Abstract
Information about water flow, detected by lateral line organs, is critical to the behavior and survival of fish and amphibians. While certain aspects of water flow processing have been revealed through electrophysiology, we lack a comprehensive description of the neurons that respond to water flow and the network that they form. Here, we use brain-wide calcium imaging in combination with microfluidic stimulation to map out, at cellular resolution, neuronal responses involved in perceiving and processing water flow information in larval zebrafish. We find a diverse array of neurons responding to head-to-tail (h-t) flow, tail-to-head (t-h) flow, or both. Early in this pathway, in the lateral line ganglia, neurons respond almost exclusively to the simple presence of h-t or t-h flow, but later processing includes neurons responding specifically to flow onset, representing the accumulated displacement of flow during a stimulus, or encoding the speed of the flow. The neurons reporting on these more nuanced details are located across numerous brain regions, including some not previously implicated in water flow processing. A graph theory-based analysis of the brain-wide water flow network shows that a majority of this processing is dedicated to h-t flow detection, and this is reinforced by our finding that details like flow velocity and the total accumulated flow are only encoded for the h-t direction. The results represent the first brain-wide description of processing for this important modality, and provide a departure point for more detailed studies of the flow of information through this network.SIGNIFICANCE STATEMENT In aquatic animals, the lateral line is important for detecting water flow stimuli, but the brain networks that interpret this information remain mysterious. Here, we have imaged the activity of individual neurons across the entire brains of larval zebrafish, revealing all response types and their brain locations as water flow processing occurs. We find neurons that respond to the simple presence of water flow, and others attuned to the direction, speed, and duration of flow, or the accumulated displacement of water that has passed during the stimulus. With this information, we modeled the underlying network, describing a system that is nuanced in its processing of water flow simulating head-to-tail motion but rudimentary in processing flow in the tail-to-head direction.