✦ LIBER ✦

Poster #S42 COMPARISON OF LARGE-SCALE HUMAN BRAIN FUNCTIONAL AND ANATOMICAL NETWORKS IN SCHIZOPHRENIA

✍ Scribed by Nelson, Brent G.; Bassett, Danielle; Camchong, Jazmin; Schulz, S Charles; Lim, Kelvin

Book ID: 122167888
Publisher: Elsevier Science
Year: 2014
Tongue: English
Weight: 88 KB
Volume: 153
Category: Article
ISSN: 0920-9964
DOI: 10.1016/S0920-9964(14)70321-4

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Large-scale brain networks in affective

Large-scale brain networks in affective and social neuroscience: towards an integrative functional architecture of the brain

✍ Barrett, Lisa Feldman; Satpute, Ajay Bhaskar 📂 Article 📅 2013 🏛 Elsevier Science 🌐 English ⚖ 839 KB

Comparison of the small-world topology b

Comparison of the small-world topology between anatomical and functional connectivity in the human brain

✍ Chang-hyun Park; Soo Yong Kim; Yun-Hee Kim; Kyungsik Kim 📂 Article 📅 2008 🏛 Elsevier Science 🌐 English ⚖ 635 KB

Age-related changes in topological effic

✍ L. Wang; YF. Li; T.S. Woodward; Y. He 📂 Article 📅 2009 🏛 Elsevier Science 🌐 English ⚖ 120 KB

Large-scale directional connections amon

Large-scale directional connections among multi resting-state neural networks in human brain: A functional MRI and Bayesian network modeling study

✍ Rui Li; Kewei Chen; Adam S. Fleisher; Eric M. Reiman; Li Yao; Xia Wu 📂 Article 📅 2011 🏛 Elsevier Science 🌐 English ⚖ 579 KB

Detecting functional nodes in large-scal

Detecting functional nodes in large-scale cortical networks with functional magnetic resonance imaging: A principal component analysis of the human visual system

✍ Christine Ecker; Emanuelle Reynaud; Steven C. Williams; Michael J. Brammer 📂 Article 📅 2007 🏛 John Wiley and Sons 🌐 English ⚖ 605 KB

## Abstract This study aimed to demonstrate how a regional variant of principal component analysis (PCA) can be used to delineate the known functional subdivisions of the human visual system. Unlike conventional eigenimage analysis, PCA was carried out as a second‐level analysis subsequent to model