Advertisement

Frequency-dependent changes in fractional amplitude of low-frequency oscillations in Alzheimer’s disease: a resting-state fMRI study

  • Liu Yang
  • Yan Yan
  • Yuxia Li
  • Xiaochen Hu
  • Jie Lu
  • Piu Chan
  • Tianyi YanEmail author
  • Ying HanEmail author
Original Research

Abstract

Alzheimer’s disease (AD) is the most common neurodegenerative disease in elderly individuals. We conducted this study to examine whether alterations in the fractional amplitudes of low-frequency fluctuations (fALFF) in the AD spectrum were frequency-dependent and symptom-relevant. A total of 43 patients with subjective cognitive decline (SCD), 52 with amnestic mild cognitive impairment (aMCI), 44 with Alzheimer’s dementia (d-AD) and 55 well-matched controls participated in resting-state functional magnetic resonance imaging (rs-fMRI) scans. The amplitudes were measured using fALFF within the slow-4 (0.027–0.073 Hz) and slow-5 (0.01–0.027 Hz) bands. Repeated-measures analysis of variance was performed on fALFF within two bands and correlated with neuropsychological test scores. The significant main effects of frequency and group on fALFF differed widely across brain regions. There were more varied areas in the slow-5 band than the slow-4 band. The fALFF associated with primary disease effects was mainly distributed in the parietal lobe. Obvious frequency band and group interaction effects were observed in the left angular gyrus, left calcarine fissure and surrounding cortex, left superior cerebellum, left cuneus and right lingual gyrus. Neuropsychological tests scores were significantly correlated with the fALFF magnitude of the left cuneus and right lingual in the slow-5 band. Our results suggested that the AD continuum had abnormal amplitudes in intrinsic brain activity, and these abnormalities were frequency-dependent and mainly associated with the slow-5 band rather than the slow-4 band. This may guide the frequency choice of future rs-fMRI studies and provide new insights into the neuropathophysiology of AD.

Keywords

Alzheimer’s disease Subjective cognitive decline Resting-state functional MRI fALFF Frequency dependence 

Notes

Acknowledgments

This article was supported by the National Key Research and Development Program of China (2016YFC1306300, 2016YFC0103000, 2017YFB1002504); the National Natural Science Foundation of China (Grants 61633018, 81430037,81471731, 31371007, 81671776, 61727807, 81522021, 81801052); Beijing Municipal Nature Science Foundation (7161009, 7132147); the Beijing Municipal Commission of Health and Family Planning (PXM2019_026283_000002); the Beijing Nova Program (Grant No. Z171100001117057, Z191100010618004); the Beijing Municipal Science & Technology Commission; and China Postdoctoral Science Foundation (2018 M641414).

Compliance with ethical standards

Ethics approval

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. The research was authorized by the Medical Research Ethics Committee and the Institutional Review Board of Xuanwu Hospital, Beijing, China.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

11682_2019_169_MOESM1_ESM.docx (6.4 mb)
ESM 1 (DOCX 6582 kb)

References

  1. Backman, L., Almkvist, O., Nyberg, L., & Andersson, J. (2000). Functional changes in brain activity during priming in Alzheimer's disease. Journal of Cognitive Neuroscience, 12(1), 134–141.  https://doi.org/10.1162/089892900561922.
  2. Bansal, R., & Peterson, B. S. (2018). Cluster-level statistical inference in fMRI datasets: The unexpected behavior of random fields in high dimensions. Magnetic Resonance Imaging, 49, 101–115.  https://doi.org/10.1016/j.mri.2018.01.004.CrossRefPubMedPubMedCentralGoogle Scholar
  3. Birn, R. M. (2012). The role of physiological noise in resting-state functional connectivity. Neuroimage, 62(2), 864–870.  https://doi.org/10.1016/j.neuroimage.2012.01.016.CrossRefPubMedGoogle Scholar
  4. Biswal, B., Yetkin, F. Z., 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.  https://doi.org/10.1002/mrm.1910340409.CrossRefPubMedGoogle Scholar
  5. Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The brain's default network: Anatomy, function, and relevance to disease. Annals of the New York Academy of Sciences, 1124, 1–38.  https://doi.org/10.1196/annals.1440.011.CrossRefGoogle Scholar
  6. Buzsaki, G., & Draguhn, A. (2004). Neuronal oscillations in cortical networks. Science, 304(5679), 1926–1929.  https://doi.org/10.1126/science.1099745.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Calhoun, V. D., Kiehl, K. A., & Pearlson, G. D. (2008). Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks. Human Brain Mapping, 29(7), 828–838.  https://doi.org/10.1002/hbm.20581.CrossRefPubMedPubMedCentralGoogle Scholar
  8. Calhoun, V. D., Sui, J., Kiehl, K., Turner, J., Allen, E., & Pearlson, G. (2011). Exploring the psychosis functional connectome: Aberrant intrinsic networks in schizophrenia and bipolar disorder. Frontiers in Psychiatry, 2, 75.  https://doi.org/10.3389/fpsyt.2011.00075.CrossRefPubMedGoogle Scholar
  9. Chang, M., Edmiston, E. K., Womer, F. Y., Thou, Q., Wei, S. N., Jiang, M. W., et al. (2019). Spontaneous low-frequency fluctuations in the neural system for emotional perception in major psychiatric disorders: Amplitude similarities and differences across frequency bands. [article]. Journal of Psychiatry & Neuroscience, 44(2), 132–141.  https://doi.org/10.1503/jpn.170226.CrossRefGoogle Scholar
  10. Cho, H., Seo, S. W., Kim, J. H., Suh, M. K., Lee, J. H., Choe, Y. S., Lee, K. H., Kim, J. S., Kim, G. H., Noh, Y., Ye, B. S., Kim, H. J., Yoon, C. W., Chin, J., & Na, D. L. (2013). Amyloid deposition in early onset versus late onset Alzheimer's disease. Journal of Alzheimer's Disease, 35(4), 813–821.  https://doi.org/10.3233/JAD-121927.CrossRefPubMedGoogle Scholar
  11. Ding, B., Ling, H. W., Zhang, Y., Huang, J., Zhang, H., Wang, T., et al. (2014). Pattern of cerebral hyperperfusion in Alzheimer's disease and amnestic mild cognitive impairment using voxel-based analysis of 3D arterial spin-labeling imaging: Initial experience. Clinical Interventions in Aging, 9, 493–500.  https://doi.org/10.2147/CIA.S58879.CrossRefPubMedPubMedCentralGoogle Scholar
  12. Dozeman, E., van Schaik, D. J., van Marwijk, H. W., Stek, M. L., van der Horst, H. E., & Beekman, A. T. (2011). The center for epidemiological studies depression scale (CES-D) is an adequate screening instrument for depressive and anxiety disorders in a very old population living in residential homes. International Journal of Geriatric Psychiatry, 26(3), 239–246.  https://doi.org/10.1002/gps.2519.CrossRefPubMedGoogle Scholar
  13. Forster, S., Yousefi, B. H., Wester, H. J., Klupp, E., Rominger, A., Forstl, H., et al. (2012). Quantitative longitudinal interrelationships between brain metabolism and amyloid deposition during a 2-year follow-up in patients with early Alzheimer's disease. European Journal of Nuclear Medicine and Molecular Imaging, 39(12), 1927–1936.  https://doi.org/10.1007/s00259-012-2230-9.CrossRefPubMedGoogle Scholar
  14. Friston, K. J., Worsley, K. J., Frackowiak, R. S., Mazziotta, J. C., & Evans, A. C. (1994). Assessing the significance of focal activations using their spatial extent. Human Brain Mapping, 1(3), 210–220.  https://doi.org/10.1002/hbm.460010306.CrossRefPubMedGoogle Scholar
  15. Gao, L., Bai, L., Zhang, Y., Dai, X. J., Netra, R., Min, Y., Zhou, F., Niu, C., Dun, W., Gong, H., & Zhang, M. (2015). Frequency-dependent changes of local resting oscillations in sleep-deprived brain. PLoS One, 10(3), e0120323.  https://doi.org/10.1371/journal.pone.0120323.CrossRefPubMedPubMedCentralGoogle Scholar
  16. Guo, Q. H., Sun, Y. T., Yu, P. M., Hong, Z., & Lv, C. Z. (2007). Norm of auditory verbal learning test in the normal aged in Chinese community. Chinese Journal of Clinical Psychology, 15(2):132–134Google Scholar
  17. Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry, 23, 56–62.  https://doi.org/10.1136/jnnp.23.1.56.CrossRefPubMedPubMedCentralGoogle Scholar
  18. Han, Y., Wang, J., Zhao, Z., Min, B., Lu, J., Li, K., He, Y., & Jia, J. (2011). Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: A resting-state fMRI study. Neuroimage, 55(1), 287–295.  https://doi.org/10.1016/j.neuroimage.2010.11.059.CrossRefPubMedGoogle Scholar
  19. He, Y., Wang, L., Zang, Y., Tian, L., Zhang, X., Li, K., & Jiang, T. (2007). Regional coherence changes in the early stages of Alzheimer's disease: A combined structural and resting-state functional MRI study. Neuroimage, 35(2), 488–500.  https://doi.org/10.1016/j.neuroimage.2006.11.042.CrossRefPubMedGoogle Scholar
  20. Hong, J. Y., Kilpatrick, L. A., Labus, J., Gupta, A., Jiang, Z., Ashe-McNalley, C., Stains, J., Heendeniya, N., Ebrat, B., Smith, S., Tillisch, K., Naliboff, B., & Mayer, E. A. (2013). Patients with chronic visceral pain show sex-related alterations in intrinsic oscillations of the resting brain. The Journal of Neuroscience, 33(29), 11994–12002.  https://doi.org/10.1523/jneurosci.5733-12.2013.CrossRefPubMedPubMedCentralGoogle Scholar
  21. Hong, Y. J., Yoon, B., Shim, Y. S., Ahn, K. J., Yang, D. W., & Lee, J. H. (2015). Gray and white matter degenerations in subjective memory impairment: Comparisons with Normal controls and mild cognitive impairment. [article]. Journal of Korean Medical Science, 30(11), 1652–1658.  https://doi.org/10.3346/jkms.2015.30.11.1652.CrossRefPubMedPubMedCentralGoogle Scholar
  22. Hou, Y., Wu, X., Hallett, M., Chan, P., & Wu, T. (2014). Frequency-dependent neural activity in Parkinson's disease. Human Brain Mapping, 35(12), 5815–5833.  https://doi.org/10.1002/hbm.22587.CrossRefPubMedGoogle Scholar
  23. Hutcheon, B., & Yarom, Y. (2000). Resonance, oscillation and the intrinsic frequency preferences of neurons. Trends in Neurosciences, 23(5), 216–222.  https://doi.org/10.1016/s0166-2236(00)01547-2.CrossRefPubMedGoogle Scholar
  24. Jessen, F., Amariglio, R. E., van Boxtel, M., Breteler, M., Ceccaldi, M., Chetelat, G., et al. (2014). A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer's disease. Alzheimers Dement, 10(6), 844–852.  https://doi.org/10.1016/j.jalz.2014.01.001.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Kang, D. W., Choi, W. H., Jung, W. S., Um, Y. H., Lee, C. U., & Lim, H. K. (2017). Impact of amyloid burden on regional functional synchronization in the cognitively Normal older adults. Scientific Reports, 7(1), 14690.  https://doi.org/10.1038/s41598-017-15001-8.CrossRefPubMedPubMedCentralGoogle Scholar
  26. Knyazev, G. G. (2007). Motivation, emotion, and their inhibitory control mirrored in brain oscillations. Neuroscience and Biobehavioral Reviews, 31(3), 377–395.  https://doi.org/10.1016/j.neubiorev.2006.10.004.CrossRefPubMedGoogle Scholar
  27. Li, C., Liu, C., Yin, X., Yang, J., Gui, L., Wei, L., & Wang, J. (2014). Frequency-dependent changes in the amplitude of low-frequency fluctuations in subcortical ischemic vascular disease (SIVD): A resting-state fMRI study. Behavioural Brain Research, 274, 205–210.  https://doi.org/10.1016/j.bbr.2014.08.019.CrossRefPubMedGoogle Scholar
  28. Li, Y., Jing, B., Liu, H., Li, Y., Gao, X., Li, Y., Mu, B., Yu, H., Cheng, J., Barker, P. B., Wang, H., & Han, Y. (2017). Frequency-dependent changes in the amplitude of low-frequency fluctuations in mild cognitive impairment with mild depression. Journal of Alzheimer's Disease, 58(4), 1175–1187.  https://doi.org/10.3233/JAD-161282.CrossRefPubMedGoogle Scholar
  29. Liang, P., Xiang, J., Liang, H., Qi, Z., Li, K., & Alzheimer's Disease NeuroImaging, I. (2014). Altered amplitude of low-frequency fluctuations in early and late mild cognitive impairment and Alzheimer's disease. Current Alzheimer Research, 11(4), 389–398.  https://doi.org/10.2174/1567205011666140331225335.CrossRefPubMedGoogle Scholar
  30. Liu, X., Wang, S., Zhang, X., Wang, Z., Tian, X., & He, Y. (2014). Abnormal amplitude of low-frequency fluctuations of intrinsic brain activity in Alzheimer's disease. Journal of Alzheimer's Disease, 40(2), 387–397.  https://doi.org/10.3233/jad-131322.CrossRefPubMedGoogle Scholar
  31. Llinas, R. R. (1988). The intrinsic electrophysiological properties of mammalian neurons: Insights into central nervous system function. Science, 242(4886), 1654–1664.  https://doi.org/10.1126/science.3059497.CrossRefPubMedGoogle Scholar
  32. Lu, J., Li, D., Li, F., Zhou, A., Wang, F., Zuo, X., Jia, X. F., Song, H., & Jia, J. (2011). Montreal cognitive assessment in detecting cognitive impairment in Chinese elderly individuals: A population-based study. Journal of Geriatric Psychiatry and Neurology, 24(4), 184–190.  https://doi.org/10.1177/0891988711422528.CrossRefPubMedGoogle Scholar
  33. Ma, X., Li, Z., Jing, B., Liu, H., Li, D., Li, H., & the Alzheimer’s Disease Neuroimaging Initiative. (2016). Identify the atrophy of Alzheimer's Disease, mild cognitive impairment and Normal aging using morphometric MRI analysis. Frontiers in Aging Neuroscience, 8, 243.  https://doi.org/10.3389/fnagi.2016.00243.CrossRefPubMedPubMedCentralGoogle Scholar
  34. Mascali, D., DiNuzzo, M., Gili, T., Moraschi, M., Fratini, M., Maraviglia, B., Serra, L., Bozzali, M., & Giove, F. (2015). Intrinsic patterns of coupling between correlation and amplitude of low-frequency fMRI fluctuations are disrupted in degenerative dementia mainly due to functional disconnection. PLoS One, 10(4), e0120988.  https://doi.org/10.1371/journal.pone.0120988.CrossRefPubMedPubMedCentralGoogle Scholar
  35. McDade, E., & Bateman, R. J. (2017). Stop Alzheimer's before it starts. Nature, 547(7662), 153–155.  https://doi.org/10.1038/547153a.CrossRefPubMedGoogle Scholar
  36. McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Jr., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., Mohs, R. C., Morris, J. C., Rossor, M. N., Scheltens, P., Carrillo, M. C., Thies, B., Weintraub, S., & Phelps, C. H. (2011). The diagnosis of dementia due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement, 7(3), 263–269.  https://doi.org/10.1016/j.jalz.2011.03.005.CrossRefPubMedPubMedCentralGoogle Scholar
  37. Morris, J. C. (1993). The clinical dementia rating (CDR): Current version and scoring rules. Neurology, 43(11), 2412–2414.  https://doi.org/10.1212/WNL.43.11.2412-a.CrossRefPubMedGoogle Scholar
  38. Pan, P., Zhu, L., Yu, T., Shi, H., Zhang, B., Qin, R., Zhu, X., Qian, L., Zhao, H., Zhou, H., & Xu, Y. (2017). Aberrant spontaneous low-frequency brain activity in amnestic mild cognitive impairment: A meta-analysis of resting-state fMRI studies. Ageing Research Reviews, 35, 12–21.  https://doi.org/10.1016/j.arr.2016.12.001.CrossRefPubMedGoogle Scholar
  39. Penttonen, M., & Buzsaki, G. (2003). Natural logarithmic relationship between brain oscillators. [article]. Thalamus & Related Systems, 2(2), 145–152.  https://doi.org/10.1016/s1472-9288(03)00007-4.CrossRefGoogle Scholar
  40. Peraza, L. R., Colloby, S. J., Deboys, L., O'Brien, J. T., Kaiser, M., & Taylor, J. P. (2016). Regional functional synchronizations in dementia with Lewy bodies and Alzheimer's disease. International Psychogeriatrics, 28(7), 1143–1151.  https://doi.org/10.1017/S1041610216000429.CrossRefPubMedPubMedCentralGoogle Scholar
  41. Power, J. D., Plitt, M., Laumann, T. O., & Martin, A. (2017). Sources and implications of whole-brain fMRI signals in humans. Neuroimage, 146, 609–625.  https://doi.org/10.1016/j.neuroimage.2016.09.038.CrossRefPubMedGoogle Scholar
  42. Prvulovic, D., Hubl, D., Sack, A. T., Melillo, L., Maurer, K., Frolich, L., et al. (2002). Functional imaging of visuospatial processing in Alzheimer's disease. Neuroimage, 17(3), 1403–1414.  https://doi.org/10.1006/nimg.2002.1271.CrossRefPubMedGoogle Scholar
  43. Rabin, L. A., Smart, C. M., & Amariglio, R. E. (2017). Subjective cognitive decline in preclinical Alzheimer's Disease. Annual Review of Clinical Psychology, 13, 369–396.  https://doi.org/10.1146/annurev-clinpsy-032816-045136.CrossRefPubMedGoogle Scholar
  44. Riederer, I., Bohn, K. P., Preibisch, C., Wiedemann, E., Zimmer, C., Alexopoulos, P., & Förster, S. (2018). Alzheimer Disease and mild cognitive impairment: Integrated pulsed arterial spin-labeling MRI and (18)F-FDG PET. Radiology, 288(1), 198–206.  https://doi.org/10.1148/radiol.2018170575.CrossRefPubMedGoogle Scholar
  45. Risacher, S. L., Kim, S., Shen, L., Nho, K., Foroud, T., Green, R. C., et al. (2013). The role of apolipoprotein E (APOE) genotype in early mild cognitive impairment (E-MCI). Frontiers in Aging Neuroscience, 5, 11.  https://doi.org/10.3389/fnagi.2013.00011.CrossRefPubMedPubMedCentralGoogle Scholar
  46. Scarmeas, N., Anderson, K. E., Hilton, J., Park, A., Habeck, C., Flynn, J., Tycko, B., & Stern, Y. (2004). APOE-dependent PET patterns of brain activation in Alzheimer disease. Neurology, 63(5), 913–915.  https://doi.org/10.1212/01.WNL.0000137274.93125.46 CrossRefPubMedPubMedCentralGoogle Scholar
  47. Seo, E. H., Lee, D. Y., Lee, J. M., Park, J. S., Sohn, B. K., Choe, Y. M., Byun, M. S., Choi, H. J., & Woo, J. I. (2013). Influence of APOE genotype on whole-brain functional networks in cognitively normal elderly. PLoS One, 8(12), e83205.  https://doi.org/10.1371/journal.pone.0083205.CrossRefPubMedPubMedCentralGoogle Scholar
  48. Silverman, D. H., Small, G. W., Chang, C. Y., Lu, C. S., Kung De Aburto, M. A., Chen, W., et al. (2001). Positron emission tomography in evaluation of dementia: Regional brain metabolism and long-term outcome. JAMA, 286(17), 2120–2127.  https://doi.org/10.1001/jama.286.17.2120.CrossRefPubMedGoogle Scholar
  49. Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., Iwatsubo, T., Jack, C. R., Jr., Kaye, J., Montine, T. J., Park, D. C., Reiman, E. M., Rowe, C. C., Siemers, E., Stern, Y., Yaffe, K., Carrillo, M. C., Thies, B., Morrison-Bogorad, M., Wagster, M. V., & Phelps, C. H. (2011). Toward defining the preclinical stages of Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement, 7(3), 280–292.  https://doi.org/10.1016/j.jalz.2011.03.003.CrossRefPubMedPubMedCentralGoogle Scholar
  50. Wang, Z., Jia, X., Liang, P., Qi, Z., Yang, Y., Zhou, W., & Li, K. (2012). Changes in thalamus connectivity in mild cognitive impairment: Evidence from resting state fMRI. European Journal of Radiology, 81(2), 277–285.  https://doi.org/10.1016/j.ejrad.2010.12.044.CrossRefPubMedGoogle Scholar
  51. Wei, L., Duan, X., Zheng, C., Wang, S., Gao, Q., Zhang, Z., Lu, G., & Chen, H. (2014). Specific frequency bands of amplitude low-frequency oscillation encodes personality. Human Brain Mapping, 35(1), 331–339.  https://doi.org/10.1002/hbm.22176.CrossRefPubMedGoogle Scholar
  52. Worsley, K. J., Marrett, S., Neelin, P., Vandal, A. C., Friston, K. J., & Evans, A. C. (1996). A unified statistical approach for determining significant signals in images of cerebral activation. Human Brain Mapping, 4(1), 58–73.CrossRefGoogle Scholar
  53. Xue, S. W., Li, D., Weng, X. C., Northoff, G., & Li, D. W. (2014). Different neural manifestations of two slow frequency bands in resting functional magnetic resonance imaging: A systemic survey at regional, interregional, and network levels. Brain Connectivity, 4(4), 242–255.  https://doi.org/10.1089/brain.2013.0182.CrossRefPubMedGoogle Scholar
  54. Yan, T., Wang, W., Yang, L., Chen, K., Chen, R., & Han, Y. (2018). Rich club disturbances of the human connectome from subjective cognitive decline to Alzheimer's disease. Theranostics, 8(12), 3237–3255.  https://doi.org/10.7150/thno.23772.CrossRefPubMedPubMedCentralGoogle Scholar
  55. Yang, C., Sun, X., Tao, W., Li, X., Zhang, J., Jia, J., Chen, K., & Zhang, Z. (2016). Multistage grading of amnestic mild cognitive impairment: The associated brain gray matter volume and cognitive behavior characterization. Frontiers in Aging Neuroscience, 8, 332.  https://doi.org/10.3389/fnagi.2016.00332.CrossRefPubMedGoogle Scholar
  56. Yang, L., Yan, Y., Wang, Y., Hu, X., Lu, J., Chan, P., Yan, T., & Han, Y. (2018). Gradual disturbances of the amplitude of low-frequency fluctuations (ALFF) and fractional ALFF in Alzheimer Spectrum. Frontiers in Neuroscience, 12.:975  https://doi.org/10.3389/fnins.2018.00975.
  57. Yue, Y., Jia, X., Hou, Z., Zang, Y., & Yuan, Y. (2015). Frequency-dependent amplitude alterations of resting-state spontaneous fluctuations in late-onset depression. BioMed Research International, 2015, 505479–505479.  https://doi.org/10.1155/2015/505479.CrossRefPubMedPubMedCentralGoogle Scholar
  58. Zang, Y. F., He, Y., Zhu, C. Z., Cao, Q. J., Sui, M. Q., Liang, M., et al. (2007). Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. [article]. Brain & Development, 29(2), 83–91.  https://doi.org/10.1016/j.braindev.2006.07.002.CrossRefGoogle Scholar
  59. Zhang, Z., Hong, X., & Hui, L. I. (1999). The minimental state examination in the Chinese residents population aged 55 years and over in the urban and rural areas of Beijing. Chinese Journal of Neurology, 32, 149–153.Google Scholar
  60. Zhao, W. N., Wang, X. T., Yin, C. H., He, M. F., Li, S. Y., & Han, Y. (2019). Trajectories of the hippocampal subfields atrophy in the Alzheimer's Disease: A structural imaging study. Frontiers in Neuroinformatics, 13, 9.  https://doi.org/10.3389/fninf.2019.00013.CrossRefGoogle Scholar
  61. 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.  https://doi.org/10.1016/j.jneumeth.2008.04.012.CrossRefPubMedPubMedCentralGoogle Scholar
  62. Zuo, X. N., Di Martino, A., Kelly, C., Shehzad, Z. E., Gee, D. G., Klein, D. F., et al. (2010). The oscillating brain: Complex and reliable. Neuroimage, 49(2), 1432–1445.  https://doi.org/10.1016/j.neuroimage.2009.09.037.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
  2. 2.School of Life ScienceBeijing Institute of TechnologyBeijingChina
  3. 3.Department of Psychiatry and Psychotherapy, Medical FacultyUniversity of CologneCologneGermany
  4. 4.Department of RadiologyXuanwu Hospital of Capital Medical UniversityBeijingChina
  5. 5.Beijing Institute of GeriatricsBeijingChina
  6. 6.National Clinical Research Center for Geriatric DisordersBeijingChina
  7. 7.Center of Alzheimer’s DiseaseBeijing Institute for Brain DisordersBeijingChina

Personalised recommendations