Cedars-Sinai researchers have created the most biorealistic and complex computer models of individual brain cells – in unparalleled quantities. Their research, published today in the peer-reviewed journal Mobile Studies, details how these models could one day answer questions about neurological disorders — and even the human intellect — that are unattainable to explore by through biological experiments.
“These models capture the shape, timing and speed of electrical signals that neurons fire in order to communicate between them, which is considered the foundation of brain function,” said Costas Anastassiou, PhD, a researcher in the Department of Neurosurgery at Cedars-Sinai, and lead author of the study. “This allows us to reproduce brain activity at the level of a single cell.”
The models are the first to combine data sets from different kinds of experiments laboratory to present a comprehensive impression of the electrical, genetic and biological activity of individual neurons. The models can be used to test theories that would require dozens of experiments to review in the lab, Anastassiou said.
“Imagine you wanted to study how 50 different genes affect a cell’s biological processes,” Anastassiou said. “You would need to create a separate experiment to ‘knock out’ each gene and see what happens. Thanks to our computer models, we will be able to modify the recipes of these genetic markers for as many genes as we want and predict what will happen.”
Another advantage of the models is that they allow researchers to completely control the experimental conditions. This opens up the possibility of establishing that a parameter, such as a protein expressed by a neuron, causes a change in the cell or a disease, such as epileptic seizures, Anastassiou said. In the laboratory, investigators can often show an association, but it is difficult to prove an induce.
“In laboratory experiments, the researcher does not control everything,” said Anastassiou. “Biology controls a lot of things. But in a computer simulation, all parameters are under the control of the creator. In a model, I can change one parameter and see how it affects another, which is very difficult to do in a biological experiment.”
To create their models, Anastassiou and his team at the Anastassiou Lab — members of the departments of neurology and neurosurgery, the Board of Governors Regenerative Medication Institute, and the Center for Neural Science and Medicine at Cedars-Sinai, used two different datasets. on the mouse’s primary visual cortex, the area of the brain that processes information from the eyes.
The first data set presented complete genetic images of tens of thousands of individual cells. The second linked the electrical responses and physical characteristics of 230 cells in the same brain region. The researchers used machine learning to integrate these two datasets and create biorealistic models of 9 200 unique neurons and their electrical activity.
“This work represents a significant advance in high-performance computing,” said Keith L. Black, MD, chair of the Department of Neurosurgery and Ruth and Lawrence Harvey Chair in Neuroscience at Cedars- Sinaï. “It also gives researchers the ability to look for relationships within and between cell types and to better understand the function of cell types in the brain.”
The study was conducted in collaboration with the Allen Institute for Brain Science in Seattle, which also provided data.
“This work led by Dr. Anastassiou fits well with the Cedars-Sinai’s commitment to bringing together math, statistics, and computer science with technology to answer all important questions in biomedical research and healthcare,” said Jason Moore, PhD, chair of the Department of Computational Biomedicine . “Ultimately, this computer race will help us understand the deepest mysteries of the human brain.”
Anastassiou and his team then work to create computer models of human cells to study brain function and disease in humans.
Funding: The research was funded by grant number RO1 NS120300-01 from the Countrywide Institutes of Wellbeing.