NeuroMechFly, a neuromechanical model of adult<i>Drosophila melanogaster</i>

Rios, Lobato; Ozdil, P. Gizem; Ramalingasetty, Shravan Tata; Arreguit, Jonathan; Rosset,; Knott, Graham; Ijspeert, Auke Jan; Ramdya, Pavan

doi:10.1101/2021.04.17.440214

Cited by 8 publications

(4 citation statements)

References 108 publications

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“…The outlier was then replaced with the triangulation result of the pair of cameras with the smallest reprojection error. The output was further processed and converted to angles as described in [60].…”

Section: Methodsmentioning

confidence: 99%

Ascending neurons convey behavioral state to integrative sensory and action selection centers in the brain

Chen

Aymanns

Minegishi

et al. 2022

Preprint

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Knowledge of one's own behavioral state---whether one is walking, grooming, or resting---is critical for contextualizing sensory cues including interpreting visual motion and tracking odor sources. Additionally, awareness of one's own posture is important to avoid initiating destabilizing or physically impossible actions. Ascending neurons (ANs), interneurons in the vertebrate spinal cord or insect ventral nerve cord (VNC) that project to the brain, may provide such high-fidelity behavioral state signals. However, little is known about what ANs encode and where they convey signals in any brain. To address this gap, we performed a large-scale functional screen of AN movement encoding, brain targeting, and motor system patterning in the adult fly, Drosophila melanogaster. Using a new library of AN sparse driver lines, we measured the functional properties of 247 genetically-identifiable ANs by performing two-photon microscopy recordings of neural activity in tethered, behaving flies. Quantitative, deep network-based neural and behavioral analyses revealed that ANs nearly exclusively encode high-level behaviors---primarily walking as well as resting and grooming---rather than low-level joint or limb movements. ANs that convey self-motion---resting, walking, and responses to gust-like puff stimuli---project to the brain's anterior ventrolateral protocerebrum (AVLP), a multimodal, integrative sensory hub, while those that encode discrete actions---eye grooming, turning, and proboscis extension---project to the brain's gnathal ganglion (GNG), a locus for action selection. The structure and polarity of AN projections within the VNC are predictive of their functional encoding and imply that ANs participate in motor computations while also relaying state signals to the brain. Illustrative of this are ANs that temporally integrate proboscis extensions over tens-of-seconds, likely through recurrent interconnectivity. Thus, in line with long-held theoretical predictions, ascending populations convey high-level behavioral state signals almost exclusively to brain regions implicated in sensory feature contextualization and action selection.

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Section: Methodsmentioning

confidence: 99%

Ascending neurons convey behavioral state to integrative sensory and action selection centers in the brain

Chen

Aymanns

Minegishi

et al. 2022

Preprint

Self Cite

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“…Large-scale whole nervous system models 120 will be of critical importance for integrating connectomic, transcriptomic, neural activity and animal behavior measurements across labs, scales, and the nervous system 2 . Further, with the recent development of detailed biomechanical body models for the fruit fly 122 and rodent 123 , we can now contemplate constructing whole animal models spanning brain and body. (a) Responses to moving edges in different directions for T4 and T5 subtypes from the DMN with best task performance.…”

Section: Discussionmentioning

confidence: 99%

Connectome-constrained deep mechanistic networks predict neural responses across the fly visual system at single-neuron resolution

Lappalainen

Tschopp

Prakhya

et al. 2023

Preprint

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We can now measure the connectivity of every neuron in a neural circuit, but we are still blind to other biological details, including the dynamical characteristics of each neuron. The degree to which connectivity measurements alone can inform understanding of neural computation is an open question. Here we show that with only measurements of the connectivity of a biological neural network, we can predict the neural activity underlying neural computation. We constructed a model neural network with the experimentally determined connectivity for 64 cell types in the motion pathways of the fruit fly optic lobe but with unknown parameters for the single neuron and single synapse properties. We then optimized the values of these unknown parameters using techniques from deep learning, to allow the model network to detect visual motion. Our mechanistic model makes detailed experimentally testable predictions for each neuron in the connectome. We found that model predictions agreed with experimental measurements of neural activity across 24 studies. Our work demonstrates a strategy for generating detailed hypotheses about the mechanisms of neural circuit function from connectivity measurements. We show that this strategy is more likely to be successful when neurons are sparsely connected---a universally observed feature of biological neural networks across species and brain regions.

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“…Animal studies have already taken advantage of a synthetic mouse model [84] to generate training data for pose estimation. In other work, a synthetic full body drosophila model [85] permits virtual force measurements. We anticipate that human 3D body models will be both tools for addressing relevant physiological questions concerning human form and providing specific forms of training, validation and testing datasets that lead to improved quantification of both normal function and disease.…”

Section: Discussionmentioning

confidence: 99%

Towards a Visualizable, De-identified Synthetic Biomarker of Human Movement Disorders

Xiao

Rhodin

et al. 2022

JPD

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Human motion analysis has been a common thread across modern and early medicine. While medicine evolves, analysis of movement disorders is mostly based on clinical presentation and trained observers making subjective assessments using clinical rating scales. Currently, the field of computer vision has seen exponential growth and successful medical applications. While this has been the case, neurology, for the most part, has not embraced digital movement analysis. There are many reasons for this including: the limited size of labeled datasets, accuracy and nontransparent nature of neural networks, and potential legal and ethical concerns. We hypothesize that a number of opportunities are made available by advancements in computer vision that will enable digitization of human form, movements, and will represent them synthetically in 3D. Representing human movements within synthetic body models will potentially pave the way towards objective standardized digital movement disorder diagnosis and building sharable open-source datasets from such processed videos. We provide a perspective of this emerging field and describe how clinicians and computer scientists can navigate this new space. Such digital movement capturing methods will be important for both machine learning-based diagnosis and computer vision-aided clinical assessment. It would also supplement face-to-face clinical visits and be used for longitudinal monitoring and remote diagnosis.

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NeuroMechFly, a neuromechanical model of adultDrosophila melanogaster

Cited by 8 publications

References 108 publications

Ascending neurons convey behavioral state to integrative sensory and action selection centers in the brain

Ascending neurons convey behavioral state to integrative sensory and action selection centers in the brain

Connectome-constrained deep mechanistic networks predict neural responses across the fly visual system at single-neuron resolution

Towards a Visualizable, De-identified Synthetic Biomarker of Human Movement Disorders

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