Researchers use personalized simulations of the brain to uncover nature of internal processes

Aug 3 2016

The brain is an adaptable and highly complex system. However, much remains to be known about the way in which individual components of the brain interact in order to produce cognitive performance. Under the leadership of Dr. Petra Ritter, a team of researchers at Charité - Universitätsmedizin Berlin are using personalized simulations to help them uncover the nature of the internal processes involved. Their work is being funded by the European Research Council (ERC), which has allocated a total of €1.87 million over five years.

There are different levels at which brain function may be studied, through the measuring of data related to individual nerve cells, small neural networks or the entire brain. So far, however, we have lacked the ability to simultaneously collect data on all of the brain's spatial and temporal scales. For this reason, we still know very little about the way in which different levels of the brain interact. We also continue to have difficulties in linking perception, understanding and behavior with specific cellular processes. "The aim of our current project is to develop a theoretical network model that will demonstrate and quantify the degree of connectivity between different levels," explains Dr. Ritter, Associate Professor at the Department of Neurology and experimental Neurology.

Dr. Ritter, whose interest in personalized brain simulations dates back a number of years, describes her vision as follows: "We hope to be able to visualize how information is transmitted within the brain. Ultimately, we hope to be in a position to decode both neurological processes and pathologies." The researchers will use data collected on individual processes to develop simulations of the whole brain. Functional imaging techniques continue to be of limited use when dealing with individual patients, and individual prognoses are usually impossible to make. "We need to change this, and we need to develop a kind of 'mathematical microscope' for the brain," explains the researcher. "We want to show that, when combined with imaging technology, computer simulations can help us link up the brain's temporal and spatial scales."

Electroencephalography, functional magnetic resonance imaging, and diffusion tensor imaging - each of these three tools/techniques form a part of the repertoire of Dr. Ritter's interdisciplinary team, to be used to measure brain structure and function. All of these technologies are known to produce enormous data sets. The challenge awaiting the researchers will, therefore, be to combine all of these data into one coherent theory. They will then need to assemble them into a model of the brain. In cooperation with international partners, the researchers have developed 'The Virtual Brain', a brain-modeling platform that allows mathematical models of individual brains to be standardized, in order to create fully reproducible simulations. Aside from promoting collaborations between different research teams from across the globe, this open-source platform also encourages the exchange of information - both of which are of crucial importance for the project, as they help maximize the collection of data sets and comprehensive theories.

The researchers will use supercomputers to process enormously complex and detailed simulations, in the hope of learning more about how the brain works and, by extension, what happens when it stops to function normally. "We are hoping that these brain simulations will enable us to make prognoses regarding certain conditions and the ways in which they might progress. Our technology, which is able to provide information on the effect of drug-based interventions and interventions aimed at changing the anatomy of the brain, may even prove to be key to the development of new biomarkers and therapies," says Dr. Ritter. As part of this ERC-funded project, the researchers will use non-invasive imaging data from healthy adults to draw inferences about the processes associated with age-related changes in brain function.