Behavioral encoding across timescales by region-specific dopamine dynamics.

The dorsal (DS) and ventral striatum (VS) receive dopaminergic projections that control motor functions and reward-related behavior. It remains poorly understood how dopamine release dynamics across different temporal scales in these regions are coupled to behavioral outcomes. Here, we employ the dopamine sensor dLight1.3b together with multiregion fiber photometry and machine learning-based analysis to decode dopamine dynamics across the striatum during self-paced exploratory behavior in mice. Our data show a striking coordination of rapidly fluctuating signal in the DS, carrying information across dopamine levels, with a slower signal in the VS, consisting mainly of slow-paced transients. Importantly, these release dynamics correlated with discrete behavioral motifs, such as turns, running, and grooming on a subsecond-to-minute time scale. Disruption of dopamine dynamics with cocaine caused randomization of action selection sequencing and disturbance of DS-VS coordination. The data suggest that distinct dopamine dynamics of DS and VS jointly encode behavioral sequences during unconstrained activity with DS modulating the stringing together of actions and VS the signal to initiate and sustain the selected action.

Early impairments of visually-driven neuronal ensemble dynamics in the rTg4510 tauopathy mouse model.

Tau protein pathology is a hallmark of many neurodegenerative diseases, including Alzheimer's Disease or frontotemporal dementia. Synaptic dysfunction and abnormal visual evoked potentials have been reported in murine models of tauopathy, but little is known about the state of the network activity on a single neuronal level prior to brain atrophy. In the present study, oscillatory rhythms and single-cell calcium activity of primary visual cortex pyramidal neuron population were investigated in basal and light evoked states in the rTg4510 tauopathy mouse model prior to neurodegeneration. We found a decrease in their responsivity and overall activity which was insensitive to GABAergic modulation. Despite an enhancement of basal state coactivation of cortical pyramidal neurons, a loss of input-output synchronicity was observed. Dysfunction of cortical pyramidal function was also reflected in a reduction of basal theta oscillations and enhanced susceptibility to a sub-convulsive dose of pentylenetetrazol in rTg4510 mice. Our results unveil impairments in visual cortical pyramidal neuron processing and define aberrant oscillations as biomarker candidates in early stages of neurodegenerative tauopathies.

Representation of spontaneous movement by dopaminergic neurons is cell-type selective and disrupted in parkinsonism.

Midbrain dopaminergic neurons are essential for appropriate voluntary movement, as epitomized by the cardinal motor impairments arising in Parkinson's disease. Understanding the basis of such motor control requires understanding how the firing of different types of dopaminergic neuron relates to movement and how this activity is deciphered in target structures such as the striatum. By recording and labeling individual neurons in behaving mice, we show that the representation of brief spontaneous movements in the firing of identified midbrain dopaminergic neurons is cell-type selective. Most dopaminergic neurons in the substantia nigra pars compacta (SNc), but not in ventral tegmental area or substantia nigra pars lateralis, consistently represented the onset of spontaneous movements with a pause in their firing. Computational modeling revealed that the movement-related firing of these dopaminergic neurons can manifest as rapid and robust fluctuations in striatal dopamine concentration and receptor activity. The exact nature of the movement-related signaling in the striatum depended on the type of dopaminergic neuron providing inputs, the striatal region innervated, and the type of dopamine receptor expressed by striatal neurons. Importantly, in aged mice harboring a genetic burden relevant for human Parkinson's disease, the precise movement-related firing of SNc dopaminergic neurons and the resultant striatal dopamine signaling were lost. These data show that distinct dopaminergic cell types differentially encode spontaneous movement and elucidate how dysregulation of their firing in early Parkinsonism can impair their effector circuits.

Insights into Parkinson's disease from computational models of the basal ganglia.

Movement disorders arise from the complex interplay of multiple changes to neural circuits. Successful treatments for these disorders could interact with these complex changes in myriad ways, and as a consequence their mechanisms of action and their amelioration of symptoms are incompletely understood. Using Parkinson's disease as a case study, we review here how computational models are a crucial tool for taming this complexity, across causative mechanisms, consequent neural dynamics and treatments. For mechanisms, we review models that capture the effects of losing dopamine on basal ganglia function; for dynamics, we discuss models that have transformed our understanding of how beta-band (15-30 Hz) oscillations arise in the parkinsonian basal ganglia. For treatments, we touch on the breadth of computational modelling work trying to understand the therapeutic actions of deep brain stimulation. Collectively, models from across all levels of description are providing a compelling account of the causes, symptoms and treatments for Parkinson's disease.

Functionally Distinct Dopamine Signals in Nucleus Accumbens Core and Shell in the Freely Moving Rat.

Dynamic signaling of mesolimbic dopamine (DA) neurons has been implicated in reward learning, drug abuse, and motivation. However, this system is complex because firing patterns of these neurons are heterogeneous; subpopulations receive distinct synaptic inputs, and project to anatomically and functionally distinct downstream targets, including the nucleus accumbens (NAc) shell and core. The functional roles of these cell populations and their real-time signaling properties in freely moving animals are unknown. Resolving the real-time DA signal requires simultaneous knowledge of the synchronized activity of DA cell subpopulations and assessment of the down-stream functional effect of DA release. Because this is not yet possible solely by experimentation in vivo, we combine computational modeling and fast-scan cyclic voltammetry data to reconstruct the functionally relevant DA signal in DA neuron subpopulations projecting to the NAc core and shell in freely moving rats. The approach provides a novel perspective on real-time DA neuron firing and concurrent activation of presynaptic autoreceptors and postsynaptic targets. We first show that individual differences in DA release arise from differences in autoreceptor feedback. The model predicts that extracellular DA concentrations in NAc core result from constant baseline DA firing, whereas DA concentrations in NAc shell reflect highly dynamic firing patters, including synchronized burst firing and pauses. Our models also predict that this anatomical difference in DA signaling is exaggerated by intravenous infusion of cocaine. SIGNIFICANCE STATEMENT: Orchestrated signaling from mesolimbic dopamine (DA) neurons is important for initiating appropriate behavior in response to salient stimuli. Thus, subpopulations of mesolimbic DA neurons show different in vitro properties and synaptic inputs depending on their specific projections to the core and shell subterritories of the nucleus accumbens (NAc). However, the functional consequence of these differences is unknown. Here we analyze and model DA dynamics in different areas of the NAc to establish the real-time DA signal. In freely behaving animals, we find that the DA signal from mesencephalic neurons projecting to the NAc shell is dominated by synchronized bursts and pauses, whereas signaling is uniform for core-projecting neurons; this difference is amplified by cocaine.

How compensation breaks down in Parkinson's disease: Insights from modeling of denervated striatum.

The bradykinesia and other motor signs of Parkinson's disease (PD) are linked to progressive loss of substantia nigra dopamine (DA) neurons innervating the striatum. However, the emergence of idiopathic PD is likely preceded by a prolonged subclinical phase, which may be masked by a variety of pre- and postsynaptic compensatory mechanisms. It is often considered self-evident that the signs of PD manifest only when nigrostriatal degeneration has proceeded to such an extent that putative compensatory mechanisms fail to accommodate the depletion of striatal DA levels. However, the precise nature of the compensatory mechanisms, and the reason for their ultimate failure, has been elusive. In a recent computational study we modeled the effects of progressive denervation, including changes in the dynamics of interstitial DA and also adaptive or compensatory changes in postsynaptic responsiveness to DA signaling in the course of progressive nigrostriatal degeneration. In particular, we found that failure of DA signaling can occur by different mechanisms at different disease stages. We review these results and discuss their relevance for clinical and translational research, and we draw a number of predictions from our model that might be tested in preclinical experiments.

Three mechanisms by which striatal denervation causes breakdown of dopamine signaling.

Progressive loss of nigrostriatal dopamine (DA) neurons is the neuropathological hallmark of Parkinson's disease (PD). Symptoms of the disease can often be treated by DA D2 agonists and thus seem related to disinhibition of the indirect striatal pathway. However, there is no evidence that symptoms arise by low extracellular DA concentration or are associated with reduced D2 receptor binding. Here I provide a theoretical analysis of the pathophysiology and postsynaptic adaptation resulting from striatal DA denervation. I found that progressive denervation may alter DA signaling by three independent mechanisms depending on degree of denervation and macroscopic morphology of the lesion. As long as the remaining innervation stays anatomically coherent, denervation reduces phasic variations in extracellular DA, but the DA tone is not changed. The reduction of phasic signaling can be partially compensated by upregulating postsynaptic signaling cascades. However, changes in DA dynamics evade compensation. With 80-99% denervation, a persistent aberrant signal develops in D2-regulated pathways caused by random fluctuations in tonic DA release. Permanent low DA levels occur in regions completely void of innervation. Simulation of l-dopa therapy reduced the aberrant D2 signal. With a high degree of denervation, l-dopa enhanced another aberrant signal, this time in the D1 pathway. This analysis provides a quantitative, physiologically consistent view of the early and late stages of PD, the effect of main therapeutic medications, and potential side effects. The mechanisms described here may also provide an explanation to currently inexplicable pathological phenomena such as psycho stimulant-induced contraversive rotations in animal models.

Mathematical model of dopamine autoreceptors and uptake inhibitors and their influence on tonic and phasic dopamine signaling.

Dopamine (DA) D2-like autoreceptors are an important component of the DA system, but their influence on postsynaptic DA signaling is not well understood. They are, directly or indirectly, involved in drug abuse and in treatment of schizophrenia and attention deficit hyperactive disorder: DA autoreceptors influence the behavioral effect of cocaine and methylphenidate and may be the target of antipsychotic medications such as haloperidol. DA autoreceptors are active at two levels: Somatodendritic autoreceptors mainly influence firing rate of DA neurons, and presynaptic autoreceptors control release of neurotransmitter at axonal terminals. Here we develop a mathematical model that captures the dynamics of this dual autoregulation system. Our model predicts a biphasic autoreceptor response between DA terminals and somatodendritic regions that influences the postsynaptic integration of DAergic firing patterns. We applied our model to study how DA uptake inhibition affects the translation of DA cell firing into activation of postsynaptic DA receptors. While uptake inhibition increased tonic activation of low-affinity postsynaptic receptors, high-affinity state receptors saturated and thus became insensitive to phasic DA signaling. This effect had remarkable regional specificity: While high-affinity DA receptors saturated at low levels of uptake inhibition in nucleus accumbens, they only saturated at higher levels of uptake inhibition in dorsal striatum. Based on high-affinity receptor saturation, the model predicted that removal of autoreceptor control would lead to cocaine hypersensitivity.

Within-Mice Comparison of Microdialysis and Fiber Photometry-Recorded Dopamine Biosensor during Amphetamine Response.

A fundamental concept in neuroscience is the transmission of information between neurons via neurotransmitters, -modulators, and -peptides. For the past decades, the gold standard for measuring neurochemicals in awake animals has been microdialysis (MD). The emergence of genetically encoded fluorescence-based biosensors, as well as in vivo optical techniques such as fiber photometry (FP), has introduced technologically distinct means of measuring neurotransmission. To directly compare MD and FP, we performed concurrent within-animal recordings of extracellular dopamine (DA) in the dorsal striatum (DS) before and after administration of amphetamine in awake, freely behaving mice expressing the dopamine sensor dLight1.3b. We show that despite temporal differences, MD- and FP-based readouts of DA correlate well within mice. Down-sampling of FP data showed temporal correlation to MD data, with less variance observed using FP. We also present evidence that DA fluctuations periodically reach low levels, and naïve animals have rapid, predrug DA dynamics measured with FP that correlate to the subsequent pharmacodynamics of amphetamine as measured with MD and FP.

Biophysical Modeling of Dopaminergic Denervation Landscapes in the Striatum Reveals New Therapeutic Strategy.

Parkinson's disease (PD) results from a loss of dopaminergic neurons. What triggers the break-down of neuronal signaling, and how this might be compensated, is not understood. The age of onset, progression and symptoms vary between patients, and our understanding of the clinical variability remains incomplete. In this study, we investigate this, by characterizing the dopaminergic landscape in healthy and denervated striatum, using biophysical modeling. Based on currently proposed mechanisms, we model three distinct denervation patterns, and show how this affect the dopaminergic network. Depending on the denervation pattern, we show how local and global differences arise in the activity of striatal neurons. Finally, we use the mathematical formalism to suggest a cellular strategy for maintaining normal dopamine (DA) signaling following neuronal denervation. This strategy is characterized by dual enhancement of both the release and uptake capacity of DA in the remaining neurons. Overall, our results derive a new conceptual framework for the impaired dopaminergic signaling related to PD and offers testable predictions for future research directions.

Influence of phasic and tonic dopamine release on receptor activation.

Tonic and phasic dopamine release is implicated in learning, motivation, and motor functions. However, the relationship between spike patterns in dopaminergic neurons, the extracellular concentration of dopamine, and activation of dopamine receptors remains unresolved. In the present study, we develop a computational model of dopamine signaling that give insight into the relationship between the dynamics of release and occupancy of D(1) and D(2) receptors. The model is derived from first principles using experimental data. It has no free parameters and offers unbiased estimation of the boundaries of dopaminergic volume transmission. Bursts primarily increase occupancy of D(1) receptors, whereas pauses translate into low occupancy of D(1) and D(2) receptors. Phasic firing patterns, composed of bursts and pauses, reduce the average D(2) receptor occupancy and increase average D(1) receptor occupancy compared with equivalent tonic firing. Receptor occupancy is crucially dependent on synchrony and the balance between tonic and phasic firing modes. Our results provide quantitative insight in the dynamics of volume transmission and complement experimental data obtained with electrophysiology, positron emission tomography, microdialysis, amperometry, and voltammetry.

Synchronicity: The Role of Midbrain Dopamine in Whole-Brain Coordination.

Midbrain dopamine seems to play an outsized role in motivated behavior and learning. Widely associated with mediating reward-related behavior, decision making, and learning, dopamine continues to generate controversies in the field. While many studies and theories focus on what dopamine cells encode, the question of how the midbrain derives the information it encodes is poorly understood and comparatively less addressed. Recent anatomical studies suggest greater diversity and complexity of afferent inputs than previously appreciated, requiring rethinking of prior models. Here, we elaborate a hypothesis that construes midbrain dopamine as implementing a Bayesian selector in which individual dopamine cells sample afferent activity across distributed brain substrates, comprising evidence to be evaluated on the extent to which stimuli in the on-going sensorimotor stream organizes distributed, parallel processing, reflecting implicit value. To effectively generate a temporally resolved phasic signal, a population of dopamine cells must exhibit synchronous activity. We argue that synchronous activity across a population of dopamine cells signals consensus across distributed afferent substrates, invigorating responding to recognized opportunities and facilitating further learning. In framing our hypothesis, we shift from the question of how value is computed to the broader question of how the brain achieves coordination across distributed, parallel processing. We posit the midbrain is part of an "axis of agency" in which the prefrontal cortex (PFC), basal ganglia (BGS), and midbrain form an axis mediating control, coordination, and consensus, respectively.

Unsupervised Idealization of Ion Channel Recordings by Minimum Description Length: Application to Human PIEZO1-Channels.

Researchers can investigate the mechanistic and molecular basis of many physiological phenomena in cells by analyzing the fundamental properties of single ion channels. These analyses entail recording single channel currents and measuring current amplitudes and transition rates between conductance states. Since most electrophysiological recordings contain noise, the data analysis can proceed by idealizing the recordings to isolate the true currents from the noise. This de-noising can be accomplished with threshold crossing algorithms and Hidden Markov Models, but such procedures generally depend on inputs and supervision by the user, thus requiring some prior knowledge of underlying processes. Channels with unknown gating and/or functional sub-states and the presence in the recording of currents from uncorrelated background channels present substantial challenges to such analyses. Here we describe and characterize an idealization algorithm based on Rissanen's Minimum Description Length (MDL) Principle. This method uses minimal assumptions and idealizes ion channel recordings without requiring a detailed user input or a priori assumptions about channel conductance and kinetics. Furthermore, we demonstrate that correlation analysis of conductance steps can resolve properties of single ion channels in recordings contaminated by signals from multiple channels. We first validated our methods on simulated data defined with a range of different signal-to-noise levels, and then showed that our algorithm can recover channel currents and their substates from recordings with multiple channels, even under conditions of high noise. We then tested the MDL algorithm on real experimental data from human PIEZO1 channels and found that our method revealed the presence of substates with alternate conductances.

A three-step reconstruction method for fluorescence molecular tomography based on compressive sensing.

Fluorescence molecular tomography (FMT) is a promising tool for real time in vivo quantification of neurotransmission (NT) as we pursue in our BRAIN initiative effort. However, the acquired image data are noisy and the reconstruction problem is ill-posed. Further, while spatial sparsity of the NT effects could be exploited, traditional compressive-sensing methods cannot be directly applied as the system matrix in FMT is highly coherent. To overcome these issues, we propose and assess a three-step reconstruction method. First, truncated singular value decomposition is applied on the data to reduce matrix coherence. The resultant image data are input to a homotopy-based reconstruction strategy that exploits sparsity via ℓ1 regularization. The reconstructed image is then input to a maximum-likelihood expectation maximization (MLEM) algorithm that retains the sparseness of the input estimate and improves upon the quantitation by accurate Poisson noise modeling. The proposed reconstruction method was evaluated in a three-dimensional simulated setup with fluorescent sources in a cuboidal scattering medium with optical properties simulating human brain cortex (reduced scattering coefficient: 9.2 cm-1, absorption coefficient: 0.1 cm-1) and tomographic measurements made using pixelated detectors. In different experiments, fluorescent sources of varying size and intensity were simulated. The proposed reconstruction method provided accurate estimates of the fluorescent source intensity, with a 20% lower root mean square error on average compared to the pure-homotopy method for all considered source intensities and sizes. Further, compared with conventional ℓ2 regularized algorithm, overall, the proposed method reconstructed substantially more accurate fluorescence distribution. The proposed method shows considerable promise and will be tested using more realistic simulations and experimental setups.

Quantitative approach to small-scale nonequilibrium systems.

In a nanoscale system out of thermodynamic equilibrium, it is important to account for thermal fluctuations. Typically, the thermal noise contributes fluctuations, e.g., of distances that are substantial in comparison to the size of the system and typical distances measured. If the thermal fluctuations are ignored, misinterpretation of measured quantities such as interaction forces, potentials, and constants may result. Here, we consider a particle moving in a time-dependent landscape, as, e.g., in an optical tweezers or atomic force nanoscopic measurement. Based on the Kramers equation [H. A. Kramers, Physica 7, 284 (1940)], we propose an approximate but quantitative way of dealing with such an out-of-equilibrium system. The limits of this approximate description of the escape process are determined through optical tweezers experiments and comparison to simulations. Also, this serves as a recipe for how to use the proposed method to obtain knowledge about the underlying energy landscape from a set of experimental measurements. Finally, we perform estimates of the error made if thermal fluctuations are ignored.

Novel optical and statistical methods reveal colloid-wall interactions inconsistent with DLVO and Lifshitz theories.

We present an experimental method based on video microscopy to perform nanometer scale position detection of a micrometer bead in the direction along the propagation of the detection light. Using the same bead for calibration and detection significantly improves the in depth resolution in comparison to video microscopy methods from literature. This method is used together with an optical trap to measure interaction potentials between a glass surface and colloids made of polystyrene or silica at different electrolyte concentrations. The results are confirmed by an independent method where the optical trap is used in connection with a quadrant photodiode. Also, we present a maximum likelihood analysis method which considerably improves the spatial resolution of interaction potentials by optimizing the underlying potential function to fit all observed position distributions. The measured interaction potentials agree well with DLVO theory for small electrolyte concentrations; however, for larger electrolyte concentrations the potentials differ qualitatively from both DLVO and Lifshitz theory.

[Optogenetics brings hidden neural mechanisms into the light and could become a future therapy]. / Optogenetik bringer neurale mekanismer frem i lyset og kan blive en ny terapiform.

Optogenetics is an emergent technology that combines light-sensitive proteins derived from algae, so-called opsins, with genetics. Viral vectors encoding opsins are injected into selective brain regions whereby specific cell populations can be controlled with high precision light pulses delivered via implanted optical fibres. This review focuses on explaining basic principles of optogenetics and describes important insights into neuropsychiatric mechanisms provided by the technology.

Spreading dynamics of a functionalized polymer latex.

Functionalized polymer nanoparticles are used as binders for inorganic materials in everyday technologies such as paper and coatings. However, the functionalization can give rise to two opposing effects: It can promote adhesion via specific interactions to the substrate, but a high degree of functionalization can also hamper spreading on substrates. Here, we studied the spreading kinetics of individual functionalized vinyl acetate-co-ethylene polymer nanoparticles on inorganic substrates by atomic force microscopy (AFM) imaging. We found that the kinetics underwent a transition from a fast initial regime to a slower regime. The transition was independent of functionalization of the particles but depended on the wettability of the substrate. Furthermore, the transition from the fast regime to the slow regime occurred at a size-dependent contact angle, leading to a h ∼ a(3/2) scaling dependence between the height (h) and the width (a) of the spreading particles. Thereafter, spreading continued on a slower time scale. In the slow regime, the kinetics was blocked by a high degree of functionalization. We interpret the observations in terms of a nanoscale stick-slip transition occurring at interface stress around 6 kPa. We develop models that describe the scaling relations between the particle height and width on different substrates.