  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.
