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  • Book
    Dewen Hu, Ling-Li Zeng.
    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.

    Intro; Contents; 1 Introduction; 1 Multimodal Brain Imaging; 2 sMRI-Based Structural Connectivity; 3 DTI-Based Anatomical Connectivity; 4 fMRI-Based Functional Connectivity; 5 Dynamic Functional Connectivity; 6 Multivariate Pattern Analysis; 7 Feature Extraction; 8 Dimensionality Reduction; 9 Classifier Design and Performance Evaluation; 10 The Content of the Book; References; 2 Multivariate Pattern Analysis of Whole-Brain Functional Connectivity in Major Depression; 1 Introduction; 2 Subjects; 3 Image Acquisition and Preprocessing; 4 Identification of Features with High Discriminative Power 2 Participants3 Image Acquisition and Preprocessing; 4 ALFF-FC Map of Dynamic Functional Connectivity; 5 Partial Least-Squares Analysis and Age Prediction; 6 Age-Dependent Changes in the Variability of the Dynamic FC During Maturation; 7 Control Analysis; 8 Reproducibility; 9 Discussion; Functional Connectivity Fluctuations Decode Individual Brain Maturity; Specific Brain Networks Exhibit Changed Connectivity Fluctuation with Age; Inter-network Rather Than Within-Network Connectivity Dynamics Shows Strong DevelopmentalTrends; Control Analysis, Limitations, and Directions for Future Research 5 DiscussionReferences; 7 Locality Preserving Projection of Functional Connectivity for Regression; 1 Introduction; 2 Data Acquisition and Preprocessing; 3 Parametric Curve Fitting and Age-Related Changes in Interregional Functional Connectivity; 4 Low-Dimensional Embeddings; 5 Locally Adjusted Support Vector Regression (LASVR) for Age Prediction; 6 Discussion; References; 8 Intrinsic Discriminant Analysis of Functional Connectivity for Multiclass Classification; 1 Introduction; 2 Participants; 3 Data Acquisition and Preprocessing; 4 IDA Algorithm and Intrinsicconnectomes 5 Support Vector Classification and Performance Evaluation6 Altered Resting-State Functional Connectivityin Major Depression; 7 Discussion; References; 3 Discriminative Analysis of Nonlinear Functional Connectivity in Schizophrenia; 1 Introduction; 2 Participants; 3 Imaging Acquisition and Preprocessing; 4 MIC and eMIC; 5 High Discriminative Connectivity Features; 6 Support Vector Classification and Performance Evaluation; 7 Functional Connectivity Changes; 8 Discussion; References; 4 Predicting Individual Brain Maturity Using Window-Based Dynamic Functional Connectivity; 1 Introduction
    Digital Access Springer 2019