A research collaboration headed up at the National University of Singapore (NUS) has successfully employed machine learning to investigate the cellular architecture of the human brain. The approach uses functional MRI (fMRI) data to automatically estimate brain parameters, enabling neuroscientists to infer the cellular properties of different brain regions without having to surgically probe the brain. The researchers say that their technique could potentially be used to assess treatment of neurological disorders or develop new therapies (Science Advances 10.1126/sciadv.aat7854). "The underlying pathways of many diseases occur at the cellular level, and many pharmaceuticals operate at the microscale level," explains team leader Thomas Yeo. "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." Currently, most human brain studies employ non-invasive approaches such as MRI, which limits examination of the brain at a cellular level.
Jan-12-2019, 11:02:31 GMT