Skip to main content
Springer Nature Link
Account

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

  • Conference proceedings
  • © 2012

Overview

Editors:
  1. Georg Langs

    1. Department of Radiology, Medical University of Vienna, Wien, Austria

  2. Irina Rish

    1. Computational Biology Center, IBM T.J. Watson Research Center, Yorktown Heights, USA

  3. Moritz Grosse-Wentrup

    1. Max Planck Institute for Intelligent Systems, Tübingen, Germany

  4. Brian Murphy

    1. Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA

  • State-of-the-art contributions
  • Interdisciplinary research
  • Unique visibility

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

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 49.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

About this book

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.

Similar content being viewed by others

Keywords

Table of contents (32 papers)

  1. Front Matter

    Download chapter PDF

  2. Coding and Decoding

    1. A Comparative Study of Algorithms for Intra- and Inter-subjects fMRI Decoding

      • Vincent Michel, Alexandre Gramfort, Evelyn Eger, Gaël Varoquaux, Bertrand Thirion
      Pages 1-8

    2. Beyond Brain Reading: Randomized Sparsity and Clustering to Simultaneously Predict and Identify

      • Alexandre Gramfort, Gaël Varoquaux, Bertrand Thirion
      Pages 9-16

    3. Searchlight Based Feature Extraction

      • Shahar Jamshy, Omri Perez, Yehezkel Yeshurun, Talma Hendler, Nathan Intrator
      Pages 17-25

    4. Looking Outside the Searchlight

      • Joset A. Etzel, Michael W. Cole, Todd S. Braver
      Pages 26-33

    5. Population Codes Representing Musical Timbre for High-Level fMRI Categorization of Music Genres

      • Michael Casey, Jessica Thompson, Olivia Kang, Rajeev Raizada, Thalia Wheatley
      Pages 34-41

    6. Induction in Neuroscience with Classification: Issues and Solutions

      • Emanuele Olivetti, Susanne Greiner, Paolo Avesani
      Pages 42-50

    7. A New Feature Selection Method Based on Stability Theory – Exploring Parameters Space to Evaluate Classification Accuracy in Neuroimaging Data

      • Jane M. Rondina, John Shawe-Taylor, Janaina Mourão-Miranda
      Pages 51-59

    8. Identification of OCD-Relevant Brain Areas through Multivariate Feature Selection

      • Emilio Parrado-Hernández, Vanessa Gómez-Verdejo, Manel Martinez-Ramon, Pino Alonso, Jesús Pujol, José M. Menchón et al.
      Pages 60-67

    9. Deformation-Invariant Sparse Coding for Modeling Spatial Variability of Functional Patterns in the Brain

      • George H. Chen, Evelina G. Fedorenko, Nancy G. Kanwisher, Polina Golland
      Pages 68-75

    10. Decoding Complex Cognitive States Online by Manifold Regularization in Real-Time fMRI

      • Toke Jansen Hansen, Lars Kai Hansen, Kristoffer Hougaard Madsen
      Pages 76-83

  3. Neuroscience

    1. Modality Neutral Techniques for Brain Image Understanding

      • David B. Keator
      Pages 84-92

    2. How Does the Brain Represent Visual Scenes? A Neuromagnetic Scene Categorization Study

      • Pavan Ramkumar, Sebastian Pannasch, Bruce C. Hansen, Adam M. Larson, Lester C. Loschky
      Pages 93-100

    3. Finding Consistencies in MEG Responses to Repeated Natural Speech

      • Miika Koskinen
      Pages 101-107

    4. Categorized EEG Neurofeedback Performance Unveils Simultaneous fMRI Deep Brain Activation

      • Sivan Kinreich, Ilana Podlipsky, Nathan Intrator, Talma Hendler
      Pages 108-115

    5. Predicting Clinically Definite Multiple Sclerosis from Onset Using SVM

      • Philip P. Kwok, Olga Ciccarelli, Declan T. Chard, David H. Miller, Daniel C. Alexander
      Pages 116-123

    6. MKL-Based Sample Enrichment and Customized Outcomes Enable Smaller AD Clinical Trials

      • Chris Hinrichs, N. Maritza Dowling, Sterling C. Johnson, Vikas Singh
      Pages 124-131

    7. Pairwise Analysis for Longitudinal fMRI Studies

      • Diego Sona, Paolo Avesani, Stefano Magon, Gianpaolo Basso, Gabriele Miceli
      Pages 132-139

  4. Dynamics

    1. Non-separable Spatiotemporal Brain Hemodynamics Contain Neural Information

      • Felix Bießmann, Yusuke Murayama, Nikos K. Logothetis, Klaus-Robert Müller, Frank C. Meinecke
      Pages 140-147

    2. The Dynamic Beamformer

      • Ali Bahramisharif, Marcel A. J. van Gerven, Jan-Mathijs Schoffelen, Zoubin Ghahramani, Tom Heskes
      Pages 148-155
Back to top

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

Accessibility Information

Accessibility information for this book is coming soon. We're working to make it available as quickly as possible. Thank you for your patience.

Bibliographic Information

Publish with us

Policies and ethics

Back to top