Pattern Analysis of the Human Connectome

Description

This book presents recent advances in pattern analysis of the human connectome. The human connectome, measured by magnetic resonance imaging at the macroscale, provides a comprehensive description of how brain regions are connected. Based on machine learning methods, multiviarate pattern analysis can directly decode psychological or cognitive states from brain connectivity patterns. Although there are a number of works with chapters on conventional human connectome encoding (brain-mapping), there are few resources on human connectome decoding (brain-reading). Focusing mainly on advances made over the past decade in the field of manifold learning, sparse coding, multi-task learning, and deep learning of the human connectome and applications, this book helps students and researchers gain an overall picture of pattern analysis of the human connectome. It also offers valuable insights for clinicians involved in the clinical diagnosis and treatment evaluation of neuropsychiatric disorders. Front Matter ....Pages i-viii Introduction (Dewen Hu, Ling-Li Zeng)....Pages 1-16 Multivariate Pattern Analysis of Whole-Brain Functional Connectivity in Major Depression (Dewen Hu, Ling-Li Zeng)....Pages 17-33 Discriminative Analysis of Nonlinear Functional Connectivity in Schizophrenia (Dewen Hu, Ling-Li Zeng)....Pages 35-54 Predicting Individual Brain Maturity Using Window-Based Dynamic Functional Connectivity (Dewen Hu, Ling-Li Zeng)....Pages 55-81 Locally Linear Embedding of Functional Connectivity for Classification (Dewen Hu, Ling-Li Zeng)....Pages 83-102 Locally Linear Embedding of Anatomical Connectivity for Classification (Dewen Hu, Ling-Li Zeng)....Pages 103-122 Locality Preserving Projection of Functional Connectivity for Regression (Dewen Hu, Ling-Li Zeng)....Pages 123-147 Intrinsic Discriminant Analysis of Functional Connectivity for Multiclass Classification (Dewen Hu, Ling-Li Zeng)....Pages 149-168 Sparse Representation of Dynamic Functional Connectivity in Depression (Dewen Hu, Ling-Li Zeng)....Pages 169-181 Low-Rank Learning of Functional Connectivity Reveals Neural Traits of Individual Differences (Dewen Hu, Ling-Li Zeng)....Pages 183-203 Multi-task Learning of Structural MRI for Multi-site Classification (Dewen Hu, Ling-Li Zeng)....Pages 205-226 Deep Discriminant Autoencoder Network for Multi-site fMRI Classification (Dewen Hu, Ling-Li Zeng)....Pages 227-258