Selective filtering of excitatory inputs to nucleus accumbens by dopamine and serotonin

The detailed mechanisms by which dopamine (DA) and serotonin (5-HT) act in the nucleus accumbens (NAc) to influence motivated behaviors in distinct ways remain largely unknown. Here, we examined whether DA and 5-HT selectively modulate excitatory synaptic transmission in NAc medium spiny neurons in an input-specific manner. DA reduced excitatory postsynaptic currents (EPSCs) generated by paraventricular thalamus (PVT) inputs but not by ventral hippocampus (vHip), basolateral amygdala (BLA), or medial prefrontal cortex (mPFC) inputs. In contrast, 5-HT reduced EPSCs generated by inputs from all areas except the mPFC. Release of endogenous DA and 5-HT by methamphetamine (METH) and (±)3,4-methylenedioxymethamphetamine (MDMA), respectively, recapitulated these input-specific synaptic effects. Optogenetic inhibition of PVT inputs enhanced cocaine-conditioned place preference, whereas mPFC input inhibition reduced the enhancement of sociability elicited by MDMA. These findings suggest that the distinct, input-specific filtering of excitatory inputs in the NAc by DA and 5-HT contribute to their discrete behavioral effects.

Synaptic Effect of Aδ-Fibers by Pulse-Train Electrical Stimulation

Electrical stimulation of specific small fibers (Aδ- and C-fibers) is used in basic studies on nociception and neuropathic pain and to diagnose neuropathies. For selective stimulation of small fibers, the optimal stimulation waveform parameters are an important aspect together with the study of electrode design. However, determining an optimal stimulation condition is challenging, as it requires the characterization of the response of the small fibers to electrical stimulation. The perception thresholds are generally characterized using single-pulse stimulation based on the strength-duration curve. However, this does not account for the temporal effects of the different waveforms used in practical applications. In this study, we designed an experiment to characterize the effects of multiple pulse stimulation and proposed a computational model that considers electrostimulation of fibers and synaptic effects in a multiscale model. The measurements of perception thresholds showed that the pulse dependency of the threshold was an exponential decay with a maximum reduction of 55%. In addition, the frequency dependence of the threshold showed a U-shaped response with a reduction of 25% at 30 Hz. Moreover, the computational model explained the synaptic effects, which were also confirmed by evoked potential recordings. This study further characterized the activation of small fibers and clarified the synaptic effects, demonstrating the importance of waveform selection.

Model-based detection of putative synaptic connections from spike recordings with latency and type constraints

Detecting synaptic connections using large-scale extracellular spike recordings is a difficult statistical problem. Here, we develop an extension of a generalized linear model that explicitly separates fast synaptic effects and slow background fluctuations in cross-correlograms between pairs of neurons while incorporating circuit properties learned from the whole network. This model outperforms two previously developed synapse detection methods in the simulated networks and recovers plausible connections from hundreds of neurons in in vitro multielectrode array data.

Valproic Acid and Epilepsy: From Molecular Mechanisms to Clinical Evidences

After more than a century from its discovery, valproic acid (VPA) still represents one of the most efficient antiepileptic drugs (AEDs). Pre and post-synaptic effects of VPA depend on a very broad spectrum of actions, including the regulation of ionic currents and the facilitation of GABAergic over glutamatergic transmission. As a result, VPA indirectly modulates neurotransmitter release and strengthens the threshold for seizure activity. However, even though participating to the anticonvulsant action, such mechanisms seem to have minor impact on epileptogenesis. Nonetheless, VPA has been reported to exert anti-epileptogenic effects. Epigenetic mechanisms, including histone deacetylases (HDACs), BDNF and GDNF modulation are pivotal to orientate neurons toward a neuroprotective status and promote dendritic spines organization. From such broad spectrum of actions comes constantly enlarging indications for VPA. It represents a drug of choice in child and adult with epilepsy, with either general or focal seizures, and is a consistent and safe IV option in generalized convulsive status epilepticus. Moreover, since VPA modulates DNA transcription through HDACs, recent evidences point to its use as an anti-nociceptive in migraine prophylaxis, and, even more interestingly, as a positive modulator of chemotherapy in cancer treatment. Furthermore, VPA-induced neuroprotection is under investigation for benefit in stroke and traumatic brain injury. Hence, VPA has still got its place in epilepsy, and yet deserves attention for its use far beyond neurological diseases. In this review, we aim to highlight, with a translational intent, the molecular basis and the clinical indications of VPA.

Modeling the short-term dynamics of in vivo excitatory spike transmission

Information transmission in neural networks is influenced by both short-term synaptic plasticity (STP) as well as non-synaptic factors, such as after-hyperpolarization currents and changes in excitability. Although these effects have been widely characterized in vitro using intracellular recordings, how they interact in vivo is unclear. Here we develop a statistical model of the short-term dynamics of spike transmission that aims to disentangle the contributions of synaptic and non-synaptic effects based only on observed pre- and postsynaptic spiking. The model includes a dynamic functional connection with short-term plasticity as well as effects due to the recent history of postsynaptic spiking and slow changes in postsynaptic excitability. Using paired spike recordings, we find that the model accurately describes the short-term dynamics of in vivo spike transmission at a diverse set of identified and putative excitatory synapses, including a thalamothalamic connection in mouse, a thalamocortical connection in a female rabbit, and an auditory brainstem synapse in a female gerbil. We illustrate the utility of this modeling approach by showing how the spike transmission patterns captured by the model may be sufficient to account for stimulus-dependent differences in spike transmission in the auditory brainstem (endbulb of Held). Finally, we apply this model to large-scale multi-electrode recordings to illustrate how such an approach has the potential to reveal cell-type specific differences in spike transmission in vivo. Although short-term synaptic plasticity parameters estimated from ongoing pre- and postsynaptic spiking are highly uncertain, our results are partially consistent with previous intracellular observations in these synapses.Significance StatementAlthough synaptic dynamics have been extensively studied and modeled using intracellular recordings of post-synaptic currents and potentials, inferring synaptic effects from extracellular spiking is challenging. Whether or not a synaptic current contributes to postsynaptic spiking depends not only on the amplitude of the current, but also on many other factors, including the activity of other, typically unobserved, synapses, the overall excitability of the postsynaptic neuron, and how recently the postsynaptic neuron has spiked. Here we developed a model that, using only observations of pre- and postsynaptic spiking, aims to describe the dynamics of in vivo spike transmission by modeling both short-term synaptic plasticity and non-synaptic effects. This approach may provide a novel description of fast, structured changes in spike transmission.