Summary: The researchers claim to have created the most biorealistic and complex computer models of individual brain cells.
Source: Cedars of Sinai
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 Cell reportsdetails how these models could one day answer questions about neurological disorders — and even the human intellect — that are impossible to explore through biological experiments.
“These patterns capture the shape, timing, and speed of electrical signals that neurons fire in order to communicate with each other, which is believed to be the basis of brain function,” said Costas Anastassiou, Ph.D., a researcher at 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 types of laboratory experiments to present a complete picture 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 biological processes in a cell,” Anastassiou said. “You would need to create a separate experiment to ‘knock out’ each gene and see what happens. Using our computer models, we will be able to modify the recipes for these genetic markers for as many genes as we want and predict what will happen.
Another advantage of 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 a association, but it is difficult to prove a cause.
“In lab experiments, the researcher doesn’t control everything,” Anastassiou said. “Biology controls a lot. 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 Medicine Institute, and the Center for Neural Science and Medicine at Cedars-Sinai, used two different datasets on the primary mouse visual cortex, the area of the brain that processes information from the eyes.
The first dataset presented complete genetic images of tens of thousands of individual cells. The second linked the electrical responses and physical characteristics of 230 cells from 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, chairman of the Department of Neurosurgery and Ruth and Lawrence Harvey Chair in Neuroscience at Cedars-Sinai. “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 aligns well with 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, Ph.D., chair of the Department of Computational Biomedicine. “Ultimately, this computational leadership 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.
About this neuroscience research news
Original research: The findings will appear in Cell reports