Neural Control of Economic Decision-Making
Michael Nitabach, Yale
Work by Dipon Ghosh
Copious amounts of free Indian food for lunch notwithstanding, this neuroscience seminar was quite interesting — and a bit more accessible than some for someone lacking a background in anything even remotely brain related. The speaker, Michael Nitabach, is a professor at Yale who made an eight year detour into law between his doctoral and postdoctoral work before coming back to science; there’s a Story Collider podcast about his career journey.
Animals in the wild are often faced with conflicting threats and rewards that they must balance out to make a decision. For example — in Disney’s Cinderella, Gus (the chunky mouse) must make a decision between collecting more cheese and facing the wrath of Lucifer (the cat) or running away from Lucifer and missing out on the cheese. The decision that a real-life Gus would make depends on many factors — how good the cheese is, how dangerous the cat is, how hungry Gus is, etc. If Gus is so hungry that he will die regardless if he doesn’t get food, he might as well brave the danger of a confrontation with Lucifer, whereas if he’s full, he has no reason to brave even a low level of danger to get food.
To study this in a lab setting, Nitabach turned to my favorite model organism — C. elegans. C. elegans are nematodes, or small worms, often used as model systems because of their easy maintenance, rapid life cycle, transparency for imaging, and very simple nervous system; there are only 302 neurons in the hermaphrodite worm as compared to humans’ 86 billion neurons!
These worms live in soil and rotting organic material and subsist on the bacteria they find there. While navigating through these living spaces, worms will encounter different concentrations of diacetyl, a compound that bacteria produce. A high concentration indicates a payload of bacteria to eat, hence, a “reward.” As a “threat,” consider the concept of osmolarity. Osmolarity is a measure of the concentration of a solution, or how many particles of something there are in a particular volume. If there is a very high concentration of a compound outside of the worm’s body — meaning a lot of particles, not much water — and a very low concentration comparatively inside of the worm, water from inside the worm will move outside to make the concentrations equal out, leaving our poor little worm all dried out. Because of this, worms sense higher concentration areas — areas with higher osmolarity — as a threat. With a threat and a reward defined for the worms, the researchers were able to develop an experimental set-up:
Worms are placed in the middle of a petri dish surrounded by a circle of high osmolarity fructose (the “danger ring”). Two drops of diacetyl are placed on opposite sides of the petri dish outside of the danger ring. In order to reach these spots, the worms must risk passing through the danger ring. How does a worm with only 302 neurons gauge the danger level of the threat versus the magnitude of the reward — in this case repulsive and attractive scents — and determine how to react? While their nervous system is small, there is still a lot of complex behavior possible with 302 neurons. Information about the scents is picked up by sensory neurons which then send a signal — by way of an interneuron, or a neuron that relays information between neurons — to the neurons that control the worm’s motion. If the scent is repulsive, this signaling pathway leads to backward motion away from the scent; if it is attractive, forward motion toward the scent. Thus, whenever a worm encounters the danger ring, it will need to choose to either retreat from the danger towards the center of the ring or exit through it and continue to the diacetyl.
Using different osmolarity danger rings, where a higher osmolarity means higher danger, the researchers found that, as expected, worms are more likely to exit through a lower osmolarity ring. However, when the worms had been starved for a length of time, they were more likely to choose to exit through a higher danger ring to reach the reward. The more starved they were, the more likely they were to exit, even at a high danger level. By quantifying this data and developing a computational neural network model, Nitabach suggests the existence of an osmolarity threshold that determines whether the worms try to exit the danger ring or not. The hunger level of the worms can raise or lower this threshold, dictating when the worms will brave the high osmolarity to reach the diacetyl.