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Multifractal analysis of resting state fMRI series in default mode network: age and gender effects

  • Article
  • Biophysics
  • Published:
Chinese Science Bulletin

Abstract

Age-related changes of resting state in default mode network (DMN) may provide new clues to the developing mechanism of normal brain as well as early diagnosis and therapy of some neuropsychiatric disorders. The application of multifractal theory to functional magnetic resonance imaging (fMRI) signals has recently raised increasing attention. We aim to explore the multifractal characteristics underlying the resting state functional magnetic resonance imaging (rs-fMRI) series extracted from DMN, and two issues are mainly discussed: (1) whether there exist multifractals in rs-fMRI series; (2) whether it is possible to distinguish between the different ages or genders by means of multifractal characteristics. Results demonstrated the existence of multifractals in rs-fMRI series in DMN. In addition, slight differences between young subjects and middle-aged or elderly subjects can be successfully detected by \( \Delta_{\text{as}} \alpha \), a modified measure we proposed. Furthermore, it is revealed that the rs-fMRI series from young subjects possess smaller averaged scale index and weaker long range correlation, while those from middle-aged or elderly people present increasing averaged scale index and stronger long range correlation. Whereas no significant statistical differences has been found between male and female group. Our results, therefore, highlight the potential usefulness of multifractal analysis in fMRI series of a certain brain region, and provide important insights into healthy aging in DMN.

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References

  1. Andrews-Hanna JR, Snyder AZ, Vincent JL et al (2007) Disruption of large-scale brain systems in advanced aging. Neuron 56:924–935

    Article  Google Scholar 

  2. Takahashi T, Murata T, Omori M et al (2004) Quantitative evaluation of age-related white matter microstructural changes on MRI by multifractal analysis. J Neurol Sci 225:33–37

    Article  Google Scholar 

  3. Raichle ME, Macleod AM, Snyder AZ et al (2001) A default mode of brain function. Proc Natl Acad Sci USA 98:676–682

    Article  Google Scholar 

  4. Raichle ME (2011) The restless brain. Brain Connect 1:3–12

    Article  Google Scholar 

  5. Greicius M (2008) Resting-state functional connectivity in neuropsychiatric disorders. Curr Opin Neurol 21:424–430

    Article  Google Scholar 

  6. Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8:700–711

    Article  Google Scholar 

  7. Jones DT, Machulda MM, Vemuri P et al (2011) Age-related changes in the default mode network are more advanced in alzheimer disease. Neurology 77:1524–1531

    Article  Google Scholar 

  8. Filippini N, Nickerson LD, Beckmann CF et al (2012) Age-related adaptations of brain function during a memory task are also present at rest. Neuroimage 59:3821–3828

    Article  Google Scholar 

  9. Tomasi D, Volkow ND (2012) Aging and functional brain networks. Mol Psychiatr 17:549–558

    Article  Google Scholar 

  10. Koch W, Teipel S, Mueller S et al (2010) Effects of aging on default mode network activity in resting state fMRI: does the method of analysis matter? Neuroimage 51:280–287

    Article  Google Scholar 

  11. Whitfield-Gabrieli S, Ford JM (2011) Default mode network activity and connectivity in psychopathology. Annu Rev Clin Psychol 8:49–76

    Article  Google Scholar 

  12. Killgore WD, Yurgelun-Todd DA (2001) Sex differences in amygdala activation during the perception of facial affect. Neuroreport 12:2543–2547

    Article  Google Scholar 

  13. Shirao N, Okamoto Y, Mantani T et al (2005) Gender differences in brain activity generated by unpleasant word stimuli concerning body image: an fMRI study. Br J Psychiatry 186:48–53

    Article  Google Scholar 

  14. Yurgelun-Todd DA, Killgore WDS (2006) Fear-related activity in the prefrontal cortex increases with age during adolescence: a preliminary fMRI study. Neurosci Lett 406:194–199

    Article  Google Scholar 

  15. Weissman-Fogel I, Moayedi M, Taylor KS et al (2010) Cognitive and default-mode resting state networks: do male and female brains “rest” differently? Hum Brain Mapp 31:1713–1726

    Google Scholar 

  16. Park DC, Polk TA, Hebrank AC et al (2010) Age differences in default mode activity on easy and difficult spatial judgment tasks. Front Human Neurosci 3:75

    Google Scholar 

  17. Wu JT, Wu HZ, Yan CG et al (2011) Aging-related changes in the default mode network and its anti-correlated networks: a resting-state fMRI study. Neurosci Lett 504:62–67

    Article  Google Scholar 

  18. Lopez-Larson MP, Anderson JS, Ferguson MA et al (2011) Local brain connectivity and associations with gender and age. Dev Cognit Neurosci 1:187–197

    Article  Google Scholar 

  19. Bluhm RL, Osuch EA, Lanius RA et al (2008) Default mode network connectivity: effects of age sex, and analytic approach. Neuroreport 19:887–891

    Article  Google Scholar 

  20. Liu CY, Krishnan AP, Yan L et al (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

    Article  Google Scholar 

  21. Long CJ, Brown EN, Triantafyllou C et al (2005) Nonstationary noise estimation in functional MRI. Neuroimage 28:890–903

    Article  Google Scholar 

  22. Maxim V, Sendur L, Fadili J et al (2005) Fractional Gaussian noise, functional MRI and Alzheimer’s disease. Neuroimage 25:141–158

    Article  Google Scholar 

  23. Herman P, Sanganahalli BG, Hyder F et al (2011) Fractal analysis of spontaneous fluctuations of the bold signal in rat brain. Neuroimage 58:1060–1069

    Article  Google Scholar 

  24. Shimizu Y, Barth M, Windischberger C et al (2004) Wavelet-based multifractal analysis of fMRI time series. Neuroimage 22:1195–1202

    Article  Google Scholar 

  25. Zarahn E, Aguirre GK, D’Esposito M (1997) Empirical analyses of bold fMRI statistics. Neuroimage 5:179–197

    Article  Google Scholar 

  26. Anderson CM, Lowen SB, Renshaw PF (2006) Emotional task-dependent low-frequency fluctuations and methylphenidate: wavelet scaling analysis of 1/f-type fluctuations in fMRI of the cerebellar vermis. J Neurosci Methods 151:52–61

    Article  Google Scholar 

  27. Goldberger AL, Amaral LAN, Hausdorff JM et al (2002) Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci USA 99:2466–2472

    Article  Google Scholar 

  28. Wink AM, Bullmore E, Barnes A et al (2008) Monofractal and multifractal dynamics of low frequency endogenous brain oscillations in functional MRI. Hum Brain Mapp 29:791–801

    Article  Google Scholar 

  29. Suckling J, Wink AM, Bernard FA et al (2008) Endogenous multifractal brain dynamics are modulated by age, cholinergic blockade and cognitive performance. J Neurosci Method 174:292–300

    Article  Google Scholar 

  30. Lee JM, Hu J, Gao JB et al (2005) Identification of brain activity by fractal scaling analysis of functional MRI data. In: 30th Proc. IEEE ICASSP, Philadelphia, 2005, 2: 137–140

  31. Ciuciu P, Varoquaux G, Abry P et al (2012) Scale-free and multifractal time dynamics of fMRI signals during rest and task. Frontiers Physiol 3:186

    Article  Google Scholar 

  32. Dutta S (2010) EEG pattern of normal and epileptic rats: monofractal or multifractal? Fractals 18:425–431

    Article  Google Scholar 

  33. Lopes R, Betrouni N (2009) Fractal and multifractal analysis: a review. Med Image Anal 13:634

    Article  Google Scholar 

  34. Eke A, Herman P, Sanganahalli BG et al (2012) Pitfalls in fractal time series analysis: fMRI bold as an exemplary case. Frontiers Physiol 3:417

    Article  Google Scholar 

  35. Miranda CR, Soares F, Sousa I et al (2011) Multifractal analysis of blood oxygen level dependent functional magnetic resonance imaging. In: 2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). 2011: 270–275

  36. Chhabra A, Jensen RV (1989) Direct determination of the f(α) singularity spectrum. Phys Rev Lett 62:1327–1330

    Article  Google Scholar 

  37. Cuevas E (2003) F(α) multifractal spectrum at strong and weak disorder. Phys Rev B 68:024206

    Article  Google Scholar 

  38. Perrier E, Tarquis AM, Dathe A (2006) A program for fractal and multifractal analysis of two-dimensional binary images: computer algorithms versus mathematical theory. Geoderma 134:284–294

    Article  Google Scholar 

  39. Wang J, Ning XB, Ma QL et al (2005) Multiscale multifractality analysis of a 12-lead electrocardiogram. Phys Rev E 71:062902

    Article  Google Scholar 

  40. Wang W, Ning XB, Wang J et al (2003) Interleaving distribution of multifractal strength of 16-channel EEG signals. Chin Sci Bull 48:1700–1703

    Article  Google Scholar 

  41. Yang XD, He AJ, Zhou Y et al (2010) Multifractal mass exponent spectrum of complex physiological time series. Chin Sci Bull 55:1996–2003

    Article  Google Scholar 

  42. Chen Y, Nash MP, Ning XB et al (2006) The sard variety of multifractality of ventricular epicardial mapping during ischemia. Chin Sci Bull 51:809–814

    Article  Google Scholar 

  43. Xu Y, Qian C, Pan L et al (2012) Comparing monofractal and multifractal analysis of corrosion damage evolution in reinforcing bars. PLoS One 7:e29956

    Article  Google Scholar 

  44. Takahashi T, Murata T, Omori M et al (2001) Quantitative evaluation of magnetic resonance imaging of deep white matter hyperintensity in geriatric patients by multifractal analysis. Neurosci Lett 314:143–146

    Article  Google Scholar 

  45. Kantelhardt JW, Zschiegner SA, Koscielny-Bunde E et al (2002) Multifractal detrended fluctuation analysis of nonstationary time series. Physica A 316:87–114

    Article  Google Scholar 

  46. Ma QL, Ning XB, Wang J et al (2006) A new measure to characterize multifractality of sleep electroencephalogram. Chin Sci Bull 51:3059–3064

    Article  Google Scholar 

  47. Biswal BB, Mennes M, Zuo XN et al (2010) Toward discovery science of human brain function. Proc Natl Acad Sci USA 107:4734–4739

    Article  Google Scholar 

  48. Yan CG, Zang YF (2010) Dparsf: a matlab toolbox for “pipeline” data analysis of resting-state fMRI. Frontiers Syst Neurosci 4:13

    Google Scholar 

  49. Song XW, Dong ZY, Long XY et al (2011) Rest: a toolkit for resting-state functional magnetic resonance imaging data processing. PLoS One 6:e25031

    Article  Google Scholar 

  50. Yan C, Liu D, He Y et al (2009) Spontaneous brain activity in the default mode network is sensitive to different resting-state conditions with limited cognitive load. PLoS One 4:e5743

    Article  Google Scholar 

  51. Fair DA, Cohen AL, Dosenbach N et al (2008) The maturing architecture of the brain’s default network. Proc Natl Acad Sci USA 105:4028–4032

    Article  Google Scholar 

  52. Muzy JF, Bacry E, Arneodo A (1993) Multifractal formalism for fractal signals: the structure-function approach versus the wavelet-transform modulus-maxima method. Phys Rev E 47:875–884

    Article  Google Scholar 

  53. Uddin LQ, Supekar K, Menon V (2010) Typical and atypical development of functional human brain networks: insights from resting-state fMRI. Front Syst Neurosci 4:21

    Google Scholar 

  54. Wink AM, Bernard F, Salvador R et al (2006) Age and cholinergic effects on hemodynamics and functional coherence of human hippocampus. Neurobiol Aging 27:1395–1404

    Article  Google Scholar 

  55. Rangarajan G, Ding M (2000) Integrated approach to the assessment of long range correlation in time series data. Phys Rev E 61:4991–5001

    Article  Google Scholar 

  56. Buckner RL (2004) Memory and executive function in aging and ad: multiple factors that cause decline and reserve factors that compensate. Neuron 44:195–208

    Article  Google Scholar 

Download references

Acknowledgment

This work was supported by the Natural Science Foundation of Jiangsu Province (BK2011565) and the National Natural Science Foundation of China (61271079 and 61271082).

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Authors and Affiliations

  1. School of Electronic Science and Engineering, Nanjing University, Nanjing, 210093, China

    Huangjing Ni, Xiaolin Huang, Xinbao Ning, Chengyu Huo & De Ben

  2. School of Physics and Electronic Engineering, Changshu Institute of Technology, Changshu, 215500, China

    Chengyu Huo

  3. Department of Biomedical Engineering, Jinling Hospital, Nanjing, 210002, China

    Tiebing Liu

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  1. Huangjing Ni
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  2. Xiaolin Huang
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  3. Xinbao Ning
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  4. Chengyu Huo
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Correspondence to Xiaolin Huang or Xinbao Ning.

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Ni, H., Huang, X., Ning, X. et al. Multifractal analysis of resting state fMRI series in default mode network: age and gender effects. Chin. Sci. Bull. 59, 3107–3113 (2014). https://doi.org/10.1007/s11434-014-0355-x

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  • DOI: https://doi.org/10.1007/s11434-014-0355-x

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