Machine Learning and Interpretation in Neuroimaging [electronic resource] : International Workshop, MLINI 2011, Held at NIPS 2011, Sierra Nevada, Spain, December 16-17, 2011, Revised Selected and Invited Contributions / edited by Georg Langs, Irina Rish, Moritz Grosse-Wentrup, Brian Murphy.

A Comparative Study of Algorithms for Intra- and Inter-subjects fMRI Decoding -- Beyond Brain Reading: Randomized Sparsity and Clustering to Simultaneously Predict and Identify -- Searchlight Based Feature Extraction -- Looking Outside the Searchlight -- Population Codes Representing Musical Timbre for High-Level fMRI Categorization of Music Genres -- Induction in Neuroscience with Classification: Issues and Solutions -- A New Feature Selection Method Based on Stability Theory - Exploring Parameters Space to Evaluate Classification Accuracy in Neuroimaging Data -- Identification of OCD-Relevant Brain Areas through Multivariate Feature Selection -- Deformation-Invariant Sparse Coding for Modeling Spatial Variability of Functional Patterns in the Brain -- Decoding Complex Cognitive States Online by Manifold Regularization in Real-Time fMRI -- Modality Neutral Techniques for Brain Image Understanding -- How Does the Brain Represent Visual Scenes? A Neuromagnetic Scene Categorization Study -- Finding Consistencies in MEG Responses to Repeated Natural Speech -- Categorized EEG Neurofeedback Performance Unveils Simultaneous fMRI Deep Brain Activation -- Predicting Clinically Definite Multiple Sclerosis from Onset Using SVM -- MKL-Based Sample Enrichment and Customized Outcomes Enable Smaller AD Clinical Trials -- Pairwise Analysis for Longitudinal fMRI Studies -- Non-separable Spatiotemporal Brain Hemodynamics Contain Neural Information -- The Dynamic Beamformer -- Covert Attention as a Paradigm for Subject-Independent Brain-Computer Interfacing -- The Neural Dynamics of Visual Processing in Monkey Extrastriate Cortex: A Comparison between Univariate and Multivariate Techniques -- Statistical Learning for Resting-State fMRI: Successes and Challenges -- Relating Brain Functional Connectivity to Anatomical Connections: Model Selection -- Information-Theoretic Connectivity-Based Cortex Parcellation -- Inferring Brain Networks through Graphical Models with Hidden Variables -- Pitfalls in EEG-Based Brain Effective Connectivity Analysis -- Data-Driven Modeling of BOLD Drug Response Curves Using Gaussian Process Learning -- Variational Bayesian Learning of Sparse Representations and Its Application in Functional Neuroimaging -- Identification of Functional Clusters in the Striatum Using Infinite Relational Modeling -- A Latent Feature Analysis of the Neural Representation of Conceptual Knowledge -- Real-Time Functional MRI Classification of Brain States Using Markov-SVM Hybrid Models: Peering Inside the rt-fMRI Black Box -- Restoring the Generalizability of SVM Based Decoding in High Dimensional Neuroimage Data.