Biology subjects

Aragon, M. J.

Publications and source records attributed to Aragon, M. J..

4 recordsLinked to original sources

bioRxiv2026

Social feedback structures stay/switch decision-making in Drosophila

Within natural environments, different sensory stimuli can drive mutually-exclusive behaviors such as fighting, feeding, or courting. When experiencing competing cues, how do animals decide to continue executing the current behavior or to switch to a new behavior? To address this question, we developed a closed-loop behavioral paradigm that presents actively courting male Drosophila with a fictive heat stimulus, forcing the male to choose between two competing behaviors: continue courting (stay) or escape the heat (switch). By modeling the sensorimotor transformations underlying this decision process, we discovered that male flies continually evaluate social cues, including female speed and her distance, in order to make a stay/switch decision. We investigated the neural correlates of stay/switch decision-making via brain-wide calcium imaging in head-fixed male flies presented with both fictive female visual cues and heat stimuli. We built models that predicted stay/switch decisions from neural data, which revealed that activity in a small number of brain regions, including the anterior optic tubercle, predicted decision outcomes up to 4 seconds prior to stimulus onset, whereas after stimulus onset, distributed activity across the brain was correlated with the behavioral decision. Together, these results demonstrate that Drosophila continually evaluate social feedback to inform decisions about how to respond to environmental cues, and that brain activity changes prior to presentation of the environmental cue can inform upcoming decisions. Overall, this study provides new insight into the processes underlying naturalistic decision-making in Drosophila.

neuroscience↗

bioRxiv2023

From connectome to effectome: learning the causal interaction map of the fly brain

A long-standing goal of neuroscience is to obtain a causal model of the nervous system. This would allow neuroscientists to explain animal behavior in terms of the dynamic interactions between neurons. The recently reported whole-brain fly connectome [1-7] specifies the synaptic paths by which neurons can affect each other but not whether, or how, they do affect each other in vivo. To overcome this limitation, we introduce a novel combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the "effectome". Specifically, we propose an estimator for a dynamical systems model of the fly brain that uses stochastic optogenetic perturbation data to accurately estimate causal effects and the connectome as a prior to drastically improve estimation efficiency. We then analyze the connectome to propose circuits that have the greatest total effect on the dynamics of the fly nervous system. We discover that, fortunately, the dominant circuits significantly involve only relatively small populations of neurons--thus imaging, stimulation, and neuronal identification are feasible. Intriguingly, we find that this approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, our analyses of the connectome provide evidence that global dynamics of the fly brain are generated by a large collection of small and often anatomically localized circuits operating, largely, independently of each other. This in turn implies that a causal model of a brain, a principal goal of systems neuroscience, can be feasibly obtained in the fly.

neuroscience↗

bioRxiv2021

Flexible circuit mechanisms for context-dependent song sequencing

Many sequenced behaviors, including locomotion, reaching, and vocalization, are patterned differently in different contexts, enabling animals to adjust to their current environments. However, how contextual information shapes neural activity to flexibly alter the patterning of actions is not yet understood. Prior work indicates such flexibility could be achieved via parallel motor circuits, with differing sensitivities to sensory context [1, 2, 3]; instead we demonstrate here how a single neural pathway operates in two different regimes dependent on recent sensory history. We leverage the Drosophila song production system [4] to investigate the neural mechanisms that support male song sequence generation in two contexts: near versus far from the female. While previous studies identified several song production neurons[5, 6, 7, 8], how these neurons are organized to mediate song patterning was unknown. We find that male flies sing simple trains of only one syllable or mode far from the female but complex song sequences consisting of alternations between modes when near to her. We characterize the male song circuit from the brain to the ventral nerve cord (VNC), and find that the VNC song pre-motor circuit is shaped by two key computations: mutual inhibition and rebound excitability [9] between nodes driving the two modes of song. Weak sensory input to a direct brain-to-VNC excitatory pathway (via pC2 brain and pIP10 descending neurons) drives simple song far from the female. Strong sensory input to the same pathway enables complex song production via simultaneous recruitment of P1a neuron-mediated disinhibition of the VNC song pre-motor circuit. Thus, proximity to the female effectively unlocks motor circuit dynamics in the correct sensory context. We construct a compact circuit model to demonstrate that these few computations are sufficient to replicate natural context-dependent song dynamics. These results have broad implications for neural population-level models of context-dependent behavior [10] and highlight that canonical circuit motifs [11, 12, 13] can be combined in novel ways to enable circuit flexibility required for dynamic communication.

neuroscience↗

bioRxiv2019

Non-invasive multiphoton imaging of neural structure and activity in Drosophila

We developed a multiphoton imaging method to capture neural structure and activity in behaving flies through the intact cuticles. Our measurements show that the fly head cuticle has surprisingly high transmission at wavelengths > 900 nm, and the difficulty of through-cuticle imaging is due to the air sacs and/or fat tissue underneath the head cuticle. By compressing the air sacs, we performed deep multiphoton imaging of fly brains through the intact cuticle. Our anatomical and functional imaging results show that 2- and 3-photon imaging are comparable in superficial regions such as the mushroom body, but 3-photon imaging is superior in deeper regions such as the central complex and beyond. We further demonstrated 2-photon through-cuticle functional imaging of odor-evoked calcium responses from the mushroom body {gamma}-lobes in behaving flies short-term and long-term (12 consecutive hours). The through-cuticle imaging method developed here extends the time limits of in vivo imaging in flies, and opens up new ways to capture neural structure and activity from the intact fly brain.

neuroscience↗