Multivariate analysis of neuroimaging data has gained ground very rapidly in the community over the past few years, leading to impressive results in cognitive, affective, and clinical neurosciences. Pattern recognition and machine learning conferences regularly feature a neuroimaging workshop, while neuroscientific meetings dedicate sessions to new approaches to neural data analysis. Thus, a rich two-way flow has been established between disciplines. The goal of the workshop was to facilitate exchange of ideas between scientific communities, with a particular interest in new approaches to the interpretation of neural data driven by new developments in pattern recognition and machine learning.

Workshop Program and Abstracts

Speakers and Talks

John Dylan Haynes (Bernstein Center for Computational Neuroscience Berlin)

• MVPA: Principles, Pitfalls, and Perspectives

Dirk Ostwald (AB Computational Cognitive Neuroscience, Freie Universität Berlin)

• An Introduction to Support Vector Classification

Johannes Stelzer (MPI für Biologische Kybernetik in Tübingen)

• Statistical Inference and Multiple Testing Correction for MVPA

Douglas Garrett (Max Planck Institute for Human Development)

• A Partial Least Squares Framework for Neuroimaging

Stefan Bode (Melbourne School of Psychological Sciences)

• The Application of MVPA to Event-Related Potentials

Martin Hebart (Institut für Systemische Neurowissenschaften Hamburg)

• Design of MVPA Studies

Kai Görgen (Bernstein Center for Computational Neuroscience Berlin)

• Detect, Avoid & Eliminate Confounds in MVPA

Kai Görgen und Martin Hebart

• The Decoding Toolbox

Organizing Committee:

Carmen Morawetz

Contact email:

mvpaworkshop@gmail.com