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

Autoři: Xi-Ze Jia aff001;  Jia-Wei Sun aff003;  Gong-Jun Ji aff001;  Wei Liao aff001;  Ya-Ting Lv aff001;  Jue Wang aff001;  Ze Wang aff001;  Han Zhang aff001;  Dong-Qiang Liu aff001;  Yu-Feng Zang aff001
Působiště autorů: Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China aff001;  Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China aff002;  School of Information and Electronics Technology, Jiamusi University, Jiamusi, Heilongjiang, China aff003;  Department of Medical Psychology, Chaohu Clinical Medical College, Anhui Medical University, Hefei, China aff004
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article


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.

Klíčová slova:

Auditory cortex – Central nervous system – Eyes – Functional magnetic resonance imaging – Neuroimaging – Preprocessing – Research validity – Simulation and modeling


1. Biswal B, Zerrin Yetkin F, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magn Reson Med. 1995;34: 537–541. doi: 10.1002/mrm.1910340409 8524021

2. Zang Y-F, He Y, Zhu C-Z, Cao Q-J, Sui M-Q, Liang M, et al. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev. 2007;29: 83–91. doi: 10.1016/j.braindev.2006.07.002 16919409

3. Yan C-G, Craddock RC, Zuo X-N, Zang Y-F, Milham MP. Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes. NeuroImage. 2013;80: 246–262. doi: 10.1016/j.neuroimage.2013.04.081 23631983

4. Zuo X-N, Di Martino A, Kelly C, Shehzad ZE, Gee DG, Klein DF, et al. The oscillating brain: Complex and reliable. NeuroImage. 2010;49: 1432–1445. doi: 10.1016/j.neuroimage.2009.09.037 19782143

5. Zou Q-H, Zhu C-Z, Yang Y-H, Zuo X-N, Long X-Y, Cao Q-J, et al. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Methods. 2008;172: 137–141. doi: 10.1016/j.jneumeth.2008.04.012 18501969

6. Kalcher K, Boubela RN, Huf W, Biswal BB, Baldinger P, Sailer U, et al. RESCALE: Voxel-specific task-fMRI scaling using resting state fluctuation amplitude. NeuroImage. 2013;70: 80–88. doi: 10.1016/j.neuroimage.2012.12.019 23266702

7. Küblböck M, Woletz M, Höflich A, Sladky R, Kranz GS, Hoffmann A, et al. Stability of low-frequency fluctuation amplitudes in prolonged resting-state fMRI. NeuroImage. 2014;103: 249–257. doi: 10.1016/j.neuroimage.2014.09.038 25251869

8. Han Y, Wang J, Zhao Z, Min B, Lu J, Li K, et al. Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: A resting-state fMRI study. NeuroImage. 2011;55: 287–295. doi: 10.1016/j.neuroimage.2010.11.059 21118724

9. Wee C-Y, Yap P-T, Denny K, Browndyke JN, Potter GG, Welsh-Bohmer KA, et al. Resting-State Multi-Spectrum Functional Connectivity Networks for Identification of MCI Patients. He Y, editor. PLoS ONE. 2012;7: e37828. doi: 10.1371/journal.pone.0037828 22666397

10. Esposito F, Tessitore A, Giordano A, De Micco R, Paccone A, Conforti R, et al. Rhythm-specific modulation of the sensorimotor network in drug-naive patients with Parkinson’s disease by levodopa. Brain J Neurol. 2013;136: 710–725. doi: 10.1093/brain/awt007 23423673

11. Wei L, Duan X, Zheng C, Wang S, Gao Q, Zhang Z, et al. Specific frequency bands of amplitude low-frequency oscillation encodes personality. Hum Brain Mapp. 2014;35: 331–339. doi: 10.1002/hbm.22176 22987723

12. Huang Z, Dai R, Wu X, Yang Z, Liu D, Hu J, et al. The self and its resting state in consciousness: An investigation of the vegetative state: Self and Resting State in Consciousness. Hum Brain Mapp. 2014;35: 1997–2008. doi: 10.1002/hbm.22308 23818102

13. Yu R, Chien Y-L, Wang H-LS, Liu C-M, Liu C-C, Hwang T-J, et al. Frequency-specific alternations in the amplitude of low-frequency fluctuations in schizophrenia. Hum Brain Mapp. 2014;35: 627–637. doi: 10.1002/hbm.22203 23125131

14. Yue Y, Jia X, Hou Z, Zang Y, Yuan Y. Frequency-dependent amplitude alterations of resting-state spontaneous fluctuations in late-onset depression. BioMed Res Int. 2015;2015: 505479. doi: 10.1155/2015/505479 25705666

15. Malinen S, Vartiainen N, Hlushchuk Y, Koskinen M, Ramkumar P, Forss N, et al. Aberrant temporal and spatial brain activity during rest in patients with chronic pain. Proc Natl Acad Sci. 2010;107: 6493–6497. doi: 10.1073/pnas.1001504107 20308545

16. Otti A, Guendel H, Wohlschläger A, Zimmer C, Noll-Hussong M. Frequency shifts in the anterior default mode network and the salience network in chronic pain disorder. BMC Psychiatry. 2013;13: 84. doi: 10.1186/1471-244X-13-84 23497482

17. Yuan B-K, Wang J, Zang Y-F, Liu D-Q. Amplitude differences in high-frequency fMRI signals between eyes open and eyes closed resting states. Front Hum Neurosci. 2014;8: 503. doi: 10.3389/fnhum.2014.00503 25071530

18. Kanwisher N, McDermott J, Chun MM. The fusiform face area: a module in human extrastriate cortex specialized for face perception. J Neurosci Off J Soc Neurosci. 1997;17: 4302–4311.

19. Kanwisher N, Tong F, Nakayama K. The effect of face inversion on the human fusiform face area. Cognition. 1998;68: B1–11. doi: 10.1016/s0010-0277(98)00035-3 9775518

20. Grill-Spector K, Knouf N, Kanwisher N. The fusiform face area subserves face perception, not generic within-category identification. Nat Neurosci. 2004;7: 555–562. doi: 10.1038/nn1224 15077112

21. Tambini A, Ketz N, Davachi L. Enhanced Brain Correlations during Rest Are Related to Memory for Recent Experiences. Neuron. 2010;65: 280–290. doi: 10.1016/j.neuron.2010.01.001 20152133

22. Zou Q, Yuan B-K, Gu H, Liu D, Wang DJJ, Gao J-H, et al. Detecting static and dynamic differences between eyes-closed and eyes-open resting states using ASL and BOLD fMRI. PloS One. 2015;10: e0121757. doi: 10.1371/journal.pone.0121757 25816237

23. Yan C, Liu D, He Y, Zou Q, Zhu C, Zuo X, et al. Spontaneous Brain Activity in the Default Mode Network Is Sensitive to Different Resting-State Conditions with Limited Cognitive Load. García AV, editor. PLoS ONE. 2009;4: e5743. doi: 10.1371/journal.pone.0005743 19492040

24. Liu D, Dong Z, Zuo X, Wang J, Zang Y. Eyes-open/eyes-closed dataset sharing for reproducibility evaluation of resting state fMRI data analysis methods. Neuroinformatics. 2013;11: 469–476. doi: 10.1007/s12021-013-9187-0 23836389

25. Zhao N, Yuan L-X, Jia X-Z, Zhou X-F, Deng X-P, He H-J, et al. Intra- and Inter-Scanner Reliability of Voxel-Wise Whole-Brain Analytic Metrics for Resting State fMRI. Front Neuroinformatics. 2018;12: 54. doi: 10.3389/fninf.2018.00054 30186131

26. Yuan L-X, Wang J-B, Zhao N, Li Y-Y, Ma Y, Liu D-Q, et al. Intra- and Inter-scanner Reliability of Scaled Subprofile Model of Principal Component Analysis on ALFF in Resting-State fMRI Under Eyes Open and Closed Conditions. Front Neurosci. 2018;12: 311. doi: 10.3389/fnins.2018.00311 29887795

27. Song X-W, Dong Z-Y, Long X-Y, Li S-F, Zuo X-N, Zhu C-Z, et al. REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PloS One. 2011;6: e25031. doi: 10.1371/journal.pone.0025031 21949842

28. Jia X-Z, Wang J, Sun H-Y, Zhang H, Liao W, Wang Z, et al. RESTplus: an improved toolkit for resting-state functional magnetic resonance imaging data processing. Sci Bull. 2019;64: 953–954. doi: 10.1016/j.scib.2019.05.008

29. Ashburner J. SPM: A history. NeuroImage. 2012;62: 791–800. doi: 10.1016/j.neuroimage.2011.10.025 22023741

30. Yan C-G, Zang Y-F. DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI. Front Syst Neurosci. 2010;4: 13. doi: 10.3389/fnsys.2010.00013 20577591

31. Cox RW. AFNI: What a long strange trip it’s been. NeuroImage. 2012;62: 743–747. doi: 10.1016/j.neuroimage.2011.08.056 21889996

32. Friston KJ, Williams S, Howard R, Frackowiak RSJ, Turner R. Movement-Related effects in fMRI time-series. Magn Reson Med. 1996;35: 346–355. doi: 10.1002/mrm.1910350312 8699946

33. Yan C-G, Cheung B, Kelly C, Colcombe S, Craddock RC, Di Martino A, et al. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. NeuroImage. 2013;76: 183–201. doi: 10.1016/j.neuroimage.2013.03.004 23499792

34. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage. 2012;59: 2142–2154. doi: 10.1016/j.neuroimage.2011.10.018 22019881

35. Yan C-G, Wang X-D, Zuo X-N, Zang Y-F. DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics. 2016;14: 339–351. doi: 10.1007/s12021-016-9299-4 27075850

36. Shehzad Z, Kelly AMC, Reiss PT, Gee DG, Gotimer K, Uddin LQ, et al. The Resting Brain: Unconstrained yet Reliable. Cereb Cortex. 2009;19: 2209–2229. doi: 10.1093/cercor/bhn256 19221144

37. Zuo X-N, Kelly C, Adelstein JS, Klein DF, Castellanos FX, Milham MP. Reliable intrinsic connectivity networks: Test–retest evaluation using ICA and dual regression approach. NeuroImage. 2010;49: 2163–2177. doi: 10.1016/j.neuroimage.2009.10.080 19896537

38. Liao X-H, Xia M-R, Xu T, Dai Z-J, Cao X-Y, Niu H-J, et al. Functional brain hubs and their test–retest reliability: A multiband resting-state functional MRI study. NeuroImage. 2013;83: 969–982. doi: 10.1016/j.neuroimage.2013.07.058 23899725

39. Margulies DS, Böttger J, Long X, Lv Y, Kelly C, Schäfer A, et al. Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity. Magn Reson Mater Phys Biol Med. 2010;23: 289–307. doi: 10.1007/s10334-010-0228-5 20972883

40. Wong CW, DeYoung PN, Liu TT. Differences in the resting-state fMRI global signal amplitude between the eyes open and eyes closed states are related to changes in EEG vigilance. NeuroImage. 2016;124: 24–31. doi: 10.1016/j.neuroimage.2015.08.053 26327245

41. Li Z, Zang Y-F, Ding J, Wang Z. Assessing the mean strength and variations of the time-to-time fluctuations of resting-state brain activity. Med Biol Eng Comput. 2017;55: 631–640. doi: 10.1007/s11517-016-1544-3 27402343

42. McAvoy M, Larson-Prior L, Nolan TS, Vaishnavi SN, Raichle ME, d’Avossa G. Resting States Affect Spontaneous BOLD Oscillations in Sensory and Paralimbic Cortex. J Neurophysiol. 2008;100: 922–931. doi: 10.1152/jn.90426.2008 18509068

43. Jao T, Vértes PE, Alexander-Bloch AF, Tang I-N, Yu Y-C, Chen J-H, et al. Volitional eyes opening perturbs brain dynamics and functional connectivity regardless of light input. NeuroImage. 2013;69: 21–34. doi: 10.1016/j.neuroimage.2012.12.007 23266698

Článek vyšel v časopise


2020 Číslo 1
Nejčtenější tento týden