rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Pattern Analysis of the Human Connectome
  name:
    name part: Hu, Dewen.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Zeng, Ling-Li.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: si
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: VIII, 258 p. 86 illus., 81 illus. in color. online resource.
  abstract: 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.
  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.
  note: by Dewen Hu, Ling-Li Zeng.
  subject:
    topic: Neurosciences
  subject:
    topic: Biomathematics
  subject:
    topic: Biomedical engineering
  subject:
    topic: Neurosciences
  subject:
    topic: Mathematical and Computational Biology
  subject:
    topic: Biomedical Engineering/Biotechnology
  classification: RC321-580
  classification: 612.8
  related item:
    title info:
      title: Springer eBooks
  identifier: 9789813295230
  identifier: https://doi.org/10.1007/978-981-32-9523-0
  record info:
    record creation date: 191112
    record change date: 20191112103807.0
    record identifier: 978-981-32-9523-0