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

  • Han Zhang
  • Panteleimon Giannakopoulos
  • Sven Haller
  • Dinggang ShenEmail author
  • Seong-Whan Lee
  • Shijun QiuEmail author
Original Article


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.


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



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.


  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.PubMedPubMedCentralGoogle 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.PubMedGoogle 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.PubMedGoogle 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.PubMedGoogle 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.PubMedGoogle 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.PubMedPubMedCentralGoogle 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.PubMedPubMedCentralGoogle Scholar
  9. Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nat Rev Neurosci, 10, 186–198.PubMedGoogle 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.PubMedGoogle 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.PubMedPubMedCentralGoogle 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.PubMedGoogle 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.PubMedGoogle 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.PubMedGoogle 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.PubMedGoogle 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.PubMedPubMedCentralGoogle Scholar
  17. Eickhoff, S. B., Thirion, B., Varoquaux, G., & Bzdok, D. (2015). Connectivity-based parcellation: Critique and implications. Hum Brain Mapp, 36, 4771–4792.PubMedGoogle 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.PubMedPubMedCentralGoogle 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.PubMedGoogle Scholar
  21. Greicius, M. (2008). Resting-state functional connectivity in neuropsychiatric disorders. Curr Opin Neurol, 21, 424–430.PubMedGoogle 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.PubMedPubMedCentralGoogle 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.PubMedPubMedCentralGoogle 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.PubMedGoogle 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.PubMedGoogle 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.PubMedPubMedCentralGoogle 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.PubMedGoogle 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.PubMedGoogle 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.PubMedGoogle 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.PubMedPubMedCentralGoogle 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.PubMedPubMedCentralGoogle 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.PubMedGoogle 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.PubMedPubMedCentralGoogle Scholar
  36. Menon, V. (2011). Large-scale brain networks and psychopathology: A unifying triple network model. Trends Cogn Sci, 15, 483–506.PubMedGoogle 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.PubMedGoogle 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.PubMedGoogle 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.PubMedPubMedCentralGoogle 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.PubMedGoogle 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.PubMedPubMedCentralGoogle Scholar
  42. Newman, M. E. (2006). Modularity and community structure in networks. Proc Natl Acad Sci U S A, 103, 8577–8582.PubMedPubMedCentralGoogle 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.PubMedGoogle 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.PubMedGoogle 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.PubMedPubMedCentralGoogle 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.PubMedPubMedCentralGoogle 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.PubMedGoogle 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.PubMedGoogle 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.PubMedPubMedCentralGoogle 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.PubMedPubMedCentralGoogle Scholar
  51. Sporns, O., & Betzel, R. F. (2016). Modular brain networks. Annu Rev Psychol, 67, 613–640.Google Scholar
  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.Google Scholar
  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.PubMedGoogle 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.PubMedPubMedCentralGoogle 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.PubMedPubMedCentralGoogle 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.PubMedGoogle 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.PubMedGoogle 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.PubMedGoogle Scholar
  59. Varoquaux, G., & Craddock, R. C. (2013). Learning and comparing functional connectomes across subjects. Neuroimage, 80, 405–415.PubMedGoogle 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.PubMedGoogle 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.PubMedGoogle 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.PubMedPubMedCentralGoogle 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.PubMedGoogle 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.PubMedPubMedCentralGoogle 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.PubMedPubMedCentralGoogle 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.PubMedGoogle Scholar
  67. Zalesky, A., Fornito, A., & Bullmore, E. T. (2010). Network-based statistic: Identifying differences in brain networks. Neuroimage, 53, 1197–1207.PubMedGoogle Scholar
  68. Zhang, D., & Raichle, M. E. (2010). Disease and the brain's dark energy. Nat Rev Neurol, 6, 15–28.PubMedGoogle 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.PubMedGoogle 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.PubMedPubMedCentralGoogle 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.PubMedGoogle 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.PubMedPubMedCentralGoogle 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.PubMedPubMedCentralGoogle 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.PubMedPubMedCentralGoogle 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.PubMedGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Division of PsychiatryGeneva University HospitalsGenevaSwitzerland
  3. 3.Affidea CDRC - Centre Diagnostique Radiologique de CarougeCarougeSwitzerland
  4. 4.Department of Surgical Sciences, RadiologyUppsala UniversityUppsalaSweden
  5. 5.Department of NeuroradiologyUniversity Hospital FreiburgFreiburgGermany
  6. 6.Faculty of MedicineUniversity of GenevaGenevaSwitzerland
  7. 7.Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea
  8. 8.Department of RadiologyThe First Affiliated Hospital of Guangzhou University of Chinese MedicineGuangzhouChina

Personalised recommendations