Biology subjects

Kvitsiani, D.

Publications and source records attributed to Kvitsiani, D..

5 recordsLinked to original sources

bioRxiv2023

Altered reactivity to threatening stimuli in Drosophila models of Parkinson's disease, revealed by a trial-based assay

The fruit fly Drosophila melanogaster emerges as an affordable, genetically tractable model of behavior and brain diseases. However, despite the surprising level of evolutionary conservation from flies to humans, significant genetic, circuit-level and behavioral differences hinder the interpretability of fruit fly models for human disease. Therefore, to allow a more direct fly-versus-human comparison, we surveyed the rarely exploited, rich behavioral repertoire of fruit flies with genetic alterations relevant to Parkinsons disease (PD), including overexpression of human mutant Parkin or -synuclein proteins and mutations in dopamine receptors. Flies with different genetic backgrounds displayed variable behaviors, including freezing, slowing and running, in response to predator-mimicking passing shadows used as threatening stimuli in a single-animal trial-based assay. We found that the expression of human mutant Parkin in flies resulted in reduced walking speed and decreased reactivity to passing shadows. Flies with dopamine receptor mutations showed similar alterations, consistent with the motor and cognitive deficits typical in humans with PD. We also found age-dependent trends in behavioral choice during the fly lifespan, while dopamine receptor mutant flies maintained their decreased general reactivity throughout all age groups. Our data demonstrate that single-trial behavioral analysis can reveal subtle behavioral changes in mutant flies that can be used to further our understanding of disease pathomechanisms and help gauge the validity of genetic Drosophila models of neurodegeneration, taking us one step closer to bridging the gap in fly-to-human translation.

neuroscience↗

bioRxiv2021

Profiling the transcallosal response of rat motor cortex evoked by contralateral optogenetic stimulation of glutamatergic cortical neurons

BackgroundEfficient interhemispheric integration of neural activity between left and right primary motor cortex (M1) is critical for inter-limb motor control. ObjectiveWe employed optogenetic stimulation to establish a framework for probing transcallosal M1-M1 interactions in rats. MethodsIn male rats, we optogenetically stimulated glutamatergic neurons in right M1 and recorded the transcallosally evoked potential with chronically implanted electrodes in contralateral left M1 during dexmedetomidine anesthesia. We systematically varied the stimulation intensity and duration to characterize the relationship between stimulation parameters in right M1 and the characteristics of the evoked intracortical potentials in left M1. ResultsOptogenetic stimulation of right M1 consistently evoked a transcallosal response in left M1 with a consistent negative peak (N1) that sometimes was preceded by a smaller positive peak (P1). Higher stimulation intensity or longer stimulation duration gradually increased N1 amplitude and reduced N1 variability across trials. Median N1 latencies remained stable, once stimulation elicited a reliable N1 peak and did not display a systematic shortening with increasing stimulation intensity or duration. ConclusionsOptogenetically stimulated glutamatergic neurons in M1 can reliably evoke a transcallosal response in anesthetized rats and can be used to characterize the relationship between "stimulation dose" and "response magnitude" (i.e., the gain function) of transcallosal M1-to-M1 glutamatergic connections. Detailed knowledge of the stimulus-response relationship is needed to optimize the efficacy of optogenetic stimulation. Since transcallosal M1-M1 interactions can also be probed non-invasively with transcranial magnetic stimulation in humans, our optogenetic stimulation approach bears translational potential for studying how unilateral M1 stimulation can induce interhemispheric plasticity.

neuroscience↗

bioRxiv2020

A normative account of choice history effects in mice and humans

Choice history effects describe how future choices depend on the history of past choices. Choice history effects are typically framed as a bias rather than an adaptive phenomenon because the phenomenon generally degrades reward rates in experimental tasks. How-ever, in natural habitats, choices made in the past constrain choices that can be made in the future. For foraging animals, the probability of obtaining a reward in a given patch depends on the degree to which the animals have exploited the patch in the past. One problem with many experimental tasks that show choice history effects is that such tasks artificially decouple choice history from its consequences in regard to reward availability over time. To circumvent this, we used a variable interval (VI) reward schedule that reinstates a more natural contingency between past choices and future reward availability. By manipulating first- and second-order statistics of the environment, we dissociated choice history, reward history, and reaction times. We found that choice history effects reflect the growth rate of the reward probability of the unchosen option, reward history effects reflect environmental volatility, and reaction time reflects overall reward rate. By testing in mice and humans, we show that the same choice history effects can be generalized across species and that these effects are similar to those observed in optimal agents. Furthermore, we develop a new reinforcement learning model that explicitly incorporates choice history over multiple timescales into the decision process, and we examine its predictive adequacy in accounting for the associated behavioral data. We show that this new variant, known as the double trace model, has a higher predictive adequacy of choice data, in addition to better reward harvesting efficiency in simulated environments. Finally, we show that the choice history effects emerge in optimal models of foraging in habitats with diminishing returns, thus linking this phenomenon to a wider class of optimality models in behavioral ecology. These results suggests that choice history effects may be adaptive for natural contingencies between consumption and reward availability. This concept lends credence to a normative account of choice history effects that extends beyond its description as a bias.

neuroscience↗

bioRxiv2019

Foraging fruit flies mix navigational and learning-based decision-making strategies

Animals often navigate environments that are uncertain, volatile and complex, making it challenging to locate reliable food sources. Therefore, it is not surprising that many species evolved multiple, parallel and complementary foraging strategies to survive. Current research on animal behavior is largely driven by a reductionist approach and attempts to study one particular aspect of behavior in isolation. This is justified by the huge success of past and current research in understanding neural circuit mechanisms of behaviors. But focusing on only one aspect of behaviors obscures their inherent multidimensional nature. To fill this gap we aimed to identify and characterize distinct behavioral modules using a simple reward foraging assay. For this we developed a single-animal, trial-based probabilistic foraging task, where freely walking fruit flies experience optogenetic sugar-receptor neuron stimulation. By carefully analyzing the walking trajectories of flies, we were able to dissect the animals foraging decisions into multiple underlying systems. We show that flies perform local searches, cue-based navigation and learn task relevant contingencies. Using probabilistic reward delivery allowed us to bid several competing reinforcement learning (RL) models against each other. We discover that flies accumulate chosen option values, forget unchosen option values and seek novelty. We further show that distinct behavioral modules -learning and navigation-based systems-cooperate, suggesting that reinforcement learning in flies operates on dimensionality reduced representations. We therefore argue that animals will apply combinations of multiple behavioral strategies to generate foraging decisions.

neuroscience↗

bioRxiv2018

D.sort: template based automatic spike sorting tool

Understanding how populations of neurons represent and compute internal or external variables requires precise and objective metrics for tracing the individual spikes that belong to a given neuron. Despite recent progress in the development of accurate and fast spike sorting tools, the scarcity of ground truth data makes it difficult to settle on the best performing spike sorting algorithm. Besides, the use of different configurations of electrodes and ways to acquire signal (e.g. anesthetized, head fixed, freely behaving animal recordings, tetrode vs. silicone probes, etc.) makes it even harder to develop a universal spike sorting tool that will perform well without human intervention. Some of the prevalent problems in spike sorting are: units separating due to drift, clustering bursting cells, and dealing with nonstationarity in background noise. The last is particularly problematic in freely behaving animals where the noises from the electrophysiological activity of hundreds or thousands of neurons are intermixed with noise arising from movement artifacts. We address these problems by developing a new spike sorting tool that is based on a template matching algorithm. The spike waveform templates are used to perform normalized cross correlation (NCC) with an acquired signal for spike detection. The normalization addresses problems with drift, bursting, and nonstationarity of noise and provides normative scoring to compare different units in terms of cluster quality. Our spike sorting algorithm, D.sort, runs on the graphic processing unit (GPU) to accelerate computations. D.sort is a freely available software package (https://github.com/1804MB/Kvistiani-lab_Dsort).

neuroscience↗