C. elegans. [Arturo Agostino] To understand the full relationship between brain activity and behavior, scientists need a way to map this relationship for all of the neurons across a whole brain, something that has so far remained an unsolved challenge. Researchers at the Picower Institute for Learning and Memory at MIT have now developed technologies that can record high-fidelity brain wide activity in the model organism Caenorhabditis elegans , and devised a mathematical model to help interpret how each neuron in the tiny worm encodes behavior.
Applying that model specifically to each cell, the team produced an atlas of how most of the brain cells, and the circuits they take part in, encode the animal’s essential behaviors, such as movement and feeding. The resulting atlas effectively outlines the underlying “logic” of how the worm’s brain produces a sophisticated and flexible repertoire of behaviors, even as its environmental circumstances change.
“This study provides a global map of how the animal’s nervous system is organized to control behavior,” said Steven Flavell, associate professor in MIT’s Department of Brain and Cognitive Sciences. “It shows how the many defined nodes that make up the animal’s nervous system encode precise behavioral features, and how this depends on factors like the animal’s recent experience and current state.” Flavell is senior author of the team’s published paper in Cell , which is titled “ Brain-wide representations of behavior spanning multiple timescales and states in C. elegans .” The team has made its data, and the findings of their model and atlas, available at the WormWideWeb . Changes in an animal’s behavior and internal state are accompanied by widespread changes in activity across its brain, the authors wrote, and while the neural circuits that control these behaviors are distributed across the brain, how neurons encode behavior, and how this encoding is impacted by state isn’t well understood. “Animals must adapt their behavior to a constantly changing environment,” they pointed out. However, given the vast number of cell types in mammals that may be involved in behavior, and their broad spatial distributions in the brain, characterizing this entire system has not been tractable, the team further stated. “… it is challenging to record activity across the brain of a freely moving animal and relate brain-wide activity to comprehensive behavioral information. For this reason, it has remained unclear how neurons and circuits across entire nervous systems represent an animal’s varied behavioral repertoire and how this flexibly changes depending on context or state.”
In contrast to the complexity in mammals, C. elegans may represent a model system that could allow investigators to better study these relationships. The C. elegans nervous system comprises just 302 neurons with known connectivity. The animal exhibits a well-defined repertoire of motor functions, from locomotion, to feeding, head oscillation, defecation, egg-laying, and postural changes. C. elegans also expresses different behaviors as it switches states, the investigators continued. For example, the organism enters sleep-like states after intense stress, while awake animals exhibit different foraging states, and aversive stimuli trigger sustained states of heightened arousal. “In C. elegans , it may be feasible to decipher how behavior is encoded across an entire nervous system and how this can flexibly change across behavioral states,” the researchers suggested. The results of previous studies, including brain recordings in immobilized animals, have indicated that many neurons carry behavioral information in the worm, but, as the team stated, “we still lack an understanding of how quantitative behavioral features are encoded by most C. elegans neurons.”
To make the measurements needed to develop their model, Flavell’s lab developed a new type of microscope and software system that automatically tracks almost all behaviors of the worm—movement, feeding, sleeping, egg-laying, etc.—and the activity of every neuron in its head, using a fluorescence system in which the cells are engineered to flash when calcium ions build up. “We built a microscopy platform for brain-wide calcium imaging in freely moving animals and wrote software to automate processing of these recordings,” the team stated.
Reliably distinguishing and tracking separate neurons as the worms moved or bent also required writing custom software, utilizing the latest tools from machine learning. “We also wrote software that extracts behavioral variables from the brightfield images: velocity, body posture, feeding (or pharyngeal pumping), angular velocity, and head curvature (bending of the head, associated with steering).”
The team confirmed the platform to be 99.7% accurate in sampling the activity of individual neurons, with greatly improved signal-to-noise compared to previous systems. The team then used the system to record simultaneous behavior and neural data from more than 60 worms as they moved freely about their environment.
Data analysis revealed three novel observations about neural activity in the worm: that neurons track behavior not only of the present moment but also the recent past; that neurons also tuned their encoding of behaviors, such as motion, based on a surprising variety of factors; and that that many neurons simultaneously encode multiple behaviors.
For example, while the behavior of wriggling around a lab dish might seem like a very simple act, neurons represented factors such as speed, steering, and whether the worm was eating or not. In some cases they represented the animal’s motion spanning back in time by about a minute. “Most neurons primarily encoded current behavior, but a sizable subset weighed past behavior,” the team stated. By encoding recent, rather than just current motion, these neurons could then help the worm compute how its past actions influenced its current outcome. Many neurons also combined behavioral information to execute more complex maneuvers. Akin to a human driver remembering to steer the car in the opposite way when going in reverse, compared with when going forwards, certain neurons in the worm’s brain integrated the animal’s direction of motion and steering direction.
By carefully analyzing these kinds of patterns of how neural activity correlated with behaviors the scientists developed the C. elegans Probabilistic Neural Encoding Model (CePNEM). The model, encapsulated in a single equation, accounts for how each neuron represents various factors to accurately predict whether and how the neural […]