Gaby Schneider

K-ATP channels in dopamine substantia nigra neurons control bursting and novelty-induced exploration

Phasic activation of the dopamine (DA) midbrain system in response to unexpected reward or novelty is critical for adaptive behavioral strategies. This activation of DA midbrain neurons occurs via a synaptically triggered switch from low-frequency background spiking to transient high-frequency burst firing. We found that, in medial DA neurons of the substantia nigra (SN), activity of ATP-sensitive potassium (K-ATP) channels enabled NMDA-mediated bursting in vitro as well as spontaneous in vivo burst firing in anesthetized mice. Cell-selective silencing of K-ATP channel activity in medial SN DA neurons revealed that their K-ATP channel–gated burst firing was crucial for novelty-dependent exploratory behavior. We also detected a transcriptional upregulation of K-ATP channel and NMDA receptor subunits, as well as high in vivo burst firing, in surviving SN DA neurons from Parkinsons disease patients, suggesting that burst-gating K-ATP channel function in DA neurons affects phenotypes in both disease and health.

Spatiotemporal Structure in Large Neuronal Networks Detected from Cross-Correlation

The analysis of neuronal information involves the detection of spatiotemporal relations between neuronal discharges. We propose a method that is based on the positions (phase offsets) of the central peaks obtained from pairwise cross-correlation histograms. Data complexity is reduced to a one-dimensional representation by using redundancies in the measured phase offsets such that each unit is assigned a preferred firing time relative to the other units in the group. We propose two procedures to examine the applicability of this method to experimental data sets. In addition, we propose methods that help the investigation of dynamical changes in the preferred firing times of the units. All methods are applied to a sample data set obtained from cat visual cortex.

Effect sizes in experimental pain produced by gender, genetic variants and sensitization procedures.

Background Various effects on pain have been reported with respect to their statistical significance, but a standardized measure of effect size has been rarely added. Such a measure would ease comparison of the magnitude of the effects across studies, for example the effect of gender on heat pain with the effect of a genetic variant on pressure pain. Methodology/Principal Findings Effect sizes on pain thresholds to stimuli consisting of heat, cold, blunt pressure, punctuate pressure and electrical current, administered to 125 subjects, were analyzed for 29 common variants in eight human genes reportedly modulating pain, gender and sensitization procedures using capsaicin or menthol. The genotype explained 0–5.9% of the total interindividual variance in pain thresholds to various stimuli and produced mainly small effects (Cohens d 0–1.8). The largest effect had the TRPA1 rs13255063T/rs11988795G haplotype explaining >5% of the variance in electrical pain thresholds and conferring lower pain sensitivity to homozygous carriers. Gender produced larger effect sizes than most variant alleles (1–14.8% explained variance, Cohens d 0.2–0.8), with higher pain sensitivity in women than in men. Sensitization by capsaicin or menthol explained up to 63% of the total variance (4.7–62.8%) and produced largest effects according to Cohens d (0.4–2.6), especially heat sensitization by capsaicin (Cohens d = 2.6). Conclusions Sensitization, gender and genetic variants produce effects on pain in the mentioned order of effect sizes. The present report may provide a basis for comparative discussions of factors influencing pain.

Increased dopamine D2 receptor activity in the striatum alters the firing pattern of dopamine neurons in the ventral tegmental area.

Significance Patients with schizophrenia suffer from cognitive and negative deficits that are largely resistant to current therapeutic strategies. Here, using a genetic mouse model that displays phenotypes similar to these cognitive and negative symptoms, we found that increased postsynaptic D2 receptor (D2R) activity in the striatum leads to changes in the firing pattern of presynaptic dopamine (DA) neurons of the midbrain. These alterations occur in the ventral tegmental area (VTA) of the midbrain, but not in the substantia nigra, suggesting that DA pathways may be differently regulated by striatal D2R hyperactivity. The changes in neuron firing patterns were accompanied by a reduction in NMDA receptor subunits selectively in dopaminergic VTA neurons, providing a potential new target for the treatment of schizophrenia symptoms. There is strong evidence that the core deficits of schizophrenia result from dysfunction of the dopamine (DA) system, but details of this dysfunction remain unclear. We previously reported a model of transgenic mice that selectively and reversibly overexpress DA D2 receptors (D2Rs) in the striatum (D2R-OE mice). D2R-OE mice display deficits in cognition and motivation that are strikingly similar to the deficits in cognition and motivation observed in patients with schizophrenia. Here, we show that in vivo, both the firing rate (tonic activity) and burst firing (phasic activity) of identified midbrain DA neurons are impaired in the ventral tegmental area (VTA), but not in the substantia nigra (SN), of D2R-OE mice. Normalizing striatal D2R activity by switching off the transgene in adulthood recovered the reduction in tonic activity of VTA DA neurons, which is concordant with the rescue in motivation that we previously reported in our model. On the other hand, the reduction in burst activity was not rescued, which may be reflected in the observed persistence of cognitive deficits in D2R-OE mice. We have identified a potential molecular mechanism for the altered activity of DA VTA neurons in D2R-OE mice: a reduction in the expression of distinct NMDA receptor subunits selectively in identified mesolimbic DA VTA, but not nigrostriatal DA SN, neurons. These results suggest that functional deficits relevant for schizophrenia symptoms may involve differential regulation of selective DA pathways.

Unilateral entorhinal denervation leads to long-lasting dendritic alterations of mouse hippocampal granule cells

Following brain injury, neurons efferently connected from the lesion site are denervated and remodel their dendritic tree. Denervation-induced dendritic reorganization of granule cells was investigated in the dentate gyrus of the Thy1-GFP mouse. After mechanical transection of the perforant path, single granule cells were 3D-reconstructed at different time points post-lesion (3d, 7d, 10d, 30 d, 90 d and 180 d) and their soma size, total dendritic length, number of dendritic segments and dendritic branch orders were studied. Changes in spine densities were determined using 3D-analysis of individual dendritic segments. Following entorhinal denervation the granule cell arbor progressively atrophied until 90 d post-lesion (reduction of total dendritic length to ~50% of control). Dendritic alterations occurred selectively in the denervated outer molecular layer, where a loss of distal dendritic segments and a reduction of mean segment length were seen. At 180 d post-lesion total dendritic length partially recovered (~70% of control). This recovery appeared to be the result of a re-elongation of surviving dendrites rather than dendritic re-branching, since the number of dendritic segments did not recover. In contrast to the protracted dendritic changes, spine density changes followed a faster time course. In the denervated layer spine densities dropped to ~65% of control values and fully recovered by 30 d post-lesion. We conclude that entorhinal denervation in mouse causes protracted and long-term structural alterations of the granule cell dendritic tree. Spontaneously occurring reinnervation processes, such as the sprouting of surviving afferent fibers, are insufficient to maintain the granule cell dendritic arbor.

Detection and assessment of near-zero delays in neuronal spiking activity

Cross-correlation histograms (CCHs) have been widely used to study the temporal relationship between pairwise recordings of neuronal signals. One interesting parameter of a CCH is the time position of the central peak which indicates delays between signals. In order to study the potential relevance of these delays which can be as small as 1 ms, it is necessary to measure them with high precision. We present a method for the estimation of the central peaks position that is based on fitting a cosine function to the CCH and show that the precision of this estimate can be tracked analytically. We validate the resulting formula by simulations and by the analysis of a sample dataset obtained from cat visual cortex. The results indicate that the time position of the center peak can be estimated with submillisecond precision. The formula allows one also to develop a test of statistical significance for differences between two sets of measurements.

Time-lapse imaging of granule cells in mouse entorhino-hippocampal slice cultures reveals changes in spine stability after entorhinal denervation

Principal neurons that are partially denervated after brain injury remodel their synaptic connections and show biphasic changes in their dendritic spine density: during an early phase after denervation spine density decreases and during a late phase spine density recovers again. It has been hypothesized that these changes in spine density are caused by a period of increased spine loss followed by a period of increased spine formation. We have tested this hypothesis, which is based on data from fixed tissues, by using time‐lapse imaging of denervated dentate granule cells in organotypic entorhino‐hippocampal slice cultures of Thy1‐GFP mice. Our data show that nondenervated granule cells turn over spines spontaneously while keeping their spine density constant. Denervation influenced this equilibrium and induced biphasic changes in the spine loss rate but not in the rate of spine formation: during the early phase after denervation the spine loss rate was increased and during the late phase after denervation the spine loss rate was decreased compared with nondenervated control cultures. In line with these observations, time‐lapse imaging of identified spines formed after the lesion revealed that the stability of these spines was decreased during the early phase and increased during the late phase after the lesion. We conclude that biphasic changes in spine loss rate and spine stability but not in the rate of spine formation play a central role in the reorganization of dentate granule cells after entorhinal denervation in vitro. J. Comp. Neurol. 520:1891–1902, 2012.

Measuring burstiness and regularity in oscillatory spike trains.

The ability of neurons to emit different firing patterns such as bursts or oscillations is important for information processing in the brain. In dopaminergic neurons, prominent patterns include repetitive, oscillatory bursts, regular pacemakers, and irregular spike trains with nonstationary properties. In order to describe and measure the variability of these patterns, we describe burstiness and regularity in a single model framework. We present a doubly stochastic spike train model in which a background oscillation with independent and normally distributed intervals gives rise to either single spikes or bursty spike events with Gaussian firing intensities. Five easily interpretable parameters allow a classification into bursty or single spike and irregularly or regularly oscillating firing patterns. This classification is based primarily on features of the autocorrelation histogram which are usually studied qualitatively by visual inspection. The present model provides a quantitative and objective classification scheme and relates these features directly to the underlying processes. In addition, confidence intervals visualize the uncertainty of parameter estimation and classification precision. We apply the model to a data set obtained from single dopaminergic substantia nigra neurons recorded extracellularly in vivo. The model is able to represent a high variety of discharge patterns observed empirically, and the classification agrees closely with visual inspection. In addition, changes in the parameters can be studied quantitatively, including also the properties related to bursting behavior. Thus, the proposed model can be used for the description of neuronal firing patterns and the investigation of their dynamical changes with cellular and experimental conditions.

A stochastic model for EEG microstate sequence analysis.

The analysis of spontaneous resting state neuronal activity is assumed to give insight into the brain function. One noninvasive technique to study resting state activity is electroencephalography (EEG) with a subsequent microstate analysis. This technique reduces the recorded EEG signal to a sequence of prototypical topographical maps, which is hypothesized to capture important spatio-temporal properties of the signal. In a statistical EEG microstate analysis of healthy subjects in wakefulness and three stages of sleep, we observed a simple structure in the microstate transition matrix. It can be described with a first order Markov chain in which the transition probability from the current state (i.e., map) to a different map does not depend on the current map. The resulting transition matrix shows a high agreement with the observed transition matrix, requiring only about 2% of mass transport (1/2 L1-distance). In the second part, we introduce an extended framework in which the simple Markov chain is used to make inferences on a potential underlying time continuous process. This process cannot be directly observed and is therefore usually estimated from discrete sampling points of the EEG signal given by the local maxima of the global field power. Therefore, we propose a simple stochastic model called sampled marked intervals (SMI) model that relates the observed sequence of microstates to an assumed underlying process of background intervals and thus, complements approaches that focus on the analysis of observable microstate sequences.

A multiple filter test for the detection of rate changes in renewal processes with varying variance

Nonstationarity of the event rate is a persistent problem in modeling time series of events, such as neuronal spike trains. Motivated by a variety of patterns in neurophysiological spike train recordings, we define a general class of renewal processes. This class is used to test the null hypothesis of stationary rate versus a wide alternative of renewal processes with finitely many rate changes (change points). Our test extends ideas from the filtered derivative approach by using multiple moving windows simultaneously. To adjust the rejection threshold of the test, we use a Gaussian process, which emerges as the limit of the filtered derivative process. We also develop a multiple filter algorithm, which can be used when the null hypothesis is rejected in order to estimate the number and location of change points. We analyze the benefits of multiple filtering and its increased detection probability as compared to a single window approach. Application to spike trains recorded from dopamine midbrain neurons in anesthetized mice illustrates the relevance of the proposed techniques as preprocessing steps for methods that assume rate stationarity. In over 70% of all analyzed spike trains classified as rate nonstationary, different change points were detected by different window sizes.