How Neuroimaging Can Be Better Utilized to Yield Diagnostic Information about Individuals
Published:27 Mar.2023 Source:Health News
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.
Prior research on brain-wide associated studies (termed 'BWAS') has shown that links between brain function and structure and traits are so weak that thousands of participants are needed to detect replicable effects. However, according to a new commentary published in Nature, 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.
The new article explains that the very weak effects found in the earlier paper do not apply to all brain images and all traits, but rather are limited to specific cases. It outlines how fMRI data from hundreds of participants, as opposed to thousands, can be better leveraged to yield important diagnostic information about individuals. The team explains that the weak associations found in the earlier analysis, particularly through brain images, were collected while people were simply resting in the scanner, rather than performing tasks. But fMRI can also capture brain activity linked to specific moment-by-moment thoughts and experiences.