An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation

• Published in: Frontiers in neuroscience Vol. 10; p. 123
• Main Authors: Wang, Yikai, Kang, Jian, Kemmer, Phebe B., Guo, Ying
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 31.03.2016; Frontiers Media S.A
• Subjects: Brain; Brain architecture; Brain mapping; Correlation analysis; fMRI; functional connectivity; Functional magnetic resonance imaging; Hemispheric laterality; L1 Regularization; Linear programming; Medical imaging; Network analysis; Neural networks; Neuroimaging; Neuroscience; NMR; Nuclear magnetic resonance; Partial Correlation; Precision matrix; Public health; Regularization methods; Sparsity; Statistical analysis; Statistics; United States > US; fMRI; functional connectivity; L1 regularization; network analysis; CLIME; partial correlation; precision matrix

Currently, network-oriented analysis of fMRI data has become an important tool for understanding brain organization and brain networks. Among the range of network modeling methods, partial correlation has shown great promises in accurately detecting true brain network connections. However, the appli...