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Machine Learning and Interpretation in Neuroimaging

Overview of attention for book
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 11: Predicting Short-Term Cognitive Change from Longitudinal Neuroimaging Analysis
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Summary
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Chapter title
Predicting Short-Term Cognitive Change from Longitudinal Neuroimaging Analysis
Chapter number 11
Book title
Machine Learning and Interpretation in Neuroimaging
Published by
Springer, Cham, December 2014
DOI 10.1007/978-3-319-45174-9_11
Book ISBNs
978-3-31-945173-2, 978-3-31-945174-9
Authors

Michael H. Coen, M. Hidayath Ansari, Barbara B. Bendlin

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 33%
Unknown 2 67%

Demographic breakdown

Readers by professional status Count As %
Researcher 1 33%
Student > Master 1 33%
Unknown 1 33%
Readers by discipline Count As %
Computer Science 1 33%
Unknown 2 67%

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