© 2012

Machine Learning and Interpretation in Neuroimaging

International Workshop, MLINI 2011, Held at NIPS 2011, Sierra Nevada, Spain, December 16-17, 2011, Revised Selected and Invited Contributions

  • Georg Langs
  • Irina Rish
  • Moritz Grosse-Wentrup
  • Brian Murphy


  • State-of-the-art contributions

  • Interdisciplinary research

  • Unique visibility

Conference proceedings

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7263)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 7263)

Table of contents

  1. Front Matter
  2. Coding and Decoding

    1. Vincent Michel, Alexandre Gramfort, Evelyn Eger, Gaël Varoquaux, Bertrand Thirion
      Pages 1-8
    2. Alexandre Gramfort, Gaël Varoquaux, Bertrand Thirion
      Pages 9-16
    3. Shahar Jamshy, Omri Perez, Yehezkel Yeshurun, Talma Hendler, Nathan Intrator
      Pages 17-25
    4. Joset A. Etzel, Michael W. Cole, Todd S. Braver
      Pages 26-33
    5. Michael Casey, Jessica Thompson, Olivia Kang, Rajeev Raizada, Thalia Wheatley
      Pages 34-41
    6. Emanuele Olivetti, Susanne Greiner, Paolo Avesani
      Pages 42-50
    7. Emilio Parrado-Hernández, Vanessa Gómez-Verdejo, Manel Martinez-Ramon, Pino Alonso, Jesús Pujol, José M. Menchón et al.
      Pages 60-67
    8. George H. Chen, Evelina G. Fedorenko, Nancy G. Kanwisher, Polina Golland
      Pages 68-75
    9. Toke Jansen Hansen, Lars Kai Hansen, Kristoffer Hougaard Madsen
      Pages 76-83
  3. Neuroscience

    1. Pavan Ramkumar, Sebastian Pannasch, Bruce C. Hansen, Adam M. Larson, Lester C. Loschky
      Pages 93-100
    2. Sivan Kinreich, Ilana Podlipsky, Nathan Intrator, Talma Hendler
      Pages 108-115
    3. Philip P. Kwok, Olga Ciccarelli, Declan T. Chard, David H. Miller, Daniel C. Alexander
      Pages 116-123
    4. Chris Hinrichs, N. Maritza Dowling, Sterling C. Johnson, Vikas Singh
      Pages 124-131
    5. Diego Sona, Paolo Avesani, Stefano Magon, Gianpaolo Basso, Gabriele Miceli
      Pages 132-139
  4. Dynamics

    1. Felix Bießmann, Yusuke Murayama, Nikos K. Logothetis, Klaus-Robert Müller, Frank C. Meinecke
      Pages 140-147
    2. Ali Bahramisharif, Marcel A. J. van Gerven, Jan-Mathijs Schoffelen, Zoubin Ghahramani, Tom Heskes
      Pages 148-155

About these proceedings


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.


classification data mining fMRI multivariate encoding multivariate pattern analysis (MVPA)

Editors and affiliations

  • Georg Langs
    • 1
  • Irina Rish
    • 2
  • Moritz Grosse-Wentrup
    • 3
  • Brian Murphy
    • 4
  1. 1.Department of RadiologyMedical University of ViennaWienAustria
  2. 2.Computational Biology CenterIBM T.J. Watson Research CenterYorktown HeightsUSA
  3. 3.Max Planck Institute for Intelligent SystemsTübingenGermany
  4. 4.Machine Learning DepartmentCarnegie Mellon UniversityPittsburghUSA

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