Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage

• Published in: NeuroImage (Orlando, Fla.) Vol. 172; pp. 478 - 491
• Main Authors: Mejia, Amanda F., Nebel, Mary Beth, Barber, Anita D., Choe, Ann S., Pekar, James J., Caffo, Brian S., Lindquist, Martin A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.05.2018; Elsevier Limited
• Subjects: Bayes Theorem; Bayesian analysis; Bayesian statistics; Brain - anatomy & histology; Brain - physiology; Connectome; Connectome - methods; Economic models; Estimates; Functional connectivity; Functional magnetic resonance imaging; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Measurement error; Nerve Net - anatomy & histology; Nerve Net - physiology; Neurosciences; Partial correlation; Reliability; Resting-state fMRI; Shrinkage; Statistical analysis; Time series; Functional connectivity; Bayesian statistics; Resting-state fMRI; Connectome; Shrinkage; Reliability; Measurement error; Partial correlation

Reliability of subject-level resting-state functional connectivity (FC) is determined in part by the statistical techniques employed in its estimation. Methods that pool information across subjects to inform estimation of subject-level effects (e.g., Bayesian approaches) have been shown to enhance r...