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Striatal dopamine explains novelty-induced behavioral dynamics and individual variability in threat prediction.

ABSTRACT: Animals both explore and avoid novel objects in the environment, but the neural mechanisms that underlie these behaviors and their dynamics remain uncharacterized. Here, we used multi-point tracking (DeepLabCut) and behavioral segmentation (MoSeq) to characterize the behavior of mice freely interacting with a novel object. Novelty elicits a characteristic sequence of behavior, starting with investigatory approach and culminating in object engagement or avoidance. Dopamine in the tail of the striatum (TS) suppresses engagement, and dopamine responses were predictive of individual variability in behavior. Behavioral dynamics and individual variability are explained by a reinforcement-learning (RL) model of threat prediction in which behavior arises from a novelty-induced initial threat prediction (akin to "shaping bonus") and a threat prediction that is learned through dopamine-mediated threat prediction errors. These results uncover an algorithmic similarity between reward- and threat-related dopamine sub-systems.

SUBMITTER: Akiti K

PROVIDER: S-EPMC9671833 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Striatal dopamine explains novelty-induced behavioral dynamics and individual variability in threat prediction.

Akiti Korleki K Tsutsui-Kimura Iku I Xie Yudi Y Mathis Alexander A Markowitz Jeffrey E JE Anyoha Rockwell R Datta Sandeep Robert SR Mathis Mackenzie Weygandt MW Uchida Naoshige N Watabe-Uchida Mitsuko M

Neuron 20220920 22

Animals both explore and avoid novel objects in the environment, but the neural mechanisms that underlie these behaviors and their dynamics remain uncharacterized. Here, we used multi-point tracking (DeepLabCut) and behavioral segmentation (MoSeq) to characterize the behavior of mice freely interacting with a novel object. Novelty elicits a characteristic sequence of behavior, starting with investigatory approach and culminating in object engagement or avoidance. Dopamine in the tail of the stri ...[more]

PMID: 36130595

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Project description:BackgroundNovelty-seeking (NS) and impulsive personality traits have been proposed to reflect an interplay between fronto-cortical and limbic systems, including the limbic striatum (LS). Although neuroimaging studies have provided some evidence for this, most are comprised of small samples and many report surprisingly large effects given the challenges of trying to relate a snapshot of brain function or structure to an entity as complex as personality. The current work tested a priori hypotheses about associations between striatal dopamine (DA) release, cortical thickness (CT), and NS in a large sample of healthy adults.MethodsFifty-two healthy adults (45M/7F; age: 23.8±4.93) underwent two positron emission tomography scans with [11C]raclopride (specific for striatal DA D2/3 receptors) with or without amphetamine (0.3 mg/kg, p.o.). Structural magnetic resonance image scans were acquired, as were Tridimensional Personality Questionnaire data. Amphetamine-induced changes in [11C]raclopride binding potential values (ΔBPND) were examined in the limbic, sensorimotor (SMS) and associative (AST) striatum. CT measures, adjusted for whole brain volume, were extracted from the dorsolateral sensorimotor and ventromedial/limbic cortices.ResultsBPND values were lower in the amphetamine vs. no-drug sessions, with the largest effect in the LS. When comparing low vs. high LS ΔBPND groups (median split), higher NS2 (impulsiveness) scores were found in the high ΔBPND group. Partial correlations (age and gender as covariates) yielded a negative relation between ASTS ΔBPND and sensorimotor CT; trends for inverse associations existed between ΔBPND values in other striatal regions and frontal CT. In other words, the greater the amphetamine-induced striatal DA response, the thinner the frontal cortex.ConclusionsThese data expand upon previously reported associations between striatal DA release in the LS and both NS related impulsiveness and CT in the largest sample reported to date. The findings add to the plausibility of these associations while suggesting that the effects are likely weaker than has been previously proposed.

Project description:Collective cell migration is a common phenotype in epithelial cancers, which is associated with tumor cell metastasis and poor patient survival. However, the interplay between physiologically relevant pro-migratory stimuli and the underlying mechanical cell-cell interactions are poorly understood. We investigated the migratory behavior of different collectively migrating non-small cell lung cancer cell lines in response to motogenic growth factors (e.g. epidermal growth factor) or clinically relevant small compound inhibitors. Depending on the treatment, we observed distinct behaviors in a classical lateral migration assay involving traveling fronts, finger-shapes or the development of cellular bridges. Particle image velocimetry analysis revealed characteristic speed dynamics (evolution of the average speed of all cells in a frame) in all experiments exhibiting initial acceleration and subsequent deceleration of the cell populations. To better understand the mechanical properties of individual cells leading to the observed speed dynamics and the phenotypic differences we developed a mathematical model based on a Langevin approach. This model describes intercellular forces, random motility, and stimulation of active migration by mechanical interaction between cells. Simulations show that the model is able to reproduce the characteristic spatio-temporal speed distributions as well as most migratory phenotypes of the studied cell lines. A specific strength of the proposed model is that it identifies a small set of mechanical features necessary to explain all phenotypic and dynamical features of the migratory response of non-small cell lung cancer cells to chemical stimulation/inhibition. Furthermore, all processes included in the model can be associated with potential molecular components, and are therefore amenable to experimental validation. Thus, the presented mathematical model may help to predict which mechanical aspects involved in non-small cell lung cancer cell migration are affected by the respective therapeutic treatment.

Dataset Information

Striatal dopamine explains novelty-induced behavioral dynamics and individual variability in threat prediction.

Publications

Striatal dopamine explains novelty-induced behavioral dynamics and individual variability in threat prediction.

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