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Poster presentation: Introduction The ability of neurons to emit different firing patterns is considered relevant for neuronal information processing. In dopaminergic neurons, prominent patterns include highly regular pacemakers with separate spikes and stereotyped intervals, processes with repetitive bursts and partial regularity, and irregular spike trains with nonstationary properties. In order to model and quantify these processes and the variability of their patterns with respect to pharmacological and cellular properties, we aim to describe the two dimensions of burstiness and regularity in a single model framework. Methods We present a stochastic spike train model in which the degree of burstiness and the regularity of the oscillation are described independently and with two simple parameters. In this model, a background oscillation with independent and normally distributed intervals gives rise to Poissonian spike packets with a Gaussian firing intensity. The variability of inter-burst intervals and the average number of spikes in each burst indicate regularity and burstiness, respectively. These parameters can be estimated by fitting the model to the autocorrelograms. This allows to assign every spike train a position in the two-dimensional space described by regularity and burstiness and thus, to investigate the dependence of the firing patterns on different experimental conditions. Finally, burst detection in single spike trains is possible within the model because the parameter estimates determine the appropriate bandwidth that should be used for burst identification. Results and Discussion We applied the model to a sample data set obtained from dopaminergic substantia nigra and ventral tegmental area neurons recorded extracellularly in vivo and studied differences between the firing activity of dopaminergic neurons in wildtype and K-ATP channel knock-out mice. The model is able to represent a variety of discharge patterns and to describe changes induced pharmacologically. It provides a simple and objective classification scheme for the observed spike trains into pacemaker, irregular and bursty processes. In addition to the simple classification, changes in the parameters can be studied quantitatively, also including the properties related to bursting behavior. Interestingly, the proposed algorithm for burst detection may be applicable also to spike trains with nonstationary firing rates if the remaining parameters are unaffected. Thus, the proposed model and its burst detection algorithm can be useful for the description and investigation of neuronal firing patterns and their variability with cellular and experimental conditions.
Poster presentation: Introduction Dopaminergic neurons in the midbrain show a variety of firing patterns, ranging from very regular firing pacemaker cells to bursty and irregular neurons. The effects of different experimental conditions (like pharmacological treatment or genetical manipulations) on these neuronal discharge patterns may be subtle. Applying a stochastic model is a quantitative approach to reveal these changes. ...
The locus coeruleus (LC) contains the majority of central noradrenergic neurons sending wide projections throughout the entire CNS. The LC is considered to be essential for multiple key brain functions including arousal, attention and adaptive stress responses as well as higher cognitive functions and memory. Electrophysiological studies of LC neurons have identified several characteristic functional features such as low-frequency pacemaker activity with broad action potentials, transient high-frequency burst discharges in response to salient stimuli and an apparently homogeneous inhibition of firing by activation of somatodendritic α2 autoreceptors (α2AR). While stress-mediated plasticity of the α2AR response has been described, it is currently unclear whether different LC neurons projecting to distinct axonal targets display differences in α2AR function. Using fluorescent beads-mediated retrograde tracing in adult C57Bl6/N mice, we compared the anatomical distributions and functional in vitro properties of identified LC neurons projecting either to medial prefrontal cortex, hippocampus or cerebellum. The functional in vitro analysis of LC neurons confirmed their mostly uniform functional properties regarding action potential generation and pacemaker firing. However, we identified significant differences in tonic and evoked α2AR-mediated responses. While hippocampal-projecting LC neurons were partially inhibited by endogenous levels of norepinephrine and almost completely silenced by application of saturating concentrations of the α2 agonist clonidine, prefrontal-projecting LC neurons were not affected by endogenous levels of norepinephrine and only partially inhibited by saturating concentrations of clonidine. Thus, we identified a limited α2AR control of electrical activity for prefrontal-projecting LC neurons indicative of functional heterogeneity in the LC-noradrenergic system.
The low-threshold L-type calcium channel Cav1.3 accelerates the pacemaker rate in the heart, but its functional role for the extended dynamic range of neuronal firing is still unresolved. Here, we show that Cav1.3 calcium channels act as unexpectedly simple, full-range linear amplifiers of firing rates for lateral dopamine substantia nigra (DA SN) neurons in mice. This means that they boost in vitro or in vivo firing frequencies between 2 and 50 hertz by about 30%. Furthermore, we demonstrate that clinically relevant, low nanomolar concentrations of the L-type channel inhibitor isradipine selectively reduce the in vivo firing activity of these nigrostriatal DA SN neurons at therapeutic plasma concentrations. Thus, our study identifies the pacemaker function of neuronal Cav1.3 channels and provides direct evidence that repurposing dihydropyridines such as isradipine is feasible to selectively modulate the in vivo activity of highly vulnerable DA SN subpopulations in Parkinson's disease.
Recent progress in the synaptic pathophysiology of brain diseases is reviewed. To emphasize the emergence of common motifs in synapse dysfunctions across neurodevelopmental, psychiatric and neurological disorders, conventional clinical boundaries are disregarded and a decidedly trans-diagnostic, potentially unifying view of altered synapse function is promoted. Based on the overlapping genetic architecture of brain disorders, which often converges on genes related to synaptic functions, disease-related changes in basic pre-synaptic and post-synaptic communication, neuromodulation-gated changes in Hebbian plasticity, dynamic interactions between Hebbian and homeostatic plasticity, and changes in synaptic maintenance by autophagy and glial-mediated phagocytosis are highlighted.
Abstract
Inhibition of midbrain dopamine neurons is thought to underlie the signaling of events that are less rewarding than expected and drive learning based on these negative prediction errors. It has recently been shown that Kv4.3 channels influence the integration of inhibitory inputs in specific subpopulations of dopamine neurons. The functional properties of Kv4.3 channels are themselves strongly determined by the binding of auxiliary β-subunits; among them KChIP4a stands-out for its unique combination of modulatory effects. These include decreasing surface membrane trafficking and slowing inactivation kinetics. Therefore, we hypothesized that KChIP4a expression in dopamine neurons could play a crucial role in behavior, in particular by affecting the computation of negative prediction errors. We developed a mouse line where the alternative exon that codes for the KChIP4a splice variant was selectively deleted in midbrain dopamine neurons. In a reward-based reinforcement learning task, we observed that dopamine neuron-specific KChIP4a deletion selectively accelerated the rate of extinction learning, without impacting the acquisition of conditioned responses. We further found that this effect was due to a faster decrease in the initiation rate of goal-directed behaviors, and not faster increases in action disengagement. Furthermore, computational fitting of the behavioral data with a Rescorla-Wagner model confirmed that the observed phenotype was attributable to a selective increase in the learning rate from negative prediction errors. Finally, KChIP4a deletion did not affect performance in other dopamine-sensitive behavioral tasks that did not involve learning from disappointing events, including an absence of effects on working memory, locomotion and novelty preference. Taken together, our results demonstrate that an exon- and midbrain dopamine neuron-specific deletion of an A-type K+ channel β-subunit leads to a selective gain of function in extinction learning.
One Sentence Summary
Exon- and midbrain dopamine neuron-specific deletion of the Kv4 channel β-subunit KChIP4a selectively accelerates extinction learning
Poster presentation from Twentieth Annual Computational Neuroscience Meeting: CNS*2011 Stockholm, Sweden. 23-28 July 2011. In statistical spike train analysis, stochastic point process models usually assume stationarity, in particular that the underlying spike train shows a constant firing rate (e.g. [1]). However, such models can lead to misinterpretation of the associated tests if the assumption of rate stationarity is not met (e.g. [2]). Therefore, the analysis of nonstationary data requires that rate changes can be located as precisely as possible. However, present statistical methods focus on rejecting the null hypothesis of stationarity without explicitly locating the change point(s) (e.g. [3]). We propose a test for stationarity of a given spike train that can also be used to estimate the change points in the firing rate. Assuming a Poisson process with piecewise constant firing rate, we propose a Step-Filter-Test (SFT) which can work simultaneously in different time scales, accounting for the high variety of firing patterns in experimental spike trains. Formally, we compare the numbers N1=N1(t,h) and N2=N2(t,h) of spikes in the time intervals (t-h,t] and (h,t+h]. By varying t within a fine time lattice and simultaneously varying the interval length h, we obtain a multivariate statistic D(h,t):=(N1-N2)/V(N1+N2), for which we prove asymptotic multivariate normality under homogeneity. From this a practical, graphical device to spot changes of the firing rate is constructed. Our graphical representation of D(h,t) (Figure 1A) visualizes the changes in the firing rate. For the statistical test, a threshold K is chosen such that under homogeneity, |D(h,t)|<K holds for all investigated h and t with probability 0.95. This threshold can indicate potential change points in order to estimate the inhomogeneous rate profile (Figure 1B). The SFT is applied to a sample data set of spontaneous single unit activity recorded from the substantia nigra of anesthetized mice. In this data set, multiple rate changes are identified which agree closely with visual inspection. In contrast to approaches choosing one fixed kernel width [4], our method has advantages in the flexibility of h.
Denervation-induced changes in excitatory synaptic strength were studied following entorhinal deafferentation of hippocampal granule cells in mature (≥3 weeks old) mouse organotypic entorhino-hippocampal slice cultures. Whole-cell patch-clamp recordings revealed an increase in excitatory synaptic strength in response to denervation during the first week after denervation. By the end of the second week synaptic strength had returned to baseline. Because these adaptations occurred in response to the loss of excitatory afferents, they appeared to be in line with a homeostatic adjustment of excitatory synaptic strength. To test whether denervation-induced changes in synaptic strength exploit similar mechanisms as homeostatic synaptic scaling following pharmacological activity blockade, we treated denervated cultures at 2 days post lesion for 2 days with tetrodotoxin. In these cultures, the effects of denervation and activity blockade were not additive, suggesting that similar mechanisms are involved. Finally, we investigated whether entorhinal denervation, which removes afferents from the distal dendrites of granule cells while leaving the associational afferents to the proximal dendrites of granule cells intact, results in a global or a local up-scaling of granule cell synapses. By using computational modeling and local electrical stimulations in Strontium (Sr2+)-containing bath solution, we found evidence for a lamina-specific increase in excitatory synaptic strength in the denervated outer molecular layer at 3–4 days post lesion. Taken together, our data show that entorhinal denervation results in homeostatic functional changes of excitatory postsynapses of denervated dentate granule cells in vitro.
Over the past two decades, our understanding of Parkinson's disease (PD) has been gleaned from the discoveries made in familial and/or sporadic forms of PD in the Caucasian population. The transferability and the clinical utility of genetic discoveries to other ethnically diverse populations are unknown. The Indian population has been under-represented in PD research. The Genetic Architecture of PD in India (GAP-India) project aims to develop one of the largest clinical/genomic bio-bank for PD in India. Specifically, GAP-India project aims to: (1) develop a pan-Indian deeply phenotyped clinical repository of Indian PD patients; (2) perform whole-genome sequencing in 500 PD samples to catalog Indian genetic variability and to develop an Indian PD map for the scientific community; (3) perform a genome-wide association study to identify novel loci for PD and (4) develop a user-friendly web-portal to disseminate results for the scientific community. Our “hub-spoke” model follows an integrative approach to develop a pan-Indian outreach to develop a comprehensive cohort for PD research in India. The alignment of standard operating procedures for recruiting patients and collecting biospecimens with international standards ensures harmonization of data/bio-specimen collection at the beginning and also ensures stringent quality control parameters for sample processing. Data sharing and protection policies follow the guidelines established by local and national authorities.We are currently in the recruitment phase targeting recruitment of 10,200 PD patients and 10,200 healthy volunteers by the end of 2020. GAP-India project after its completion will fill a critical gap that exists in PD research and will contribute a comprehensive genetic catalog of the Indian PD population to identify novel targets for PD.