Connectomics in NeuroImaging

Chapter 1 Unsupervised Feature Selection via Adaptive Embedding and Sparse Learning for Parkinson’s Disease Diagnosis

Chapter 2 A Novel Graph Neural Network to Localize Eloquent Cortex in Brain Tumor Patients from Resting-State fMRI Connectivity

Chapter 3 Graph Morphology-Based Genetic Algorithm for Classifying Late Dementia States

Chapter 4 Covariance Shrinkage for Dynamic Functional Connectivity

Chapter 5 Rapid Acceleration of the Permutation Test via Transpositions

Chapter 6 Heat Kernels with Functional Connectomes Reveal Atypical Energy Transport in Peripheral Subnetworks in Autism

Chapter 7 A Mass Multivariate Edge-wise Approach for Combining Multiple Connectomes to Improve the Detection of Group Differences

Chapter 8 Adversarial Connectome Embedding for Mild Cognitive Impairment Identification Using Cortical Morphological Networks

Chapter 9 A Machine Learning Framework for Accurate Functional Connectome Fingerprinting and an Application of a Siamese Network

Chapter 10 Test-Retest Reliability of Functional Networks for Evaluation of Data-Driven Parcellation

Chapter 11 Constraining Disease Progression Models Using Subject Specific Connectivity Priors

Chapter 12 Hemodynamic Matrix Factorization for Functional Magnetic Resonance Imaging

Chapter 13 Network Dependency Index Stratified Subnetwork Analysis of Functional Connectomes: An Application to Autism