Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data

• Published in: NeuroImage (Orlando, Fla.) Vol. 181; pp. 692 - 717
• Main Authors: Glasser, Matthew F., Coalson, Timothy S., Bijsterbosch, Janine D., Harrison, Samuel J., Harms, Michael P., Anticevic, Alan, Van Essen, David C., Smith, Stephen M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.11.2018; Elsevier Limited
• Subjects: Adult; Blood flow; Brain; Brain - diagnostic imaging; Brain - physiology; Brain mapping; Brain Mapping - methods; Brain Mapping - standards; Connectome; Female; Functional magnetic resonance imaging; Humans; Image Processing, Computer-Assisted - methods; Image Processing, Computer-Assisted - standards; Laboratory animals; Learning algorithms; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - standards; Male; Neural networks; Neuroimaging; Neurosciences; Noise; Physiology; Power; Sensitivity and Specificity; Signal to noise ratio; Young Adult
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2018.04.076

Temporal fluctuations in functional Magnetic Resonance Imaging (fMRI) have been profitably used to study brain activity and connectivity for over two decades. Unfortunately, fMRI data also contain structured temporal “noise” from a variety of sources, including subject motion, subject physiology, an...