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Assessing the mean strength and variations of the time-to-time fluctuations of resting-state brain activity

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Abstract

The time-to-time fluctuations (TTFs) of resting-state brain activity as captured by resting-state fMRI (rsfMRI) have been repeatedly shown to be informative of functional brain structures and disease-related alterations. TTFs can be characterized by the mean and the range of successive difference. The former can be measured with the mean squared successive difference (MSSD), which is mathematically similar to standard deviation; the latter can be calculated by the variability of the successive difference (VSD). The purpose of this study was to evaluate both the resting state-MSSD and VSD of rsfMRI regarding their test–retest stability, sensitivity to brain state change, as well as their biological meanings. We hypothesized that MSSD and VSD are reliable in resting brain; both measures are sensitive to brain state changes such as eyes-open compared to eyes-closed condition; both are predictive of age. These hypotheses were tested with three rsfMRI datasets and proven true, suggesting both MSSD and VSD as reliable and useful tools for resting-state studies.

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References

  1. Aron AR, Gluck MA, Poldrack RA (2006) Long-term test-retest reliability of functional MRI in a classification learning task. Neuroimage 29:1000–1006. doi:10.1016/j.neuroimage.2005.08.010

    Article  PubMed  Google Scholar 

  2. Attwell D, Laughlin SB (2001) An energy budget for signaling in the grey matter of the brain. J Cereb Blood Flow Metab 21:1133–1145. doi:10.1097/00004647-200110000-00001

    Article  CAS  PubMed  Google Scholar 

  3. Basalyga G, Salinas E (2006) When response variability increases neural network robustness to synaptic noise. Neural Comput 18:1349–1379. doi:10.1162/neco.2006.18.6.1349

    Article  PubMed  Google Scholar 

  4. Bennett CM, Wolford GL, Miller MB (2009) The principled control of false positives in neuroimaging. Soc Cogn Affect Neurosci 4:417–422. doi:10.1093/scan/nsp053

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  6. Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM, Beckmann CF, Adelstein JS, Buckner RL, Colcombe S, Dogonowski AM, Ernst M, Fair D, Hampson M, Hoptman MJ, Hyde JS, Kiviniemi VJ, Kotter R, Li SJ, Lin CP, Lowe MJ, Mackay C, Madden DJ, Madsen KH, Margulies DS, Mayberg HS, McMahon K, Monk CS, Mostofsky SH, Nagel BJ, Pekar JJ, Peltier SJ, Petersen SE, Riedl V, Rombouts SA, Rypma B, Schlaggar BL, Schmidt S, Seidler RD, Siegle GJ, Sorg C, Teng GJ, Veijola J, Villringer A, Walter M, Wang L, Weng XC, Whitfield-Gabrieli S, Williamson P, Windischberger C, Zang YF, Zhang HY, Castellanos FX, Milham MP (2010) Toward discovery science of human brain function. Proc Natl Acad Sci USA 107:4734–4739. doi:10.1073/pnas.0911855107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Buckner RL, Andrews-Hanna JR, Schacter DL (2008) The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci 1124:1–38. doi:10.1196/annals.1440.011

    Article  PubMed  Google Scholar 

  8. Challis RE, Kitney RI (1990) Biomedical signal processing (in four parts). Part 1. Time-domain methods. Med Biol Eng Comput 28:509–524

    Article  CAS  PubMed  Google Scholar 

  9. Cicchetti DV (2001) The precision of reliability and validity estimates re-visited: distinguishing between clinical and statistical significance of sample size requirements. J Clin Exp Neuropsychol 23:695–700. doi:10.1076/jcen.23.5.695.1249

    Article  CAS  PubMed  Google Scholar 

  10. Cicchetti DV, Sparrow SA (1981) Developing criteria for establishing interrater reliability of specific items: applications to assessment of adaptive behavior. Am J Ment Defic 86:127–137

    CAS  PubMed  Google Scholar 

  11. Cox RW (2012) AFNI: what a long strange trip it’s been. Neuroimage 62:743–747. doi:10.1016/j.neuroimage.2011.08.056

    Article  PubMed  Google Scholar 

  12. Damoiseaux JS, Beckmann CF, Arigita EJ, Barkhof F, Scheltens P, Stam CJ, Smith SM, Rombouts SA (2008) Reduced resting-state brain activity in the “default network” in normal aging. Cereb Cortex 18:1856–1864. doi:10.1093/cercor/bhm207

    Article  CAS  PubMed  Google Scholar 

  13. Faisal AA, Selen LPJ, Wolpert DM (2008) Noise in the nervous system. Nat Rev Neurosci 9:292–303. doi:10.1038/Nrn2258

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Fonseca-Azevedo K, Herculano-Houzel S (2012) Metabolic constraint imposes tradeoff between body size and number of brain neurons in human evolution. Proc Natl Acad Sci USA 109:18571–18576. doi:10.1073/pnas.1206390109

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Fransson P (2005) Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis. Hum Brain Mapp 26:15–29. doi:10.1002/hbm.20113

    Article  PubMed  Google Scholar 

  16. Garrett DD, Kovacevic N, McIntosh AR, Grady CL (2010) Blood oxygen level-dependent signal variability is more than just noise. J Neurosci 30:4914–4921. doi:10.1523/Jneurosci.5166-09.2010

    Article  CAS  PubMed  Google Scholar 

  17. Goto M, Abe O, Miyati T, Yamasue H, Gomi T, Takeda T (2015) Head motion and correction methods in resting-state functional MRI. Magn Reson Med Sci. doi:10.2463/mrms.rev.2015-0060

    PubMed  Google Scholar 

  18. He BJ (2011) Scale-free properties of the functional magnetic resonance imaging signal during rest and task. J Neurosci 31:13786–13795. doi:10.1523/JNEUROSCI.2111-11.2011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Huang HH, Lee YH, Chan HL, Wang YP, Huang CH, Fan SZ (2008) Using a short-term parameter of heart rate variability to distinguish awake from isoflurane anesthetic states. Med Biol Eng Comput 46:977–984. doi:10.1007/s11517-008-0342-y

    Article  PubMed  Google Scholar 

  20. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM (2012) FSL. Neuroimage 62:782–790. doi:10.1016/j.neuroimage.2011.09.015

    Article  PubMed  Google Scholar 

  21. Kaneoke Y, Donishi T, Iwatani J, Ukai S, Shinosaki K, Terada M (2012) Variance and autocorrelation of the spontaneous slow brain activity. PLoS One 7:e38131. doi:10.1371/journal.pone.0038131

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Li Z, Kadivar A, Pluta J, Dunlop J, Wang Z (2012) Test-retest stability analysis of resting brain activity revealed by blood oxygen level-dependent functional MRI. J Magn Reson Imaging 36:344–354. doi:10.1002/jmri.23670

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Li Z, Zhu Y, Childress AR, Detre JA, Wang Z (2012) Relations between BOLD fMRI-derived resting brain activity and cerebral blood flow. PLoS One 7:e44556. doi:10.1371/journal.pone.0044556

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Lieberman MD, Cunningham WA (2009) Type I and type II error concerns in fMRI research: re-balancing the scale. Soc Cogn Affect Neurosci 4:423–428. doi:10.1093/scan/nsp052

    Article  PubMed  PubMed Central  Google Scholar 

  25. Lipsitz LA (2004) Physiological complexity, aging, and the path to frailty. Sci Aging Knowl Environ SAGE KE 2004:pe16. doi:10.1126/sageke.2004.16.pe16

    Google Scholar 

  26. Lipsitz LA, Goldberger AL (1992) Loss of complexity and aging—potential applications of fractals and chaos theory to senescence. JAMA 267:1806–1809. doi:10.1001/jama.1992.03480130122036

    Article  CAS  PubMed  Google Scholar 

  27. Liu CY, Krishnan AP, Yan L, Smith RX, Kilroy E, Alger JR, Ringman JM, Wang DJ (2013) Complexity and synchronicity of resting state blood oxygenation level-dependent (BOLD) functional MRI in normal aging and cognitive decline. J Magn Reson Imaging 38:36–45. doi:10.1002/jmri.23961

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  29. Maxim V, Sendur L, Fadili J, Suckling J, Gould R, Howard R, Bullmore E (2005) Fractional Gaussian noise, functional MRI and Alzheimer’s disease. NeuroImage 25:141–158. doi:10.1016/j.neuroimage.2004.10.044

    Article  PubMed  Google Scholar 

  30. McDonnell MD, Ward LM (2011) The benefits of noise in neural systems: bridging theory and experiment. Nat Rev Neurosci 12:U415–U489. doi:10.1038/Nrn3061

    Article  Google Scholar 

  31. Mikl M, Marecek R, Hlustik P, Pavlicova M, Drastich A, Chlebus P, Brazdil M, Krupa P (2008) Effects of spatial smoothing on fMRI group inferences. Magn Reson Imaging 26:490–503. doi:10.1016/j.mri.2007.08.006

    Article  PubMed  Google Scholar 

  32. Niazy RK, Xie J, Miller K, Beckmann CF, Smith SM (2011) Spectral characteristics of resting state networks. Prog Brain Res 193:259–276. doi:10.1016/B978-0-444-53839-0.00017-X

    Article  PubMed  Google Scholar 

  33. Portney LG, Watkins MP (2000) Foundations of clinical research: applications to practice, vol 2. Prentice Hall, Upper Saddle River

    Google Scholar 

  34. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL (2001) A default mode of brain function. PNAS 98:676–682. doi:10.1073/pnas.98.2.676

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Shehzad Z, Kelly AMC, Reiss PT, Gee DG, Gotimer K, Uddin LQ, Lee SH, Margulies DS, Roy AK, Biswal BB, Petkova E, Castellanos FX, Milham MP (2009) The resting brain: unconstrained yet reliable. Cereb Cortex 19:2209–2229. doi:10.1093/cercor/bhn256

    Article  PubMed  PubMed Central  Google Scholar 

  36. Shew WL, Yang H, Yu S, Roy R, Plenz D (2011) Information capacity and transmission are maximized in balanced cortical networks with neuronal avalanches. Journal Neurosci 31:55–63. doi:10.1523/JNEUROSCI.4637-10.2011

    Article  CAS  Google Scholar 

  37. Shrout P, Fleiss J (1979) Intraclass correlations: uses in assessing rater reliability. Psychol Bull 86:420–428

    Article  CAS  PubMed  Google Scholar 

  38. Sokunbi MO, Staff RT, Waiter GD, Ahearn TS, Fox HC, Deary IJ, Starr JM, Whalley LJ, Murray AD (2011) Inter-individual differences in fMRI entropy measurements in old age. IEEE Trans Biomed Eng 58:3206–3214. doi:10.1109/TBME.2011.2164793

    Article  PubMed  Google Scholar 

  39. von Neumann J, Bellinson HR, Hart BI (1941) The mean square successive difference. Ann Math Stat 12:153–162

    Article  Google Scholar 

  40. Wang Z, Li Y, Childress AR, Detre JA (2014) Brain entropy mapping using fMRI. PLoS One 9:e89948. doi:10.1371/journal.pone.0089948

    Article  PubMed  PubMed Central  Google Scholar 

  41. Wilke SD, Eurich CW (2002) On the functional role of noise correlations in the nervous system. Neurocomputing 44:1023–1028. doi:10.1016/S0925-2312(02)00506-4

    Article  Google Scholar 

  42. Yang AC, Huang CC, Yeh HL, Liu ME, Hong CJ, Tu PC, Chen JF, Huang NE, Peng CK, Lin CP, Tsai SJ (2013) Complexity of spontaneous BOLD activity in default mode network is correlated with cognitive function in normal male elderly: a multiscale entropy analysis. Neurobiol Aging 34:428–438. doi:10.1016/j.neurobiolaging.2012.05.004

    Article  CAS  PubMed  Google Scholar 

  43. Yuan BK, Wang J, Zang YF, Liu DQ (2014) Amplitude differences in high-frequency fMRI signals between eyes open and eyes closed resting states. Front Hum Neurosci 8:503. doi:10.3389/fnhum.2014.00503

    Article  PubMed  PubMed Central  Google Scholar 

  44. Zang Y, Jiang T, Lu Y, He Y, Tian L (2004) Regional homogeneity approach to fMRI data analysis. Neuroimage 22:394–400. doi:10.1016/j.neuroimage.2003.12.030

    Article  PubMed  Google Scholar 

  45. Zang YF, He Y, Zhu CZ, Cao QJ, Sui MQ, Liang M, Tian LX, Jiang TZ, Wang YF (2007) Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev 29:83–91. doi:10.1016/j.braindev.2006.07.002

    Article  PubMed  Google Scholar 

  46. Zou QH, Yuan BK, Gu H, Liu DQ, Wang DJJ, Gao JH, Yang YH, Zang YF (2015) Detecting static and dynamic differences between eyes-closed and eyes-open resting states using ASL and BOLD fMRI. PLoS One. doi:10.1371/journal.pone.0121757

    Google Scholar 

  47. Zuo XN, Di Martino A, Kelly C, Shehzad ZE, Gee DG, Klein DF, Castellanos FX, Biswal BB, Milham MP (2010) The oscillating brain: complex and reliable. NeuroImage 49:1432–1445. doi:10.1016/j.neuroimage.2009.09.037

    Article  PubMed  Google Scholar 

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Acknowledgments

This study was supported by the Hangzhou Qianjiang Endowed Professor Program, the Youth 1000 Talent Program of China, Natural Science Foundation of Zhejiang Province Grant LZ15H180001, and NIH Grant 1R56DA036556. Data acquisition for dataset 3 was supported by National Natural Science Foundation of China Grant 30770594 and the National High Technology Program of China (863) Grant 2008AA02Z405.

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Authors and Affiliations

  1. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3900 Chestnut St, Philadelphia, PA, 19104, USA

    Zhengjun Li

  2. Center for Cognition and Brain Disorders, Institutes of Neurological Science, Hangzhou Normal University, Hangzhou, 310005, Zhejiang Province, China

    Yu-Feng Zang & Ze Wang

  3. Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, 310005, Zhejiang Province, China

    Yu-Feng Zang & Ze Wang

  4. Affiliated Hospital, Hangzhou Normal University, 126 Wenzhou Rd, Building 7, MRI Room, Hangzhou, 310015, Zhejiang Province, China

    Jianping Ding & Ze Wang

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  1. Zhengjun Li
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Correspondence to Ze Wang.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. For this type of retrospective study, formal consent is not required.

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Li, Z., Zang, YF., Ding, J. et al. Assessing the mean strength and variations of the time-to-time fluctuations of resting-state brain activity. Med Biol Eng Comput 55, 631–640 (2017). https://doi.org/10.1007/s11517-016-1544-3

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