Brain Imaging and Behavior

, Volume 8, Issue 4, pp 542–557 | Cite as

Predictive models of resting state networks for assessment of altered functional connectivity in mild cognitive impairment

  • Xi Jiang
  • Dajiang Zhu
  • Kaiming Li
  • Tuo Zhang
  • Lihong Wang
  • Dinggang Shen
  • Lei Guo
  • Tianming LiuEmail author
Original Research


Due to the difficulties in establishing correspondences between functional regions across individuals and populations, systematic elucidation of functional connectivity alterations in mild cognitive impairment (MCI) in comparison with normal controls (NC) is still a challenging problem. In this paper, we assessed the functional connectivity alterations in MCI via novel, alternative predictive models of resting state networks (RSNs) learned from multimodal resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI) data. First, ICA-clustering was used to construct RSNs from R-fMRI data in NC group. Second, since the RSNs in MCI are already altered and can hardly be constructed directly from R-fMRI data, structural landmarks derived from DTI data were employed as the predictive models of RSNs for MCI. Third, given that the landmarks are structurally consistent and correspondent across NC and MCI, functional connectivities in MCI were assessed based on the predicted RSNs and compared with those in NC. Experimental results demonstrated that the predictive models of RSNs based on multimodal R-fMRI and DTI data systematically and comprehensively revealed widespread functional connectivity alterations in MCI in comparison with NC.


Mild cognitive impairment Resting state networks DTI Resting state fMRI Predictive models of networks Functional connectivity 



T Liu was supported by NIH Career Award (NIH EB-006878), NSF CAREER Award (IIS-1149260), NIH R01 DA-033393, NIH R01 AG-042599, and NSF BME-1302089. L Guo was supported by the NWPU Foundation for Fundamental Research. K Li and T Zhang were supported by the China Government Scholarship. L Wang was supported by the Paul B. Beeson Career Developmental Awards (K23-AG028982) and a National Alliance for Research in Schizophrenia and Depression Young Investigator Award. The authors would like to thank the anonymous reviewers for their constructive comments.


  1. Bai, F., Zhang, Z., Yu, H., Shi, Y., Yuan, Y., Zhu, W., et al. (2008). Default-mode network activity distinguishes amnestic type mild cognitive impairment from healthy aging: a combined structural and resting-state functional MRI study. Neuroscience Letters, 438(1), 111–115.PubMedCrossRefGoogle Scholar
  2. Beckmann, C. F., De Luca, M., Devlin, J. T., & Smith, S. M. (2005). Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 360(1457), 1001–1013.PubMedCentralPubMedCrossRefGoogle Scholar
  3. Binnewijzend, M.A., Schoonheim, M.M., Sanz-Arigita, E., Wink, A.M., van der Flier W.M., Tolboom N., et al. (2011). Resting-state fMRI changes in Alzheimer’s disease and mild cognitive impairment. Neurobiol Aging.Google Scholar
  4. Bozzali, M., Falini, A., Franceschi, M., Cercignani, M., Zuffi, M., Scotti, G., et al. (2002). White matter damage in Alzheimer’s disease assessed in vivo using diffusion tensor magnetic resonance imaging. Journal of Neurology, Neurosurgery, and Psychiatry, 72(6), 742–746.PubMedCentralPubMedCrossRefGoogle Scholar
  5. Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J. (2001). A method for making group inferences from functional MRI data using independent component analysis. Human Brain Mapping, 14(3), 140–151.PubMedCrossRefGoogle Scholar
  6. Calhoun, V. D., Pekar, J. J., & Pearlson, G. D. (2004). Alcohol intoxication effects on simulated driving: exploring alcohol-dose effects on brain activation using functional MRI. Neuropsychopharmacology, 29, 2097–3017.PubMedCrossRefGoogle Scholar
  7. Celone, K. A., Calhoun, V. D., Dickerson, B. C., Atri, A., Chua, E. F., Miller, S. L., et al. (2006). Alterations in memory networks in mild cognitive impairment and Alzheimer’s disease: an independent component analysis. The Journal of Neuroscience, 26(40), 10222–10231.PubMedCrossRefGoogle Scholar
  8. Damoiseaux, J. S., Rombouts, S. A., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., et al. (2006). Consistent resting-state networks across healthy subjects. PNAS, 103(37), 13848–13853.PubMedCentralPubMedCrossRefGoogle Scholar
  9. De Luca, M., Beckmann, C. F., De Stefano, N., Matthews, P. M., & Smith, S. M. (2006). fMRI resting state networks define distinct modes of long-distance interactions in the human brain. NeuroImage, 29(4), 1359–1367.PubMedCrossRefGoogle Scholar
  10. Dickerson, B. C., & Sperling, R. A. (2009). Large-scale functional brain network abnormalities in Alzheimer’s disease: Insights from functional neuroimaging. Behavioural Neurology, 21(1), 63–75.PubMedCentralPubMedCrossRefGoogle Scholar
  11. Fillard, P., Descoteaux, M., Goh, A., Gouttard, S., Jeurissen, B., Malcolm, J., et al. (2011). Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom. NeuroImage, 56(1), 220–234.PubMedCrossRefGoogle Scholar
  12. Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004). Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proceedings of the National Academy of Sciences of the United States of America, 101(13), 4637–4642.PubMedCentralPubMedCrossRefGoogle Scholar
  13. Grundman, M., Petersen, R. C., Ferris, S. H., Thomas, R. G., Aisen, P. S., Bennett, D. A., et al. (2004). Mild cognitive impairment can be distinguished from Alzheimer disease and normal aging for clinical trials. Archives of Neurology, 61(1), 59–66.PubMedCrossRefGoogle Scholar
  14. He, Y., Wang, L., Zang, Y., Tian, L., Zhang, X., Li, K., et al. (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.PubMedCrossRefGoogle Scholar
  15. Head, D., Buckner, R. L., Shimony, J. S., Williams, L. E., Akbudak, E., Conturo, T. E., et al. (2004). Differential vulnerability of anterior white matter in nondemented aging with minimal acceleration in dementia of the Alzheimer type: evidence from diffusion tensor imaging. Cerebral Cortex, 14(4), 410–423.PubMedCrossRefGoogle Scholar
  16. Jack, C. R., Jr., Bernstein, M. A., Borowski, B. J., Gunter, J. L., Fox, N. C., Thompson, P. M., et al. (2010). Update on the magnetic resonance imaging core of the Alzheimer’s disease neuroimaging initiative. Alzheimers Dement, 6(3), 212–220.PubMedCentralPubMedCrossRefGoogle Scholar
  17. Li, K., Guo, L., Zhu, D., Hu, X., Han, J., Liu T. (2012). Individual Functional ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles, in press, Neuroinformatics.Google Scholar
  18. Li, K., Zhu, D., Guo, L., Li, Z., Lynch, M.E., Coles, C. et al. (2012b). Connectomics Signatures of Prenatal Cocaine Exposure Affected Adolescent Brains, accepted, Human Brain Mapping.Google Scholar
  19. Li, Y. O., Adali, T., & Calhoun, V. D. (2007). Estimating the number of independent components for functional magnetic resonance imaging data. Human Brain Mapping, 28(11), 1251–1266.PubMedCrossRefGoogle Scholar
  20. Li, K., Guo, L., Nie, J., Li, G., & Liu, T. (2009). Review of Methods for Functional Brain Connectivity Detection Using fMRI. Computerized Medical Imaging and Graphics, 33(2), 131–139.PubMedCentralPubMedCrossRefGoogle Scholar
  21. Li, K., Guo, L., Faraco, C.C., Zhu, D., Deng, F., Zhang, T. et al. (2010). Individualized ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles. Neural Information Processing Systems (NIPS).Google Scholar
  22. Liang, P., Wang, Z., Yang, Y., Jia, X., & Li, K. (2011). Functional Disconnection and Compensation in Mild Cognitive Impairment: Evidence from DLPFC Connectivity Using Resting-State fMRI. PLoS One, 6(7), e22153.PubMedCentralPubMedCrossRefGoogle Scholar
  23. Linsker, R. (1997). A local learning rule that enables information maximization for arbitrary input distributions. Neural Computation, 9(8), 1661–1665.CrossRefGoogle Scholar
  24. Liu, T. (2011). A few thoughts on Brain ROIs. Brain Imaging and Behavior, 5(3), 189–202.PubMedCentralPubMedCrossRefGoogle Scholar
  25. Liu, T., Li, H., Wong, K., Tarokh, A., Guo, L., & Wong, S. T. (2007). Brain Tissue Segmentation Based on DTI Data. NeuroImage, 38(1), 114–123.PubMedCentralPubMedCrossRefGoogle Scholar
  26. Liu, Y., Wang, K., Yu, C., He, Y., Zhou, Y., Liang, M., et al. (2008a). Regional homogeneity, functional connectivity and imaging markers of Alzheimer’s disease: A review of resting-state fMRI studies. Neuropsychologia, 46(6), 1648–1656.PubMedCrossRefGoogle Scholar
  27. Liu, T., Nie, J., Tarokh, A., Guo, L., & Wong, S. T. (2008b). Reconstruction of Central Cortical Surface from MRI Brain Images: Method and Application. NeuroImage, 40(3), 991–1002.PubMedCentralPubMedCrossRefGoogle Scholar
  28. Logothetis, N. K. (2008). What we can do and what we cannot do with fMRI. Nature, 453(7197), 869–878.PubMedCrossRefGoogle Scholar
  29. Lynall, M. E., Bassett, D. S., Kerwin, R., McKenna, P. J., Kitzbichler, M., Muller, U., et al. (2010). Functional connectivity and brain networks in schizophrenia. The Journal of Neuroscience, 30(28), 9477–9487.PubMedCentralPubMedCrossRefGoogle Scholar
  30. McKeown, M. J., Makeig, S., Brown, G. G., Jung, T. P., Kindermann, S. S., Bell, A. J., et al. (1998). Analysis of fMRI data by blind separation into independent spatial components. Human Brain Mapping, 6(3), 160–188.PubMedCrossRefGoogle Scholar
  31. Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, 167–202.PubMedCrossRefGoogle Scholar
  32. Passingham, R. E., Stephan, K. E., & Kötter, R. (2002). The anatomical basis of functional localization in the cortex. Nature Reviews Neuroscience, 3(8), 606–616.PubMedGoogle Scholar
  33. Rademacher, J., Morosan, P., Schormann, T., Schleicher, A., Werner, C., Freund, H. J., et al. (2001). Probabilistic mapping and volume measurement of human primary auditory cortex. NeuroImage, 13(4), 669–683.PubMedCrossRefGoogle Scholar
  34. 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(2), 676–682.PubMedCentralPubMedCrossRefGoogle Scholar
  35. Rivier, F., & Clarke, S. (1997). Cytochrome oxidase, acetylcholinesterase, and NADPH-diaphorase staining in human supratemporal and insular cortex: evidence for multiple auditory areas. NeuroImage, 6(4), 288–304.PubMedCrossRefGoogle Scholar
  36. Rombouts, S. A., Barkhof, F., Goekoop, R., Stam, C. J., & Scheltens, P. (2005). Altered resting state networks in mild cognitive impairment and mild Alzheimer’s disease: an fMRI study. Human Brain Mapping, 26(4), 231–239.PubMedCrossRefGoogle Scholar
  37. Salat, D. H., Tuch, D. S., van der Kouwe, A. J., Greve, D. N., Pappu, V., Lee, S. Y., et al. (2010). White matter pathology isolates the hippocampal formation in Alzheimer’s disease. Neurobiology of Aging, 31(2), 244–256.PubMedCentralPubMedCrossRefGoogle Scholar
  38. Salvador, R., Suckling, J., Coleman, M. R., Pickard, J. D., Menon, D., & Bullmore, E. (2005). Neurophysiological architecture of functional magnetic resonance images of human brain. Cerebral Cortex, 15(9), 1332–1342.PubMedCrossRefGoogle Scholar
  39. Schmithorst, V. J., & Holland, S. K. (2004). Comparison of three methods for generating group statistical inferences from independent component analysis of functional magnetic resonance imaging data. Journal of Magnetic Resonance Imaging, 19(3), 365–368.PubMedCentralPubMedCrossRefGoogle Scholar
  40. Sorg, C., Riedl, V., Mühlau, M., Calhoun, V. D., Eichele, T., Läer, L., et al. (2007). Selective changes of resting-state networks in individuals at risk for Alzheimer’s disease. PNAS, 104(47), 18760–18765.PubMedCentralPubMedCrossRefGoogle Scholar
  41. Sporns, O., Tononi, G., & Kötter, R. (2005). The human connectome: a structural description of the human brain. PLoS Computational Biology, 1(4), e42.PubMedCentralPubMedCrossRefGoogle Scholar
  42. Stahl, R., Dietrich, O., Teipel, S. J., Hampel, H., Reiser, M. F., & Schoenberg, S. O. (2007). White matter damage in Alzheimer’s disease and mild cognitive impairment: assessment with diffusion tensor MR imaging and parallel imaging techniques. Radiology, 243(2), 482–492.CrossRefGoogle Scholar
  43. Stebbins, G. T., & Murphy, C. M. (2009). Diffusion tensor imaging in Alzheimer’s disease and mild cognitive impairment. Behavioural Neurology, 21(1), 39–49.PubMedCentralPubMedCrossRefGoogle Scholar
  44. Tournier, J. D., Calamante, F., & Connelly, A. (2012). MRtrix: Diffusion tractography in crossing fiber regions. International Journal of Imaging Systems and Technology, 22(1), 53–66.CrossRefGoogle Scholar
  45. van den Heuvel, M., Mandl, R., & Hulshoff, P. H. (2008). Normalized cut group clustering of resting-state FMRI data. PLoS One, 3(4), e2001.PubMedCentralPubMedCrossRefGoogle Scholar
  46. Wang, K., Liang, M., Wang, L., Tian, L., Zhang, X., et al. (2007). Altered functional connectivity in early Alzheimer’s disease: a resting-state fMRI study. Human Brain Mapping, 28(10), 967–978.PubMedCrossRefGoogle Scholar
  47. Wee, C.-Y., Yap, P.-T., Li, W., Denny, K., Browndyke, J. N., Potter, G. G., et al. (2011). Enriched White Matter Connectivity Networks for Accurate Identification of MCI Patients. NeuroImage, 54(3), 1812–1822.PubMedCentralPubMedCrossRefGoogle Scholar
  48. Zalesky, A., Fornito, A., & Bullmore, E. T. (2010). Network-based statistic: Identifying differences in brain networks. NeuroImage, 53(4), 1197–1207.PubMedCrossRefGoogle Scholar
  49. Zang, Y., Jiang, T., Lu, Y., He, Y., & Tian, L. (2004). Regional homogeneity approach to fMRI data analysis. NeuroImage, 22(1), 394–400.PubMedCrossRefGoogle Scholar
  50. Zhang, Y., Schuff, N., Jahng, G. H., Bayne, W., Mori, S., Schad, L., et al. (2007). Diffusion tensor imaging of cingulum fibers in mild cognitive impairment and Alzheimer’s disease. Neurology, 68(1), 13–19.PubMedCentralPubMedCrossRefGoogle Scholar
  51. Zhang, D., Wang, Y., Zhou, L., Yuan, H., & Shen, D. (2011a). Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment. NeuroImage, 55(3), 856–867.PubMedCentralPubMedCrossRefGoogle Scholar
  52. Zhang, T., Guo, L., Li, K., Jing, C., Yin, Y., Zhu, D., et al. (2011b). Predicting functional cortical ROIs based on fiber shape models. Cerebral Cortex, 22(4), 854–864.PubMedCentralPubMedCrossRefGoogle Scholar
  53. Zhang, D., & Shen, D. (2012). Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease. Neuroimage 59(2), 895–907.Google Scholar
  54. Zhu, D., Li, K., Faraco, C. C., Deng, F., Zhang, D., Guo, L., et al. (2011a). Optimization of functional brain ROIs via maximization of consistency of structural connectivity profiles. NeuroImage, 59(2), 1382–1393.PubMedCentralPubMedCrossRefGoogle Scholar
  55. Zhu, D., Zhang, D., Faraco, C., Li, K., Deng, F., Chen, H., et al. (2011b). Discovering dense and consistent landmarks in the brain. IPMI, 22, 97–110.Google Scholar
  56. Zhu, D., Li, K., Guo, L., Jiang, X., Zhang, T., Zhang, D., et al. (2012). DICCCOL: Dense Individualized and Common Connectivity-based Cortical Landmarks, in press, Cerebral Cortex.Google Scholar
  57. Zhu, D., Li, K., Douglas, P., Terry, A., Puente, N., Wang, al. (2013). Connectome-scale Assessments of Structural and Functional Connectivity in MCI, in press, Human Brain Mapping.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Xi Jiang
    • 1
  • Dajiang Zhu
    • 1
  • Kaiming Li
    • 2
    • 1
  • Tuo Zhang
    • 2
    • 1
  • Lihong Wang
    • 3
  • Dinggang Shen
    • 4
  • Lei Guo
    • 2
  • Tianming Liu
    • 1
    Email author
  1. 1.Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research CenterUniversity of GeorgiaAthensUSA
  2. 2.School of AutomationNorthwestern Polytechnical UniversityXi’anChina
  3. 3.Brain Imaging and Analysis CenterDuke UniversityDurhamUSA
  4. 4.Department of RadiologyUNC Chapel HillChapel HillUSA

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