What drives decision-making in the brain? Imagine a behavioral task during which a mouse in a maze learns that a cue means it should turn left to get a reward. After weeks of training to learn this, the cue shows up, and the mouse needs to decide to turn left or right. What makes the mouse turn left? During the task, neurons in a specific part of the cortex - called the posterior parietal cortex or PPC - become associated with left or right turns. These neurons are not only active during a turn but also before the mouse turns, so their activity can predict a turn direction. Are these neurons driving decisions? This age-old question is hard to answer with neuronal activity studies alone, and more knowledge was needed about how left- and right-turn neurons connect with each other. And that’s exactly what connectomics is about.
Connectomics is a research field that aims to make comprehensive wiring maps of brain circuits. To build those maps, researchers use electron microscopy (EM) to image thousands of very thin sections of the brain. In a new study published in Nature on February 21, Aaron Kuan, PhD, assistant professor of Neuroscience, and his collaborators used a custom-built automated EM system to image a decision-making circuit within the mouse brain. They needed months of uninterrupted (and automated) imaging time, and an even longer period to trace synapses and dendrites from each image, to finally reconstruct the circuit. By combining this circuit map, or connectome, with a behavioral decision-making task and real-time recording of neurons, the team discovered a pattern of circuit connections – a circuit motif – they called opponent inhibition: when an excitatory left-turn neuron is activated, it suppresses right-turn activity through inhibitory neurons.
It intuitively makes sense in the context of binary decision-making. If the neuronal activity starts telling you “Ok, I’m kind of thinking about turning left”, the circuit makes it become a very strong left-turn signal and doesn’t tolerate any indecisive, both left and right, signal.
Aaron Kuan, PhD
Kuan went then further and collaborated with the team of Stefano Panzeri, PhD, to model these circuits to understand how they compute information. They showed that this opponent inhibition motif does help make more accurate decisions. Opponent inhibition has been a potential mechanism suggested by previous theoretical work in decision-making, but it’s the first time that researchers have shown it in the cortex.
You have these networks of thousands, or even millions of neurons working together. In this study, we've shown that our combination of techniques is enough to reveal really interesting and important connectivity motifs and relate that to the activity of the neurons and the function of the circuit in general.
Aaron Kuan, PhD
"You could apply this method to see patterns that are important for any behavior you are studying," adds Kuan. This opens the door to comparative work - finding and understanding differences between brain regions - but it could also be used to study disease models, development, or aging.
Kuan is bringing to Yale some of this high-throughput electron microscopy technology, emphasizing both tool development and the biology of neural circuits in his independent research program. The Kuan lab is currently hiring. Prospective Kuan lab members should contact Aaron Kuan via email or the lab website’s contact form.
The findings were published on February 21 in the journal Nature. The co-first author is a new Yale Neuroscience assistant professor, Aaron Kuan. The work was conducted during his postdoctoral training at Harvard Medical School in the labs of Christopher Harvey and Wei-Chung Lee, in collaboration with the lab of Stefano Panzeri at Istituto Italiano di Tecnologia, Genoa (Italy) and UKE, Hamburg (Germany).