Regulation of neuromodulator receptor efficacy—implications for whole-neuron and synaptic plasticity

Methylation pattern and mRNA expression of synapse-relevant genes in the MAM model of schizophrenia in the time-course of adolescence

Schizophrenia is highly heritable and aggregating in families, but genetics alone does not exclusively explain the pathogenesis. Many risk factors, including childhood trauma, viral infections, migration, and the use of cannabis, are associated with schizophrenia. Adolescence seems to be the critical period where symptoms of the disease manifest. This work focuses on studying an epigenetic regulatory mechanism (the role of DNA methylation) and its interaction with mRNA expression during development, with a particular emphasis on adolescence. The presumptions regarding the role of aberrant neurodevelopment in schizophrenia were tested in the Methyl-Azoxy-Methanol (MAM) animal model. MAM treatment induces neurodevelopmental disruptions and behavioral deficits in off-springs of the treated animals reminiscent of those observed in schizophrenia and is thus considered a promising model for studying this pathology. On a gestational day-17, adult pregnant rats were treated with the antimitotic agent MAM. Experimental animals were divided into groups and subgroups according to substance treatment (MAM and vehicle agent [Sham]) and age of analysis (pre-adolescent and post-adolescent). Methylation and mRNA expression analysis of four candidate genes, which are often implicated in schizophrenia, with special emphasis on the Dopamine hypothesis i.e., Dopamine receptor D₂ (Drd2), and the "co-factors" Disrupted in schizophrenia 1 (DISC1), Synaptophysin (Syp), and Dystrobrevin-binding protein 1 (Dtnbp1), was performed in the Gyrus cingulum (CING) and prefrontal cortex (PFC). Data were analyzed to observe the effect of substance treatment between groups and the impact of adolescence within-group. We found reduced pre-adolescent expression levels of Drd2 in both brain areas under the application of MAM. The "co-factor genes" did not show high deviations in mRNA expression levels but high alterations of methylation rates under the application of MAM (up to ~20%), which diminished in the further time course, reaching a comparable level like in Sham control animals after adolescence. The pre-adolescent reduction in DRD2 expression might be interpreted as downregulation of the receptor due to hyperdopaminergic signaling from the ventral tegmental area (VTA), eventually even to both investigated brain regions. The notable alterations of methylation rates in the three analyzed co-factor genes might be interpreted as attempt to compensate for the altered dopaminergic neurotransmission.

Endogenous Cholinergic Signaling Modulates Sound-Evoked Responses of the Medial Nucleus of the Trapezoid Body

The medial nucleus of trapezoid body (MNTB) is a major source of inhibition in auditory brainstem circuitry. The MNTB projects well-timed inhibitory output to principal sound-localization nuclei in the superior olive (SOC) as well as other computationally important centers. Acoustic information is conveyed to MNTB neurons through a single calyx of Held excitatory synapse arising from the cochlear nucleus. The encoding efficacy of this large synapse depends on its activity rate, which is primarily determined by sound intensity and stimulus frequency. However, MNTB activity rate is additionally influenced by inhibition and possibly neuromodulatory inputs, albeit their functional role is unclear. Happe and Morley (2004) discovered prominent expression of α7 nAChRs in rat SOC, suggesting possible engagement of ACh-mediated modulation of neural activity in the MNTB. However, the existence and nature of this putative modulation have never been physiologically demonstrated. We probed nicotinic cholinergic influences on acoustic responses of MNTB neurons from adult gerbils (Meriones unguiculatus) of either sex. We recorded tone-evoked MNTB single-neuron activity in vivo using extracellular single-unit recording. Piggyback multibarrel electrodes enabled pharmacological manipulation of nAChRs by reversibly applying antagonists to two receptor types, α7 and α4β2. We observed that tone-evoked responses are dependent on ACh modulation by both nAChR subtypes. Spontaneous activity was not affected by antagonist application. Functionally, we demonstrate that ACh contributes to sustaining high discharge rates and enhances signal encoding efficacy. Additionally, we report anatomic evidence revealing novel cholinergic projections to MNTB arising from pontine and superior olivary nuclei.SIGNIFICANCE STATEMENT This study is the first to physiologically probe how acetylcholine, a pervasive neuromodulator in the brain, influences the encoding of acoustic information by the medial nucleus of trapezoid body, the most prominent source of inhibition in brainstem sound-localization circuitry. We demonstrate that this cholinergic input enhances neural discrimination of tones from noise stimuli, which may contribute to processing important acoustic signals, such as speech. Additionally, we describe novel anatomic projections providing cholinergic input to the MNTB. Together, these findings shed new light on the contribution of neuromodulation to fundamental computational processes in auditory brainstem circuitry and to a more holistic understanding of modulatory influences in sensory processing.

Spatial dynamics of the insulin receptor in living neurons

Insulin signaling through the insulin receptor has long been studied in classic target organs, such as adipose tissue and skeletal muscle, where one of its effects is to increase glucose uptake. Insulin and insulin receptor are present in many areas of the brain, but the functions of brain insulin signaling outside feeding circuits are not well defined. It has been proposed that hippocampal insulin signaling is important for memory, that brain insulin signaling is deficient in Alzheimer's disease, and that intranasal insulin treatment improves cognition, but the mechanisms remain unclear and do not seem to involve increased glucose uptake by neurons. The molecular behavior of the insulin receptor itself is not well known in living neurons; therefore, we investigated the spatial dynamics of the insulin receptor on somatodendritic membranes of live rat hippocampal neurons in culture. Using single-molecule tracking of quantum dot-tagged insulin receptors and single-particle tracking photoactivation localization microscopy, we show that the insulin receptor is distributed over both dendritic shafts and spines. Using colocalization with synaptic markers, we also show that in contrast to the glutamate receptor subunit glutamate receptor subunit A1, the dynamics of the insulin receptor are not affected by association with excitatory synapses; however, the insulin receptor is immobilized by components of inhibitory synapses. The mobility of the insulin receptor is reduced both by low concentrations of the pro-inflammatory cytokine tumor necrosis factor α and by cholesterol depletion, suggesting an association with sphingolipid-rich membrane domains. On the other hand, the insulin receptor dynamics in hippocampal neurons are not affected by increased excitatory signaling. Finally, using real-time single-event quantification, we find evidence of strong insulin receptor exocytosis on dendritic shafts. Our results suggest an association of the neuronal insulin receptor with specific elements of the dendritic shaft, rather than excitatory synapses.

Neuromodulation influences synchronization and intrinsic read-out

Background: The roles of neuromodulation in a neural network, such as in a cortical microcolumn, are still incompletely understood. Neuromodulation influences neural processing by presynaptic and postsynaptic regulation of synaptic efficacy. Neuromodulation also affects ion channels and intrinsic excitability.

Methods: Synaptic efficacy modulation is an effective way to rapidly alter network density and topology. We alter network topology and density to measure the effect on spike synchronization. We also operate with differently parameterized neuron models which alter the neuron's intrinsic excitability, i.e., activation function.

Results: We find that (a) fast synaptic efficacy modulation influences the amount of correlated spiking in a network. Also, (b) synchronization in a network influences the read-out of intrinsic properties. Highly synchronous input drives neurons, such that differences in intrinsic properties disappear, while asynchronous input lets intrinsic properties determine output behavior. Thus, altering network topology can alter the balance between intrinsically vs. synaptically driven network activity.

Conclusion: We conclude that neuromodulation may allow a network to shift between a more synchronized transmission mode and a more asynchronous intrinsic read-out mode. This has significant implications for our understanding of the flexibility of cortical computations.

Neuromodulation influences synchronization and intrinsic read-out

Background: The roles of neuromodulation in a neural network, such as in a cortical microcolumn, are still incompletely understood. Neuromodulation influences neural processing by presynaptic and postsynaptic regulation of synaptic efficacy. Neuromodulation also affects ion channels and intrinsic excitability. Methods: Synaptic efficacy modulation is an effective way to rapidly alter network density and topology. We alter network topology and density to measure the effect on spike synchronization. We also operate with differently parameterized neuron models which alter the neuron's intrinsic excitability, i.e., activation function. Results: We find that (a) fast synaptic efficacy modulation influences the amount of correlated spiking in a network. Also, (b) synchronization in a network influences the read-out of intrinsic properties. Highly synchronous input drives neurons, such that differences in intrinsic properties disappear, while asynchronous input lets intrinsic properties determine output behavior. Thus, altering network topology can alter the balance between intrinsically vs. synaptically driven network activity. Conclusion: We conclude that neuromodulation may allow a network to shift between a more synchronized transmission mode and a more asynchronous intrinsic read-out mode. This has significant implications for our understanding of the flexibility of cortical computations.

Logarithmic distributions prove that intrinsic learning is Hebbian

In this paper, we present data for the lognormal distributions of spike rates, synaptic weights and intrinsic excitability (gain) for neurons in various brain areas, such as auditory or visual cortex, hippocampus, cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of heavy-tailed, specifically lognormal, distributions for rates, weights and gains in all brain areas examined. The difference between strongly recurrent and feed-forward connectivity (cortex vs. striatum and cerebellum), neurotransmitter (GABA (striatum) or glutamate (cortex)) or the level of activation (low in cortex, high in Purkinje cells and midbrain nuclei) turns out to be irrelevant for this feature. Logarithmic scale distribution of weights and gains appears to be a general, functional property in all cases analyzed. We then created a generic neural model to investigate adaptive learning rules that create and maintain lognormal distributions. We conclusively demonstrate that not only weights, but also intrinsic gains, need to have strong Hebbian learning in order to produce and maintain the experimentally attested distributions. This provides a solution to the long-standing question about the type of plasticity exhibited by intrinsic excitability.

Logarithmic distributions prove that intrinsic learning is Hebbian

In this paper, we document lognormal distributions for spike rates, synaptic weights and intrinsic excitability (gain) for neurons in various brain areas, such as auditory or visual cortex, hippocampus, cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of heavy-tailed, specifically lognormal, distributions for rates, weights and gains in all brain areas. The difference between strongly recurrent and feed-forward connectivity (cortex vs. striatum and cerebellum), neurotransmitter (GABA (striatum) or glutamate (cortex)) or the level of activation (low in cortex, high in Purkinje cells and midbrain nuclei) turns out to be irrelevant for this feature. Logarithmic scale distribution of weights and gains appears as a functional property that is present everywhere.  Secondly, we created a generic neural model to show that Hebbian learning will create and maintain lognormal distributions. We could prove with the model that not only weights, but also intrinsic gains, need to have strong Hebbian learning in order to produce and maintain the experimentally attested distributions. This settles a long-standing question about the type of plasticity exhibited by intrinsic excitability.

Comodulation of dopamine and serotonin on prefrontal cortical rhythms: a theoretical study

The prefrontal cortex (PFC) is implicated to play an important role in cognitive control. Abnormal PFC activities and rhythms have been observed in some neurological and neuropsychiatric disorders, and evidences suggest influences from the neuromodulators dopamine (DA) and serotonin (5-HT). Despite the high level of interest in these brain systems, the combined effects of DA and 5-HT modulation on PFC dynamics remain unknown. In this work, we build a mathematical model that incorporates available experimental findings to systematically study the comodulation of DA and 5-HT on the network behavior, focusing on beta and gamma band oscillations. Single neuronal model shows pyramidal cells with 5-HT1A and 2A receptors can be non-monotonically modulated by 5-HT. Two-population excitatory-inhibitory type network consisting of pyramidal cells with D1 receptors can provide rich repertoires of oscillatory behavior. In particular, 5-HT and DA can modulate the amplitude and frequency of the oscillations, which can emerge or cease, depending on receptor types. Certain receptor combinations are conducive for the robustness of the oscillatory regime, or the existence of multiple discrete oscillatory regimes. In a multi-population heterogeneous model that takes into account possible combination of receptors, we demonstrate that robust network oscillations require high DA concentration. We also show that selective D1 receptor antagonists (agonists) tend to suppress (enhance) network oscillations, increase the frequency from beta toward gamma band, while selective 5-HT1A antagonists (agonists) act in opposite ways. Selective D2 or 5-HT2A receptor antagonists (agonists) can lead to decrease (increase) in oscillation amplitude, but only 5-HT2A antagonists (agonists) can increase (decrease) the frequency. These results are comparable to some pharmacological effects. Our work illustrates the complex mechanisms of DA and 5-HT when operating simultaneously through multiple receptors.

Learning intrinsic excitability in medium spiny neurons

We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP) which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parameterization of individual ion channels on the neuronal membrane potential-curent relationship (activation function). We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. We emphasize that the effects of intrinsic neuronal modulation on spiking behavior require a distributed mode of synaptic input and can be eliminated by strongly correlated input. We show how modulation and adaptivity in ion channel conductances can be utilized to store patterns without an additional contribution by synaptic plasticity (SP). The adaptation of the spike response may result in either "positive" or "negative" pattern learning. However, read-out of stored information depends on a distributed pattern of synaptic activity to let intrinsic modulation determine spike response. We briefly discuss the implications of this conditional memory on learning and addiction.

The mellow years?: neural basis of improving emotional stability over age

Contrary to the pervasive negative stereotypes of human aging, emotional functions may improve with advancing age. However, the brain mechanisms underlying changes in emotional function over age remain unknown. Here, we demonstrate that emotional stability improves linearly over seven decades (12-79 years) of the human lifespan. We used both functional magnetic resonance imaging and event-related potential recording to examine the neural basis of this improvement. With these multimodal techniques, we show that better stability is predicted by a shift toward greater medial prefrontal control over negative emotional input associated with increased activity later in the processing sequence (beyond 200 ms after stimulus) and less control over positive input, related to a decrease in early activity (within 150 ms). This shift was independent from gray matter loss, indexed by structural magnetic resonance data. We propose an integrative model in which accumulated life experience and the motivation for meaning over acquisition in older age contribute to plasticity of medial prefrontal systems, achieving a greater selective control over emotional functions.