- An exciting in vivo function of ATP-sensitive potassium channels in substantia nigra dopamine neurons : implications for burst firing and novelty coding (2012)
- An exciting in vivo function of ATP-sensitive potassium channels in substantia nigra dopamine neurons Ð Implications for burst firing and novelty coding ÐPhasic burst activity is a key feature of dopamine (DA) midbrain neurons. This particular pattern of excitation of DA neurons occurs via a synaptically triggered transition from low-frequency background spiking to transient high-frequency discharges. Burst-firing mediated phasic DA release is critical for flexible switching of behavioural strategies in response to unexpected rewards, novelty and other salient stimuli. However, the cellular and molecular bases of burst signalling in distinct DA subpopulations of the substantia nigra (SN) or the ventral tegmental area (VTA) are unknown. DA neuron excitability is controlled by synaptic network inputs, neurotransmitter receptors and ion channels, which generate action potentials and determine frequency and pattern of electrical activity in a complex interplay. ATP-sensitive potassium (K-ATP) channels are widely expressed throughout the brain, where in most cases they are believed to act as metabolically-controlled 'excitation brakes' by matching excitability to cellular energy states. However, their precise physiological in vivo function in DA neurons remains elusive. To study burst firing and the underlying ionic mechanisms with single cell resolution, in vivo single-unit recordings were combined with juxtacellular neurobiotin labelling as well as immunohistochemical and anatomical identification of individual DA neurons. In vivo recordings were performed in adult isoflurane-anaesthetised wildtype (WT) and global K-ATP channel knockout mice, lacking the pore forming Kir6.2 subunit (Kir6.2-/-). In addition, DA cell-selective functional silencing of K-ATP channel activity in vivo was established using virus-mediated expression of dominant-negative Kir6.2 subunits. Careful control experiments ruled out any significant contributions from nonDA neurons as transduction was effectively limited to SN DA neurons rather than affecting those cells that innervate them. Virus-based K-ATP channel silencing in combination with juxtacellular recording and labelling was achieved to define the electrophysiological phenotype of individually identified, virally-transduced DA neurons in vivo. Single-unit recordings revealed that K-ATP channels Ð in contrast to their conventional hyperpolarising role Ð in a subpopulation of DA neurons located in the medial SN (m-SN) act as cell-type selective gates for excitatory burst firing in vivo. The percentage of spikes in bursts was threefold reduced in Kir6.2-/- compared to WT mice. Classification of firing patterns based on visual inspection of autocorrelation histograms and on a newly developed spike-train-model confirmed the dramatic shift from phasic burst to tonic single-spike oscillatory firing in Kir6.2-/-. This significant decrease of burstiness was selective for m-SN DA neurons and was not exhibited by DA cells in the lateral SN or VTA. Virus-based K-ATP channel silencing in vivo unequivocally demonstrated that the activity of postsynaptic K-ATP channels was sufficient to disrupt bursting in m-SN DA neuron subtypes. Patch-clamp recordings in brain slices indicated an essential role of K-ATP channels for NMDA-mediated in vitro bursting. In accordance with previous studies in DA midbrain neurons, NMDA receptor stimulation triggered burst-like firing in m-SN DA cells in vitro, but only when K-ATP channels were co-activated in these neurons. K-ATP channel-gated burst firing in m-SN DA neurons might be functionally relevant in awake, freely moving mice. To explore the behavioural consequences of SN DA neuron subtype-selective K-ATP channel suppression, spontaneous open field (OF) behaviour of mice with bilateral K-ATP silencing across the whole SN (medial + lateral) or in only the lateral SN was tested. Analysis of WT and global Kir6.2-/- mice showed reduced exploratory locomotor activity of Kir6.2-/- in a novel OF environment. Remarkably, K-ATP channel silencing in m-SN DA neurons phenocopied this novelty-exploration deficit, indicating that K-ATP channel-gated burst firing in medial but not lateral SN DA neurons is crucial for WT-like novelty-dependent exploratory behaviour. In summary, a novel role of K-ATP channels in promoting the excitatory switch from tonic to phasic firing in vivo in a cell-type specific manner was discovered. The present PhD thesis provides several important insights into the pivotal function of K-ATP channels in medial SN DA cells, which project to the dorsomedial striatum, for burst firing and its important consequences for context-dependent exploratory behaviour. In collaboration with two other research groups transcriptional up-regulation of K-ATP channel and NMDA receptor subunits and high levels of in vivo burst firing were detected in surviving SN DA neurons from Parkinson's disease (PD) patients Ð providing a potential link of K-ATP channel activity to neurodegenerative pathomechanisms of PD. Using high-resolution fMRI imaging another study in humans has recently identified distinct DA midbrain regions that are preferentially activated by either reward or novelty. Taken together, these human data and the results of the present PhD thesis suggest that burst-gating K-ATP channel function in SN DA neurons impacts on phenotypes in disease as well as in health.
- Detection and localization of multiple rate changes in Poisson spike trains : poster presentation from Twentieth Annual Computational Neuroscience Meeting CNS*2011 Stockholm, Sweden, 23 - 28 July 2011 (2011)
- 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. ). However, such models can lead to misinterpretation of the associated tests if the assumption of rate stationarity is not met (e.g. ). 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. ). 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 , our method has advantages in the flexibility of h.
- A simple Hidden Markov Model for midbrain dopaminergic neurons (2009)
- 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. ...
- A model for the joint evaluation of burstiness and regularity in oscillatory spike trains (2009)
- 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.