LEADER 07732nam 22008535 450 001 996466318403316 005 20200701084537.0 010 $a3-642-34713-4 024 7 $a10.1007/978-3-642-34713-9 035 $a(CKB)3400000000102839 035 $a(SSID)ssj0000810207 035 $a(PQKBManifestationID)11956453 035 $a(PQKBTitleCode)TC0000810207 035 $a(PQKBWorkID)10832934 035 $a(PQKB)10368512 035 $a(DE-He213)978-3-642-34713-9 035 $a(MiAaPQ)EBC6288045 035 $a(MiAaPQ)EBC5596326 035 $a(Au-PeEL)EBL5596326 035 $a(OCoLC)820879063 035 $a(PPN)168327430 035 $a(EXLCZ)993400000000102839 100 $a20121116d2012 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aMachine Learning and Interpretation in Neuroimaging$b[electronic resource] $eInternational Workshop, MLINI 2011, Held at NIPS 2011, Sierra Nevada, Spain, December 16-17, 2011, Revised Selected and Invited Contributions /$fedited by Georg Langs, Irina Rish, Moritz Grosse-Wentrup, Brian Murphy 205 $a1st ed. 2012. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2012. 215 $a1 online resource (XIV, 266 p. 83 illus.) 225 1 $aLecture Notes in Artificial Intelligence ;$v7263 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-642-34712-6 320 $aIncludes bibliographical references and author index. 327 $aA 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. 330 $aBrain imaging brings together the technology, methodology, research questions and approaches of a wide range of scientific fields including physics, statistics, computer science, neuroscience, biology, and engineering. Thus, methodological and technological advances that enable us to obtain measurements, examine relationships across observations, and link these data to neuroscientific hypotheses happen in a highly interdisciplinary environment. The dynamic field of machine learning with its modern approach to data mining provides many relevant approaches for neuroscience and enables the exploration of open questions. This state-of-the-art survey offers a collection of papers from the Workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2011, held at the 25th Annual Conference on Neural Information Processing, NIPS 2011, in the Sierra Nevada, Spain, in December 2011. Additionally, invited speakers agreed to contribute reviews on various aspects of the field, adding breadth and perspective to the volume. The 32 revised papers were carefully selected from 48 submissions. At the interface between machine learning and neuroimaging the papers aim at shedding some light on the state of the art in this interdisciplinary field. They are organized in topical sections on coding and decoding, neuroscience, dynamcis, connectivity, and probabilistic models and machine learning. 410 0$aLecture Notes in Artificial Intelligence ;$v7263 606 $aOptical data processing 606 $aPattern recognition 606 $aData mining 606 $aMathematical statistics 606 $aApplication software 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22005 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aProbability and Statistics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17036 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aComputer Applications$3https://scigraph.springernature.com/ontologies/product-market-codes/I23001 615 0$aOptical data processing. 615 0$aPattern recognition. 615 0$aData mining. 615 0$aMathematical statistics. 615 0$aApplication software. 615 14$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aPattern Recognition. 615 24$aData Mining and Knowledge Discovery. 615 24$aProbability and Statistics in Computer Science. 615 24$aImage Processing and Computer Vision. 615 24$aComputer Applications. 676 $a006.3/1 702 $aLangs$b Georg$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRish$b Irina$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGrosse-Wentrup$b Moritz$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMurphy$b Brian$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aMLINI 2011 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466318403316 996 $aMachine Learning and Interpretation in Neuroimaging$92829824 997 $aUNISA