How (not) to interpret Multivariate Decoding Models in Neuroimaging
Multivariate decoding models are replacing traditional univariate statistical tests in the analysis of neuroimaging data. Their interpretation, however, is far from trivial. In this presentation, I outline various pitfalls and discuss under which conditions they can provide insights into the (causal) question of how neuronal activity gives rise to cognition and behavior.
Prof. Dr. Moritz Grosse-Wentrup is full professor and head of the Research Group Neuroinformatics at the University of Vienna, Austria. He develops machine learning algorithms that provide insights into how large-scale neural activity gives rise to (disorders of) cognition, and applies these algorithms in the domain of cognitive neural engineering, e.g., to build brain-computer interfaces for communication with severely paralyzed patients, design closed-loop neural interfaces for stroke rehabilitation, and develop personalized brain stimulation paradigms. He has received numerous awards for his work, including the 2011 Annual Brain-Computer Interface Research Award, the 2014 Teaching Award of the Graduate School of Neural Information Processing at the University of Tübingen, and the 2016 IEEE Brain Initiative Best Paper Award.