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

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Abstract

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.

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References

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed Central  PubMed  Google Scholar 

  • 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.

  • 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.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed Central  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed Central  PubMed  Google Scholar 

  • 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.

  • 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.

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed Central  PubMed  Google Scholar 

  • 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).

  • 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.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Linsker, R. (1997). A local learning rule that enables information maximization for arbitrary input distributions. Neural Computation, 9(8), 1661–1665.

    Article  Google Scholar 

  • Liu, T. (2011). A few thoughts on Brain ROIs. Brain Imaging and Behavior, 5(3), 189–202.

    Article  PubMed Central  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed Central  PubMed  Google Scholar 

  • Logothetis, N. K. (2008). What we can do and what we cannot do with fMRI. Nature, 453(7197), 869–878.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, 167–202.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed Central  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Sporns, O., Tononi, G., & Kötter, R. (2005). The human connectome: a structural description of the human brain. PLoS Computational Biology, 1(4), e42.

    Article  PubMed Central  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Stebbins, G. T., & Murphy, C. M. (2009). Diffusion tensor imaging in Alzheimer’s disease and mild cognitive impairment. Behavioural Neurology, 21(1), 39–49.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • van den Heuvel, M., Mandl, R., & Hulshoff, P. H. (2008). Normalized cut group clustering of resting-state FMRI data. PLoS One, 3(4), e2001.

    Article  PubMed Central  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed Central  PubMed  Google Scholar 

  • Zalesky, A., Fornito, A., & Bullmore, E. T. (2010). Network-based statistic: Identifying differences in brain networks. NeuroImage, 53(4), 1197–1207.

    Article  PubMed  Google Scholar 

  • Zang, Y., Jiang, T., Lu, Y., He, Y., & Tian, L. (2004). Regional homogeneity approach to fMRI data analysis. NeuroImage, 22(1), 394–400.

    Article  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • 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.

    Article  PubMed Central  PubMed  Google Scholar 

  • 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.

    Article  PubMed Central  PubMed  Google Scholar 

  • 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 

  • 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.

    Article  PubMed Central  PubMed  Google Scholar 

  • 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 

  • 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.

  • Zhu, D., Li, K., Douglas, P., Terry, A., Puente, N., Wang, L.et al. (2013). Connectome-scale Assessments of Structural and Functional Connectivity in MCI, in press, Human Brain Mapping.

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Acknowledgments

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.

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Authors and Affiliations

  1. Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, GA, USA

    Xi Jiang, Dajiang Zhu, Kaiming Li, Tuo Zhang & Tianming Liu

  2. School of Automation, Northwestern Polytechnical University, Xi’an, China

    Kaiming Li, Tuo Zhang & Lei Guo

  3. Brain Imaging and Analysis Center, Duke University, Durham, NC, USA

    Lihong Wang

  4. Department of Radiology, UNC Chapel Hill, Chapel Hill, NC, USA

    Dinggang Shen

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  1. Xi Jiang
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  2. Dajiang Zhu
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  3. Kaiming Li
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  8. Tianming Liu
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Correspondence to Tianming Liu.

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Jiang, X., Zhu, D., Li, K. et al. Predictive models of resting state networks for assessment of altered functional connectivity in mild cognitive impairment. Brain Imaging and Behavior 8, 542–557 (2014). https://doi.org/10.1007/s11682-013-9280-x

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