brain areas and when ChR2 was expressed in neurons or astrocytes. In particular, ChR2-mediated excitation of striatal astrocytes was sufficient to increase medium spiny neuron (MSN) excitability and immediate early gene expression. The effects on MSN excitability were recapitulated in silico with a computational MSN model and detected in vivo as increased action potential firing in awake, behaving mice. We show that transient, physiologically consequential increases in extracellular potassium ions accompany ChR2 optogenetic excitation. This coincidental effect may be important to consider during astrocyte studies employing ChR2 to interrogate neural circuits and animal behavior.

Using multiple approaches, Octeau et al. discover that optogenetic excitation of ChR2-expressing cells leads to significant transient extracellular potassium ion elevations that increase neuronal excitability and immediate early gene expression in neurons following in vivo stimulation.

An electrodiffusive, ion conserving Pinsky-Rinzel model with homeostatic mechanisms

In most neuronal models, ion concentrations are assumed to be constant, and effects of concentration variations on ionic reversal potentials, or of ionic diffusion on electrical potentials are not accounted for. Here, we present the electrodiffusive Pinsky-Rinzel (edPR) model, which we believe is the first multicompartmental neuron model that accounts for electrodiffusive ion concentration dynamics in a way that ensures a biophysically consistent relationship between ion concentrations, electrical charge, and electrical potentials in both the intra- and extracellular space. The edPR model is an expanded version of the two-compartment Pinsky-Rinzel (PR) model of a hippocampal CA3 neuron. Unlike the PR model, the edPR model includes homeostatic mechanisms and ion-specific leakage currents, and keeps track of all ion concentrations (Na⁺, K⁺, Ca²⁺, and Cl⁻), electrical potentials, and electrical conductivities in the intra- and extracellular space. The edPR model reproduces the membrane potential dynamics of the PR model for moderate firing activity. For higher activity levels, or when homeostatic mechanisms are impaired, the homeostatic mechanisms fail in maintaining ion concentrations close to baseline, and the edPR model diverges from the PR model as it accounts for effects of concentration changes on neuronal firing. We envision that the edPR model will be useful for the field in three main ways. Firstly, as it relaxes commonly made modeling assumptions, the edPR model can be used to test the validity of these assumptions under various firing conditions, as we show here for a few selected cases. Secondly, the edPR model should supplement the PR model when simulating scenarios where ion concentrations are expected to vary over time. Thirdly, being applicable to conditions with failed homeostasis, the edPR model opens up for simulating a range of pathological conditions, such as spreading depression or epilepsy.

Neurons generate their electrical signals by letting ions pass through their membranes. Despite this fact, most models of neurons apply the simplifying assumption that ion concentrations remain effectively constant during neural activity. This assumption is often quite good, as neurons contain a set of homeostatic mechanisms that make sure that ion concentrations vary quite little under normal circumstances. However, under some conditions, these mechanisms can fail, and ion concentrations can vary quite dramatically. Standard models are thus not able to simulate such conditions. Here, we present what to our knowledge is the first multicompartmental neuron model that accounts for ion concentration variations in a way that ensures complete and consistent ion concentration and charge conservation. In this work, we use the model to explore under which activity conditions the ion concentration variations become important for predicting the neurodynamics. We expect the model to be of great value for the field of neuroscience, as it can be used to simulate a range of pathological conditions, such as spreading depression or epilepsy, which are associated with large changes in extracellular ion concentrations.

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.

A review of computational modeling and deep brain stimulation: applications to Parkinson's disease

Biophysical computational models are complementary to experiments and theories, providing powerful tools for the study of neurological diseases. The focus of this review is the dynamic modeling and control strategies of Parkinson's disease (PD). In previous studies, the development of parkinsonian network dynamics modeling has made great progress. Modeling mainly focuses on the cortex-thalamus-basal ganglia (CTBG) circuit and its sub-circuits, which helps to explore the dynamic behavior of the parkinsonian network, such as synchronization. Deep brain stimulation (DBS) is an effective strategy for the treatment of PD. At present, many studies are based on the side effects of the DBS. However, the translation from modeling results to clinical disease mitigation therapy still faces huge challenges. Here, we introduce the progress of DBS improvement. Its specific purpose is to develop novel DBS treatment methods, optimize the treatment effect of DBS for each patient, and focus on the study in closed-loop DBS. Our goal is to review the inspiration and insights gained by combining the system theory with these computational models to analyze neurodynamics and optimize DBS treatment.

Morphine Differentially Alters the Synaptic and Intrinsic Properties of D1R- and D2R-Expressing Medium Spiny Neurons in the Nucleus Accumbens

Exposure to opioids reshapes future reward and motivated behaviors partially by altering the functional output of medium spiny neurons (MSNs) in the nucleus accumbens shell. Here, we investigated how morphine, a highly addictive opioid, alters synaptic transmission and intrinsic excitability on dopamine D1-receptor (D1R) expressing and dopamine D2-receptor (D2R) expressing MSNs, the two main output neurons in the nucleus accumbens shell. Using whole-cell electrophysiology recordings, we show, that 24 h abstinence following repeated non-contingent administration of morphine (10 mg/kg, i.p.) in mice reduces the miniature excitatory postsynaptic current (mEPSC) frequency and miniature inhibitory postsynaptic current (mIPSC) frequency on D2R-MSNs, with concomitant increases in D2R-MSN intrinsic membrane excitability. We did not observe any changes in synaptic or intrinsic changes on D1R-MSNs. Last, in an attempt to determine the integrated effect of the synaptic and intrinsic alterations on the overall functional output of D2R-MSNs, we measured the input-output efficacy by measuring synaptically-driven action potential firing. We found that both D1R-MSN and D2R-MSN output was unchanged following morphine treatment.

A computational model for gonadotropin releasing cells in the teleost fish medaka

Pituitary endocrine cells fire action potentials (APs) to regulate their cytosolic Ca²⁺ concentration and hormone secretion rate. Depending on animal species, cell type, and biological conditions, pituitary APs are generated either by TTX-sensitive Na⁺ currents (I_Na), high-voltage activated Ca²⁺ currents (I_Ca), or by a combination of the two. Previous computational models of pituitary cells have mainly been based on data from rats, where I_Na is largely inactivated at the resting potential, and spontaneous APs are predominantly mediated by I_Ca. Unlike in rats, spontaneous I_Na-mediated APs are consistently seen in pituitary cells of several other animal species, including several species of fish. In the current work we develop a computational model of gonadotropin releasing cells in the teleost fish medaka (Oryzias latipes). The model stands out from previous modeling efforts by being (1) the first model of a pituitary cell in teleosts, (2) the first pituitary cell model that fires sponateous APs that are predominantly mediated by I_Na, and (3) the first pituitary cell model where the kinetics of the depolarizing currents, I_Na and I_Ca, are directly fitted to voltage-clamp data. We explore the firing properties of the model, and compare it to the properties of previous models that fire I_Ca-based APs. We put a particular focus on how the big conductance K⁺ current (I_BK) modulates the AP shape. Interestingly, we find that I_BK can prolong AP duration in models that fire I_Ca-based APs, while it consistently shortens the duration of the predominantly I_Na-mediated APs in the medaka gonadotroph model. Although the model is constrained to experimental data from gonadotroph cells in medaka, it may likely provide insights also into other pituitary cell types that fire I_Na-mediated APs.

Excitable cells elicit electrical pulses called action potentials (APs), which are generated and shaped by a combination of ion channels in the cell membrane. Since one type of ion channels is permeable to Ca²⁺ ions, there is typically an influx of Ca²⁺ during an AP. Pituitary cells therefore use AP firing to regulate their cytosolic Ca²⁺ concentration, which in turn controls their hormone secretion rate. The amount of Ca²⁺ that enters during an AP depends strongly on how long it lasts, and it is therefore important to understand the mechanisms that control this. Pituitary APs are generally mediated by a combination of Ca²⁺ channels and Na⁺ channels, and the relative contributions of from the two vary between cell types, animal species and biological conditions. Previous computer models have predominantly been adapted to data from pituitary cells which tend to fire Ca²⁺-based APs. Here we develop a new model, adapted to data from pituitary cells in the fish medaka, which APs that are predominantly Na⁺-based, and compare its dynamical properties to the previous models that fire Ca²⁺-based APs.

Biophysical modeling of C. elegans neurons: Single ion currents and whole-cell dynamics of AWCon and RMD

C. elegans neuronal system constitutes the ideal framework for studying simple, yet realistic, neuronal activity, since the whole nervous system is fully characterized with respect to the exact number of neurons and the neuronal connections. Most recent efforts are devoted to investigate and clarify the signal processing and functional connectivity, which are at the basis of sensing mechanisms, signal transmission, and motor control. In this framework, a refined modelof whole neuron dynamics constitutes a key ingredient to describe the electrophysiological processes, both at thecellular and at the network scale. In this work, we present Hodgkin-Huxley-based models of ion channels dynamics black, built on data available both from C. elegans and from other organisms, expressing homologous channels. We combine these channel models to simulate the electrical activity oftwo among the most studied neurons in C. elegans, which display prototypical dynamics of neuronal activation, the chemosensory AWCON and the motor neuron RMD. Our model properly describes the regenerative responses of the two cells. We analyze in detail the role of ion currents, both in wild type and in in silico knockout neurons. Moreover, we specifically investigate the behavior of RMD, identifying a heterogeneous dynamical response which includes bistable regimes and sustained oscillations. We are able to assess the critical role of T-type calcium currents, carried by CCA-1 channels, and leakage currents in the regulation of RMD response. Overall, our results provide new insights in the activity of key C. elegans neurons. The developed mathematical framework constitute a basis for single-cell and neuronal networks analyses, opening new scenarios in the in silico modeling of C. elegans neuronal system.

Altered GluA1 (Gria1) Function and Accumbal Synaptic Plasticity in the ClockΔ19 Model of Bipolar Mania

BACKGROUND

Disruptions in circadian rhythms are associated with an increased risk for bipolar disorder. Moreover, studies show that the circadian protein CLOCK (circadian locomotor output cycles kaput) is involved in regulating monoaminergic systems and mood-related behavior. However, the molecular and synaptic mechanisms underlying this relationship remain poorly understood.

METHODS

Using ex vivo whole-cell patch-clamp electrophysiology in ClockΔ19 mutant and wild-type mice we characterized alterations in excitatory synaptic transmission, strength, and intrinsic excitability of nucleus accumbens (NAc) neurons. We performed protein crosslinking and Western blot analysis to examine surface and intracellular levels and rhythm of the glutamate receptor subunit, GluA1, in the NAc. Viral-mediated overexpression of Gria1 in the NAc and behavioral assays were also used.

RESULTS

Compared with wild-type mice, ClockΔ19 mice display reduced alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor-mediated excitatory synaptic responses at NAc medium spiny neurons. These alterations are likely postsynaptic, as presynaptic release of glutamate onto medium spiny neurons is unaltered in mutant mice. Additionally, NAc surface protein levels and the rhythm of GRIA1 are decreased in ClockΔ19 mice diurnally, consistent with reduced functional synaptic response. Furthermore, we observed a significantly hyperpolarized resting membrane potential of ClockΔ19 medium spiny neurons, suggesting lowered intrinsic excitability. Last, overexpression of functional Gria1 in the NAc of mutant mice was able to normalize increased exploratory drive and reward sensitivity behavior when mice are in a manic-like state.

CONCLUSIONS

Together, our findings demonstrate that NAc excitatory signaling via Gria1 expression is integral to the effects of Clock gene disruption on manic-like behaviors.

Automated Metadata Suggestion During Repository Submission

Knowledge discovery via an informatics resource is constrained by the completeness of the resource, both in terms of the amount of data it contains and in terms of the metadata that exists to describe the data. Increasing completeness in one of these categories risks reducing completeness in the other because manually curating metadata is time consuming and is restricted by familiarity with both the data and the metadata annotation scheme. The diverse interests of a research community may drive a resource to have hundreds of metadata tags with few examples for each making it challenging for humans or machine learning algorithms to learn how to assign metadata tags properly. We demonstrate with ModelDB, a computational neuroscience model discovery resource, that using manually-curated regular-expression based rules can overcome this challenge by parsing existing texts from data providers during user data entry to suggest metadata annotations and prompt them to suggest other related metadata annotations rather than leaving the task to a curator. In the ModelDB implementation, analyzing the abstract identified 6.4 metadata tags per abstract at 79% precision. Using the full-text produced higher recall with low precision (41%), and the title alone produced few (1.3) metadata annotations per entry; we thus recommend data providers use their abstract during upload. Grouping the possible metadata annotations into categories (e.g. cell type, biological topic) revealed that precision and recall for the different text sources varies by category. Given this proof-of-concept, other bioinformatics resources can likewise improve the quality of their metadata by adopting our approach of prompting data uploaders with relevant metadata at the minimal cost of formalizing rules for each potential metadata annotation.

Early-life adversity selectively impairs α2-GABAA receptor expression in the mouse nucleus accumbens and influences the behavioral effects of cocaine

Haplotypes of the Gabra2 gene encoding the α2-subunit of the GABAA receptor (GABAAR) are associated with drug abuse, suggesting that α2-GABAARs may play an important role in the circuitry underlying drug misuse. The genetic association of Gabra2 haplotypes with cocaine addiction appears to be evident primarily in individuals who had experienced childhood trauma. Given this association of childhood trauma, cocaine abuse and the Gabra2 haplotypes, we have explored in a mouse model of early life adversity (ELA) whether such events influence the behavioral effects of cocaine and if, as suggested by the human studies, α2-GABAARs in the nucleus accumbens (NAc) are involved in these perturbed behaviors. In adult mice prior ELA caused a selective decrease of accumbal α2-subunit mRNA, resulting in a selective decrease in the number and size of the α2-subunit (but not the α1-subunit) immunoreactive clusters in NAc core medium spiny neurons (MSNs). Functionally, in adult MSNs ELA decreased the amplitude and frequency of GABAAR-mediated miniature inhibitory postsynaptic currents (mIPSCs), a profile similar to that of α2 "knock-out" (α2-/-) mice. Behaviourally, adult male ELA and α2-/- mice exhibited an enhanced locomotor response to acute cocaine and blunted sensitisation upon repeated cocaine administration, when compared to their appropriate controls. Collectively, these findings reveal a neurobiological mechanism which may relate to the clinical observation that early trauma increases the risk for substance abuse disorder (SAD) in individuals harbouring haplotypic variations in the Gabra2 gene.

Differential changes to D1 and D2 medium spiny neurons in the 12-month-old Q175+/- mouse model of Huntington's Disease

Huntington's Disease (HD) is an autosomal dominant, progressive neurodegenerative disorder caused by deleterious expansion of CAG repeats in the Huntingtin gene and production of neurotoxic mutant Huntingtin protein (mHTT). The key pathological feature of HD is a profound degeneration of the striatum and a loss of cortical volume. The initial loss of indirect pathway (D2) medium spiny neuron (MSN) projections in early stages of HD, followed by a loss of direct pathway (D1) projections in advanced stages has important implications for the trajectory of motor and cognitive dysfunction in HD, but is not yet understood. Mouse models of HD have yielded important information on the effects and mechanisms of mHTT toxicity; however, whether these models recapitulate differential vulnerability of D1 vs. D2 MSNs is unknown. Here, we employed 12-month-old Q175+/- x D2-eGFP mice to examine the detailed structural and functional properties of D1 vs. D2 MSNs. While both D1 and D2 MSNs exhibited increased input resistance, depolarized resting membrane potentials and action potential threshold, only D1 MSNs showed reduced rheobase, action potential amplitude and frequency of spontaneous excitatory postsynaptic currents. Furthermore, D1 but not D2 MSNs showed marked proliferative changes to their dendritic arbors and reductions in spine density. Immunohistochemical assessment showed no loss of glutamatergic afferent inputs from cortical and subcortical sources onto identified D1 and D2 MSNs. Computational models constrained by empirical data predict that the increased dendritic complexity in Q175+/- D1 MSNs likely leads to greater dendritic filtering and attenuation of signals propagating to the soma from the dendrites. Together these findings reveal that, by twelve months, D1 and D2 MSNs exhibit distinctive responses to the presence of mHTT in this important mouse model of HD. This further highlights the need to incorporate findings from D1 and D2 MSNs independently in the context of HD models.

Basal Ganglia Neuromodulation Over Multiple Temporal and Structural Scales-Simulations of Direct Pathway MSNs Investigate the Fast Onset of Dopaminergic Effects and Predict the Role of Kv4.2

The basal ganglia are involved in the motivational and habitual control of motor and cognitive behaviors. Striatum, the largest basal ganglia input stage, integrates cortical and thalamic inputs in functionally segregated cortico-basal ganglia-thalamic loops, and in addition the basal ganglia output nuclei control targets in the brainstem. Striatal function depends on the balance between the direct pathway medium spiny neurons (D1-MSNs) that express D1 dopamine receptors and the indirect pathway MSNs that express D2 dopamine receptors. The striatal microstructure is also divided into striosomes and matrix compartments, based on the differential expression of several proteins. Dopaminergic afferents from the midbrain and local cholinergic interneurons play crucial roles for basal ganglia function, and striatal signaling via the striosomes in turn regulates the midbrain dopaminergic system directly and via the lateral habenula. Consequently, abnormal functions of the basal ganglia neuromodulatory system underlie many neurological and psychiatric disorders. Neuromodulation acts on multiple structural levels, ranging from the subcellular level to behavior, both in health and disease. For example, neuromodulation affects membrane excitability and controls synaptic plasticity and thus learning in the basal ganglia. However, it is not clear on what time scales these different effects are implemented. Phosphorylation of ion channels and the resulting membrane effects are typically studied over minutes while it has been shown that neuromodulation can affect behavior within a few hundred milliseconds. So how do these seemingly contradictory effects fit together? Here we first briefly review neuromodulation of the basal ganglia, with a focus on dopamine. We furthermore use biophysically detailed multi-compartmental models to integrate experimental data regarding dopaminergic effects on individual membrane conductances with the aim to explain the resulting cellular level dopaminergic effects. In particular we predict dopaminergic effects on Kv4.2 in D1-MSNs. Finally, we also explore dynamical aspects of the onset of neuromodulation effects in multi-scale computational models combining biochemical signaling cascades and multi-compartmental neuron models.

Effects of location and extent of spine clustering on synaptic integration in striatal medium spiny neurons-a computational study

The nucleus accumbens (NAc) is known widely for its role in the reward circuit, which is dysregulated in a number of psychological disorders. Recent evidence also suggests the contribution of this structure in spatial and gustatory memories. Because of its role in different types of memories, similar to the hippocampus, we assumed the formation of spine clusters, which are engrams of memory, to be present on dendrites of medium spiny neurons (MSNs). We found that the activation of clustered inputs resulted in sublinear summation when clusters were present on the same branch and also when inputs were distributed on different branches. The size, as well as the location of clusters, was found to affect the summation. With an increase in cluster size and distance from soma, the summation was increasingly sublinear. When the temporal integration window was measured for clustered spines, it was found to be narrower as compared to that for a single spine. Also, distally located clusters resulted in a wider temporal window, as compared to proximal clusters. Our results suggest that depending on the location of clusters, the modes of integration will differ in MSNs possessing clustered spines.

The role of Ca2+ -dependent K+ - channels at the rat corticostriatal synapses revealed by paired pulse stimulation

Potassium channels play an important role in modulating synaptic activity both at presynaptic and postsynaptic levels. We have shown before that presynaptically located K_V and K_IR channels modulate the strength of corticostriatal synapses in rat brain, but the role of other types of potassium channels at these synapses remains largely unknown. Here, we show that calcium-dependent potassium channels BK-type but not SK-type channels are located presynaptically in corticostriatal synapses. We stimulated cortical neurons in rat brain slices and recorded postsynaptic excitatory potentials (EPSP) in medium spiny neurons (MSN) in dorsal neostriatum. By using a paired pulse protocol, we induced synaptic facilitation before applying either BK- or SK-specific toxins. Thus, we found that blockage of BK_Ca with iberiotoxin (10 nM) reduces synaptic facilitation and increases the amplitude of the EPSP, while exposure to SK-blocker apamin (100 nM) has no effect. Additionally, we induced train action potentials on striatal MSN by current injection before and after the exposure to K_Ca toxins. We found that the action potential becomes broader when the MSN is exposed to iberiotoxin, although it has no impact on frequency. In contrast, exposure to apamin results in loss of afterhyperpolarization phase and an increase of spike frequency. Therefore, we concluded that postsynaptic SK channels are involved in afterhyperpolarization and modulation of spike frequency while the BK channels are involved on the late repolarization phase of the action potential. Altogether, our results show that calcium-dependent potassium channels modulate both input towards and output from the striatum.

T2N as a new tool for robust electrophysiological modeling demonstrated for mature and adult-born dentate granule cells

Compartmental models are the theoretical tool of choice for understanding single neuron computations. However, many models are incomplete, built ad hoc and require tuning for each novel condition rendering them of limited usability. Here, we present T2N, a powerful interface to control NEURON with Matlab and TREES toolbox, which supports generating models stable over a broad range of reconstructed and synthetic morphologies. We illustrate this for a novel, highly detailed active model of dentate granule cells (GCs) replicating a wide palette of experiments from various labs. By implementing known differences in ion channel composition and morphology, our model reproduces data from mouse or rat, mature or adult-born GCs as well as pharmacological interventions and epileptic conditions. This work sets a new benchmark for detailed compartmental modeling. T2N is suitable for creating robust models useful for large-scale networks that could lead to novel predictions. We discuss possible T2N application in degeneracy studies.

The Effects of Medium Spiny Neuron Morphologcial Changes on Basal Ganglia Network under External Electric Field: A Computational Modeling Study

The damage of dopaminergic neurons that innervate the striatum has been considered to be the proximate cause of Parkinson's disease (PD). In the dopamine-denervated state, the loss of dendritic spines and the decrease of dendritic length may prevent medium spiny neuron (MSN) from receiving too much excitatory stimuli from the cortex, thereby reducing the symptom of Parkinson's disease. However, the reduction in dendritic spine density obtained by different experiments is significantly different. We developed a biological-based network computational model to quantify the effect of dendritic spine loss and dendrites tree degeneration on basal ganglia (BG) signal regulation. Through the introduction of error index (EI), which was used to measure the attenuation of the signal, we explored the amount of dendritic spine loss and dendritic trees degradation required to restore the normal regulatory function of the network, and found that there were two ranges of dendritic spine loss that could reduce EI to normal levels in the case of dopamine at a certain level, this was also true for dendritic trees. However, although these effects were the same, the mechanisms of these two cases were significant difference. Using the method of phase diagram analysis, we gained insight into the mechanism of signal degradation. Furthermore, we explored the role of cortex in MSN morphology changes dopamine depletion-induced and found that proper adjustments to cortical activity do stop the loss in dendritic spines induced by dopamine depleted. These results suggested that modifying cortical drive onto MSN might provide a new idea on clinical therapeutic strategies for Parkinson's disease.

Comprehensive review: Computational modelling of schizophrenia

Computational modelling has been used to address: (1) the variety of symptoms observed in schizophrenia using abstract models of behavior (e.g. Bayesian models - top-down descriptive models of psychopathology); (2) the causes of these symptoms using biologically realistic models involving abnormal neuromodulation and/or receptor imbalance (e.g. connectionist and neural networks - bottom-up realistic models of neural processes). These different levels of analysis have been used to answer different questions (i.e. understanding behavioral vs. neurobiological anomalies) about the nature of the disorder. As such, these computational studies have mostly supported diverging hypotheses of schizophrenia's pathophysiology, resulting in a literature that is not always expanding coherently. Some of these hypotheses are however ripe for revision using novel empirical evidence. Here we present a review that first synthesizes the literature of computational modelling for schizophrenia and psychotic symptoms into categories supporting the dopamine, glutamate, GABA, dysconnection and Bayesian inference hypotheses respectively. Secondly, we compare model predictions against the accumulated empirical evidence and finally we identify specific hypotheses that have been left relatively under-investigated.

Cell-type-specific inhibition of the dendritic plateau potential in striatal spiny projection neurons

Striatal spiny projection neurons (SPNs) receive convergent excitatory synaptic inputs from the cortex and thalamus. Activation of spatially clustered and temporally synchronized excitatory inputs at the distal dendrites could trigger plateau potentials in SPNs. Such supralinear synaptic integration is crucial for dendritic computation. However, how plateau potentials interact with subsequent excitatory and inhibitory synaptic inputs remains unknown. By combining computational simulation, two-photon imaging, optogenetics, and dual-color uncaging of glutamate and GABA, we demonstrate that plateau potentials can broaden the spatiotemporal window for integrating excitatory inputs and promote spiking. The temporal window of spiking can be delicately controlled by GABAergic inhibition in a cell-type-specific manner. This subtle inhibitory control of plateau potential depends on the location and kinetics of the GABAergic inputs and is achieved by the balance between relief and reestablishment of NMDA receptor Mg²⁺ block. These findings represent a mechanism for controlling spatiotemporal synaptic integration in SPNs.

Pallidostriatal Projections Promote β Oscillations in a Dopamine-Depleted Biophysical Network Model

In the basal ganglia, focused rhythmicity is an important feature of network activity at certain stages of motor processing. In disease, however, the basal ganglia develop amplified rhythmicity. Here, we demonstrate how the cellular architecture and network dynamics of an inhibitory loop in the basal ganglia yield exaggerated synchrony and locking to β oscillations, specifically in the dopamine-depleted state. A key component of this loop is the pallidostriatal pathway, a well-characterized anatomical projection whose function has long remained obscure. We present a synaptic characterization of this pathway in mice and incorporate these data into a computational model that we use to investigate its influence over striatal activity under simulated healthy and dopamine-depleted conditions. Our model predicts that the pallidostriatal pathway influences striatal output preferentially during periods of synchronized activity within GPe. We show that, under dopamine-depleted conditions, this effect becomes a key component of a positive feedback loop between the GPe and striatum that promotes synchronization and rhythmicity. Our results generate novel predictions about the role of the pallidostriatal pathway in shaping basal ganglia activity in health and disease.

SIGNIFICANCE STATEMENT

This work demonstrates that functional connections from the globus pallidus externa (GPe) to striatum are substantially stronger onto fast-spiking interneurons (FSIs) than onto medium spiny neurons. Our circuit model suggests that when GPe spikes are synchronous, this pallidostriatal pathway causes synchronous FSI activity pauses, which allow a transient window of disinhibition for medium spiny neurons. In simulated dopamine-depletion, this GPe-FSI activity is necessary for the emergence of strong synchronization and the amplification and propagation of β oscillations, which are a hallmark of parkinsonian circuit dysfunction. These results suggest that GPe may play a central role in propagating abnormal circuit activity to striatum, which in turn projects to downstream basal ganglia structures. These findings warrant further exploration of GPe as a target for interventions for Parkinson's disease.

Exploring the neural mechanisms of finasteride: a proteomic analysis in the nucleus accumbens

The enzyme 5α-reductase (5αR) catalyzes the conversion of progesterone and testosterone into neuroactive steroids implicated in a wide array of behavioral functions. The prototypical 5αR inhibitor, finasteride (FIN), is clinically approved for the treatment of androgenic alopecia and benign prostatic hyperplasia. Recent evidence has shown that FIN, albeit generally well tolerated, can induce untoward psychological effects in a subset of patients; furthermore, this drug may have therapeutic efficacy for a number of different neuropsychiatric conditions, ranging from Tourette syndrome to schizophrenia. In rat models of these conditions, FIN has been shown to block the effects of dopamine receptors in the nucleus accumbens (NAcc), a key terminal of the dopamine mesolimbic system. The biological underpinnings of these effects, however, remain mostly elusive. To elucidate the neurochemical networks that may be responsible for the behavioral effects of FIN, we evaluated the proteomic profile of the NAcc following acute (100mg/kg, IP) and subchronic (7 days; 100mg/kg/day, IP) treatment with this drug, in comparison with vehicle treatment (n=5/group). Two-dimensional electrophoresis (2-DE) analysis coupled to mass spectrometry revealed significant changes in the expression of nine proteins (CRMP2, PSMD1, STX18, KCNC3, CYP255, GABRP, GABT, PRPS1, CYP2B3), which were further analyzed by ontological classification (PANTHER). These results point to a number of novel potential chemical targets of FIN, and may help elucidate the underpinnings of FIN's behavioral effects and therapeutic potential for neuropsychiatric disorders.

A model-based analysis of physiological properties of the striatal medium spiny neuron

As the principal cell of the striatum, medium spiny neurons (MSNs) are closely associated with various motor dysfunctional diseases. In this paper, we describe an electric compartment model constructed in NEURON with a realistic morphology. Based on a 554-compartment computational model, we researched the influence of external current stimuli, different ions conductance, and the removal of partial dendrites on the physiological properties of the MSN. The main results are the following: (1) in the case of external current stimuli, various firing patterns appear in the MSN and the model produces a clear period-adding bifurcation phenomenon; (2) the effect of distinct types of ion channels vary and significant differences in discharge rhythm exist even among ion channels of the same type; (3) the closer the removed dendrite was to the soma, the larger the impact this had on the discharge pattern of the MSN.

Calcium dynamics predict direction of synaptic plasticity in striatal spiny projection neurons

The striatum is a major site of learning and memory formation for sensorimotor and cognitive association. One of the mechanisms used by the brain for memory storage is synaptic plasticity - the long-lasting, activity-dependent change in synaptic strength. All forms of synaptic plasticity require an elevation in intracellular calcium, and a common hypothesis is that the amplitude and duration of calcium transients can determine the direction of synaptic plasticity. The utility of this hypothesis in the striatum is unclear in part because dopamine is required for striatal plasticity and in part because of the diversity in stimulation protocols. To test whether calcium can predict plasticity direction, we developed a calcium-based plasticity rule using a spiny projection neuron model with sophisticated calcium dynamics including calcium diffusion, buffering and pump extrusion. We utilized three spike timing-dependent plasticity (STDP) induction protocols, in which postsynaptic potentials are paired with precisely timed action potentials and the timing of such pairing determines whether potentiation or depression will occur. Results show that despite the variation in calcium dynamics, a single, calcium-based plasticity rule, which explicitly considers duration of calcium elevations, can explain the direction of synaptic weight change for all three STDP protocols. Additional simulations show that the plasticity rule correctly predicts the NMDA receptor dependence of long-term potentiation and the L-type channel dependence of long-term depression. By utilizing realistic calcium dynamics, the model reveals mechanisms controlling synaptic plasticity direction, and shows that the dynamics of calcium, not just calcium amplitude, are crucial for synaptic plasticity.

Endocannabinoid dynamics gate spike-timing dependent depression and potentiation

Synaptic plasticity is a cardinal cellular mechanism for learning and memory. The endocannabinoid (eCB) system has emerged as a pivotal pathway for synaptic plasticity because of its widely characterized ability to depress synaptic transmission on short- and long-term scales. Recent reports indicate that eCBs also mediate potentiation of the synapse. However, it is not known how eCB signaling may support bidirectionality. Here, we combined electrophysiology experiments with mathematical modeling to question the mechanisms of eCB bidirectionality in spike-timing dependent plasticity (STDP) at corticostriatal synapses. We demonstrate that STDP outcome is controlled by eCB levels and dynamics: prolonged and moderate levels of eCB lead to eCB-mediated long-term depression (eCB-tLTD) while short and large eCB transients produce eCB-mediated long-term potentiation (eCB-tLTP). Moreover, we show that eCB-tLTD requires active calcineurin whereas eCB-tLTP necessitates the activity of presynaptic PKA. Therefore, just like glutamate or GABA, eCB form a bidirectional system to encode learning and memory.

Circadian Mechanisms Underlying Reward-Related Neurophysiology and Synaptic Plasticity

Evidence from clinical and preclinical research provides an undeniable link between disruptions in the circadian clock and the development of psychiatric diseases, including mood and substance abuse disorders. The molecular clock, which controls daily patterns of physiological and behavioral activity in living organisms, when desynchronized, may exacerbate or precipitate symptoms of psychiatric illness. One of the outstanding questions remaining in this field is that of cause and effect in the relationship between circadian rhythm disruption and psychiatric disease. Focus has recently turned to uncovering the role of circadian proteins beyond the maintenance of homeostatic systems and outside of the suprachiasmatic nucleus (SCN), the master pacemaker region of the brain. In this regard, several groups, including our own, have sought to understand how circadian proteins regulate mechanisms of synaptic plasticity and neurotransmitter signaling in mesocorticolimbic brain regions, which are known to be critically involved in reward processing and mood. This regulation can come in the form of direct transcriptional control of genes central to mood and reward, including those associated with dopaminergic activity in the midbrain. It can also be seen at the circuit level through indirect connections of mesocorticolimbic regions with the SCN. Circadian misalignment paradigms as well as genetic models of circadian disruption have helped to elucidate some of the complex interactions between these systems and neural activity influencing behavior. In this review, we explore findings that link circadian protein function with synaptic adaptations underlying plasticity as it may contribute to the development of mood disorders and addiction. In light of recent advances in technology and sophisticated methods for molecular and circuit-level interrogation, we propose future directions aimed at teasing apart mechanisms through which the circadian system modulates mood and reward-related behavior.

Brain rhythms connect impaired inhibition to altered cognition in schizophrenia

In recent years, schizophrenia research has focused on inhibitory interneuron dysfunction at the level of neurobiology and on cognitive impairments at the psychological level. Reviewing both experimental and computational findings, we show how the temporal structure of the activity of neuronal populations, exemplified by brain rhythms, can begin to bridge these levels of complexity. Oscillations in neuronal activity tie the pathophysiology of schizophrenia to alterations in local processing and large-scale coordination, and these alterations in turn can lead to the cognitive and perceptual disturbances observed in schizophrenia.

Intrinsic plasticity: an emerging player in addiction

Exposure to drugs of abuse, such as cocaine, leads to plastic changes in the activity of brain circuits, and a prevailing view is that these changes play a part in drug addiction. Notably, there has been intense focus on drug-induced changes in synaptic excitability and much less attention on intrinsic excitability factors (that is, excitability factors that are remote from the synapse). Accumulating evidence now suggests that intrinsic factors such as K+ channels are not only altered by cocaine but may also contribute to the shaping of the addiction phenotype.

Pyramidal cell selective ablation of N-methyl-D-aspartate receptor 1 causes increase in cellular and network excitability

BACKGROUND

Neuronal activity at gamma frequency is impaired in schizophrenia (SZ) and is considered critical for cognitive performance. Such impairments are thought to be due to reduced N-methyl-D-aspartate receptor (NMDAR)-mediated inhibition from parvalbumin interneurons, rather than a direct role of impaired NMDAR signaling on pyramidal neurons. However, recent studies suggest a direct role of pyramidal neurons in regulating gamma oscillations. In particular, a computational model has been proposed in which phasic currents from pyramidal cells could drive synchronized feedback inhibition from interneurons. As such, impairments in pyramidal neuron activity could lead to abnormal gamma oscillations. However, this computational model has not been tested experimentally and the molecular mechanisms underlying pyramidal neuron dysfunction in SZ remain unclear.

METHODS

In the present study, we tested the hypothesis that SZ-related phenotypes could arise from reduced NMDAR signaling in pyramidal neurons using forebrain pyramidal neuron specific NMDA receptor 1 knockout mice.

RESULTS

The mice displayed increased baseline gamma power, as well as sociocognitive impairments. These phenotypes were associated with increased pyramidal cell excitability due to changes in inherent membrane properties. Interestingly, mutant mice showed decreased expression of GIRK2 channels, which has been linked to increased neuronal excitability.

CONCLUSIONS

Our data demonstrate for the first time that NMDAR hypofunction in pyramidal cells is sufficient to cause electrophysiological, molecular, neuropathological, and behavioral changes related to SZ.

Tau-mediated NMDA receptor impairment underlies dysfunction of a selectively vulnerable network in a mouse model of frontotemporal dementia

Frontotemporal dementia (FTD) is a neurodegenerative behavioral disorder that selectively affects the salience network, including the ventral striatum and insula. Tau mutations cause FTD, but how mutant tau impairs the salience network is unknown. Here, we address this question using a mouse model expressing the entire human tau gene with an FTD-associated mutation (V337M). Mutant, but not wild-type, human tau transgenic mice had aging-dependent repetitive and disinhibited behaviors, with synaptic deficits selectively in the ventral striatum and insula. There, mutant tau depleted PSD-95, resulting in smaller postsynaptic densities and impaired synaptic localization of NMDA receptors (NMDARs). In the ventral striatum, decreased NMDAR-mediated transmission reduced striatal neuron firing. Pharmacologically enhancing NMDAR function with the NMDAR co-agonist cycloserine reversed electrophysiological and behavioral deficits. These results indicate that NMDAR hypofunction critically contributes to FTD-associated behavioral and electrophysiological alterations and that this process can be therapeutically targeted by a Food and Drug Administration-approved drug.

Effects of NMDA receptor antagonists and antipsychotics on high frequency oscillations recorded in the nucleus accumbens of freely moving mice

RATIONALE

Abnormal oscillatory activity associated with N-methyl-D-aspartate (NMDA) receptor hypofunction is widely considered to contribute to the symptoms of schizophrenia.

OBJECTIVE

This study aims to characterise the changes produced by NMDA receptor antagonists and antipsychotics on accumbal high-frequency oscillations (HFO; 130-180 Hz) in mice.

METHODS

Local field potentials were recorded from the nucleus accumbens of freely moving mice.

RESULTS

Systemic injection of ketamine and MK801 both dose-dependently increased the power of HFO and produced small increases in HFO frequency. The atypical antipsychotic drug, clozapine, produced a robust dose-dependent reduction in the frequency of MK801-enhanced HFO, whilst haloperidol, a typical antipsychotic drug, had little effect. Stimulation of NMDA receptors (directly or through the glycine site) as well as activation of 5-HT1A receptors, reduced the frequency of MK801-enhanced HFO, but other receptors known to be targets for clozapine, namely 5-HT2A, 5-HT7 and histamine H3 receptors had no effect.

CONCLUSIONS

NMDA receptor antagonists and antipsychotics produce broadly similar fundamental effects on HFO, as reported previously for rats, but we did observe several notable differences. In mice, HFO at baseline were weak or not detectable unlike rats. Post-injection of NMDA receptor antagonists HFO was also weaker but significantly faster. Additionally, we found that atypical antipsychotic drugs may reduce the frequency of HFO by interacting with NMDA and/or 5-HT1A receptors.

Lateral and feedforward inhibition suppress asynchronous activity in a large, biophysically-detailed computational model of the striatal network

Striatal medium spiny neurons (MSNs) receive lateral inhibitory projections from other MSNs and feedforward inhibitory projections from fast-spiking, parvalbumin-containing striatal interneurons (FSIs). The functional roles of these connections are unknown, and difficult to study in an experimental preparation. We therefore investigated the functionality of both lateral (MSN-MSN) and feedforward (FSI-MSN) inhibition using a large-scale computational model of the striatal network. The model consists of 2744 MSNs comprised of 189 compartments each and 121 FSIs comprised of 148 compartments each, with dendrites explicitly represented and almost all known ionic currents included and strictly constrained by biological data as appropriate. Our analysis of the model indicates that both lateral inhibition and feedforward inhibition function at the population level to limit non-ensemble MSN spiking while preserving ensemble MSN spiking. Specifically, lateral inhibition enables large ensembles of MSNs firing synchronously to strongly suppress non-ensemble MSNs over a short time-scale (10–30 ms). Feedforward inhibition enables FSIs to strongly inhibit weakly activated, non-ensemble MSNs while moderately inhibiting activated ensemble MSNs. Importantly, FSIs appear to more effectively inhibit MSNs when FSIs fire asynchronously. Both types of inhibition would increase the signal-to-noise ratio of responding MSN ensembles and contribute to the formation and dissolution of MSN ensembles in the striatal network.

Spiny neurons of amygdala, striatum, and cortex use dendritic plateau potentials to detect network UP states

Spiny neurons of amygdala, striatum, and cerebral cortex share four interesting features: (1) they are the most abundant cell type within their respective brain area, (2) covered by thousands of thorny protrusions (dendritic spines), (3) possess high levels of dendritic NMDA conductances, and (4) experience sustained somatic depolarizations in vivo and in vitro (UP states). In all spiny neurons of the forebrain, adequate glutamatergic inputs generate dendritic plateau potentials (“dendritic UP states”) characterized by (i) fast rise, (ii) plateau phase lasting several hundred milliseconds, and (iii) abrupt decline at the end of the plateau phase. The dendritic plateau potential propagates toward the cell body decrementally to induce a long-lasting (longer than 100 ms, most often 200–800 ms) steady depolarization (∼20 mV amplitude), which resembles a neuronal UP state. Based on voltage-sensitive dye imaging, the plateau depolarization in the soma is precisely time-locked to the regenerative plateau potential taking place in the dendrite. The somatic plateau rises after the onset of the dendritic voltage transient and collapses with the breakdown of the dendritic plateau depolarization. We hypothesize that neuronal UP states in vivo reflect the occurrence of dendritic plateau potentials (dendritic UP states). We propose that the somatic voltage waveform during a neuronal UP state is determined by dendritic plateau potentials. A mammalian spiny neuron uses dendritic plateau potentials to detect and transform coherent network activity into a ubiquitous neuronal UP state. The biophysical properties of dendritic plateau potentials allow neurons to quickly attune to the ongoing network activity, as well as secure the stable amplitudes of successive UP states.

Involvement of nucleus accumbens AMPA receptor trafficking in augmentation of D- amphetamine reward in food-restricted rats

RATIONALE

Chronic food restriction (FR) increases behavioral responsiveness to drugs of abuse and associated environments. Pre- and postsynaptic neuroadaptations have been identified in the mesoaccumbens dopamine pathway of FR subjects but the mechanistic basis of increased drug reward magnitude remains unclear.

OBJECTIVES

Effects of FR on basal and D-amphetamine-induced trafficking of AMPA receptor subunits to the nucleus accumbens (NAc) postsynaptic density (PSD) were examined, and AMPA receptor involvement in augmentation of D-amphetamine reward was tested.

MATERIALS AND METHODS

FR and ad libitum fed (AL) rats were injected with D-amphetamine (2.5 mg/kg, i.p.) or vehicle. Brains were harvested and subcellular fractionation and Western analyses were used to assess AMPA receptor abundance in NAc homogenate and PSD fractions. A follow-up experiment used a curve-shift protocol of intracranial self-stimulation to assess the effect of 1-naphthylacetyl spermine (1-NASPM), a blocker of Ca(2+)-permeable AMPA receptors, on rewarding effects of D-amphetamine microinjected in NAc shell.

RESULTS

FR increased GluA1 in the PSD, and D-amphetamine increased p-Ser845-GluA1, GluA1, GluA2, but not GluA3, with a greater effect in FR than AL rats. D-amphetamine lowered reward thresholds, with greater effects in FR than AL rats, and 1-NASPM selectively reversed the enhancing effect of FR.

CONCLUSIONS

Results suggest that FR leads to increased synaptic incorporation of GluA1 homomers to potentiate rewarding effects of appetitive stimuli and, as a maladaptive byproduct, D-amphetamine. The D-amphetamine-induced increase in synaptic p-Ser845-GluA1, GluA1, and GluA2 may contribute to the rewarding effect of D-amphetamine, but may also be a mechanism of synaptic strengthening and behavior modification.

N-Acetylcysteine and metabotropic glutamate receptors: implications for the treatment of schizophrenia: a literature review

The objective of this study is to review the available data regarding metabotropic glutamate receptors in the pathology of Schizophrenia. Further, to investigate the potential utility of N-acetylcysteine as it relates to metabotropic glutamate receptors. A PubMed based literature review was conducted using keywords related to glutamate receptors, Schizophrenia and N-acetylcysteine from June 2012 through August of 2012. Relevant cited references of selected articles were also reviewed. The knowledge base regarding glutamate receptors, both ionotropic and metabotropic is rapidly expanding. New agonists of various subsets of metabotropic glutamate receptors are available and have demonstrated potential utility in animal models. N-acetylcysteine indirectly stimulates presynaptic metabotropic glutamate receptors and has shown efficacy in two double-blind randomized controlled trials. Metabotropic glutamate receptors contribute to an understanding of glutamate dysfunction in Schizophrenia. Agents which lead to stimulation of metabotropic glutamate receptors, inclusive of N-acetylcysteine show promise as novel agents in the treatment of this disorder. An understanding of the various metabotropic glutamate receptors will be a growing necessity as agents which target them continue to emerge and enter clinical trials.

Differential cortical activation of the striatal direct and indirect pathway cells: reconciling the anatomical and optogenetic results by using a computational method

The corticostriatal system is considered to be crucially involved in learning and action selection. Anatomical studies have shown that two types of corticostriatal neurons, intratelencephalic (IT) and pyramidal tract (PT) cells, preferentially project to dopamine D1 or D2 receptor-expressing striatal projection neurons, respectively. In contrast, an optogenetic study has shown that stimulation of IT axons evokes comparable responses in D1 and D2 cells and that stimulation of PT axons evokes larger responses in D1 cells. Since the optogenetic study applied brief stimulation only, however, the overall impacts of repetitive inputs remain unclear. Moreover, the apparent contradiction between the anatomical and optogenetic results remains to be resolved. I addressed these issues by using a computational approach. Specifically, I constructed a model of striatal response to cortical inputs, with parameters regarding short-term synaptic plasticity and anatomical connection strength for each connection type. Under the constraint of the optogenetic results, I then explored the parameters that best explain the previously reported paired-pulse ratio of response in D1 and D2 cells to cortical and intrastriatal stimulations, which presumably recruit different compositions of IT and PT fibers. The results indicate that 1) IT→D1 and PT→D2 connections are anatomically stronger than IT→D2 and PT→D1 connections, respectively, consistent with the previous findings, and that 2) IT→D1 and PT→D2 synapses entail short-term facilitation, whereas IT→D2 and PT→D1 synapses would basically show depression, and thereby 3) repetitive IT or PT inputs have larger overall impacts on D1 or D2 cells, respectively, supporting a recently proposed hypothesis on the roles of corticostriatal circuits in reinforcement learning.

Timing and expectation of reward: a neuro-computational model of the afferents to the ventral tegmental area

Neural activity in dopaminergic areas such as the ventral tegmental area is influenced by timing processes, in particular by the temporal expectation of rewards during Pavlovian conditioning. Receipt of a reward at the expected time allows to compute reward-prediction errors which can drive learning in motor or cognitive structures. Reciprocally, dopamine plays an important role in the timing of external events. Several models of the dopaminergic system exist, but the substrate of temporal learning is rather unclear. In this article, we propose a neuro-computational model of the afferent network to the ventral tegmental area, including the lateral hypothalamus, the pedunculopontine nucleus, the amygdala, the ventromedial prefrontal cortex, the ventral basal ganglia (including the nucleus accumbens and the ventral pallidum), as well as the lateral habenula and the rostromedial tegmental nucleus. Based on a plausible connectivity and realistic learning rules, this neuro-computational model reproduces several experimental observations, such as the progressive cancelation of dopaminergic bursts at reward delivery, the appearance of bursts at the onset of reward-predicting cues or the influence of reward magnitude on activity in the amygdala and ventral tegmental area. While associative learning occurs primarily in the amygdala, learning of the temporal relationship between the cue and the associated reward is implemented as a dopamine-modulated coincidence detection mechanism in the nucleus accumbens.

NMDA receptor antagonist-enhanced high frequency oscillations: are they generated broadly or regionally specific?

Systemic administration of NMDA receptor antagonists, used to model schizophrenia, increase the power of high-frequency oscillations (130-180Hz, HFO) in a variety of neuroanatomical and functionally distinct brain regions. However, it is unclear whether HFO are independently and locally generated or instead spread from a distant source. To address this issue, we used local infusion of tetrodotoxin (TTX) to distinct brain areas to determine how accurately HFO recorded after injection of NMDAR antagonists reflect the activity actually generated at the electrode tip. Changes in power were evaluated in local field potentials (LFPs) recorded from the nucleus accumbens (NAc), prefrontal cortex and caudate and in electrocorticograms (ECoGs) from visual and frontal areas. HFO recorded in frontal and visual cortices (ECoGs) or in the prefrontal cortex, caudate (LFPs) co-varied in power and frequency with observed changes in the NAc. TTX infusion to the NAc immediately and profoundly reduced the power of accumbal HFO which correlated with changes in HFO recorded in distant cortical sites. In contrast, TTX infusion to the prefrontal cortex did not change HFO power recorded locally, although gamma power was reduced. A very similar result was found after TTX infusion to the caudate. These findings raise the possibility that the NAc is an important neural generator. Our data also support existing studies challenging the idea that high frequencies recorded in LFPs are necessarily generated at the recording site.