Swiss researchers based in Geneva and Lausanne have successfully applied a “new computational method” to the study of functional magnetic resonance imaging (fMRI), so sharpening the generally fuzzy images to which neuroscience has been accustomed and enabling the distinction of up to 13 separate, colour-coded neural networks operating at any given time within the human brain.
This is about more than just getting prettier pictures, as distinguishing separate neural networks should improve our understanding of their inter-relationships and the circumstances in which brain disorders emerge. And we can note, in passing, the happy irony that while machine learning specialists have long been interested in how the human brain works, the inspirations can work the other way too: neuroscientists using the power of computers to enhance their study of the brain.
A challenge emerges as more researchers use more powerful computers to develop deeper understandings of the workings of the most complex organ in the known universe: how do we assimilate and build upon this tsunami of data and accumulation of knowledge? Scientists at Stanford University, backed by the US National Science Foundation, have established an initiative called Open fMRI to enable the sharing of knowledge via a computer database that will be available to scientists everywhere.