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Connectomics in NeuroImaging

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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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