Biology subjects

Asahina, K.

Publications and source records attributed to Asahina, K..

7 recordsLinked to original sources

bioRxiv2026

Phasor EO-FLIM: Lifetime imaging with picosecond noise and 500 Hz frame rate

Fluorescence lifetime microscopy is limited by photon throughput, constraining the speed and dynamic range of biological measurements. We demonstrate a compact optical module and fast phasor acquisition methods to image multi-exponential lifetimes at 500 Hz in vivo and with over 1010 photons per second in static tissues. Action potentials are captured in phasor plots, and lifetime contrast is revealed from autofluorescence and wheat germ agglutinin stain with 20 picosecond pixel noise.

bioengineering↗

bioRxiv2026

Neural circuits regulating social dominance implement a strategy predicted by evolutionary game theory

Social conflict is a fundamental challenge for all animals and determines access to critical resources like mates and food. Evolutionary game theory predicts that natural selection should yield competitive strategies that balance the benefits and costs of social conflict. However, whether such strategies are embedded within the neural circuits that regulate aggression remains unclear. Here, we identify a neural circuit regulating the decision to flee during fighting in male Drosophila and show that the onset of defeat is governed by a probabilistic strategy predicted by evolutionary game theory. This mechanism arises from the inhibition of Tk-GAL4FruM neurons that promote aggressive arousal in males. Inhibition is mediated by a mushroom body circuit involving PPL1 dopaminergic neurons and V2 mushroom body output neurons, both classically associated with aversive learning. Silencing this circuit disrupts the onset of defeat, while activating it induces rapid defeat. Conversely, activation of reward-encoding PAM dopaminergic neurons promotes winning, revealing a dual role for dopamine in shaping contest dynamics. Finally, we find that internal state variables such as hunger and motivation shift the defeat onset probability distribution, consistent with game theory predictions of how payoff modulates fighting persistence. Together, our results provide direct evidence that evolutionary strategies based on payoff, long described by game theory, are implemented as circuit-level computations that regulate aggression.

neuroscience↗

bioRxiv2026

Cellular and functional dissection of the octopaminergic system in the Drosophila brain

Octopamine (OA) is a major biogenic amine in the invertebrate nervous system and is often considered a functional analog of vertebrate noradrenaline. Along with its immediate precursor tyramine (TA), OA influences diverse physiological and behavioral processes, including sensory processing and social behavior. However, understanding the neural basis of its multifunctionality has been constrained by the limited genetic access to defined OA/TA neuron types. Here, we present a curated set of transgenic driver strains that provide selective access to nearly all long-range OA/TA cell types in the brain of common fruit flies, Drosophila melanogaster. Using these tools, we map cell-type-specific innervation patterns, compare male and female neuroanatomy, and cross-reference identified neuron types with electron microscopy connectome datasets. As a proof of principle, we show that distinct optic lobe-projecting OA/TA neuron types differentially modulate visually guided behaviors, and we identify a novel OA/TA cell type that suppresses aggression in both sexes. This resource establishes a practical and conceptual foundation for cell-type-resolved analysis of OA/TA circuit function and enables direct integration of genetics, anatomy, and connectomics for studies of neuromodulatory circuit organization.

neuroscience↗

bioRxiv2025

A switch in arousal circuit architecture shapes sleep across the lifespan

Sleep is a continuous behavior across the lifespan, yet its features and functions evolve markedly with development1-4. In Drosophila melanogaster, as in mammals, early life sleep differs from mature sleep5,6, but it is unknown whether disparate sleep regulatory mechanisms underlie these changes. Here, we identify distinct populations of octopaminergic (OA) neurons that promote arousal in larval and adult flies, thus revealing a developmental switch in sleep-wake circuit architecture. Of eight OA neurons present in the sub-esophageal zone (SEZ) of the nervous system at both life stages, dedicated, non-overlapping subsets drive arousal in larvae versus adults. Morphologic and connectomic analyses show that larval OA arousal neurons project primarily to the ventral nerve cord and lack substantial sensory input, suggesting a circuit logic optimized for internally driven arousal during early development. In contrast, adult OA arousal neurons target higher brain regions involved in cognition and receive rich multimodal sensory input, supporting wakefulness in response to environmental cues. These findings highlight a developmental transition in arousal circuitry that mirrors changing ecological demands, with juvenile systems organized to prioritize growth and feeding, insulated from sensory disturbance, and mature systems supporting sensory-guided behavior. Our results support a model of sleep regulation as a developmentally dynamic process, in which shared neuromodulators like OA operate through distinct cellular substrates tailored to life stage- specific behavioral priorities.

neuroscience↗

bioRxiv2021

Drosophila tachykininergic neurons modulate the activity of two groups of receptor-expressing neurons to regulate aggressive tone

Neuropeptides influence animal behaviors through complex molecular and cellular mechanisms, many of which are difficult to predict solely from synaptic connectivity. Here, we uncovered two separate downstream targets that are differentially modulated by the neuropeptide tachykinin, which promotes Drosophila aggression. Tachykinin from a single sexually dimorphic group of neurons recruits two separate downstream groups of neurons. One downstream group, synaptically connected to the tachykinergic neurons, expresses the receptor TkR86C and is necessary for aggression. Tachykinin supports the strength of cholinergic excitatory synaptic transmission between the tachykinergic and TkR86C downstream neurons. The other downstream group expresses the TkR99D receptor and is recruited primarily when tachykinin is over-expressed in the source neurons. This circuit reconfiguration correlates with the quantitative and qualitative enhancement of aggression observed when tachykinin is present in excess. Our data highlight how the amount of neuropeptide released from a small number of neurons can reshape the activity patterns of multiple downstream neural populations.

neuroscience↗

bioRxiv2020

A neurogenetic mechanism of experience-dependent suppression of aggression

Aggression is an ethologically important social behavior1 but excessive aggression can be detrimental to animal fitness2,3. Social experiences among conspecific individuals reduce aggression in a wide range of animals4. However, the genetic and neural basis for the experience-dependent suppression of aggression remains largely unknown. Here we found that nervy (nvy), a Drosophila homolog of vertebrate myeloid translocation gene (MTG)5 involved in transcriptional regulation6-8, suppresses aggression via its action in a specific subset of neurons. Loss-of-function mutation of the nvy gene resulted in hyper-aggressiveness only in socially experienced flies, whereas overexpression of nvy suppressed spontaneous aggression in socially naive flies. The loss-of-function nvy mutant exhibited persistent aggression under various contexts in which wild-type flies transition to escape or courtship behaviors. Knockdown of nvy in octopaminergic/tyraminergic (OA/TA) neurons increased aggression, phenocopying the nvy mutation. We found that a subpopulation of OA/TA cells specifically labeled by nvy is required for the social-experience-dependent suppression of aggression. Moreover, cell-type-specific transcriptomics on nvy-expressing OA/TA neurons revealed aggression-controlling genes that are likely downstream of nvy. Our results are the first to describe the presence of a specific neuronal subpopulation in the central brain that actively suppresses aggression in a social-experience-dependent manner, illuminating the underlying genetic mechanism.

neuroscience↗

bioRxiv2020

Quantitative comparison of Drosophila behavior annotations by human observers and a machine learning algorithm

Automated quantification of behavior is increasingly prevalent in neuroscience research. Human judgments can influence machine-learning-based behavior classification at multiple steps in the process, for both supervised and unsupervised approaches. Such steps include the design of the algorithm for machine learning, the methods used for animal tracking, the choice of training images, and the benchmarking of classification outcomes. However, how these design choices contribute to the interpretation of automated behavioral classifications has not been extensively characterized. Here, we quantify the effects of experimenter choices on the outputs of automated classifiers of Drosophila social behaviors. Drosophila behaviors contain a considerable degree of variability, which was reflected in the confidence levels associated with both human and computer classifications. We found that a diversity of sex combinations and tracking features was important for robust performance of the automated classifiers. In particular, features concerning the relative position of flies contained useful information for training a machine-learning algorithm. These observations shed light on the importance of human influence on tracking algorithms, the selection of training images, and the quality of annotated sample images used to benchmark the performance of a classifier (the ‘ground truth’). Evaluation of these factors is necessary for researchers to accurately interpret behavioral data quantified by a machine-learning algorithm and to further improve automated classifications.Significance Statement Accurate quantification of animal behaviors is fundamental to neuroscience. Here, we quantitatively assess how human choices influence the performance of automated classifiers trained by a machine-learning algorithm. We found that human decisions about the computational tracking method, the training images, and the images used for performance evaluation impact both the classifier outputs and how human observers interpret the results. These factors are sometimes overlooked but are critical, especially because animal behavior is itself inherently variable. Automated quantification of animal behavior is becoming increasingly prevalent: our results provide a model for bridging the gap between traditional human annotations and computer-based annotations. Systematic assessment of human choices is important for developing behavior classifiers that perform robustly in a variety of experimental conditions.Competing Interest StatementThe authors have declared no competing interest.View Full Text

animal behavior and cognition↗