Machine Learning and Interpretation in Neuroimaging

1. Front Matter

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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

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)

• 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

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