Percent amplitude of fluctuation: A simple measure for resting-state fMRI signal at single voxel level

• Published in: PloS one Vol. 15; no. 1; p. e0227021
• Main Authors: Jia, Xi-Ze, Sun, Jia-Wei, Ji, Gong-Jun, Liao, Wei, Lv, Ya-Ting, Wang, Jue, Wang, Ze, Zhang, Han, Liu, Dong-Qiang, Zang, Yu-Feng
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08.01.2020; Public Library of Science (PLoS)
• Subjects: Adult; Algorithms; Amplitudes; Biology and Life Sciences; Brain - diagnostic imaging; Brain - physiology; Brain Mapping - methods; Brain research; Cognition & reasoning; Computer and Information Sciences; Diagnostic imaging; Engineering and Technology; Female; Frequencies; Functional magnetic resonance imaging; Head movement; Humans; Image Processing, Computer-Assisted - methods; Laboratories; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medicine and Health Sciences; Neuroimaging; Oxygenation; Regression analysis; Reliability analysis; Research and Analysis Methods; Standardization; Statistical analysis; Statistics; Studies; Variation; Young Adult; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0227021

The amplitude of low-frequency fluctuation (ALFF) measures resting-state functional magnetic resonance imaging (RS-fMRI) signal of each voxel. However, the unit of blood oxygenation level-dependent (BOLD) signal is arbitrary and hence ALFF is sensitive to the scale of raw signal. A well-accepted standardization procedure is to divide each voxel's ALFF by the global mean ALFF, named mALFF. Although fractional ALFF (fALFF), a ratio of the ALFF to the total amplitude within the full frequency band, offers possible solution of the standardization, it actually mixes with the fluctuation power within the full frequency band and thus cannot reveal the true amplitude characteristics of a given frequency band. The current study borrowed the percent signal change in task fMRI studies and proposed percent amplitude of fluctuation (PerAF) for RS-fMRI. We firstly applied PerAF and mPerAF (i.e., divided by global mean PerAF) to eyes open (EO) vs. eyes closed (EC) RS-fMRI data. PerAF and mPerAF yielded prominently difference between EO and EC, being well consistent with previous studies. We secondly performed test-retest reliability analysis and found that (PerAF ≈ mPerAF ≈ mALFF) > (fALFF ≈ mfALFF). Head motion regression (Friston-24) increased the reliability of PerAF, but decreased all other metrics (e.g. mPerAF, mALFF, fALFF, and mfALFF). The above results suggest that mPerAF is a valid, more reliable, more straightforward, and hence a promising metric for voxel-level RS-fMRI studies. Future study could use both PerAF and mPerAF metrics. For prompting future application of PerAF, we implemented PerAF in a new version of REST package named RESTplus.