LEADER 03690nam 22005775 450 001 9910373914703321 005 20251113205630.0 010 $a981-329-523-6 024 7 $a10.1007/978-981-32-9523-0 035 $a(CKB)4100000009845081 035 $a(DE-He213)978-981-32-9523-0 035 $a(MiAaPQ)EBC5978029 035 $a(PPN)260300934 035 $a(MiAaPQ)EBC5977997 035 $a(EXLCZ)994100000009845081 100 $a20191112d2019 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPattern Analysis of the Human Connectome /$fby Dewen Hu, Ling-Li Zeng 205 $a1st ed. 2019. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2019. 215 $a1 online resource (VIII, 258 p. 86 illus., 81 illus. in color.) 311 08$a981-329-522-8 320 $aIncludes bibliographical references. 327 $aIntroduction -- 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. 330 $aThis 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. 606 $aNeurosciences 606 $aBiomathematics 606 $aBiotechnology 606 $aNeuroscience 606 $aMathematical and Computational Biology 606 $aBiotechnology 615 0$aNeurosciences. 615 0$aBiomathematics. 615 0$aBiotechnology. 615 14$aNeuroscience. 615 24$aMathematical and Computational Biology. 615 24$aBiotechnology. 676 $a612.8 700 $aHu$b Dewen$4aut$4http://id.loc.gov/vocabulary/relators/aut$0904021 702 $aZeng$b Ling-Li$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910373914703321 996 $aPattern Analysis of the Human Connectome$92020957 997 $aUNINA