Inter-Network High-Order Functional Connectivity (IN-HOFC) and its Alteration in Patients with Mild Cognitive Impairment

Abstract

Little is known about the high-order interactions among brain regions measured by the similarity of higher-order features (other than the raw blood-oxygen-level-dependent signals) which can characterize higher-level brain functional connectivity (FC). Previously, we proposed FC topographical profile-based high-order FC (HOFC) and found that this metric could provide supplementary information to traditional FC for early Alzheimer’s disease (AD) detection. However, whether such findings apply to network-level brain functional integration is unknown. In this paper, we propose an extended HOFC method, termed inter-network high-order FC (IN-HOFC), as a useful complement to the traditional inter-network FC methods, for characterizing more complex organizations among the large-scale brain networks. In the IN-HOFC, both network definition and inter-network FC are defined in a high-order manner. To test whether IN-HOFC is more sensitive to cognition decline due to brain diseases than traditional inter-network FC, 77 mild cognitive impairments (MCIs) and 89 controls are compared among the conventional methods and our IN-HOFC. The result shows that IN-HOFCs among three temporal lobe-related high-order networks are dampened in MCIs. The impairment of IN-HOFC is especially found between the anterior and posterior medial temporal lobe and could be a potential MCI biomarker at the network level. The competing network-level low-order FC methods, however, either revealing less or failing to detect any group difference. This work demonstrates the biological meaning and potential diagnostic value of the IN-HOFC in clinical neuroscience studies.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Allen, E. A., Erhardt, E. B., Damaraju, E., Gruner, W., Segall, J. M., Silva, R. F., Havlicek, M., Rachakonda, S., Fries, J., Kalyanam, R., Michael, A. M., Caprihan, A., Turner, J. A., Eichele, T., Adelsheim, S., Bryan, A. D., Bustillo, J., Clark, V. P., Feldstein Ewing, S. W., Filbey, F., Ford, C. C., Hutchison, K., Jung, R. E., Kiehl, K. A., Kodituwakku, P., Komesu, Y. M., Mayer, A. R., Pearlson, G. D., Phillips, J. P., Sadek, J. R., Stevens, M., Teuscher, U., Thoma, R. J., & Calhoun, V. D. (2011). A baseline for the multivariate comparison of resting-state networks. Front Syst Neurosci, 5, 2.

    PubMed  PubMed Central  Google Scholar 

  2. Bai, F., Zhang, Z., Yu, H., Shi, Y., Yuan, Y., Zhu, W., Zhang, X., & Qian, Y. (2008). Default-mode network activity distinguishes amnestic type mild cognitive impairment from healthy aging: A combined structural and resting-state functional MRI study. Neurosci Lett, 438, 111–115.

    CAS  PubMed  Google Scholar 

  3. Barkhof, F., Haller, S., & Rombouts, S. A. (2014). Resting-state functional MR imaging: A new window to the brain. Radiology, 272, 29–49.

    PubMed  Google Scholar 

  4. Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005). Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond Ser B Biol Sci, 360, 1001–1013.

    Google Scholar 

  5. Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., & Evans, A. C. (2010). Multi-level bootstrap analysis of stable clusters in resting-state fMRI. Neuroimage, 51, 1126–1139.

    PubMed  Google Scholar 

  6. Bijsterbosch, J., Smith, S., Forster, S., John, O. P., & Bishop, S. J. (2014). Resting state correlates of subdimensions of anxious affect. J Cogn Neurosci, 26, 914–926.

    PubMed  Google Scholar 

  7. Bonner, M. F., & Price, A. R. (2013). Where is the anterior temporal lobe and what does it do? J Neurosci, 33, 4213–4215.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Brier, M. R., Thomas, J. B., Snyder, A. Z., Benzinger, T. L., Zhang, D., Raichle, M. E., Holtzman, D. M., Morris, J. C., & Ances, B. M. (2012). Loss of intranetwork and internetwork resting state functional connections with Alzheimer's disease progression. J Neurosci, 32, 8890–8899.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nat Rev Neurosci, 10, 186–198.

    CAS  PubMed  Google Scholar 

  10. 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. Hum Brain Mapp, 14, 140–151.

    CAS  PubMed  Google Scholar 

  11. Cohen, A. L., Fair, D. A., Dosenbach, N. U., Miezin, F. M., Dierker, D., Van Essen, D. C., Schlaggar, B. L., & Petersen, S. E. (2008). Defining functional areas in individual human brains using resting functional connectivity MRI. Neuroimage, 41, 45–57.

    PubMed  PubMed Central  Google Scholar 

  12. Cordes, D., Haughton, V., Carew, J. D., Arfanakis, K., & Maravilla, K. (2002). Hierarchical clustering to measure connectivity in fMRI resting-state data. Magn Reson Imaging, 20, 305–317.

    PubMed  Google Scholar 

  13. Craddock, R. C., James, G. A., Holtzheimer, P. E., 3rd, Hu, X. P., & Mayberg, H. S. (2012). A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum Brain Mapp, 33, 1914–1928.

    PubMed  Google Scholar 

  14. Desikan, R. S., Segonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., Albert, M. S., & Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage, 31, 968–980.

    PubMed  Google Scholar 

  15. Di Paola, M., Macaluso, E., Carlesimo, G. A., Tomaiuolo, F., Worsley, K. J., Fadda, L., & Caltagirone, C. (2007). Episodic memory impairment in patients with Alzheimer's disease is correlated with entorhinal cortex atrophy. A voxel-based morphometry study. Journal of Neurology, 254, 774–781.

    PubMed  Google Scholar 

  16. Dosenbach, N. U., Nardos, B., Cohen, A. L., Fair, D. A., Power, J. D., Church, J. A., Nelson, S. M., Wig, G. S., Vogel, A. C., Lessov-Schlaggar, C. N., Barnes, K. A., Dubis, J. W., Feczko, E., Coalson, R. S., Pruett, J. R., Jr., Barch, D. M., Petersen, S. E., & Schlaggar, B. L. (2010). Prediction of individual brain maturity using fMRI. Science, 329, 1358–1361.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Eickhoff, S. B., Thirion, B., Varoquaux, G., & Bzdok, D. (2015). Connectivity-based parcellation: Critique and implications. Hum Brain Mapp, 36, 4771–4792.

    PubMed  Google Scholar 

  18. Filippini, N., MacIntosh, B. J., Hough, M. G., Goodwin, G. M., Frisoni, G. B., Smith, S. M., Matthews, P. M., Beckmann, C. F., & Mackay, C. E. (2009). Distinct patterns of brain activity in young carriers of the APOE-epsilon4 allele. Proc Natl Acad Sci U S A, 106, 7209–7214.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Frisoni, G. B., & Coleman, P. D. (2011). Mild cognitive impairment: Instructions for use at neurobiology of aging. Neurobiology of aging. Neurobiology of Aging, 32, 761–762.

    Google Scholar 

  20. Gour, N., Ranjeva, J. P., Ceccaldi, M., Confort-Gouny, S., Barbeau, E., Soulier, E., Guye, M., Didic, M., & Felician, O. (2011). Basal functional connectivity within the anterior temporal network is associated with performance on declarative memory tasks. Neuroimage, 58, 687–697.

    PubMed  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  22. 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. Proc Natl Acad Sci U S A, 101, 4637–4642.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Han, C. E., Yoo, S. W., Seo, S. W., Na, D. L., & Seong, J. K. (2013). Cluster-based statistics for brain connectivity in correlation with behavioral measures. PLoS One, 8, e72332.

    PubMed  PubMed Central  Google Scholar 

  24. He, Y., Chen, Z., & Evans, A. (2008). Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer's disease. J Neurosci, 28, 4756–4766.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Hirose, S., Watanabe, T., Wada, H., Imai, Y., Machida, T., Shirouzu, I., Miyashita, Y., & Konishi, S. (2013). Functional relevance of micromodules in the human association cortex delineated with high-resolution FMRI. Cereb Cortex, 23, 2863–2871.

    PubMed  Google Scholar 

  26. Ing, A., & Schwarzbauer, C. (2014). Cluster size statistic and cluster mass statistic: Two novel methods for identifying changes in functional connectivity between groups or conditions. PLoS One, 9, e98697.

    PubMed  PubMed Central  Google Scholar 

  27. Jafri, M. J., Pearlson, G. D., Stevens, M., & Calhoun, V. D. (2008). A method for functional network connectivity among spatially independent resting-state components in schizophrenia. Neuroimage, 39, 1666–1681.

    PubMed  Google Scholar 

  28. Jia, X., Zhang, H., Adeli, E., & Shen, D. (2017). 2017. Consciousness level and recovery outcome prediction using high-order brain functional connectivity network. Connectomics Neuroimaging, 10511, 17–24.

    Google Scholar 

  29. Karunanayaka, P., Eslinger, P. J., Wang, J. L., Weitekamp, C. W., Molitoris, S., Gates, K. M., Molenaar, P. C., & Yang, Q. X. (2014). Networks involved in olfaction and their dynamics using independent component analysis and unified structural equation modeling. Hum Brain Mapp, 35, 2055–2072.

    PubMed  Google Scholar 

  30. Ketchen, D. J., & Shook, C. L. (1996). The application of cluster analysis in strategic management research: An analysis and critique. Strateg Manag J, 17, 441–458.

    Google Scholar 

  31. Kiviniemi, V., Starck, T., Remes, J., Long, X., Nikkinen, J., Haapea, M., Veijola, J., Moilanen, I., Isohanni, M., Zang, Y. F., & Tervonen, O. (2009). Functional segmentation of the brain cortex using high model order group PICA. Hum Brain Mapp, 30, 3865–3886.

    PubMed  Google Scholar 

  32. Kong, Y., Eippert, F., Beckmann, C. F., Andersson, J., Finsterbusch, J., Buchel, C., Tracey, I., & Brooks, J. C. (2014). Intrinsically organized resting state networks in the human spinal cord. Proc Natl Acad Sci U S A, 111, 18067–18072.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Lee, M. H., Hacker, C. D., Snyder, A. Z., Corbetta, M., Zhang, D., Leuthardt, E. C., & Shimony, J. S. (2012). Clustering of resting state networks. PLoS One, 7, e40370.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Li, H. J., Hou, X. H., Liu, H. H., Yue, C. L., He, Y., & Zuo, X. N. (2015). Toward systems neuroscience in mild cognitive impairment and Alzheimer's disease: A meta-analysis of 75 fMRI studies. Hum Brain Mapp, 36, 1217–1232.

    CAS  PubMed  Google Scholar 

  35. Liang, P., Zhang, H., Xu, Y., Jia, W., Zang, Y., & Li, K. (2015). Disruption of cortical integration during midazolam-induced light sedation. Hum Brain Mapp, 36, 4247–4261.

    PubMed  PubMed Central  Google Scholar 

  36. Menon, V. (2011). Large-scale brain networks and psychopathology: A unifying triple network model. Trends Cogn Sci, 15, 483–506.

    PubMed  Google Scholar 

  37. Meunier, D., Achard, S., Morcom, A., & Bullmore, E. (2009). Age-related changes in modular organization of human brain functional networks. Neuroimage, 44, 715–723.

    PubMed  Google Scholar 

  38. Mezer, A., Yovel, Y., Pasternak, O., Gorfine, T., & Assaf, Y. (2009). Cluster analysis of resting-state fMRI time series. Neuroimage, 45, 1117–1125.

    PubMed  Google Scholar 

  39. Misic, B., Goni, J., Betzel, R. F., Sporns, O., & McIntosh, A. R. (2014). A network convergence zone in the hippocampus. PLoS Comput Biol, 10, e1003982.

    PubMed  PubMed Central  Google Scholar 

  40. Misic, B., Betzel, R. F., Nematzadeh, A., Goni, J., Griffa, A., Hagmann, P., Flammini, A., Ahn, Y. Y., & Sporns, O. (2015). Cooperative and competitive spreading dynamics on the human connectome. Neuron, 86, 1518–1529.

    CAS  PubMed  Google Scholar 

  41. Nelson, S. M., Cohen, A. L., Power, J. D., Wig, G. S., Miezin, F. M., Wheeler, M. E., Velanova, K., Donaldson, D. I., Phillips, J. S., Schlaggar, B. L., & Petersen, S. E. (2010). A parcellation scheme for human left lateral parietal cortex. Neuron, 67, 156–170.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Newman, M. E. (2006). Modularity and community structure in networks. Proc Natl Acad Sci U S A, 103, 8577–8582.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Nickl-Jockschat, T., Kleiman, A., Schulz, J. B., Schneider, F., Laird, A. R., Fox, P. T., Eickhoff, S. B., & Reetz, K. (2012). Neuroanatomic changes and their association with cognitive decline in mild cognitive impairment: A meta-analysis. Brain Struct Funct, 217, 115–125.

    PubMed  Google Scholar 

  44. Petersen, R. C., Doody, R., Kurz, A., Mohs, R. C., Morris, J. C., Rabins, P. V., Ritchie, K., Rossor, M., Thal, L., & Winblad, B. (2001). Current concepts in mild cognitive impairment. Arch Neurol, 58, 1985–1992.

    CAS  PubMed  Google Scholar 

  45. Putcha, D., Ross, R. S., Cronin-Golomb, A., Janes, A. C., & Stern, C. E. (2015). Altered intrinsic functional coupling between core neurocognitive networks in Parkinson's disease. Neuroimage Clin, 7, 449–455.

    PubMed  PubMed Central  Google Scholar 

  46. Qiao, L., Zhang, H., Kim, M., Teng, S., Zhang, L., & Shen, D. (2016). Estimating functional brain networks by incorporating a modularity prior. Neuroimage, 141, 399–407.

    PubMed  PubMed Central  Google Scholar 

  47. Richiardi, J., Monsch, A. U., Haas, T., Barkhof, F., Van de Ville, D., Radu, E. W., Kressig, R. W., & Haller, S. (2015). Altered cerebrovascular reactivity velocity in mild cognitive impairment and Alzheimer's disease. Neurobiol Aging, 36, 33–41.

    PubMed  Google Scholar 

  48. Ryan, L., Lin, C. Y., Ketcham, K., & Nadel, L. (2010). The role of medial temporal lobe in retrieving spatial and nonspatial relations from episodic and semantic memory. Hippocampus, 20, 11–18.

    PubMed  Google Scholar 

  49. Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., Reiss, A. L., & Greicius, M. D. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci, 27, 2349–2356.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Smith, S. M., Vidaurre, D., Beckmann, C. F., Glasser, M. F., Jenkinson, M., Miller, K. L., Nichols, T. E., Robinson, E. C., Salimi-Khorshidi, G., Woolrich, M. W., Barch, D. M., Ugurbil, K., & Van Essen, D. C. (2013). Functional connectomics from resting-state fMRI. Trends Cogn Sci, 17, 666–682.

    PubMed  PubMed Central  Google Scholar 

  51. Sporns, O., & Betzel, R. F. (2016). Modular brain networks. Annu Rev Psychol, 67, 613–640.

  52. Stam, C. J., van Straaten, E. C., Van Dellen, E., Tewarie, P., Gong, G., Hillebrand, A., Meier, J., & Van Mieghem, P. (2016). The relation between structural and functional connectivity patterns in complex brain networks. Int J Psychophysiol, 103, 149–160.

  53. Stevens, M. C., Pearlson, G. D., & Calhoun, V. D. (2009). Changes in the interaction of resting-state neural networks from adolescence to adulthood. Hum Brain Mapp, 30, 2356–2366.

    PubMed  PubMed Central  Google Scholar 

  54. Supekar, K., Menon, V., Rubin, D., Musen, M., & Greicius, M. D. (2008). Network analysis of intrinsic functional brain connectivity in Alzheimer's disease. PLoS Comput Biol, 4, e1000100.

    PubMed  PubMed Central  Google Scholar 

  55. Touroutoglou, A., Andreano, J. M., Barrett, L. F., & Dickerson, B. C. (2015). Brain network connectivity-behavioral relationships exhibit trait-like properties: Evidence from hippocampal connectivity and memory. Hippocampus, 25, 1591–1598.

    PubMed  PubMed Central  Google Scholar 

  56. Trinkler, I., King, J. A., Doeller, C. F., Rugg, M. D., & Burgess, N. (2009). Neural bases of autobiographical support for episodic recollection of faces. Hippocampus, 19, 718–730.

    PubMed  Google Scholar 

  57. Tromp, D., Dufour, A., Lithfous, S., Pebayle, T., & Despres, O. (2015). Episodic memory in normal aging and Alzheimer disease: Insights from imaging and behavioral studies. Ageing Res Rev, 24, 232–262.

    CAS  PubMed  Google Scholar 

  58. van Eijndhoven, P., van Wingen, G., Fernandez, G., Rijpkema, M., Verkes, R. J., Buitelaar, J., & Tendolkar, I. (2011). Amygdala responsivity related to memory of emotionally neutral stimuli constitutes a trait factor for depression. Neuroimage, 54, 1677–1684.

    PubMed  Google Scholar 

  59. Varoquaux, G., & Craddock, R. C. (2013). Learning and comparing functional connectomes across subjects. Neuroimage, 80, 405–415.

    PubMed  Google Scholar 

  60. Varoquaux, G., Sadaghiani, S., Pinel, P., Kleinschmidt, A., Poline, J. B., & Thirion, B. (2010). A group model for stable multi-subject ICA on fMRI datasets. Neuroimage, 51, 288–299.

    CAS  PubMed  Google Scholar 

  61. Wang, D., Qin, W., Liu, Y., Zhang, Y., Jiang, T., & Yu, C. (2014). Altered resting-state network connectivity in congenital blind. Hum Brain Mapp, 35, 2573–2581.

    PubMed  Google Scholar 

  62. Wang, S. F., Ritchey, M., Libby, L. A., & Ranganath, C. (2016). Functional connectivity based parcellation of the human medial temporal lobe. Neurobiology of Learning and Memory, 134(Pt A), 123–134.

    PubMed  PubMed Central  Google Scholar 

  63. Wig, G. S., Laumann, T. O., & Petersen, S. E. (2014). An approach for parcellating human cortical areas using resting-state correlations. Neuroimage, 93(Pt 2), 276–291.

    PubMed  Google Scholar 

  64. Yang, S. Q., Xu, Z. P., Xiong, Y., Zhan, Y. F., Guo, L. Y., Zhang, S., Jiang, R. F., Yao, Y. H., Qin, Y. Y., Wang, J. Z., Liu, Y., & Zhu, W. Z. (2016). Altered Intranetwork and internetwork functional connectivity in type 2 diabetes mellitus with and without cognitive impairment. Sci Rep, 6, 32980.

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Yao, Z., Zhang, Y., Lin, L., Zhou, Y., Xu, C., & Jiang, T. (2010). Abnormal cortical networks in mild cognitive impairment and Alzheimer's disease. PLoS Comput Biol, 6, e1001006.

    PubMed  PubMed Central  Google Scholar 

  66. Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zollei, L., Polimeni, J. R., Fischl, B., Liu, H., & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol, 106, 1125–1165.

    PubMed  Google Scholar 

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

    PubMed  Google Scholar 

  68. Zhang, D., & Raichle, M. E. (2010). Disease and the brain's dark energy. Nat Rev Neurol, 6, 15–28.

    PubMed  Google Scholar 

  69. Zhang, H., Zuo, X. N., Ma, S. Y., Zang, Y. F., Milham, M. P., & Zhu, C. Z. (2010). Subject order-independent group ICA (SOI-GICA) for functional MRI data analysis. Neuroimage, 51, 1414–1424.

    PubMed  Google Scholar 

  70. Zhang, H., Chen, X., Shi, F., Li, G., Kim, M., Giannakopoulos, P., Haller, S., & Shen, D. (2016a). Topographical information-based high-order functional connectivity and its application in abnormality detection for mild cognitive impairment. J Alzheimers Dis, 54, 1095–1112.

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Zhang, J., Cheng, W., Liu, Z., Zhang, K., Lei, X., Yao, Y., Becker, B., Liu, Y., Kendrick, K. M., Lu, G., & Feng, J. (2016b). Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders. Brain, 139, 2307–2321.

    PubMed  Google Scholar 

  72. Zhang, H., Chen, X., Zhang, Y., & Shen, D. (2017a). Test-retest reliability of "high-order" functional connectivity in young healthy adults. Front Neurosci, 11, 439.

    PubMed  PubMed Central  Google Scholar 

  73. Zhang, Y., Zhang, H., Chen, X., Lee, S. W., & Shen, D. (2017b). Hybrid high-order functional connectivity networks using resting-state functional MRI for mild cognitive impairment diagnosis. Sci Rep, 7, 6530.

    PubMed  PubMed Central  Google Scholar 

  74. Zhang, Y., Zhang, H., Chen, X., & Shen, D. (2017c). Constructing multi-frequency high-order functional connectivity network for diagnosis of mild cognitive impairment. Connectomics Neuroimaging, 2017(10511), 9–16.

    Google Scholar 

  75. Zhao, F., Zhang, H., Rekik, I., An, Z., & Shen, D. (2018). Diagnosis of autism Spectrum disorders using multi-level high-order functional networks derived from resting-state functional MRI. Front Hum Neurosci, 12, 184.

    PubMed  PubMed Central  Google Scholar 

  76. Zhu, D., Li, K., Terry, D. P., Puente, A. N., Wang, L., Shen, D., Miller, L. S., & Liu, T. (2014). Connectome-scale assessments of structural and functional connectivity in MCI. Hum Brain Mapp, 35, 2911–2923.

    PubMed  Google Scholar 

Download references

Acknowledgments

This work is supported in part by NIH grants (EB006733, EB008374, EB009634, MH100217, AG041721, AG049371 and AG042599). We have no conflict of interest to declare.

Author information

Affiliations

Authors

Corresponding authors

Correspondence to Shijun Qiu or Dinggang Shen.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, H., Giannakopoulos, P., Haller, S. et al. Inter-Network High-Order Functional Connectivity (IN-HOFC) and its Alteration in Patients with Mild Cognitive Impairment. Neuroinform 17, 547–561 (2019). https://doi.org/10.1007/s12021-018-9413-x

Download citation

Keywords

  • Functional magnetic resonance imaging (fMRI)
  • Mild cognitive impairment (MCI)
  • Alzheimer’s disease (AD)
  • Functional connectivity
  • Brain network
  • High-order