Pattern Analysis of the Human Connectome

• Main Authors: Hu, Dewen (Author, http://id.loc.gov/vocabulary/relators/aut), Zeng, Ling-Li (http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Singapore : Springer Singapore : Imprint: Springer, 2019.
• Edition: 1st ed. 2019.
• Subjects: Neurosciences.; Biomathematics.; Biomedical engineering.; Mathematical and Computational Biology.; Biomedical Engineering/Biotechnology.

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 ca...

Table of Contents:

• Introduction
• Multivariate pattern analysis of whole-brain functional connectivity in major depression
• Discriminative analysis of nonlinear functional connectivity in schizophrenia
• Predicting individual brain maturity using window-based dynamic functional connectivity
• Locally linear embedding of functional connectivity for classification
• Locally linear embedding of anatomical connectivity for classification
• Locality preserving projection of functional connectivity for regression
• Intrinsic discriminant analysis of functional connectivity for multi-class classification
• Sparse representation of dynamic functional connectivity in depression
• Low-rank learning of functional connectivity reveals neural traits of individual differences
• Multi-task learning of structural MRI for multi-site classification
• Deep discriminant auto-encoder network for multi-site fMRI classification.