Machine Learning and Interpretation in Neuroimaging

Table of Contents

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   Book Overview
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   Chapter 1 A Comparative Study of Algorithms for Intra- and Inter-subjects fMRI Decoding
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   Chapter 2 Beyond Brain Reading: Randomized Sparsity and Clustering to Simultaneously Predict and Identify
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   Chapter 3 Searchlight Based Feature Extraction
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   Chapter 4 Looking Outside the Searchlight
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   Chapter 5 Population Codes Representing Musical Timbre for High-Level fMRI Categorization of Music Genres
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   Chapter 6 Induction in Neuroscience with Classification: Issues and Solutions
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   Chapter 7 A New Feature Selection Method Based on Stability Theory – Exploring Parameters Space to Evaluate Classification Accuracy in Neuroimaging Data
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   Chapter 8 Identification of OCD-Relevant Brain Areas through Multivariate Feature Selection
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    Chapter 9 Deformation-Invariant Sparse Coding for Modeling Spatial Variability of Functional Patterns in the Brain
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    Chapter 10 Decoding Complex Cognitive States Online by Manifold Regularization in Real-Time fMRI
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    Chapter 11 Modality Neutral Techniques for Brain Image Understanding
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    Chapter 12 How Does the Brain Represent Visual Scenes? A Neuromagnetic Scene Categorization Study
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    Chapter 13 Finding Consistencies in MEG Responses to Repeated Natural Speech
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    Chapter 14 Categorized EEG Neurofeedback Performance Unveils Simultaneous fMRI Deep Brain Activation
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    Chapter 15 Predicting Clinically Definite Multiple Sclerosis from Onset Using SVM
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    Chapter 16 MKL-Based Sample Enrichment and Customized Outcomes Enable Smaller AD Clinical Trials
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    Chapter 17 Pairwise Analysis for Longitudinal fMRI Studies
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    Chapter 18 Non-separable Spatiotemporal Brain Hemodynamics Contain Neural Information
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    Chapter 19 The Dynamic Beamformer
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    Chapter 20 Covert Attention as a Paradigm for Subject-Independent Brain-Computer Interfacing
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    Chapter 21 The Neural Dynamics of Visual Processing in Monkey Extrastriate Cortex: A Comparison between Univariate and Multivariate Techniques
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    Chapter 22 Statistical Learning for Resting-State fMRI: Successes and Challenges
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    Chapter 23 Relating Brain Functional Connectivity to Anatomical Connections: Model Selection
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    Chapter 24 Information-Theoretic Connectivity-Based Cortex Parcellation
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    Chapter 25 Inferring Brain Networks through Graphical Models with Hidden Variables
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    Chapter 26 Pitfalls in EEG-Based Brain Effective Connectivity Analysis
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    Chapter 27 Data-Driven Modeling of BOLD Drug Response Curves Using Gaussian Process Learning
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    Chapter 28 Machine Learning and Interpretation in Neuroimaging
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    Chapter 29 Identification of Functional Clusters in the Striatum Using Infinite Relational Modeling
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    Chapter 30 Machine Learning and Interpretation in Neuroimaging
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    Chapter 31 Real-Time Functional MRI Classification of Brain States Using Markov-SVM Hybrid Models: Peering Inside the rt-fMRI Black Box
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    Chapter 32 Restoring the Generalizability of SVM Based Decoding in High Dimensional Neuroimage Data