A group model for stable multi-subject ICA on fMRI datasets

• Published in: NeuroImage (Orlando, Fla.) Vol. 51; no. 1; pp. 288 - 299
• Main Authors: Varoquaux, G., Sadaghiani, S., Pinel, P., Kleinschmidt, A., Poline, J.B., Thirion, B.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.05.2010; Elsevier Limited; Elsevier
• Subjects: Algorithms; Automation; Biomarkers; Brain - physiology; Brain Mapping - methods; Calibration; Cognitive science; Computer Science; Databases, Factual; Experiments; Humans; Magnetic Resonance Imaging - methods; Mathematics; Medical Imaging; Models, Statistical; Multivariate Analysis; Neural Pathways - physiology; Neuroscience; Probability; Reproducibility of Results; Signal Processing, Computer-Assisted; Standard deviation; Statistics; Statistics Theory; Studies; Time series; CCA; fMRI; group analysis; ICA; resting-state

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract sets of mutually correlated brain regions without prior information on the time course of these regions. Some of...