Neurons form a web of connections in our brains but only a small portion of how they relate to our actions, thoughts and feelings are understood. In an effort to further identify and determine how each piece of that web comes together and interacts, the National Science Foundation has awarded 17 Next Generation Networks for Neuroscience (NeuroNex) awards, two of which are projects involving Baylor College of Medicine researchers.
Untangling the web of information from our brains
A group of researchers from Baylor College of Medicine, Rice University, the University of Houston and Notre Dame University have been awarded a $4.3 million grant from the NSF to develop new methods to analyze and interpret neural data.
“We will develop statistical tools and models to generate new insight into the workings of the brain. When you have a lot of data, it becomes harder and much more important to understand how that data creates a complete picture of brain activity,” said Dr. Xaq Pitkow, co-principle investigator on this project and assistant professor of neuroscience at Baylor with a joint appointment at Rice.
Through this project, the researchers will determine how to account for activity that isn’t directly observed but still affects cell behavior.
Pitkow explains that this project is a collaborative effort, with experts from statistics to biology working together. Other researchers include Drs. Kresimir Josic, principle investigator and professor of mathematics with a joint appointment at UH and Rice; Genevera Allen, associate professor of statistics at Rice; Ankit Patel, assistant professor of neuroscience at Baylor; and Robert Rosenbaum, assistant professor of mathematics at Notre Dame University.
The initial work will involve data gathered from the visual cortex of mice, later moving to data captured in more complex situations.
Josic and Rosenbaum will study the link between cellular activity and brain function, developing theoretical models to interpret the data. Allen will lead development and validation of the proposed statistical techniques, while Patel, whose work is focused on machine learning, will focus on training artificial neural networks at tasks that parallel those in experiments. Pitkow, a theoretical neuroscientist, will lead the application of graphical models to the analysis of neural activity, stimuli and behavior for artificial and biological neural networks engaged in tasks.
“An additional goal of this project will be to make sure there is open-source data and complete sharing of information to the scientific community,” said Pitkow, who also is a McNair Scholar.
Shining the light on brain activity
Also working to understand the unknown parts of the brain include Dr. François St-Pierre, assistant professor of neuroscience and a McNair Scholar at Baylor College of Medicine. He, along with his co-principle investigator, Dr. Andreas Tolias, associate professor of neuroscience at Baylor, were awarded an $800,000 Innovation Award from the NSF NeuroNex program. These awards aim to fund development of potentially transformative technologies.
The grant will support their work to develop new sensors for imaging brain activity in animal models that will provide the research community with more powerful tools to study the brain.
“The ability to image brain activity is critical to understanding what is happening in our brain when we learn, think, sense, move, and dream,” said St-Pierre. “However, much of the activity in the brain is so rapid that we need to follow its dynamics with resolution exceeding 1/1000th of a second. Current imaging tools are just too slow to follow fast brain activity.”
At the cellular level, brain activity is represented by changes in the electrical properties or voltage of neurons. To capture this activity, St-Pierre has previously engineered “genetically encoded voltage indicators,” or light-emitting proteins whose brightness track neuronal voltage. However, greater technological advances are needed to use these tools in animal models, especially for challenging conditions when neurons are located deep in the brain.
“There is great potential for this technology, but the current tools need to be brighter and produce a larger signal for them to be routinely used by neuroscientists,” Tolias said. “We’re proposing new methodology for optimizing indicators and ensure they meet or exceed the performance required to image rapid brain activity in animal models.”
This emerging technology promises to fulfill the dream of recording electrical activity from many cells or subcellular locations and with cell type specificity.
NeuroNex is one element of Understanding the Brain, NSF’s multi-year effort to enable a scientific understanding of the full complexity of the brain. Through Understanding the Brain, NSF participates in the national Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative, an alliance of federal agencies and other partners seeking to enhance our understanding of the brain.
Get information and full descriptions of each project: