Skip to main content

Advertisement

Log in

Disrupted structural and functional brain connectomes in mild cognitive impairment and Alzheimer’s disease

  • Review
  • Published:
Neuroscience Bulletin Aims and scope Submit manuscript

Abstract

Alzheimer’s disease (AD) is the most common type of dementia, comprising an estimated 60–80% of all dementia cases. It is clinically characterized by impairments of memory and other cognitive functions. Previous studies have demonstrated that these impairments are associated with abnormal structural and functional connections among brain regions, leading to a disconnection concept of AD. With the advent of a combination of non-invasive neuroimaging (structural magnetic resonance imaging (MRI), diffusion MRI, and functional MRI) and neurophysiological techniques (electroencephalography and magnetoencephalography) with graph theoretical analysis, recent studies have shown that patients with AD and mild cognitive impairment (MCI), the prodromal stage of AD, exhibit disrupted topological organization in large-scale brain networks (i.e., connectomics) and that this disruption is significantly correlated with the decline of cognitive functions. In this review, we summarize the recent progress of brain connectomics in AD and MCI, focusing on the changes in the topological organization of large-scale structural and functional brain networks using graph theoretical approaches. Based on the two different perspectives of information segregation and integration, the literature reviewed here suggests that AD and MCI are associated with disrupted segregation and integration in brain networks. Thus, these connectomics studies open up a new window for understanding the pathophysiological mechanisms of AD and demonstrate the potential to uncover imaging biomarkers for clinical diagnosis and treatment evaluation for this disease.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, et al. The diagnosis of mild cognitive impairment 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 2011, 7: 270–279.

    Article  PubMed Central  PubMed  Google Scholar 

  2. Jack Jr CR, Albert MS, Knopman DS, McKhann GM, Sperling RA, Carrillo MC, et al. Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011, 7: 257–262.

    Article  PubMed Central  PubMed  Google Scholar 

  3. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack Jr CR, Kawas CH, et al. 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 2011, 7: 263–269.

    Article  PubMed Central  PubMed  Google Scholar 

  4. Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, et al. 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 2011, 7: 280–292.

    Article  PubMed Central  PubMed  Google Scholar 

  5. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol 1999, 56: 303–308.

    Article  PubMed  CAS  Google Scholar 

  6. Delbeuck X, Van der Linden M, Collette F. Alzheimer’s disease as a disconnection syndrome? Neuropsychol Rev 2003, 13: 79–92.

    Article  PubMed  CAS  Google Scholar 

  7. Delbeuck X, Collette F, Van der Linden M. Is Alzheimer’s disease a disconnection syndrome? Neuropsychologia 2007, 45: 3315–3323.

    Article  PubMed  CAS  Google Scholar 

  8. Bozzali M, Parker GJM, Serra L, Embleton K, Gili T, Perri R, et al. Anatomical connectivity mapping: A new tool to assess brain disconnection in Alzheimer’s disease. Neuroimage 2011, 54: 2045–2051.

    Article  PubMed  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  10. Bullmore E, Sporns O. The economy of brain network organization. Nat Rev Neurosci 2012, 13: 336–349.

    PubMed  CAS  Google Scholar 

  11. Kelly C, Biswal BB, Craddock RC, Castellanos FX, Milham MP. Characterizing variation in the functional connectome: promise and pitfalls. Trends Cogn Sci 2012, 16: 181–188.

    Article  PubMed  Google Scholar 

  12. Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang D. Complex networks: Structure and dynamics. Physics Rep 2006, 424: 175–308.

    Article  Google Scholar 

  13. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 2010, 52: 1059–1069.

    Article  PubMed  Google Scholar 

  14. Sporns O. Network attributes for segregation and integration in the human brain. Curr Opin Neurobiol 2013, 23: 162–171.

    Article  PubMed  CAS  Google Scholar 

  15. Newman ME. Modularity and community structure in networks. Proc Natl Acad Sci U S A 2006, 103: 8577–8582.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  16. Freeman LC. Centrality in social networks conceptual clarification. Soc Networks 1979, 1: 215–239.

    Article  Google Scholar 

  17. Bonacich P. Factoring and weighting approaches to status scores and clique identification. J Math Sociol 1972, 2: 113–120.

    Article  Google Scholar 

  18. Watts DJ, Strogatz SH. Collective dynamics of’ small-world’ networks. Nature 1998, 393: 440–442.

    Article  PubMed  CAS  Google Scholar 

  19. He Y, Chen Z, Gong G, Evans A. Neuronal networks inAlzheimer’s disease. Neuroscientist 2009, 15: 333–350.

    Article  PubMed  Google Scholar 

  20. Xie T, He Y. Mapping the Alzheimer’ s brain wi th connectomics. Front Psychiatry 2011, 2: 77.

    PubMed Central  PubMed  Google Scholar 

  21. Tijms BM, Wink AM, de Haan W, van der Flier WM, Stam CJ, Scheltens P, et al. Alzheimer’s disease: connecting findings from graph theoretical studies of brain networks. Neurobiol Aging 2013, 34: 2023–2036.

    Article  PubMed  Google Scholar 

  22. He Y, Chen ZJ, Evans AC. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb Cortex 2007, 17: 2407–2419.

    Article  PubMed  Google Scholar 

  23. Bassett DS, Bullmore E, Verchinski BA, Mattay VS, Weinberger DR, Meyer-Lindenberg A. Hierarchical organization of human cortical networks in health and schizophrenia. J Neurosci 2008, 28: 9239–9248.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  24. Wright IC, Sharma T, Ellison ZR, McGuire PK, Friston KJ, Brammer MJ, et al. Supra-regional Brain Systems and the Neuropathology of Schizophrenia. Cereb Cortex 1999, 9: 366–378.

    Article  PubMed  CAS  Google Scholar 

  25. Pezawas L, Meyer-Lindenberg A, Drabant EM, Verchinski BA, Munoz KE, Kolachana BS, et al. 5-HTTLPR polymorphism impacts human cingulate-amygdala interactions: a genetic susceptibility mechanism for depression. Nat Neurosci 2005, 8: 828–834.

    Article  PubMed  CAS  Google Scholar 

  26. He Y, Chen Z, Evans A. Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer’s disease. J Neurosci 2008, 28: 4756–4766.

    Article  PubMed  CAS  Google Scholar 

  27. Yao Z, Zhang Y, Lin L, Zhou Y, Xu C, Jiang T. Abnormal cortical networks in mild cognitive impairment and Alzheimer’s disease. PLoS Comput Biol 2010, 6: e1001006.

    Article  PubMed Central  PubMed  Google Scholar 

  28. Tijms BM, Moller C, Vrenken H, Wink AM, de Haan W, van der Flier WM, et al. Single-subject grey matter graphs in Alzheimer’s disease. PLoS One 2013, 8: e58921.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  29. Le Bihan D. Looking into the functional architecture of the brain with diffusion MRI. Nat Rev Neurosci 2003, 4: 469–480.

    Article  PubMed  Google Scholar 

  30. Mori S, van Zijl PC. Fiber tracking: principles and strategies — a technical review. NMR Biomed 2002, 15: 468–480.

    Article  PubMed  Google Scholar 

  31. Hagmann P, Kurant M, Gigandet X, Thiran P, Wedeen VJ, Meuli R, et al. Mapping human whole-brain structural networks with diffusion MRI. PLoS One 2007, 2: e597.

    Article  PubMed Central  PubMed  Google Scholar 

  32. Gong G, He Y, Concha L, Lebel C, Gross DW, Evans AC, et al. Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb Cortex 2009, 19: 524–536.

    Article  PubMed Central  PubMed  Google Scholar 

  33. Iturria-Medina Y, Sotero RC, Canales-Rodriguez EJ, Aleman-Gomez Y, Melie-Garcia L. Studying the human brain anatomical network via diffusion-weighted MRI and Graph Theory. Neuroimage 2008, 40: 1064–1076.

    Article  PubMed  Google Scholar 

  34. Lo CY, Wang PN, Chou KH, Wang J, He Y, Lin CP. Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer’s disease. J Neurosci 2010, 30: 16876–16885.

    Article  PubMed  CAS  Google Scholar 

  35. Reijmer YD, Leemans A, Caeyenberghs K, Heringa SM, Koek HL, Biessels GJ. Disruption of cerebral networks and cognitive impairment in Alzheimer disease. Neurology 2013, 80: 1370–1377.

    Article  PubMed  Google Scholar 

  36. Shu N, Liang Y, Li H, Zhang J, Li X, Wang L, et al. Disrupted topological organization in white matter structural networks in amnestic mild cognitive impairment: relationship to subtype. Radiology 2012, 265: 518–527.

    Article  PubMed  Google Scholar 

  37. Daianu M, Jahanshad N, Nir TM, Toga AW, Jack CR, Jr., Weiner MW, et al. Breakdown of brain connectivity between normal aging and Alzheimer’s disease: a structural k-core network analysis. Brain Connect 2013, 3: 407–422.

    Article  PubMed  Google Scholar 

  38. Bai F, Shu N, Yuan Y, Shi Y, Yu H, Wu D, et al. Topologically Convergent and Divergent Structural Connectivity Patterns between Patients with Remitted Geriatric Depression and Amnestic Mild Cognitive Impairment. J Neurosci 2012, 32: 4307–4318.

    Article  PubMed  CAS  Google Scholar 

  39. Stam CJ. Functional connectivity patterns of human magnetoencephalographic recordings: a ‘small-world’ network? Neurosci Lett 2004, 355: 25–28.

    Article  PubMed  CAS  Google Scholar 

  40. Bassett DS, Meyer-Lindenberg A, Achard S, Duke T, Bullmore E. Adaptive reconfiguration of fractal small-world human brain functional networks. Proc Natl Acad Sci U S A 2006, 103: 19518–19523.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  41. Smit DJ, Boersma M, Schnack HG, Micheloyannis S, Boomsma DI, Hulshoff Pol HE, et al. The brain matures with stronger functional connectivity and decreased randomness of its network. PLoS One 2012, 7: e36896.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  42. Stam CJ, Jones BF, Nolte G, Breakspear M, Scheltens P. Small-world networks and functional connectivity in Alzheimer’s disease. Cereb Cortex 2007, 17: 92–99.

    Article  PubMed  CAS  Google Scholar 

  43. Stam CJ, de Haan W, Daffertshofer A, Jones BF, Manshanden I, van Cappellen van Walsum AM, et al. Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer’s disease. Brain 2009, 132: 213–224.

    Article  PubMed  CAS  Google Scholar 

  44. de Haan W, Pijnenburg YA, Strijers RL, van der Made Y, van der Flier WM, Scheltens P, et al. Functional neural network analysis in frontotemporal dementia and Alzheimer’s disease using EEG and graph theory. BMC Neurosci 2009, 10: 101.

    Article  PubMed Central  PubMed  Google Scholar 

  45. de Haan W, van der Flier WM, Koene T, Smits LL, Scheltens P, Stam CJ. Disrupted modular brain dynamics reflect cognitive dysfunction in Alzheimer’s disease. Neuroimage 2012, 59: 3085–3093.

    Article  PubMed  Google Scholar 

  46. de Haan W, van der Flier WM, Wang H, Van Mieghem PF, Scheltens P, Stam C. Disruption of functional brain networks in Alzheimer’s disease: what can we learn from graph spectral analysis of resting-state MEG? Brain Connect 2012, 2: 45–55.

    Article  PubMed  Google Scholar 

  47. Buldu JM, Bajo R, Maestu F, Castellanos N, Leyva I, Gil P, et al. Reorganization of functional networks in mild cognitive impairment. PLoS One 2011, 6: e19584.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  48. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 1995, 34: 537–541.

    Article  PubMed  CAS  Google Scholar 

  49. Salvador R, Suckling J, Coleman MR, Pickard JD, Menon D, Bullmore E. Neurophysiological architecture of functional magnetic resonance images of human brain. Cereb Cortex 2005, 15: 1332–1342.

    Article  PubMed  Google Scholar 

  50. Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci 2006, 26: 63–72.

    Article  PubMed  CAS  Google Scholar 

  51. Supekar K, Menon V, Rubin D, Musen M, Greicius MD. Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. PLoS Comput Biol 2008, 4: e1000100.

    Article  PubMed Central  PubMed  Google Scholar 

  52. Sanz-Arigita EJ, Schoonheim MM, Damoiseaux JS, Rombouts SA, Maris E, Barkhof F, et al. Loss of ‘small-world’ networks in Alzheimer’s disease: graph analysis of FMRI resting-state functional connectivity. PLoS One 2010, 5: e13788.

    Article  PubMed Central  PubMed  Google Scholar 

  53. Zhao X, Liu Y, Wang X, Liu B, Xi Q, Guo Q, et al. Disrupted Small-World Brain Networks in Moderate Alzheimer’s Disease: A Resting-State fMRI Study. PLoS One 2012, 7: e33540.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  54. Chen G, Zhang HY, Xie C, Zhang ZJ, Teng GJ, Li SJ. Modular reorganization of brain resting state networks and its independent validation in Alzheimer’s disease patients. Front Hum Neurosci 2013, 7: 456.

    PubMed Central  PubMed  Google Scholar 

  55. Li Y, Qin Y, Chen X, Li W. Exploring the functional brain network of Alzheimer’s disease: based on the computational experiment. PLoS One 2013, 8: e73186.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  56. Wang J, Zuo X, Dai Z, Xia M, Zhao Z, Zhao X, et al. Disrupted Functional Brain Connectome in Individuals at Risk for Alzheimer’s Disease. Biol Psychiatry 2012, 73: 472–481.

    Article  PubMed  Google Scholar 

  57. Drzezga A, Becker JA, Van Dijk KR, Sreenivasan A, Talukdar T, Sullivan C, et al. Neuronal dysfunction and disconnection of cortical hubs in non-demented subjects with elevated amyloid burden. Brain 2011, 134: 1635–1646.

    Article  PubMed Central  PubMed  Google Scholar 

  58. Bai L, Zhang M, Chen S, Ai L, Xu M, Wang D, et al. Characterizing acupuncture de qi in mild cognitive impairment: relations with small-world efficiency of functional brain networks. Evid Based Complement Alternat Med 2013, 2013: 1–7.

    Google Scholar 

  59. Liu Z, Zhang Y, Yan H, Bai L, Dai R, Wei W, et al. Altered topological patterns of brain networks in mild cognitive impairment and Alzheimer’s disease: A resting-state fMRI study. Psychiatry Res 2012, 202: 118–125.

    Article  PubMed  Google Scholar 

  60. Liu Y, Yu C, Zhang X, Liu J, Duan Y, Alexander-Bloch AF, et al. Impaired long distance functional connectivity and weighted network architecture in Alzheimer’s disease. Cereb Cortex 2013. doi: 10.1093/cercor/bhs410

    Google Scholar 

  61. Brier M, Thomas J, Fagan A, Hassenstab J, Holtzman D, Benzinger T, et al. Functional connectivity and graph theory in preclinical Alzheimer’s disease. Neurobiol Aging 2013, [Epub ahead of print].

    Google Scholar 

  62. Sporns O, Tononi G, Kotter R. The human connectome: A structural description of the human brain. PLoS Comput Biol 2005, 1: e42.

    Article  PubMed Central  PubMed  Google Scholar 

  63. Wang J, Wang L, Zang Y, Yang H, Tang H, Gong Q, et al. Parcellation-dependent small-world brain functional networks: a resting-state fMRI study. Hum Brain Mapp 2009, 30: 1511–1523.

    Article  PubMed  Google Scholar 

  64. de Reus MA, van den Heuvel MP. The parcellation-based connectome: limitations and extensions. Neuroimage 2013, 80: 397–404.

    Article  PubMed  Google Scholar 

  65. Liang X, Wang J, Yan C, Shu N, Xu K, Gong G, et al. Effects of different correlation metrics and preprocessing factors on small-world brain functional networks: a resting-state functional MRI study. PLoS One 2012, 7: e32766.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  66. Van Dijk KR, Hedden T, Venkataraman A, Evans KC, Lazar SW, Buckner RL. Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J Neurophysiol 2010, 103: 297–321.

    Article  PubMed Central  PubMed  Google Scholar 

  67. Whitlow CT, Casanova R, Maldjian JA. Effect of resting-state functional MR imaging duration on stability of graph theory metrics of brain network connectivity. Radiology 2011, 259: 516–524.

    Article  PubMed  Google Scholar 

  68. Bassett DS, Brown JA, Deshpande V, Carlson JM, Grafton ST. Conserved and variable architecture of human white matter connectivity. Neuroimage 2011, 54: 1262–1279.

    Article  PubMed  Google Scholar 

  69. Greicius MD, Kiviniemi V, Tervonen O, Vainionpaa V, Alahuhta S, Reiss AL, et al. Persistent default-mode network connectivity during light sedation. Hum Brain Mapp 2008, 29: 839–847.

    Article  PubMed Central  PubMed  Google Scholar 

  70. Cao H, Plichta MM, Schafer A, Haddad L, Grimm O, Schneider M, et al. Test-retest reliability of fMRI-based graph theoretical properties during working memory, emotion processing, and resting state. Neuroimage 2014, 84: 888–900.

    Article  PubMed  Google Scholar 

  71. Weber MJ, Detre JA, Thompson-Schill SL, Avants BB. Reproducibility of functional network metrics and network structure: a comparison of task-related BOLD, resting ASL with BOLD contrast, and resting cerebral blood flow. Cogn Affect Behav Neurosci 2013, 13: 627–640.

    Article  PubMed  Google Scholar 

  72. Buchanan CR, Pernet CR, Gorgolewski KJ, Storkey AJ, Bastin ME. Test-retest reliability of structural brain networks from diffusion MRI. Neuroimage 2014, 86: 231–243.

    Article  PubMed  Google Scholar 

  73. Liao XH, Xia MR, Xu T, Dai ZJ, Cao XY, Niu HJ, et al. Functional brain hubs and their test-retest reliability: a multiband resting-state functional MRI study. Neuroimage 2013, 83: 969–982.

    Article  PubMed  Google Scholar 

  74. Wang JH, Zuo XN, Gohel S, Milham MP, Biswal BB, He Y. Graph theoretical analysis of functional brain networks: test-retest evaluation on short- and long-term resting-state functional MRI data. PLoS One 2011, 6: e21976.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  75. Cheng H, Wang Y, Sheng J, Kronenberger WG, Mathews VP, Hummer TA, et al. Characteristics and variability of structural networks derived from diffusion tensor imaging. Neuroimage 2012, 64: 1153–1164.

    Article  Google Scholar 

  76. Leoni V. The effect of apolipoprotein E (ApoE) genotype on biomarkers of amyloidogenesis, tau pathology and neurodegeneration in Alzheimer’s disease. Clin Chem Lab Med 2011, 49: 375–383.

    Article  PubMed  CAS  Google Scholar 

  77. Brown JA, Terashima KH, Burggren AC, Ercoli LM, Miller KJ, Small GW, et al. Brain network local interconnectivity loss in aging APOE-4 allele carriers. Proc Natl Acad Sci U S A 2011, 108: 20760–20765.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  78. Hardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 2002, 297: 353–356.

    Article  PubMed  CAS  Google Scholar 

  79. Selkoe DJ. Biochemistry and molecular biology of amyloid beta-protein and the mechanism of Alzheimer’s disease. Handb Clin Neurol 2008, 89: 245–260.

    Article  PubMed  Google Scholar 

  80. Buckner RL, Sepulcre J, Talukdar T, Krienen FM, Liu H, Hedden T, et al. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. J Neurosci 2009, 29: 1860–1873.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  81. de Haan W, Mott K, van Straaten EC, Scheltens P, Stam CJ. Activity dependent degeneration explains hub vulnerability in Alzheimer’s disease. PLoS Comput Biol 2012, 8: e1002582.

    Article  PubMed Central  PubMed  Google Scholar 

  82. Raj A, Kuceyeski A, Weiner M. A network diffusion model of disease progression in dementia. Neuron 2012, 73: 1204–1215.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  83. Zhou J, Gennatas ED, Kramer JH, Miller BL, Seeley WW. Predicting regional neurodegeneration from the healthy brain functional connectome. Neuron 2012, 73: 1216–1227.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yong He.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dai, Z., He, Y. Disrupted structural and functional brain connectomes in mild cognitive impairment and Alzheimer’s disease. Neurosci. Bull. 30, 217–232 (2014). https://doi.org/10.1007/s12264-013-1421-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12264-013-1421-0

Keywords

Navigation