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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

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
    3. Hans J. P. Wouters, Marcel A. J. van Gerven, Matthias S. Treder, Tom Heskes, Ali Bahramisharif
      Pages 156-163
    4. Gaël Varoquaux, Bertrand Thirion
      Pages 172-177
    5. Fani Deligianni, Gaël Varoquaux, Bertrand Thirion, Emma Robinson, David J. Sharp, A. David Edwards et al.
      Pages 178-185
    6. Nico S. Gorbach, Silvan Siep, Jenia Jitsev, Corina Melzer, Marc Tittgemeyer
      Pages 186-193
    7. Justin Dauwels, Hang Yu, Xueou Wang, Francois Vialatte, Charles Latchoumane, Jaeseung Jeong et al.
      Pages 194-201
    8. Stefan Haufe, Vadim V. Nikulin, Guido Nolte, Klaus-Robert Müller
      Pages 202-209
  5. Probabilistic Models and Machine Learning

    1. Orla M. Doyle, Mitul A. Mehta, Michael J. Brammer, Adam J. Schwarz, Sara De Simoni, Andre F. Marquand
      Pages 210-217
    2. Evangelos Roussos, Steven Roberts, Ingrid Daubechies
      Pages 218-225
    3. Kasper Winther Andersen, Kristoffer Hougaard Madsen, Hartwig Siebner, Lars Kai Hansen, Morten Mørup
      Pages 226-233
    4. Kai-min Chang, Brian Murphy, Marcel Just
      Pages 234-241
    5. Ariana Anderson, Dianna Han, Pamela K. Douglas, Jennifer Bramen, Mark S. Cohen
      Pages 242-255
  6. Back Matter

About these proceedings

Introduction

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.

Keywords

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

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-34713-9
  • Copyright Information Springer-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-34712-2
  • Online ISBN 978-3-642-34713-9
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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