Multivariate decoding of fMRI data: Towards a content-based cognitive neuroscience

摘要

The advent of functional magnetic resonance imaging (fMRI) of brain function 20 years ago has provided a new methodology for non-invasive measurement of brain function that is now widely used in cognitive neuroscience. Traditionally, fMRI data has been analyzed looking for overall activity changes in brain regions in response to a stimulus or a cognitive task. Now, recent developments have introduced more elaborate, content-based analysis techniques. When multivariate decoding is applied to the detailed patterning of regionally-specific fMRI signals, it can be used to assess the amount of information these encode about specific task-variables. Here we provide an overview of several developments, spanning from applications in cognitive neuroscience (perception, attention, reward, decision making, emotional communication) to methodology (information flow, surface-based searchlight decoding) and medical diagnostics.