Biology subjects

Dreyer, J. K.

Publications and source records attributed to Dreyer, J. K..

5 recordsLinked to original sources

bioRxiv2024

Modelling determinants of region-specific dopamine dynamics in the striatum

Striatal dopamine (DA) release regulates reward-related learning and motivation and is believed to consist of a short-lived phasic and continuous tonic component. Here, we build a large-scale three-dimensional model of extracellular DA dynamics in dorsal (DS) and ventral striatum (VS). The model predicts rapid dynamics in DS with little-to-no basal DA, and slower dynamics in the VS enabling build-up of tonic DA levels. These regional differences do not reflect release-related phenomena but rather differential dopamine transporter (DAT) activity. Interestingly, our simulations posit DAT nanoclustering as a possible regulator of this activity. Receptor binding simulations show that D1 receptor occupancy follows extracellular DA concentration with milliseconds delay, while D2 receptors do not respond to brief pauses in firing but rather integrate DA signal over seconds. Summarized, our model distills recent experimental observations into a computational framework that challenge prevailing paradigms of striatal DA signalling.

neuroscience↗

bioRxiv2022

Behavioral encoding across timescales by region-specificdopamine 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 multi-region fiber photometry and machine learning-based analysis to decode dopamine dynamics across 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-minutes 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. Significance StatementNew genetically encoded dopamine sensors offer unprecedented temporal resolution for measurement of dopamine release dynamics across different brain regions over extended periods. In this study, we use the dopamine sensor dLight1.3b to decipher the role of dopamine release dynamics in the dorsal (DS) and ventral striatum (VS) of mice during simple, self-paced exploratory behavior. By AI-based splitting of behavioral kinematics into individual motifs, we link differential but highly cooperative dopamine release dynamics of DS and VS with movements on a subsecond-to-minutes time scales. In addition to coupling region-specific dopamine dynamics to behavioral sequences, our study demonstrates the strength of a machine learning-based data analysis pipeline that can be readily applied to other neurotransmitters for which genetically encoded biosensors are available.

neuroscience↗

bioRxiv2022

Within-animal comparison of microdialysis and fiber photometry in amphetamine-exposed mice

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), have 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 naive animals have rapid, pre-drug DA dynamics measured with FP that correlate to the subsequent pharmacodynamics of amphetamine as measured with MD and FP.

neuroscience↗

bioRxiv2020

Theoretical analysis predicts an optimal therapeutic strategy in distinct parkinsonian landscapes of the striatum

Parkinsons disease (PD) results from a loss of dopaminergic neurons. The age of disease onset, its progression and symptoms vary significantly between patients, pointing to a complex relationship between neuron loss and PD etiology. Yet, our understanding of the clinical variability remains incomplete. Here, we use biophysical modelling to characterize the dopaminergic landscape in the healthy and denervated striatum. Based on currently proposed mechanisms causing PD, we model three distinct denervation patterns, and show notable differences in the dopaminergic network as denervation progresses. We find local and global differences in the activity of two types of striatal neurons depending on the denervation pattern. Finally, we identify an optimal cellular strategy for maintaining normal dopamine signaling when neurons degenerate stochastically within our model. Our results derive a conceptual framework in which the clinical variability of PD is rooted in distinct denervation patterns and forms testable predictions for future PD research.

biophysics↗

bioRxiv2018

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 Parkinsons 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 Parkinsons disease.

neuroscience↗