NUS scientists harness machine learning to uncover new insights into the human brain
Study uses non-invasive neuroimaging data to reveal cellular properties of different brain regions, providing a new avenue to examine neurological disorders
An inter-disciplinary research team led by scientists from the National University of Singapore (NUS) has successfully employed machine learning to uncover new insights into the cellular architecture of the human brain.
The team demonstrated an approach that automatically estimates parameters of the brain using data collected from functional magnetic resonance imaging (fMRI), enabling neuroscientists to infer the cellular properties of different brain regions without probing the brain using surgical means. This approach could potentially be used to assess treatment of neurological disorders, and to develop new therapies.
“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, who is from the Singapore Institute for Neurotechnology (SINAPSE) at NUS, and the A*STAR-NUS Clinical Imaging Research Centre (CIRC).
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