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

PLoS One. 2020 Jan 8;15(1):e0227021. doi: 10.1371/journal.pone.0227021. eCollection 2020.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms
  • Brain / diagnostic imaging
  • Brain / physiology
  • Brain Mapping / methods*
  • Female
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Magnetic Resonance Imaging / methods*
  • Male
  • Young Adult

Grant support

This study was supported by grants from the National Natural Science Foundation of China (81520108016, 81271652, 81020108022, 31471084, 81471653, 81201155, 81301210, 81201156, 81401400, 81201083). Ze Wang is supported by the State Youth 1000 Talent Program of China and Hangzhou Qian Jiang Endowed Professorship. Dr. Zang is partly supported by Qian Jiang Distinguished Professor program.