Advertisement

Brain Imaging and Behavior

, Volume 13, Issue 5, pp 1486–1495 | Cite as

Associations of brain entropy (BEN) to cerebral blood flow and fractional amplitude of low-frequency fluctuations in the resting brain

  • Donghui Song
  • Da Chang
  • Jian Zhang
  • Qiu Ge
  • Yu-Feng Zang
  • Ze WangEmail author
Original Research

Abstract

Entropy is a fundamental trait of human brain. Using fMRI-based brain entropy (BEN) mapping, interesting findings have been increasingly revealed in normal brain and neuropsychiatric disorders. As BEN is still relatively new, an often-raised question is how much new information can this measure tell about the brain compared to other more established brain activity measures. The study aimed to address that question by examining the relationship between BEN and cerebral blood flow (CBF) and the fractional amplitude of low-frequency fluctuations (fALFF), two widely used resting state brain state measures. fMRI data acquired from a large cohort of normal subjects were used to calculate the three metrics; inter-modality associations were assessed at each voxel through the Pearson correlation analysis. A moderate to high positive BEN-CBF and BEN-fALFF correlations were found in orbito-frontal cortex (OFC) and posterior inferior temporal cortex (ITC); Strong negative BEN-fALFF correlations were found in visual cortex (VC), anterior ITC, striatum, motor network, precuneus, and lateral parietal cortex. Positive CBF-fALFF correlations were found in medial OFC (MOFC), medial prefrontal cortex (MPFC), left angular gyrus, and left precuneus. Significant gender effects were observed for all three metrics and their correlations. Our data clearly demonstrated that BEN provides unique information that cannot be revealed by CBF and fALFF.

Keywords

Brain entropy Resting state fMRI Cerebral blood flow Fractional amplitude of the low-frequency fluctuations 

Notes

Funding

This study was funded by Natural Science Foundation of Zhejiang Province (Grant LZ15H180001), the Youth 1000 Talent Program of China, and Hangzhou Qianjiang Endowed Professor Program, National Natural Science Foundation of China (No. 61671198).

Compliance with ethical standards

Conflict of interest

All authors declared no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed written consents were obtained from all individual participants included in the study.

References

  1. Adibi, M., Clifford, C. W., & Arabzadeh, E. (2013). Informational basis of sensory adaptation: Entropy and single-spike efficiency in rat barrel cortex. Journal of Neuroscience the Official Journal of the Society for Neuroscience, 33(37), 14921–14926.CrossRefGoogle Scholar
  2. Baron, J. C., Lebrun-Grandie, P., Collard, P., Crouzel, C., Mestelan, G., & Bousser, M. G. (1982). Noninvasive measurement of blood flow, oxygen consumption, and glucose utilization in the same brain regions in man by positron emission tomography: Concise communication. Journal of Nuclear Medicine, 23(5), 391–399.Google Scholar
  3. Bassett, D. S., & Gazzaniga, M. S. (2011). Understanding complexity in the human brain. Trends in Cognitive Sciences, 15(5), 200.CrossRefGoogle Scholar
  4. Bhattacharya, J. (2000). Complexity analysis of spontaneous EEG. Acta Neurobiologiae Experimentalis, 60(4), 495.Google Scholar
  5. Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magnetic Resonance in Medicine, 34(4), 537–541.CrossRefGoogle Scholar
  6. Bosl, W., Tierney, A., Tager-Flusberg, H., & Nelson, C. (2011). EEG complexity as a biomarker for autism spectrum disorder risk. BMC Medicine, 9(1), 18.CrossRefGoogle Scholar
  7. Clausius, R. (1862). Ueber die wärmeleitung gasförmiger körper. Annalen der Physik, 191(1), 1–56.CrossRefGoogle Scholar
  8. Da, C., Song, D., Jian, Z., Shang, Y., Qiu, G., & Wang, Z. (2018). Caffeine caused a widespread increase of resting brain entropy. Scientific Reports, 8(1), 2700.CrossRefGoogle Scholar
  9. DeWitt, D. S., Yuan, X. Q., Becker, D. P., & Hayes, R. L. (1988). Simultaneous, quantitative measurement of local blood flow and glucose utilization in tissue samples in normal and injured feline brain. Brain Injury, 2(4), 291–303.CrossRefGoogle Scholar
  10. Fisher, R. A. (1921). On the “probable error” of a coefficient of correlation. Metron, 1, 1–32.Google Scholar
  11. Fransson, P. (2005). Spontaneous low-frequency BOLD signal fluctuations: An fMRI investigation of the resting-state default mode of brain function hypothesis. Human Brain Mapping, 26(1), 15–29.CrossRefGoogle Scholar
  12. Friston, K. J., Frith, C. D., Passingham, R. E., Dolan, R. J., Liddle, P. F., & Frackowiak, R. S. (1992). Entropy and cortical activity: Information theory and PET findings. Cerebral Cortex, 2(3), 259–267.CrossRefGoogle Scholar
  13. Friston, K. J., Tononi, G., Sporns, O., & Edelman, G. M. (2010). Characterising the complexity of neuronal interactions. Human Brain Mapping, 3(4), 302–314.CrossRefGoogle Scholar
  14. Furlow Jr., T. W., Martin, R. M., & Harrison, L. E. (1983). Simultaneous measurement of local glucose utilization and blood flow in the rat brain: An autoradiographic method using two tracers labeled with carbon-14. Journal of Cerebral Blood Flow and Metabolism, 3(1), 62–66.CrossRefGoogle Scholar
  15. Fuster, J. (2015). The prefrontal cortex (Fifth Edition). Elsevier Ltd.Google Scholar
  16. Goldberger, A. L., Peng, C. K., & Lipsitz, L. A. (2002). What is physiologic complexity and how does it change with aging and disease? Neurobiology of Aging, 23(1), 23–26.CrossRefGoogle Scholar
  17. Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences of the United States of America, 100(1), 253–258.CrossRefGoogle Scholar
  18. Gur, R. E., & Gur, R. C. (1990). Gender differences in regional cerebral blood flow. Schizophrenia Bulletin, 16(2), 247–254.CrossRefGoogle Scholar
  19. Hofman, M. A. (2014). Evolution of the human brain: When bigger is better. Frontiers in Neuroanatomy, 8, 15.CrossRefGoogle Scholar
  20. Hu, W. T., Wang, Z., Lee, V. M. Y., Trojanowski, J. Q., Detre, J. A., & Grossman, M. (2010). Distinct cerebral perfusion patterns in FTLD and AD. Neurology, 75(10), 881–888.CrossRefGoogle Scholar
  21. Jia, Y., Gu, H., & Luo, Q. (2017). Sample entropy reveals an age-related reduction in the complexity of dynamic brain. Scientific Reports, 7(1), 7990.CrossRefGoogle Scholar
  22. Kiviniemi, V., Jauhiainen, J., Tervonen, O., Pääkkö, E., Oikarinen, J., Vainionpää, V., Rantala, H., & Biswal, B. (2000). Slow vasomotor fluctuation in fMRI of anesthetized child brain. Magnetic Resonance in Medicine, 44(3), 373.CrossRefGoogle Scholar
  23. Lake, D. E., Richman, J. S., Griffin, M. P., & Moorman, J. R. (2002). Sample entropy analysis of neonatal heart rate variability. American Journal of Physiology Regulatory Integrative & Comparative Physiology, 283(3), R789.CrossRefGoogle Scholar
  24. Lebedev, A. V., Kaelen, M., Lövdén, M., Nilsson, J., Feilding, A., Nutt, D. J., & Carhart-Harris, R. L. (2016). LSD-induced entropic brain activity predicts subsequent personality change. Human Brain Mapping, 37(9), 3203.CrossRefGoogle Scholar
  25. Lee, M. H., Smyser, C. D., & Shimony, J. S. (2013). Resting state fMRI: A review of methods and clinical applications. Ajnr American Journal of Neuroradiology, 34(10), 1866–1872.CrossRefGoogle Scholar
  26. Li, Z., Zhu, Y., Childress, A. R., Detre, J. A., & Wang, Z. (2012). Relations between BOLD fMRI-derived resting brain activity and cerebral blood flow. PLoS One, 7(9), e44556.CrossRefGoogle Scholar
  27. Li, Z., Fang, Z., Hager, N., Rao, H., & Wang, Z. (2016). Hyper-resting brain entropy within chronic smokers and its moderation by sex. Scientific Reports, 6, 29435.CrossRefGoogle Scholar
  28. Lipsitz, L. A. (2004). Physiological complexity, aging, and the path to frailty. Science of Aging Knowledge Environment Sage Ke, 2004(16), pe16.Google Scholar
  29. Liu, Z. M., Schmidt, K. F., Sicard, K. M., & Duong, T. Q. (2004). Imaging oxygen consumption in forepaw somatosensory stimulation in rats under isoflurane anesthesia. Magnetic Resonance in Medicine, 52(2), 277–285.CrossRefGoogle Scholar
  30. Lowe, M. J., Mock, B. J., & Sorenson, J. A. (1998). Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations. NeuroImage, 7(2), 119.CrossRefGoogle Scholar
  31. Macphail, E. M., & Bolhuis, J. J. (2001). The evolution of intelligence: Adaptive specializations versus general process. Biological Reviews of the Cambridge Philosophical Society, 76(3), 341–364.CrossRefGoogle Scholar
  32. Mölle, M., Marshall, L., Lutzenberger, W., Pietrowsky, R., Fehm, H. L., & Born, J. (1996). Enhanced dynamic complexity in the human EEG during creative thinking. Neuroscience Letters, 208(1), 61–64.CrossRefGoogle Scholar
  33. Nelson, M. J., S. Dehaene, C. Pallier and J. T. Hale (2017). Entropy reduction correlates with temporal lobe activity. The Workshop on Cognitive Modeling & Computational Linguistics.Google Scholar
  34. Potthoff, R. F. (1966). Statistical aspects of problem of biases in psychological tests. North Carolina State University. Department of Statistics.Google Scholar
  35. Raichle, M. E. (1998). Behind the scenes of functional brain imaging: A historical and physiological perspective. Proceedings of the National Academy of Sciences of the United States of America, 95(3), 765–772.CrossRefGoogle Scholar
  36. Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. PNAS, 98, 676–682.CrossRefGoogle Scholar
  37. Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology Heart & Circulatory Physiology, 278(6), H2039.CrossRefGoogle Scholar
  38. Rilling, J. K. (2014). Comparative primate neuroimaging: Insights into human brain evolution. Trends in Cognitive Sciences, 18(1), 46–55.CrossRefGoogle Scholar
  39. Rodriguez, G., Warkentin, S., Risberg, J., & Rosadini, G. (1988). Sex differences in regional cerebral blood flow. Journal of Cerebral Blood Flow and Metabolism, 8(6), 783–789.CrossRefGoogle Scholar
  40. Roth, G., & Dicke, U. (2012). Evolution of the brain and intelligence in primates. Progress in Brain Research, 195, 413–430.CrossRefGoogle Scholar
  41. Saad, Z. S., Gotts, S. J., Murphy, K., Chen, G., Jo, H. J., Martin, A., & Cox, R. W. (2012). Trouble at rest: How correlation patterns and group differences become distorted after global signal regression. Brain Connectivity, 2(1), 25.CrossRefGoogle Scholar
  42. Sabeti, M., Katebi, S., & Boostani, R. (2009). Entropy and complexity measures for EEG signal classification of schizophrenic and control participants. Artificial Intelligence in Medicine, 47(3), 263–274.CrossRefGoogle Scholar
  43. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423–623–656.CrossRefGoogle Scholar
  44. Singer, W. (2009). The brain, a complex self-organizing system. European Review, 17(2), 321–329.CrossRefGoogle Scholar
  45. Smith, R. X., Yan, L., & Wang, D. J. J. (2014). Multiple time scale complexity analysis of resting state FMRI. Brain Imaging & Behavior, 8(2), 284.CrossRefGoogle Scholar
  46. Sokunbi, M., Fung, W., Sawlani, V., Choppin, S., Linden, D., & Thome, J. (2014a). Resting state fMRI entropy probes complexity of brain activity in adults with ADHD. Psychiatry Research: Neuroimaging, 214(3), 341–348.CrossRefGoogle Scholar
  47. Sokunbi, M. O., Gradin, V. B., Waiter, G. D., Cameron, G. G., Ahearn, T. S., Murray, A. D., Steele, D. J., & R. T. Staff. (2014b). Nonlinear complexity analysis of brain FMRI signals in schizophrenia. PLoS One, 9(5), e95146.CrossRefGoogle Scholar
  48. Song, D., Chang, D., Zhang, J., Peng, W., Shang, Y., Gao X., & Wang, Z. (2018). Reduced brain entropy by repetivive transcranial magnetic stimulation on the left dorsolateral in the healthy young adults. Brain Imaging & Behavior, 1–9.Google Scholar
  49. Tononi, G., Edelman, G. M., & Sporns, O. (1998). Complexity and coherency: Integrating information in the brain. Trends in Cognitive Sciences, 2(12), 474–484.CrossRefGoogle Scholar
  50. Vestergaard, M. B., Lindberg, U., Aachmann-Andersen, N. J., Lisbjerg, K., Christensen, S. J., Law, I., Rasmussen, P., Olsen, N. V., & Larsson, H. B. (2016). Acute hypoxia increases the cerebral metabolic rate - a magnetic resonance imaging study. Journal of Cerebral Blood Flow and Metabolism, 36(6), 1046–1058.CrossRefGoogle Scholar
  51. Wang, Z., Li, Y., Childress, A. R., & Detre, J. A. (2014). Brain entropy mapping using fMRI. PLoS One, 9(3), e89948.CrossRefGoogle Scholar
  52. Wang, B., Niu, Y., Miao, L., Cao, R., Yan, P., Guo, H., Li, D., Guo, Y., Yan, T., & Wu, J. (2017). Decreased complexity in Alzheimer's disease: Resting-state fMRI evidence of brain entropy mapping. Frontiers in Aging Neuroscience, 9, 378.CrossRefGoogle Scholar
  53. Yang, A. C., Huang, C. C., Yeh, H. L., Liu, M. E., Hong, C. J., Tu, P. C., Chen, J. F., Huang, N. E., Peng, C. K., Lin, C. P., & Tsai, S. J. (2013). Complexity of spontaneous BOLD activity in default mode network is correlated with cognitive function in normal male elderly: A multiscale entropy analysis. Neurobiology of Aging, 34(2), 428–438.CrossRefGoogle Scholar
  54. Yao, Y., Lu, W. L., Xu, B., Li, C. B., Lin, C. P., Waxman, D., & Feng, J. F. (2013). The increase of the functional entropy of the human brain with age. Scientific Reports, 3, 2853.CrossRefGoogle Scholar
  55. Ze Wang, J. S., Duan, D., Darnley, S., Jing, Y., Zhang, J., O'Brien, C., & Childress, A. R. (2017). A hypo-status in drug dependent brain revealed by multi-modal MRI. Addiction Biology, 22(6), 1622–1631.CrossRefGoogle Scholar
  56. Zhou, F., Zhuang, Y., Gong, H., Zhan, J., Grossman, M., & Wang, Z. (2016). Resting state brain entropy alterations in relapsing remitting multiple sclerosis. PLoS One, 11(1), e0146080.CrossRefGoogle Scholar
  57. Zou, Q. H., Zhu, C. Z., Yang, Y., Zuo, X. N., Long, X. Y., Cao, Q. J., Wang, Y. F., & Zang, Y. F. (2008). An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF. Journal of Neuroscience Methods, 172(1), 137–141.CrossRefGoogle Scholar
  58. Zou, Q., Wu, C. W., Stein, E. A., Zang, Y., & Yang, Y. (2009). Static and dynamic characteristics of cerebral blood flow during the resting state. NeuroImage, 48(3), 515–524.CrossRefGoogle Scholar
  59. Zou, Q., Miao, X., Liu, D., Wang, D. J. J., Zhuo, Y., & Gao, J. H. (2015). Reliability comparison of spontaneous brain activities between BOLD and CBF contrasts in eyes-open and eyes-closed resting states. Neuroimage, 121, 91–105.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Center for Cognition and Brain Disorders, Department of PsychologyHangzhou Normal UniversityHangzhouChina
  2. 2.Department of Radiology, Lewis Katz School of MedicineTemple UniversityPhiladelphiaUSA

Personalised recommendations