The Future of Brain Mapping Starts with a Zebrafish
Stuti Jain | March 3, 2022
Dr. Mika Rubinov is a Vanderbilt Assistant Professor of Biomedical Engineering with a research focus on modeling neuroscience networks. He has had a longstanding interest in applying computational techniques and while studying neuroscience, he thought that combining both those fields would make for interesting research. So, he decided to pursue a PhD in this area, and has been working on such research ever since.
Starting with zebrafish brain
The National Science Foundation recently awarded a $600,000 grant to Dr. Rubinov to develop new computational methods for analysis of large-scale brain activity data. Dr. Rubinov explains that the grant is going to be used to develop methods to infer neuromodulatory interactions in the zebrafish brain first. After these methods have been validated, the next step is to try and scale it to human brains.
The grant is in collaboration with Dr. Takashi Kawashima of the Weizmann Institute and Dr. Catie Chang, a Vanderbilt Assistant Professor of Electrical and Computer Engineering. Each collaborator has a different focus that will be used to push this project forward. Dr. Chang is an expert in human neuroimaging and in the study of neuromodulation in humans while Dr. Kawashima is an expert in zebrafish neuroimaging. Neuromodulation is a physiological process in which a neuron uses one or more chemicals to act upon and therefore regulate different populations of neurons.
Building framework for neuromodulatory circuits
This project involves the analysis of two types of brain activity: calcium imaging data from zebrafish and fMRI recordings in humans. Dr. Rubinov and his collaborators’ first step is to use a modeling framework that tries to remove types of activity that are more fast paced and less modulatory in nature, and this will be done in zebrafish. This framework will allow them to build a better map of the neuromodulatory circuits in zebrafish, especially in the subcortex. The validation of their computational methods in zebrafish will allow them to use similar methods on fMRI data.
The first part of this three-year project started in the fall. Dr. Rubinov and Dr. Chang had been working with Dr. Takashi on building simulations to validate their computational approach. They are now applying their validated approach to the data sets to test their initial hypothesis that their developed framework will allow for accurate neuromodulation inference in a non-invasive way.
Dr. Rubinov hopes that this project can help advance brain mapping technology. “In general, the idea of bridging species and scales is very exciting, and can help us develop tools that allow more accurate mapping of brain circuitry. We can do lots of different things with such tools in the future.”