Since the development of functional magnetic resonance imaging in the 1990s, the reliance on neuroimaging has skyrocketed as researchers investigate how fMRI data from the brain at rest, and anatomical brain structure itself, can be used to predict individual traits, such as depression, cognitive decline, and brain disorders. But how reliable brain imaging is for detecting traits has been a subject of wide debate. Researchers now report that stronger links between brain measures and traits can be obtained when state-of-the-art pattern recognition (or 'machine learning') algorithms are utilized, which can garner high-powered results from moderate sample sizes.
