Periodic Reporting for period 4 - activeFly (Circuit mechanisms of self-movement estimation during walking)
Berichtszeitraum: 2022-05-01 bis 2023-12-31
The control of locomotion may seem effortless, but it relies on a complex interplay between the Central Nervous System (CNS) and the environment. During this interaction, the CNS monitors movement to plan and control future actions, ensuring both posture stability and achievement of behavioral goals. This internal estimation of self-motion results from continuous coordination between signals originating from motor commands and self-generated sensory feedback, including visual and mechanosensory inputs.
While mechanosensory systems like proprioception play a crucial role in monitoring and guiding leg movements , less is known about the identity and function of visual circuits involved in self-motion estimation. Understanding how these visual signals integrate with proprioception and internal motor commands to accurately estimate self-motion and regulate locomotion is a key focus of this project.
To address these questions, we have employed different experimental strategies, including quantitative behavior analysis in controlled, virtual reality-based environments, physiology in walking flies, perturbations of neural activity through genetically encoded actuators, and connectivity analysis. Our experiments have revealed that the functional organization of a visuomotor network monitoring self-rotations is configured for context-dependent and robust encoding of the fly’s angular movements, particularly during high-speed walking.
An important feature of this network is its dynamic tuning of multimodal integration by inhibition, which optimally controls the weights of visual and non-visual information based on uncertainty. Additionally, the multilayered network enforces competitive disinhibition across brain hemispheres, facilitating the encoding of asymmetries in the fly's locomotive behavior. These asymmetries are corrected by the network via descending steering commands that interact with the head and legs on a moment-by-moment basis through the modulatory activity of ascending neurons projecting from leg neuropiles of the ventral nerve cord (VNC) back to the premotor network.
These findings represent a general mechanism by which bidirectional interactions between the VNC and brain visual circuits contribute to adaptive and high-performance locomotion control. Understanding this organization within the context of walking, a behavior conserved across species, has provided a new framework in which visual feedback, via recurrent interactions between descending and ascending pathways, orchestrates the interplay between posture and gaze stability in a goal-dependent and motor-context-specific manner. This framework may have significant implications for health, disease, and artificial systems
While mechanosensory systems like proprioception play a crucial role in monitoring and guiding leg movements , less is known about the identity and function of visual circuits involved in self-motion estimation. Understanding how these visual signals integrate with proprioception and internal motor commands to accurately estimate self-motion and regulate locomotion is a key focus of this project.
To address these questions, we have employed different experimental strategies, including quantitative behavior analysis in controlled, virtual reality-based environments, physiology in walking flies, perturbations of neural activity through genetically encoded actuators, and connectivity analysis. Our experiments have revealed that the functional organization of a visuomotor network monitoring self-rotations is configured for context-dependent and robust encoding of the fly’s angular movements, particularly during high-speed walking.
An important feature of this network is its dynamic tuning of multimodal integration by inhibition, which optimally controls the weights of visual and non-visual information based on uncertainty. Additionally, the multilayered network enforces competitive disinhibition across brain hemispheres, facilitating the encoding of asymmetries in the fly's locomotive behavior. These asymmetries are corrected by the network via descending steering commands that interact with the head and legs on a moment-by-moment basis through the modulatory activity of ascending neurons projecting from leg neuropiles of the ventral nerve cord (VNC) back to the premotor network.
These findings represent a general mechanism by which bidirectional interactions between the VNC and brain visual circuits contribute to adaptive and high-performance locomotion control. Understanding this organization within the context of walking, a behavior conserved across species, has provided a new framework in which visual feedback, via recurrent interactions between descending and ascending pathways, orchestrates the interplay between posture and gaze stability in a goal-dependent and motor-context-specific manner. This framework may have significant implications for health, disease, and artificial systems
activeFly aimed to identify neurons supplying motor information to cells processing visual feedback and elucidate the role of these visuomotor interactions. We first mapped the site of motor signals to HS and H2 cells, two classes of optic flow sensitive cells, specifically to their axon terminals. Using an electron microscopy (EM) dataset and computational analysis, we identified approximately 100 neurons receiving direct convergent information from premotor centers, including the VNC (the insect analogue of the spinal cord). Notably, we identified multiple pathways capable of providing non-visual information to HS and H2 cells, rendering a silencing screen for identifying dedicated cells obsolete.
To explore the function of the identified network, we conducted four distinct studies. Initially, we examined the role of visual feedback in walking control. This study demonstrated that flies swiftly stabilize gaze by reducing the gain of posture control reflexes. The notion that visual feedback plays a critical role in the interplay between mechanical stability and behavioral goals established a framework for exploring the underlying visuomotor circuits governing gaze stabilization. We hypothesized that HS or H2 cells would contribute to gaze stability by regulating leg placements on a moment-to-moment basis to counter postural reflexes.
Using whole-cell patch recordings, a second study described that HS cells received ascending information from the VNC. This modulation operates on a moment-by-moment bases, facilitating the cells’ rapid control of the ipsilateral foreleg. In addition, HS cells either gradually depolarized or hyperpolarized as a function of several high- vs. low-speed steps, respectively. This study unveiled the source of speed modulation in HS cells and its function: to facilitate rapid and context-dependent recruitment of optic flow-sensitive neurons for steering control.
In a third study, using whole-cell patch recordings and 2-photon optical imaging from different elements of the network confirmed their sensitivity to body rotations, thus validating predictions derived from the connectivity analysis. We used visual stimuli to precisely modulate activity at the network's input (the optic flow-sensitive cells) and assess signal propagation throughout the network. Our findings showed that the network architecture fosters competitive disinhibition between the right and left sides, enabling robust extraction of rotational information during translation.
A four study examined the interplay between visual and motor signals within the network by manipulating visual information. This study showed that optic flow-sensitive cells optimally integrate multimodal information, enhancing the weighting for non-visual signals as visual uncertainty escalates. Altogether, these findings strongly support the idea that non-visual information robustly encode self-rotations. Moreover, our work illustrates that inhibitory neurons within the network regulate optimal multimodal integration via normalization.
In summary, activeFly has shown that early visuomotor interactions optimally and robustly encode self-rotations. This information is flexible used based on the fly’s forward speed to control steering. These findings have been published in:
1. Fujiwara, et al, and Chiappe (2017) Nat Neurosci. PMID: 27798632
2. Erginkaya, et al., and Chiappe (2023) BiorXiv 552150
3. Cruz, et al., and Chiappe (2021) Curr Biol PMID: 34499851
4. Fujiwara, et al., and Chiappe (2022) Neuron PMID: 35525243
5. Cruz, Marques and Chiappe, in prep
6. Cruz and Chiappe (2023) Curr Opin in Neurobiol PMID: 37651855
7. Chiappe (2023) Curr Opin in Neurobiol PMID: 37453230
To explore the function of the identified network, we conducted four distinct studies. Initially, we examined the role of visual feedback in walking control. This study demonstrated that flies swiftly stabilize gaze by reducing the gain of posture control reflexes. The notion that visual feedback plays a critical role in the interplay between mechanical stability and behavioral goals established a framework for exploring the underlying visuomotor circuits governing gaze stabilization. We hypothesized that HS or H2 cells would contribute to gaze stability by regulating leg placements on a moment-to-moment basis to counter postural reflexes.
Using whole-cell patch recordings, a second study described that HS cells received ascending information from the VNC. This modulation operates on a moment-by-moment bases, facilitating the cells’ rapid control of the ipsilateral foreleg. In addition, HS cells either gradually depolarized or hyperpolarized as a function of several high- vs. low-speed steps, respectively. This study unveiled the source of speed modulation in HS cells and its function: to facilitate rapid and context-dependent recruitment of optic flow-sensitive neurons for steering control.
In a third study, using whole-cell patch recordings and 2-photon optical imaging from different elements of the network confirmed their sensitivity to body rotations, thus validating predictions derived from the connectivity analysis. We used visual stimuli to precisely modulate activity at the network's input (the optic flow-sensitive cells) and assess signal propagation throughout the network. Our findings showed that the network architecture fosters competitive disinhibition between the right and left sides, enabling robust extraction of rotational information during translation.
A four study examined the interplay between visual and motor signals within the network by manipulating visual information. This study showed that optic flow-sensitive cells optimally integrate multimodal information, enhancing the weighting for non-visual signals as visual uncertainty escalates. Altogether, these findings strongly support the idea that non-visual information robustly encode self-rotations. Moreover, our work illustrates that inhibitory neurons within the network regulate optimal multimodal integration via normalization.
In summary, activeFly has shown that early visuomotor interactions optimally and robustly encode self-rotations. This information is flexible used based on the fly’s forward speed to control steering. These findings have been published in:
1. Fujiwara, et al, and Chiappe (2017) Nat Neurosci. PMID: 27798632
2. Erginkaya, et al., and Chiappe (2023) BiorXiv 552150
3. Cruz, et al., and Chiappe (2021) Curr Biol PMID: 34499851
4. Fujiwara, et al., and Chiappe (2022) Neuron PMID: 35525243
5. Cruz, Marques and Chiappe, in prep
6. Cruz and Chiappe (2023) Curr Opin in Neurobiol PMID: 37651855
7. Chiappe (2023) Curr Opin in Neurobiol PMID: 37453230
The development of the exploratory walking paradigm in the Virtual Reality system (FlyVRena) has established the fly Drosophila melanogaster as a solid model to study visually guided walking control. We have made FlyVRena freely accessible (https://github.com/ChiappeLab(öffnet in neuem Fenster)) for its widespread distribution. Ongoing work is adapting FlyVRena to measure and manipulate the activity of neurons from the brain and the VNC. Future work will combine quantitative behavioral analysis with state-of-the-art genetic and connectivity tools to examine the contribution of genetically identified neurons to continuous and intermittent control.
Studying circuits for self-motion estimation provides an opportunity to gain insight into how these circuits collaborate for precise motor control. Self-motion estimation plays a crucial role as an intermediary between motor planning and execution, providing an opportunity tp develop a comprehensive understanding of the coordinated functioning throughout the Central Nervous System (CNS).
Studying circuits for self-motion estimation provides an opportunity to gain insight into how these circuits collaborate for precise motor control. Self-motion estimation plays a crucial role as an intermediary between motor planning and execution, providing an opportunity tp develop a comprehensive understanding of the coordinated functioning throughout the Central Nervous System (CNS).