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Machine Learning and Interpretation in Neuroimaging

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Cover of 'Machine Learning and Interpretation in Neuroimaging'

Table of Contents

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    Book Overview
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    Chapter 1 Multi-Task Learning for Interpretation of Brain Decoding Models
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    Chapter 2 The New Graph Kernels on Connectivity Networks for Identification of MCI
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    Chapter 3 Mapping Tractography Across Subjects
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    Chapter 4 Automated Speech Analysis for Psychosis Evaluation
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    Chapter 5 Combining Different Modalities in Classifying Phonological Categories
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    Chapter 6 Label-Alignment-Based Multi-Task Feature Selection for Multimodal Classification of Brain Disease
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    Chapter 7 Leveraging Clinical Data to Enhance Localization of Brain Atrophy
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    Chapter 8 Estimating Learning Effects: A Short-Time Fourier Transform Regression Model for MEG Source Localization
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    Chapter 9 Classification-Based Causality Detection in Time Series
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    Chapter 10 Fast and Improved SLEX Analysis of High-Dimensional Time Series
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    Chapter 11 Predicting Short-Term Cognitive Change from Longitudinal Neuroimaging Analysis
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    Chapter 12 Hyperalignment of Multi-subject fMRI Data by Synchronized Projections
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    Chapter 13 An Oblique Approach to Prediction of Conversion to Alzheimer’s Disease with Multikernel Gaussian Processes
Attention for Chapter 4: Automated Speech Analysis for Psychosis Evaluation
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