A team led by Assistant Professor Thomas Yeo from the Singapore Institute for Neurotechnology (SINAPSE) at NUS, the Department of Electrical and Computer Engineering and the A*STAR-NUS Clinical Imaging Research Centre (CIRC), has found a way to harness machine learning to uncover new insights into the cellular architecture of the human brain, which may offer novel insights into the development, and potential treatment, of various neurological disorders.
“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 Asst Prof Yeo.
Research teams around the world have used biophysical brain models to simulate brain activity and gain insights into the brain —- the most intricate organ of the human body. However, many of these models rely on overly simplistic assumptions, for instance that all brain regions have the same cellular properties.
Adopting a different approach, the research team analysed functional magnetic resonance imaging data from 452 participants of the Human Connectome Project, allowing each brain region to have distinct cellular properties and exploiting machine learning algorithms to automatically estimate the parameters of the brain, without the use of surgery.
In the process they found that brain regions involved in sensory perception — such as vision, hearing and touch — exhibit cellular properties opposite from brain regions involved in internal thoughts and memories. In addition, the spatial pattern of the brain’s cellular architecture closely reflects how the brain hierarchically processes information. This form of hierarchical processing is also a key feature of recent advances in artificial intelligence.
The study, conducted in collaboration with researchers from the Netherlands and Spain, was published in Science Advances on 9 January.
Looking forward, the team plans to use their approach to examine the brain data of individual participants in order to better understand how variation in the brain’s cellular architecture may relate to differences in cognitive abilities, and to further the development of personalised treatment plans for patients.
See press release.