There are many questions that remain unanswered about the nature of several neurological disorders. Cedars-Sinai researchers have set out to investigate this, creating the most realistic computer models of thousands of different brain cells.
“These models capture the shape, timing and speed of the electrical signals that neurons fire in order to communicate with each other, which is considered the basis of brain function,” says senior author Dr. Costas Anastassiou, PhD, a research scientist in the Department of Neurosurgery at Cedars-Sinai. “This lets us replicate brain activity at the single-cell level.”
These models are the first to have the capacity to combine data sets from several lab experiments, providing a clear picture of the exclusive electrical, genetic, and biological activities of neurons. Often, testing different hypotheses and theories requires the examination of several different experiments, so these models have helped fulfill this need.
To create the models, Anastassiou and his team utilized two different sets of data on the mouse primary visual cortex, which is the area of the brain that processes information received from the eyes. The first data set presented detailed genetic pictures of thousands of single cells. The second set connected the electrical responses and physical traits of 230 cells from the same region in the brain. Researchers then used machine learning technology to blend these two datasets and create biologically-realistic models of 9,200 neurons.
This development can allow researchers to completely be in the driving seat when it comes to experimental conditions. It’s a groundbreaking achievement because often, despite how strong a finding is, researchers are only really limited to claiming an association or correlation, but not a cause. In neurology, this is especially true, but these models may begin to change that outcome.
“In laboratory experiments, the researcher doesn’t control everything,” says Anastassiou. “Biology controls a lot. But in a computational simulation, all the parameters are under the creator’s control. In a model, I can change one parameter and see how it affects another, something that is very hard to do in a biological experiment.”
Understanding the brain’s most complex processes on a deeper level seemed far more unattainable than it does now, in light of this advancement. The work has drawn on statistical analysis, computer science, and technology ideas to answer key questions that can translate to clinical and biomedical fields. Next, the research team hopes to create similar models using human cells to study general neurological functions as well as common diseases.
This study is published in the journal Cell Reports.
