Abstract
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.
About the author
Studied Psychology at the University of Bremen, before completing a PhD in neuroimaging at the Hanse Institute for Advanced Study, the Neurology Department of Magdeburg University and the University of Bremen. Following postdoc positions at the Plymouth Institute of Neuroscience and the Institute of Cognitive Neuroscience in London, he then became head of an independent research group at the Max Planck Institute for Cognitive and Brain Sciences, Leipzig. In2006 he was appointed Professor for Theory and Analysis of Large Scale Brain Signals at the Bernstein Center for Computational Neuroscience of the Charité Berlin. Since 2009 he is also founding director of the Berlin Center for Advanced Neuroimaging, a joint institution of Charité and Humboldt University.
© 2017 by Walter de Gruyter Berlin/Boston
