A study using zebrafish reveals how brainstem neural networks guide eye movement, offering a new model that mimics these networks to predict their activity, potentially aiding eye disorder treatments and enhancing our grasp of short-term memory.
Researchers at Weill Cornell Medicine and their collaborators have uncovered how connections in a network of brainstem neurons control gaze in week-old zebrafish larvae. Published today (November 22) in Nature Neuroscience, the study revealed that a simplified artificial circuit, designed based on this neural architecture, could accurately predict the network’s activity. These findings not only enhance our understanding of how the brain manages short-term memory but also hold the potential for developing innovative treatments for eye movement disorders.
The brain is constantly processing a flood of sensory information from an ever-changing environment. To make sense of this input, it must temporarily retain key details—whether to string together words in a sentence or to maintain visual focus on a specific object. This ability to hold and use sensory information is crucial for forming a coherent understanding of the world.
“Trying to understand how these short-term memory behaviors are generated at the level of neural mechanism is the core goal of the project,” said senior author Dr. Emre Aksay, associate professor of physiology and biophysics at Weill Cornell Medicine, who led the study, together with Dr. Mark Goldman at the University of California Davis and Dr. Sebastian Seung at Princeton University.
Modeling Memory and Movement
To decode the behavior of such neuronal circuits, neuroscientists use the tools of dynamical systems, which involve building mathematical models that describe how the state of a system changes over time, where the current state determines its future states according to a set of rules. A short-term memory circuit, for example, will remain in a single preferred state until a new stimulus comes along, causing it to settle into a new activity state. In the visual-motor system, each of these states can store the memory of where an animal should be looking.
But what parameters help set up that type of dynamical system? One possibility is the anatomy of the circuit: the connections that form between each neuron and how many connections they make. Another likely possibility is the physiological strength of those connections, which is established by a myriad of factors like the amount of neurotransmitter being released, the type of synaptic receptors, and the concentration of those receptors.
To understand the contributions of circuit anatomy, Dr. Aksay and his collaborators looked at larval zebrafish. By five days of age, these fishlets are swimming around and hunting prey, a skill that involves sustained visual attention. Importantly for the research team, the brain region that controls eye movement is structurally similar in fish and mammals. However, the zebrafish system contains only 500 neurons. “So, we can analyze the entire circuit—microscopically and functionally,” Dr. Aksay said. “That’s very difficult to do in other vertebrates.”
Advanced Imaging and Computational Modeling
Using an array of advanced imaging techniques, Dr. Aksay and colleagues identified the neurons that participate in controlling the animals’ gaze and then determined how these neurons are wired together. They discovered that the system consists of two prominent feedback loops, each containing three clusters of tightly connected cells. The researchers used this distinctive architecture to build a computational model. They found that their artificial network could accurately predict activity patterns of the zebrafish circuit which they validated by comparing their results to physiological data.
“I consider myself a physiologist, first and foremost,” Dr. Aksay said. “So, I was surprised how much of the behavior of the circuit we could predict from the anatomical architecture alone.”
Next, the researchers will explore how the cells in each cluster contribute to the behavior of the circuit—and whether the neurons in the different clusters have distinct genetic signatures. Such information could allow clinicians to therapeutically target those cells that may malfunction in eye movement disorders. The findings also provide a blueprint for unraveling the more complex computational systems in the brain that rely on short-term memory, such as those involved in deciphering visual scenes or understanding speech.
Reference: “Predicting modular functions and neural coding of behavior from a synaptic wiring diagram” by Ashwin Vishwanathan, Alex Sood, Jingpeng Wu, Alexandro D. Ramirez, Runzhe Yang, Nico Kemnitz, Dodam Ih, Nicholas Turner, Kisuk Lee, Ignacio Tartavull, William M. Silversmith, Chris S. Jordan, Celia David, Doug Bland, Amy Sterling, H. Sebastian Seung, Mark S. Goldman, Emre R. F. Aksay and the Eyewirers, 22 November 2024, Nature Neuroscience.
DOI: 10.1038/s41593-024-01784-3
This study was supported in part by the National Institutes of Health grants from the National Institute of Neurological Disorders and Stroke R01 NS104926 and Brain initiative award 5U19NS104648; the National Eye Institute R01 EY027036, R01 EY021581 and K99 EY027017; and the National Cancer Institute UH2 CA203710.
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