| Description |
1 online resource : color illustrations |
| Series |
Lecture notes in computer science, 0302-9743 ; 7263 |
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Lecture notes in artificial intelligence |
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LNCS sublibrary. SL 7, Artificial intelligence |
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Lecture notes in computer science ; 7263.
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Lecture notes in computer science. Lecture notes in artificial intelligence
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LNCS sublibrary. SL 7, Artificial intelligence.
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| Contents |
A Comparative Study of Algorithms for Intra- and Inter-subjects fMRI Decoding / Vincent Michel, Alexandre Gramfort, Evelyn Eger, Gaël Varoquaux and Bertrand Thirion -- Beyond Brain Reading: Randomized Sparsity and Clustering to Simultaneously Predict and Identify / Alexandre Gramfort, Gaël Varoquaux and Bertrand Thirion -- Searchlight Based Feature Extraction / Shahar Jamshy, Omri Perez, Yehezkel Yeshurun, Talma Hendler and Nathan Intrator -- Looking Outside the Searchlight / Joset A. Etzel, Michael W. Cole and Todd S. Braver -- Population Codes Representing Musical Timbre for High-Level fMRI Categorization of Music Genres / Michael Casey, Jessica Thompson, Olivia Kang, Rajeev Raizada and Thalia Wheatley -- Induction in Neuroscience with Classification: Issues and Solutions / Emanuele Olivetti, Susanne Greiner and Paolo Avesani -- 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 and Janaina Mourão-Miranda |
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Identification of OCD-Relevant Brain Areas through Multivariate Feature Selection / Emilio Parrado-Hernández, Vanessa Gómez-Verdejo, Manel Martinez-Ramon, Pino Alonso and Jesús Pujol, et al. -- Deformation-Invariant Sparse Coding for Modeling Spatial Variability of Functional Patterns in the Brain / George H. Chen, Evelina G. Fedorenko, Nancy G. Kanwisher and Polina Golland -- Decoding Complex Cognitive States Online by Manifold Regularization in Real-Time fMRI / Toke Jansen Hansen, Lars Kai Hansen and Kristoffer Hougaard Madsen -- Modality Neutral Techniques for Brain Image Understanding / David B. Keator -- How Does the Brain Represent Visual Scenes? A Neuromagnetic Scene Categorization Study / Pavan Ramkumar, Sebastian Pannasch, Bruce C. Hansen, Adam M. Larson and Lester C. Loschky -- Finding Consistencies in MEG Responses to Repeated Natural Speech / Miika Koskinen -- Categorized EEG Neurofeedback Performance Unveils Simultaneous fMRI Deep Brain Activation / Sivan Kinreich, Ilana Podlipsky, Nathan Intrator and Talma Hendler |
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Predicting Clinically Definite Multiple Sclerosis from Onset Using SVM / Philip P. Kwok, Olga Ciccarelli, Declan T. Chard, David H. Miller and Daniel C. Alexander -- MKL-Based Sample Enrichment and Customized Outcomes Enable Smaller AD Clinical Trials / Chris Hinrichs, N. Maritza Dowling, Sterling C. Johnson and Vikas Singh -- Pairwise Analysis for Longitudinal fMRI Studies / Diego Sona, Paolo Avesani, Stefano Magon, Gianpaolo Basso and Gabriele Miceli -- Non-separable Spatiotemporal Brain Hemodynamics Contain Neural Information / Felix Bießmann, Yusuke Murayama, Nikos K. Logothetis, Klaus-Robert Müller and Frank C. Meinecke -- The Dynamic Beamformer / Ali Bahramisharif, Marcel A.J. van Gerven, Jan-Mathijs Schoffelen, Zoubin Ghahramani and Tom Heskes -- Covert Attention as a Paradigm for Subject-Independent Brain-Computer Interfacing / Hans J.P. Wouters, Marcel A.J. van Gerven, Matthias S. Treder, Tom Heskes and Ali Bahramisharif -- The Neural Dynamics of Visual Processing in Monkey Extrastriate Cortex: A Comparison between Univariate and Multivariate Techniques / Maxime Cauchoix, Ali Bilgin Arslan, Denis Fize and Thomas Serre |
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Statistical Learning for Resting-State fMRI: Successes and Challenges / Gaël Varoquaux and Bertrand Thirion -- Relating Brain Functional Connectivity to Anatomical Connections: Model Selection / Fani Deligianni, Gaël Varoquaux, Bertrand Thirion, Emma Robinson and David J. Sharp, et al. -- Information-Theoretic Connectivity-Based Cortex Parcellation / Nico S. Gorbach, Silvan Siep, Jenia Jitsev, Corina Melzer and Marc Tittgemeyer -- Inferring Brain Networks through Graphical Models with Hidden Variables / Justin Dauwels, Hang Yu, Xueou Wang, Francois Vialatte and Charles Latchoumane, et al. -- Pitfalls in EEG-Based Brain Effective Connectivity Analysis / Stefan Haufe, Vadim V. Nikulin, Guido Nolte and Klaus-Robert Müller -- Data-Driven Modeling of BOLD Drug Response Curves Using Gaussian Process Learning / Orla M. Doyle, Mitul A. Mehta, Michael J. Brammer, Adam J. Schwarz and Sara De Simoni, et al |
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Variational Bayesian Learning of Sparse Representations and Its Application in Functional Neuroimaging / Evangelos Roussos, Steven Roberts and Ingrid Daubechies -- Identification of Functional Clusters in the Striatum Using Infinite Relational Modeling / Kasper Winther Andersen, Kristoffer Hougaard Madsen, Hartwig Siebner, Lars Kai Hansen and Morten Mørup -- A Latent Feature Analysis of the Neural Representation of Conceptual Knowledge / Kai-min Chang, Brian Murphy and Marcel Just -- Real-Time Functional MRI Classification of Brain States Using Markov-SVM Hybrid Models: Peering Inside the rt-fMRI Black Box / Ariana Anderson, Dianna Han, Pamela K. Douglas, Jennifer Bramen and Mark S. Cohen -- Restoring the Generalizability of SVM Based Decoding in High Dimensional Neuroimage Data / Trine Julie Abrahamsen and Lars Kai Hansen |
| Summary |
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 |
| Analysis |
Computer science |
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Data mining |
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Computer vision |
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Optical pattern recognition |
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Computer Imaging, Vision, Pattern Recognition and Graphics |
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Pattern Recognition |
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Computer Applications |
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computerwetenschappen |
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computer sciences |
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computertechnieken |
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computer techniques |
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waarschijnlijkheid |
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probability |
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statistiek |
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statistics |
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beeldverwerking |
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image processing |
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machine vision |
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patroonherkenning |
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datamining |
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Information and Communication Technology (General) |
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Informatie- en communicatietechnologie (algemeen) |
| Bibliography |
Includes bibliographical references and index |
| Notes |
English |
| Subject |
Machine learning -- Congresses
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Artificial intelligence -- Medical applications -- Congresses
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Brain -- Imaging -- Data processing -- Congresses
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Informatique.
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Artificial intelligence -- Medical applications
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Machine learning
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| Genre/Form |
proceedings (reports)
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Conference papers and proceedings
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Conference papers and proceedings.
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Actes de congrès.
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| Form |
Electronic book
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| Author |
Langs, Georg.
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NIPS (Conference) (25th : 2011 : Granada, Spain)
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| ISBN |
9783642347139 |
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3642347134 |
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