Connectomics in NeuroImaging

Chapter 1 Connectome of Autistic Brains, Global Versus Local Characterization

Chapter 2 Constructing Multi-frequency High-Order Functional Connectivity Network for Diagnosis of Mild Cognitive Impairment

Chapter 3 Consciousness Level and Recovery Outcome Prediction Using High-Order Brain Functional Connectivity Network

Chapter 4 Discriminative Log-Euclidean Kernels for Learning on Brain Networks

Chapter 5 Interactive Computation and Visualization of Structural Connectomes in Real-Time

Chapter 6 Pairing-based Ensemble Classifier Learning using Convolutional Brain Multiplexes and Multi-view Brain Networks for Early Dementia Diagnosis

Chapter 7 High-order Connectomic Manifold Learning for Autistic Brain State Identification

Chapter 8 A Unified Bayesian Approach to Extract Network-Based Functional Differences from a Heterogeneous Patient Cohort

Chapter 9 FCNet: A Convolutional Neural Network for Calculating Functional Connectivity from Functional MRI

Chapter 10 Identifying Subnetwork Fingerprints in Structural Connectomes: A Data-Driven Approach

Chapter 11 A Simple and Efficient Cylinder Imposter Approach to Visualize DTI Fiber Tracts

Chapter 12 Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference

Chapter 13 “Evaluating Acquisition Time of rfMRI in the Human Connectome Project for Early Psychosis. How Much Is Enough?”

Chapter 14 Early Brain Functional Segregation and Integration Predict Later Cognitive Performance

Chapter 15 Measuring Brain Connectivity via Shape Analysis of fMRI Time Courses and Spectra

Chapter 16 Topological Network Analysis of Electroencephalographic Power Maps

Chapter 17 Region-Wise Stochastic Pattern Modeling for Autism Spectrum Disorder Identification and Temporal Dynamics Analysis

Chapter 18 A Whole-Brain Reconstruction Approach for FOD Modeling from Multi-Shell Diffusion MRI

Chapter 19 Topological Distances Between Brain Networks