Brain imaging brings together the technology, methodology, research questions and approaches of a wide range of scientific fields including physics, statistics, computer science, neuroscience, biology, and engineering. Thus, methodological and technological advances that enable us to obtain measurements, examine relationships across observations, and link these data to neuroscientific hypotheses happen in a highly interdisciplinary environment. The dynamic field of machine learning with its modern approach to data mining provides many relevant approaches for neuroscience and enables the exploration of open questions. This state-of-the-art survey offers a collection of papers from the Workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2011, held at the 25th Annual Conference on Neural Information Processing, NIPS 2011, in the Sierra Nevada, Spain, in December 2011. Additionally, invited speakers agreed to contribute reviews on various aspects of the field, adding breadth and perspective to the volume. The 32 revised papers were carefully selected from 48 submissions. At the interface between machine learning and neuroimaging the papers aim at shedding some light on the state of the art in this interdisciplinary field. They are organized in topical sections on coding and decoding, neuroscience, dynamcis, connectivity, and probabilistic models and machine learning.
Editors and Affiliations
Department of Radiology, Medical University of Vienna, Wien, Austria
Georg Langs
Computational Biology Center, IBM T.J. Watson Research Center, Yorktown Heights, USA
Irina Rish
Max Planck Institute for Intelligent Systems, Tübingen, Germany
Moritz Grosse-Wentrup
Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA
Brian Murphy
Bibliographic Information
Book Title: Machine Learning and Interpretation in Neuroimaging
Book Subtitle: International Workshop, MLINI 2011, Held at NIPS 2011, Sierra Nevada, Spain, December 16-17, 2011, Revised Selected and Invited Contributions
Editors: Georg Langs, Irina Rish, Moritz Grosse-Wentrup, Brian Murphy
