@article {69301, title = {Motor neurons generate pose-targeted movements via proprioceptive sculpting.}, journal = {Nature}, year = {2024}, month = {2024 Mar 20}, abstract = {

Motor neurons are the final common pathway through which the brain controls movement of the body, forming the basic elements from which all movement is composed. Yet how a single motor neuron contributes to control during natural movement remains unclear. Here we anatomically and functionally characterize the individual roles of the motor neurons that control head movement in the fly, Drosophila melanogaster. Counterintuitively, we find that activity in a single motor neuron rotates the head in different directions, depending on the starting posture of the head, such that the head converges towards a pose determined by the identity of the stimulated motor neuron. A feedback model predicts that this convergent behaviour results from motor neuron drive interacting with proprioceptive feedback. We identify and genetically suppress a single class of proprioceptive neuron that changes the motor neuron-induced convergence as predicted by the feedback model. These data suggest a framework for how the brain controls movements: instead of directly generating movement in a given direction by activating a fixed set of motor neurons, the brain controls movements by adding bias to a continuing proprioceptive-motor loop.

}, issn = {1476-4687}, doi = {10.1038/s41586-024-07222-5}, author = {Gorko, Benjamin and Siwanowicz, Igor and Close, Kari and Christoforou, Christina and Hibbard, Karen L and Kabra, Mayank and Lee, Allen and Park, Jin-Yong and Li, Si Ying and Chen, Alex B and Namiki, Shigehiro and Chen, Chenghao and Tuthill, John C and Bock, Davi D and Rouault, Herv{\'e} and Branson, Kristin and Ihrke, Gudrun and Huston, Stephen J} } @article {68933, title = {Biomechanical origins of proprioceptor feature selectivity and topographic maps in the Drosophila leg.}, journal = {Neuron}, year = {2023}, month = {2023 Aug 04}, abstract = {

Our ability to sense and move our bodies relies on proprioceptors, sensory neurons that detect mechanical forces within the body. Different subtypes of proprioceptors detect different kinematic features, such as joint position, movement, and vibration, but the mechanisms that underlie proprioceptor feature selectivity remain poorly understood. Using single-nucleus RNA sequencing (RNA-seq), we found that proprioceptor subtypes in the Drosophila leg lack differential expression of mechanosensitive ion channels. However, anatomical reconstruction of the proprioceptors and connected tendons revealed major biomechanical differences between subtypes. We built a model of the proprioceptors and tendons that identified a biomechanical mechanism for joint angle selectivity and predicted the existence of a topographic map of joint angle, which we confirmed using calcium imaging. Our findings suggest that biomechanical specialization is a key determinant of proprioceptor feature selectivity in Drosophila. More broadly, the discovery of proprioceptive maps reveals common organizational principles between proprioception and other topographically organized sensory systems.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2023.07.009}, author = {Mamiya, Akira and Sustar, Anne and Siwanowicz, Igor and Qi, Yanyan and Lu, Tzu-Chiao and Gurung, Pralaksha and Chen, Chenghao and Phelps, Jasper S and Kuan, Aaron T and Pacureanu, Alexandra and Lee, Wei-Chung Allen and Li, Hongjie and Mhatre, Natasha and Tuthill, John C} } @article {67751, title = {Functional architecture of neural circuits for leg proprioception in Drosophila.}, journal = {Current Biology}, volume = {31}, year = {2021}, month = {2021 Dec 06}, pages = {5163}, abstract = {

To effectively control their bodies, animals rely on feedback from proprioceptive mechanosensory neurons. In the Drosophila leg, different proprioceptor subtypes monitor joint position, movement direction, and vibration. Here, we investigate how these diverse sensory signals are integrated by central proprioceptive circuits. We find that signals for leg joint position and directional movement converge in second-order neurons, revealing pathways for local feedback control of leg posture. Distinct populations of second-order neurons integrate tibia vibration signals across pairs of legs, suggesting a role in detecting external substrate vibration. In each pathway, the flow of sensory information is dynamically gated and sculpted by inhibition. Overall, our results reveal parallel pathways for processing of internal and external mechanosensory signals, which we propose mediate feedback control of leg movement and vibration sensing, respectively. The existence of a functional connectivity map also provides a resource for interpreting connectomic reconstruction of neural circuits for leg proprioception.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2021.09.035}, author = {Chen, Chenghao and Agrawal, Sweta and Mark, Brandon and Mamiya, Akira and Sustar, Anne and Phelps, Jasper S and Lee, Wei-Chung Allen and Dickson, Barry J and Card, Gwyneth M and Tuthill, John C} } @article {65860, title = {Central processing of leg proprioception in Drosophila.}, journal = {eLife}, volume = {9}, year = {2020}, month = {2020 Dec 02}, abstract = {

Proprioception, the sense of self-movement and position, is mediated by mechanosensory neurons that detect diverse features of body kinematics. Although proprioceptive feedback is crucial for accurate motor control, little is known about how downstream circuits transform limb sensory information to guide motor output. Here, we investigate neural circuits in that process proprioceptive information from the fly leg. We identify three cell-types from distinct developmental lineages that are positioned to receive input from proprioceptor subtypes encoding tibia position, movement, and vibration. 13Bα neurons encode femur-tibia joint angle and mediate postural changes in tibia position. 9Aα neurons also drive changes in leg posture, but encode a combination of directional movement, high frequency vibration, and joint angle. Activating 10Bα neurons, which encode tibia vibration at specific joint angles, elicits pausing in walking flies. Altogether, our results reveal that central circuits integrate information across proprioceptor subtypes to construct complex sensorimotor representations that mediate diverse behaviors, including reflexive control of limb posture and detection of leg vibration.

}, issn = {2050-084X}, doi = {10.7554/eLife.60299}, author = {Agrawal, Sweta and Dickinson, Evyn S and Sustar, Anne and Gurung, Pralaksha and Shepherd, David and Truman, James W and Tuthill, John C} } @article {65725, title = {Dense neuronal reconstruction through X-ray holographic nano-tomography.}, journal = {Nature Neuroscience}, volume = {23}, year = {2020}, month = {2020 Dec -1}, pages = {1637-43}, abstract = {

Imaging neuronal networks provides a foundation for understanding the nervous system, but resolving dense nanometer-scale structures over large volumes remains challenging for light microscopy (LM) and electron microscopy (EM). Here we show that X-ray holographic nano-tomography (XNH) can image millimeter-scale volumes with sub-100-nm resolution, enabling reconstruction of dense wiring in Drosophila melanogaster and mouse nervous tissue. We performed correlative XNH and EM to reconstruct hundreds of cortical pyramidal cells and show that more superficial cells receive stronger synaptic inhibition on their apical dendrites. By combining multiple XNH scans, we imaged an adult Drosophila leg with sufficient resolution to comprehensively catalog mechanosensory neurons and trace individual motor axons from muscles to the central nervous system. To accelerate neuronal reconstructions, we trained a convolutional neural network to automatically segment neurons from XNH volumes. Thus, XNH bridges a key gap between LM and EM, providing a new avenue for neural circuit discovery.

}, issn = {1546-1726}, doi = {10.1038/s41593-020-0704-9}, author = {Kuan, Aaron T and Phelps, Jasper S and Thomas, Logan A and Nguyen, Tri M and Han, Julie and Chen, Chiao-Lin and Azevedo, Anthony W and Tuthill, John C and Funke, Jan and Cloetens, Peter and Pacureanu, Alexandra and Lee, Wei-Chung Allen} } @article {65723, title = {A systematic nomenclature for the Drosophila ventral nerve cord.}, journal = {Neuron}, volume = {107}, year = {2020}, month = {2020 Sep 14}, pages = {1071-79}, abstract = {

Drosophila melanogaster is an established model for neuroscience research with relevance in biology and medicine. Until recently, research on the Drosophila brain was hindered by the lack of a complete and uniform nomenclature. Recognizing this, Ito et\ al. (2014) produced an authoritative nomenclature for the adult insect brain, using Drosophila as the reference. Here, we extend this nomenclature to the adult thoracic and abdominal neuromeres, the ventral nerve cord (VNC), to provide an anatomical description of this major component of the Drosophila nervous system. The VNC is the locus for the reception and integration of sensory information and involved in generating most of the locomotor actions that underlie fly behaviors. The aim is to create a nomenclature, definitions, and spatial boundaries for the Drosophila VNC that are consistent with other insects. The work establishes an anatomical framework that provides a powerful tool for analyzing the functional organization of the VNC.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2020.08.005}, author = {Court, Robert and Namiki, Shigehiro and Armstrong, J Douglas and Borner, Jana and Card, Gwyneth and Costa, Marta and Dickinson, Michael and Duch, Carsten and Korff, Wyatt and Mann, Richard and Merritt, David and Murphey, Rod K and Seeds, Andrew M and Shirangi, Troy and Simpson, Julie H and Truman, James W and Tuthill, John C and Williams, Darren W and Shepherd, David} } @article {48657, title = {A systematic nomenclature for the Drosophila ventral nervous system.}, journal = {bioRxiv}, year = {2017}, month = {2017 Apr 26}, abstract = {

Insect nervous systems are proven and powerful model systems for neuroscience research with wide relevance in biology and medicine. However, descriptions of insect brains have suffered from a lack of a complete and uniform nomenclature. Recognising this problem the Insect Brain Name Working Group produced the first agreed hierarchical nomenclature system for the adult insect brain, using Drosophila melanogaster as the reference framework, with other insect taxa considered to ensure greater consistency and expandability (Ito et al., 2014). Ito et al. (2014) purposely focused on the gnathal regions that account for approximately 50\% of the adult CNS. We extend this nomenclature system to the sub-gnathal regions of the adult Drosophila nervous system to provide a nomenclature of the so-called ventral nervous system (VNS), which includes the thoracic and abdominal neuromeres that was not included in the original work and contains the neurons that play critical roles underpinning most fly behaviours.

}, doi = {10.1101/122952}, author = {Court, Robert Christopher and Armstrong, James Douglas and Borner, Jana and Card, Gwyneth M and Costa, Marta and Dickinson, Michael H and Duch, Carsten and Korff, Wyatt and Mann, Richard S and Merritt, David and Murphey, Rodney K and Namiki, Shigehiro and Seeds, Andrew M and Shepherd, David and Shirangi, Troy R and Simpson, Julie H and Truman, James W and Tuthill, John C and Williams, Darren W} } @article {45176, title = {Wide-field feedback neurons dynamically tune early visual processing.}, journal = {Neuron}, volume = {82}, year = {2014}, month = {2014 May 21}, pages = {887-95}, abstract = {

An important strategy for efficient neural coding is to match the range of cellular responses to the distribution of relevant input signals. However, the structure and relevance of sensory signals depend on behavioral state. Here, we show that behavior modifies neural activity at the earliest stages of fly vision. We describe a class of wide-field neurons that provide feedback to the most peripheral layer of the Drosophila visual system, the lamina. Using in vivo patch-clamp electrophysiology, we found that lamina wide-field neurons respond to low-frequency luminance fluctuations. Recordings in flying flies revealed that the gain and frequency tuning of wide-field neurons change during flight, and that these effects are mimicked by the neuromodulator octopamine. Genetically silencing wide-field neurons increased behavioral responses to slow-motion stimuli. Together, these findings identify a cell type that is gated by behavior to enhance neural coding by subtracting low-frequency signals from the inputs to motion detection circuits.

}, keywords = {Adrenergic alpha-Agonists, Anesthetics, Local, Animals, Animals, Genetically Modified, Brain, Choline O-Acetyltransferase, Drosophila, Drosophila Proteins, Feedback, Sensory, Flight, Animal, Green Fluorescent Proteins, Membrane Potentials, Motion Perception, Neurons, Octopamine, Photic Stimulation, Tetrodotoxin, Transcription Factors, Visual Pathways}, issn = {1097-4199}, doi = {10.1016/j.neuron.2014.04.023}, author = {Tuthill, John C and Nern, Aljoscha and Rubin, Gerald M and Reiser, Michael B} } @article {6091, title = {Contributions of the 12 neuron classes in the fly lamina to motion vision.}, journal = {Neuron}, volume = {79}, year = {2013}, month = {07/2013}, pages = {128-140}, abstract = {

Motion detection is a fundamental neural computation performed by many sensory systems. In the fly, local motion computation is thought to occur within the first two layers of the visual system, the lamina and medulla. We constructed specific genetic driver lines for each of the 12 neuron classes in the lamina. We then depolarized and hyperpolarized each neuron type and quantified fly behavioral responses to a diverse set of motion stimuli. We found that only a small number of lamina output neurons are essential for motion detection, while most neurons serve to sculpt and enhance these feedforward pathways. Two classes of feedback neurons (C2 and C3), and lamina output neurons (L2 and L4), are required for normal detection of directional motion stimuli. Our results reveal a prominent role for feedback and lateral interactions in motion processing and demonstrate that motion-dependent behaviors rely on contributions from nearly all lamina neuron classes.

}, doi = {http://dx.doi.org/10.1016/j.neuron.2013.05.024}, author = {Tuthill, John C and Nern, Aljoscha and Stephen L. Holtz and Rubin, Gerald M and Reiser, Michael B} } @article {3867, title = {Neural correlates of illusory motion perception in Drosophila.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {108}, year = {2011}, month = {2011 Jun 7}, pages = {9685-90}, abstract = {

When the contrast of an image flickers as it moves, humans perceive an illusory reversal in the direction of motion. This classic illusion, called reverse-phi motion, has been well-characterized using psychophysics, and several models have been proposed to account for its effects. Here, we show that Drosophila melanogaster also respond behaviorally to the reverse-phi illusion and that the illusion is present in dendritic calcium signals of motion-sensitive neurons in the fly lobula plate. These results closely match the predictions of the predominant model of fly motion detection. However, high flicker rates cause an inversion of the reverse-phi behavioral response that is also present in calcium signals of lobula plate tangential cell dendrites but not predicted by the model. The fly{\textquoteright}s behavioral and neural responses to the reverse-phi illusion reveal unexpected interactions between motion and flicker signals in the fly visual system and suggest that a similar correlation-based mechanism underlies visual motion detection across the animal kingdom.

}, keywords = {Algorithms, Animals, Dendrites, Drosophila melanogaster, Female, Humans, Male, Models, Motion, Motion Perception, Neural Pathways, Neurological, Neurons, Time Factors}, issn = {1091-6490}, doi = {10.1073/pnas.1100062108}, author = {Tuthill, John C and Chiappe, M Eugenia and Reiser, Michael B} } @article {4280, title = {Lessons from a compartmental model of a Drosophila neuron.}, journal = {The Journal of Neuroscience: The Official Journal of the Society for Neuroscience}, volume = {29}, year = {2009}, month = {2009 Sep 30}, pages = {12033-4}, abstract = {

Although the vinegar fly, Drosophila melanogaster, has been a biological model organism for over a century, its emergence as a model system for the study of neurophysiology is comparatively recent. The primary reason for this is that the vinegar fly and its neurons are tiny; up until 5 years ago, it was prohibitively difficult to record intracellularly from individual neurons in the intact Drosophila brain (Wilson et al., 2004). Today, fly electrophysiologists can genetically label neurons with GFP and reliably record from many (but not all) neurons in the fruit fly brain. Using genetic tools to drive expression of fluorescent calcium indicators, light-sensitive ion channels, or cell activity suppressors, we are beginning to understand how the external environment is represented with electrical potentials in Drosophila neurons (for review, see Olsen and Wilson, 2008).

}, keywords = {Animals, Drosophila melanogaster, Humans, Models, Neurological, Neurons}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.3348-09.2009}, author = {Tuthill, John C} }