Pattern Analysis of the Human Connectome / by Dewen Hu, Ling-Li Zeng.

Format:

Book

Author/Creator:

Hu, Dewen, author.

Zeng, Ling-Li, author.

Contributor:

SpringerLink (Online service)

Series:

Biomedical and Life Sciences (Springer-11642)

Language:

English

Subjects (All):

Neurosciences.

Biomathematics.

Biomedical engineering.

Mathematical and Computational Biology.

Biomedical Engineering/Biotechnology.

Local Subjects:

Neurosciences.

Mathematical and Computational Biology.

Biomedical Engineering/Biotechnology.

Physical Description:

1 online resource (VIII, 258 pages) : 86 illustrations, 81 illustrations in color

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Singapore : Springer Singapore : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

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.

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.

Other Format:

Printed edition:

ISBN:

978-981-32-9523-0

9789813295230

Access Restriction:

Restricted for use by site license.