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El. knyga: 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

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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 ContributionsDidesnis paveikslėlis
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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.
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-BasedBrain 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.
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