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Ridding fMRI data of motion-related influences: Removal of signals with distinct spatial and physical bases in multiecho data
Author(s) -
Jonathan D. Power,
Mark Plitt,
Stephen J. Gotts,
Prantik Kundu,
Valerie Voon,
Peter A. Bandettini,
Alex Martin
Publication year - 2018
Publication title -
proceedings of the national academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.1720985115
Subject(s) - resting state fmri , blood oxygen level dependent , functional magnetic resonance imaging , pattern recognition (psychology) , artificial intelligence , signal (programming language) , neuroscience , computer science , motion (physics) , brain activity and meditation , biological system , computer vision , psychology , electroencephalography , biology , programming language
Significance Spontaneous fMRI signals are used to understand human brain organization throughout the life span and in disease states. Spontaneous fMRI signals contain many artifacts, and removing these artifacts is vital to properly studying neurobiological signals. We report successful removal of a major artifact, spatially focal motion artifact, from resting state fMRI signals via multiecho imaging techniques. By removing motion artifact, we isolate a second kind of motion-associated signal, a respiratory signal, that occurs across the entire brain. We illustrate several techniques that remove this respiratory artifact, yielding fMRI data free of motion-related influences. These two kinds of motion-related signals have distinct physical and spatial bases, and each can strongly and differentially influence signal patterns in fMRI data.

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