Brain-wide representations of behavior spanning multiple timescales and states in C. elegans

Atanas, Adam A.; Kim, Jungsoo; Wang, Ziyu; Bueno, Eric; Becker, McCoy; Kang, Di; Park, Jungyeon; Kramer, Talya S.; Wan, Flossie K.; Baskoylu, Saba; Dag, Ugur; Kalogeropoulou, Elpiniki; Gomes, Matthew A.; Estrem, Cassi; Cohen, Netta; Mansinghka, Vikash K.; Flavell, Steven W.

doi:10.1016/j.cell.2023.07.035

Cited by 35 publications

(58 citation statements)

References 55 publications

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“…It may also be possible in a simple organism like C. elegans to estimate metabolic costs to utilize each behavioral state [109]. The multitude of genetic tools [110], the ability to image neurons in behaving animals [80–84] and to quantify behavior using our methods may make C. elegans an ideal system to look inside of an organism performing the dynamic loop of experiencing the world, making decisions based on those observations and its internal model of the world, and updating that internal model based on the outcomes of those decisions and their effect on the environment.…”

Section: Discussionmentioning

confidence: 99%

A Markovian dynamics forC. elegansbehavior across scales

Costa,

Ahamed,

Jordan

et al. 2023

Preprint

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How do we capture the breadth of behavior in animal movement, from rapid body twitches to aging? Using high-resolution videos of the nematode wormC. elegans, we show that a single dynamics connects posture-scale fluctuations with trajectory diffusion, and longer-lived behavioral states. We take short posture sequences as an instantaneous behavioral measure, fixing the sequence length for maximal prediction. Within the space of posture sequences we construct a fine-scale, maximum entropy partition so that transitions among microstates define a high-fidelity Markov model, which we also use as a means of principled coarse-graining. We translate these dynamics into movement using resistive force theory, capturing the statistical properties of foraging trajectories. Predictive across scales, we leverage the longest-lived eigenvectors of the inferred Markov chain to perform a top-down subdivision of the worm's foraging behavior, revealing both "runs-and-pirouettes" as well as previously uncharacterized finer-scale behaviors. We use our model to investigate the relevance of these fine-scale behaviors for foraging success, recovering a trade-off between local and global search strategies.

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Section: Discussionmentioning

confidence: 99%

A Markovian dynamics forC. elegansbehavior across scales

Costa,

Ahamed,

Jordan

et al. 2023

Preprint

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“…At the end of each imaging session, they were immobilized by cooling and multi-spectral images were captured for NeuroPAL annotations. The live-tracking microscope, software for extracting calcium and behavioral data, and procedure for neuron annotation have been previously described and validated 61 .…”

Section: Resultsmentioning

confidence: 99%

“…Multiple animals were added onto 500ul agar pads and immobilized with 100uM sodium azide (Sigma Aldrich). For each reporter strain, animals on OP50 and PA14 were imaged using a custom Andor spinning disk confocal system, containing a 5000 rpm Yokogawa CSU-X1 spinning disk unit and Borealis upgrade, built on a Nikon ECLIPSE Ti microscope 61 at the same laser intensity using a 10X objective (CFI Plan Fluor 10x, Nikon). Images were analyzed using Fiji where a region of interest (ROI) was drawn around the region of expression, extending from the region immediately posterior to the pharyngeal bulb along the full length of the animal’s body.…”

Section: Methodsmentioning

confidence: 99%

“…Since infected animals generally expressed dimmer fluorescence, we excited each fluorophore with higher laser intensities during freely-moving recordings than previously described 61 . During freely-moving recording, we excited GCaMP7f with a 488 nm laser at 14% of its maximal intensity and TagRFP (together with CyOFP and mNeptune) with a 561 nm laser at 19% of its maximal intensity, respectively.…”

Section: Methodsmentioning

confidence: 99%

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Pathogen infection induces sickness behaviors by recruiting neuromodulatory systems linked to stress and satiety inC. elegans

Pradhan,

Madan,

Kang

et al. 2024

Preprint

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SUMMARYWhen animals are infected by a pathogen, peripheral sensors of infection signal to the brain to coordinate a set of adaptive behavioral changes known as sickness behaviors. While the pathways that signal from the periphery to the brain have been intensively studied in recent years, how central circuits are reconfigured to elicit sickness behaviors is not well understood. Here we find that neuromodulatory systems linked to stress and satiety are recruited upon infection to drive sickness behaviors inC. elegans. Upon chronic infection by the bacteriumPseudomonas aeruginosaPA14,C. elegansdecrease their feeding behavior, then display reversible bouts of quiescence, and eventually die. The ALA neuron and its neuropeptides FLP-7, FLP-24, and NLP-8, which control stress-induced sleep in uninfected animals, promote the PA14-induced feeding reduction. However, the ALA neuropeptide FLP-13 instead acts to delay quiescence and death in infected animals. Cell-specific genetic perturbations show that the neurons that release FLP-13 to delay quiescence in infected animals are distinct from ALA. A brain-wide imaging screen reveals that infection-induced quiescence involves ASI and DAF-7/TGF-beta, which control satiety-induced quiescence in uninfected animals. Our results suggest that a common set of neuromodulators are recruited across different physiological states, acting from distinct neural sources and in distinct combinations to drive state-dependent behaviors.

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“…In the case of the model organism, Caenorhabditis elegans ( C. elegans ), such an approach can identify neuronal and genetic origins of the locomotive behavior. 1–6 The millimeter long nematode has a simple and well-characterized nervous system, which is uniquely advantageous for understanding the molecular and cellular bases of biological processes. 7,8 Because of its small size and fluorescence microscopy-based readout of neuronal activities, multi-neuronal imaging has been traditionally achieved in physically or chemically immobilized animals; 9,10 a mobile animal would readily escape the imaging field of view and introduce confounding motion artifacts.…”

Section: Introductionmentioning

confidence: 99%