Multiscale model of primary motor cortex circuits predicts in vivo cell-type-specific, behavioral state-dependent dynamics.

Understanding cortical function requires studying multiple scales: molecular, cellular, circuit, and behavioral. We develop a multiscale, biophysically detailed model of mouse primary motor cortex (M1) with over 10,000 neurons and 30 million synapses. Neuron types, densities, spatial distributions, morphologies, biophysics, connectivity, and dendritic synapse locations are constrained by experimental data. The model includes long-range inputs from seven thalamic and cortical regions and noradrenergic inputs. Connectivity depends on cell class and cortical depth at sublaminar resolution. The model accurately predicts in vivo layer- and cell-type-specific responses (firing rates and LFP) associated with behavioral states (quiet wakefulness and movement) and experimental manipulations (noradrenaline receptor blockade and thalamus inactivation). We generate mechanistic hypotheses underlying the observed activity and analyzed low-dimensional population latent dynamics. This quantitative theoretical framework can be used to integrate and interpret M1 experimental data and sheds light on the cell-type-specific multiscale dynamics associated with several experimental conditions and behaviors.

A cranial implant for stabilizing whole-cell patch-clamp recordings in behaving rodents.

BACKGROUND: In vivo patch-clamp recording techniques provide access to the sub- and suprathreshold membrane potential dynamics of individual neurons during behavior. However, maintaining recording stability throughout behavior is a significant challenge, and while methods for head restraint are commonly used to enhance stability, behaviorally related brain movement relative to the skull can severely impact the success rate and duration of whole-cell patch-clamp recordings. NEW METHOD: We developed a low-cost, biocompatible, and 3D-printable cranial implant capable of locally stabilizing brain movement, while permitting equivalent access to the brain when compared to a conventional craniotomy. RESULTS: Experiments in head-restrained behaving mice demonstrate that the cranial implant can reliably reduce the amplitude and speed of brain displacements, significantly improving the success rate of recordings across repeated bouts of motor behavior. COMPARISON WITH EXISTING METHOD(S): Our solution offers an improvement on currently available strategies for brain stabilization. Due to its small size, the implant can be retrofitted to most in vivo electrophysiology recording setups, providing a low cost, easily implementable solution for increasing intracellular recording stability in vivo. CONCLUSIONS: By facilitating stable whole-cell patch-clamp recordings in vivo, biocompatible 3D printed implants should accelerate the investigation of single neuron computations underlying behavior.

A cerebellar-thalamocortical pathway drives behavioral context-dependent movement initiation.

Executing learned motor behaviors often requires the transformation of sensory cues into patterns of motor commands that generate appropriately timed actions. The cerebellum and thalamus are two key areas involved in shaping cortical output and movement, but the contribution of a cerebellar-thalamocortical pathway to voluntary movement initiation remains poorly understood. Here, we investigated how an auditory "go cue" transforms thalamocortical activity patterns and how these changes relate to movement initiation. Population responses in dentate/interpositus-recipient regions of motor thalamus reflect a time-locked increase in activity immediately prior to movement initiation that is temporally uncoupled from the go cue, indicative of a fixed-latency feedforward motor timing signal. Blocking cerebellar or motor thalamic output suppresses movement initiation, while stimulation triggers movements in a behavioral context-dependent manner. Our findings show how cerebellar output, via the thalamus, shapes cortical activity patterns necessary for learned context-dependent movement initiation.

Cellular mechanisms underlying behavioral state-dependent bidirectional modulation of motor cortex output.

Neuronal activity in primary motor cortex (M1) correlates with behavioral state, but the cellular mechanisms underpinning behavioral state-dependent modulation of M1 output remain largely unresolved. Here, we performed in vivo patch-clamp recordings from layer 5B (L5B) pyramidal neurons in awake mice during quiet wakefulness and self-paced, voluntary movement. We show that L5B output neurons display bidirectional (i.e., enhanced or suppressed) firing rate changes during movement, mediated via two opposing subthreshold mechanisms: (1) a global decrease in membrane potential variability that reduced L5B firing rates (L5Bsuppressed neurons), and (2) a coincident noradrenaline-mediated increase in excitatory drive to a subpopulation of L5B neurons (L5Benhanced neurons) that elevated firing rates. Blocking noradrenergic receptors in forelimb M1 abolished the bidirectional modulation of M1 output during movement and selectively impaired contralateral forelimb motor coordination. Together, our results provide a mechanism for how noradrenergic neuromodulation and network-driven input changes bidirectionally modulate M1 output during motor behavior.

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

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.

Cav1.3 channels control D2-autoreceptor responses via NCS-1 in substantia nigra dopamine neurons.

Dopamine midbrain neurons within the substantia nigra are particularly prone to degeneration in Parkinson's disease. Their selective loss causes the major motor symptoms of Parkinson's disease, but the causes for the high vulnerability of SN DA neurons, compared to neighbouring, more resistant ventral tegmental area dopamine neurons, are still unclear. Consequently, there is still no cure available for Parkinson's disease. Current therapies compensate the progressive loss of dopamine by administering its precursor l-DOPA and/or dopamine D2-receptor agonists. D2-autoreceptors and Cav1.3-containing L-type Ca(2+) channels both contribute to Parkinson's disease pathology. L-type Ca(2+) channel blockers protect SN DA neurons from degeneration in Parkinson's disease and its mouse models, and they are in clinical trials for neuroprotective Parkinson's disease therapy. However, their physiological functions in SN DA neurons remain unclear. D2-autoreceptors tune firing rates and dopamine release of SN DA neurons in a negative feedback loop through activation of G-protein coupled potassium channels (GIRK2, or KCNJ6). Mature SN DA neurons display prominent, non-desensitizing somatodendritic D2-autoreceptor responses that show pronounced desensitization in PARK-gene Parkinson's disease mouse models. We analysed surviving human SN DA neurons from patients with Parkinson's disease and from controls, and detected elevated messenger RNA levels of D2-autoreceptors and GIRK2 in Parkinson's disease. By electrophysiological analysis of postnatal juvenile and adult mouse SN DA neurons in in vitro brain-slices, we observed that D2-autoreceptor desensitization is reduced with postnatal maturation. Furthermore, a transient high-dopamine state in vivo, caused by one injection of either l-DOPA or cocaine, induced adult-like, non-desensitizing D2-autoreceptor responses, selectively in juvenile SN DA neurons, but not ventral tegmental area dopamine neurons. With pharmacological and genetic tools, we identified that the expression of this sensitized D2-autoreceptor phenotype required Cav1.3 L-type Ca(2+) channel activity, internal Ca(2+), and the interaction of the neuronal calcium sensor NCS-1 with D2-autoreceptors. Thus, we identified a first physiological function of Cav1.3 L-type Ca(2+) channels in SN DA neurons for homeostatic modulation of their D2-autoreceptor responses. L-type Ca(2+) channel activity however, was not important for pacemaker activity of mouse SN DA neurons. Furthermore, we detected elevated substantia nigra dopamine messenger RNA levels of NCS-1 (but not Cav1.2 or Cav1.3) after cocaine in mice, as well as in remaining human SN DA neurons in Parkinson's disease. Thus, our findings provide a novel homeostatic functional link in SN DA neurons between Cav1.3- L-type-Ca(2+) channels and D2-autoreceptor activity, controlled by NCS-1, and indicate that this adaptive signalling network (Cav1.3/NCS-1/D2/GIRK2) is also active in human SN DA neurons, and contributes to Parkinson's disease pathology. As it is accessible to pharmacological modulation, it provides a novel promising target for tuning substantia nigra dopamine neuron activity, and their vulnerability to degeneration.

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 Parkinson's disease patients, suggesting that burst-gating K-ATP channel function in DA neurons affects phenotypes in both disease and health.

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.