Machine Learning Turns Brain Scans Into Cellular-Level Data

Instead of relying on oversimplistic assumptions, machine learning has helped scientists gain insights into the organization of the brain.

AsianScientist (Jan. 16, 2019) – Machine learning has helped scientists infer the cellular properties of different brain regions without needed to probe the brain with surgery. These findings have been published in Science Advances.

Currently, most human brain studies are limited to non-invasive approaches, such as magnetic resonance imaging (MRI). This limits the examination of the human brain at the cellular level, which may offer novel insights into the development of various neurological diseases.

“The underlying pathways of many diseases occur at the cellular level, and many pharmaceuticals operate at the microscale level. To know what really happens at the innermost levels of the human brain, it is crucial for us to develop methods that can delve into the depths of the brain non-invasively,” said team leader Assistant Professor Thomas Yeo from the National University of Singapore (NUS) and the A*STAR-NUS Clinical Imaging Research Centre.

Different research teams around the world have harnessed biophysical modelling to bridge this gap between non-invasive imaging and cellular understanding of the human brain. The biophysical brain models could be used to simulate brain activity, enabling neuroscientists to gain insights into the brain. However, many of these models rely on overly simplistic assumptions, such as, all brain regions have the same cellular properties, which scientists know to be false.

Instead, Yeo and his team allowed each brain region to have distinct cellular properties and exploited machine learning algorithms to automatically estimate the model parameters. They found that brain regions involved in sensory perception exhibit cellular properties opposite from brain regions involved in internal thought and memories.

“Our approach achieves a much better fit with real data. Furthermore, we discovered that the micro-scale model parameters estimated by the machine learning algorithm reflect how the brain processes information,” said study first author Dr. Peng Wang.

“Our study suggests that the processing hierarchy of the brain is supported by micro-scale differentiation among its regions, which may provide further clues for breakthroughs in artificial intelligence,” added Yeo.

Moving forward, the team plans to apply their approach to examine the brain data of individual participants to better understand how individual variation in the brain’s cellular architecture may relate to differences in cognitive abilities.

The article can be found at: Wang et al. (2019) Inversion of a Large-scale Circuit Model Reveals a Cortical Hierarchy in the Dynamic Resting Human Brain.


Source: National University of Singapore; Photo: Shutterstock.
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