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Connectomics in NeuroImaging

Overview of attention for book
Cover of 'Connectomics in NeuroImaging'

Table of Contents

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    Book Overview
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    Chapter 1 Unsupervised Feature Selection via Adaptive Embedding and Sparse Learning for Parkinson’s Disease Diagnosis
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    Chapter 2 A Novel Graph Neural Network to Localize Eloquent Cortex in Brain Tumor Patients from Resting-State fMRI Connectivity
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    Chapter 3 Graph Morphology-Based Genetic Algorithm for Classifying Late Dementia States
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    Chapter 4 Covariance Shrinkage for Dynamic Functional Connectivity
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    Chapter 5 Rapid Acceleration of the Permutation Test via Transpositions
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    Chapter 6 Heat Kernels with Functional Connectomes Reveal Atypical Energy Transport in Peripheral Subnetworks in Autism
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    Chapter 7 A Mass Multivariate Edge-wise Approach for Combining Multiple Connectomes to Improve the Detection of Group Differences
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    Chapter 8 Adversarial Connectome Embedding for Mild Cognitive Impairment Identification Using Cortical Morphological Networks
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    Chapter 9 A Machine Learning Framework for Accurate Functional Connectome Fingerprinting and an Application of a Siamese Network
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    Chapter 10 Test-Retest Reliability of Functional Networks for Evaluation of Data-Driven Parcellation
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    Chapter 11 Constraining Disease Progression Models Using Subject Specific Connectivity Priors
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    Chapter 12 Hemodynamic Matrix Factorization for Functional Magnetic Resonance Imaging
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    Chapter 13 Network Dependency Index Stratified Subnetwork Analysis of Functional Connectomes: An Application to Autism
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18 tweeters

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Title
Connectomics in NeuroImaging
Published by
Springer International Publishing, December 2019
DOI 10.1007/978-3-030-32391-2
ISBNs
978-3-03-032390-5, 978-3-03-032391-2
Editors

Schirmer, Markus D., Venkataraman, Archana, Rekik, Islem, Kim, Minjeong, Chung, Ai Wern

Twitter Demographics

The data shown below were collected from the profiles of 18 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 16%
Other 2 11%
Student > Postgraduate 2 11%
Student > Ph. D. Student 2 11%
Student > Doctoral Student 1 5%
Other 2 11%
Unknown 7 37%
Readers by discipline Count As %
Computer Science 3 16%
Engineering 3 16%
Neuroscience 2 11%
Energy 1 5%
Medicine and Dentistry 1 5%
Other 0 0%
Unknown 9 47%