Proceedings of the 5th Workshop on Machine Learning and Interpretation   in Neuroimaging (MLINI) at NIPS 2015
  This volume is a collection of contributions from the 5th Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI) at the Neural Information Processing Systems (NIPS 2015) conference. Modern multivariate statistical methods developed in the rapidly growing field of machine learning are being increasingly applied to various problems in neuroimaging, from cognitive state detection to clinical diagnosis and prognosis. Multivariate pattern analysis methods are designed to examine complex relationships between high-dimensional signals, such as brain images, and outcomes of interest, such as the category of a stimulus, a type of a mental state of a subject, or a specific mental disorder. Such techniques are in contrast with the traditional mass-univariate approaches that dominated neuroimaging in the past and treated each individual imaging measurement in isolation.   We believe that machine learning has a prominent role in shaping how questions in neuroscience are framed, and that the machine-learning mind set is now entering modern psychology and behavioral studies. It is also equally important that practical applications in these fields motivate a rapidly evolving line or research in the machine learning community. In parallel, there is an intense interest in learning more about brain function in the context of rich naturalistic environments and scenes. Efforts to go beyond highly specific paradigms that pinpoint a single function, towards schemes for measuring the interaction with natural and more varied scene are made. The goal of the workshop is to pinpoint the most pressing issues and common challenges across the neuroscience, neuroimaging, psychology and machine learning fields, and to sketch future directions and open questions in the light of novel methodology. 
I. Rish, L. Wehbe, G. Langs, M. Grosse-Wentrup, B. Murphy, G. Cecchi
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