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Changes in functional and structural brain connectome along the Alzheimer’s disease continuum


The aim of this study was two-fold: (i) to investigate structural and functional brain network architecture in patients with Alzheimer’s disease (AD) and amnestic mild cognitive impairment (aMCI), stratified in converters (c-aMCI) and non-converters (nc-aMCI) to AD; and to assess the relationship between healthy brain network functional connectivity and the topography of brain atrophy in patients along the AD continuum. Ninety-four AD patients, 47 aMCI patients (25 c-aMCI within 36 months) and 53 age- and sex-matched healthy controls were studied. Graph analysis and connectomics assessed global and local, structural and functional topological network properties and regional connectivity. Healthy topological features of brain regions were assessed based on their connectivity with the point of maximal atrophy (epicenter) in AD and aMCI patients. Brain network graph analysis properties were severely altered in AD patients. Structural brain network was already altered in c-aMCI patients relative to healthy controls in particular in the temporal and parietal brain regions, while functional connectivity did not change. Structural connectivity alterations distinguished c-aMCI from nc-aMCI cases. In both AD and c-aMCI, the point of maximal atrophy was located in left hippocampus (disease-epicenter). Brain regions most strongly connected with the disease-epicenter in the healthy functional connectome were also the most atrophic in both AD and c-aMCI patients. Progressive degeneration in the AD continuum is associated with an early breakdown of anatomical brain connections and follows the strongest connections with the disease-epicenter. These findings support the hypothesis that the topography of brain connectional architecture can modulate the spread of AD through the brain.

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  1. 1.

    Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991;82:239–59.

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Manly JJ, Tang MX, Schupf N, Stern Y, Vonsattel JP, Mayeux R. Frequency and course of mild cognitive impairment in a multiethnic community. Ann Neurol. 2008;63:494–506.

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Jucker M, Walker LC. Self-propagation of pathogenic protein aggregates in neurodegenerative diseases. Nature. 2013;501:45–51.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Frost B, Diamond MI. Prion-like mechanisms in neurodegenerative diseases. Nat Rev Neurosci. 2010;11:155–9.

    CAS  PubMed  Google Scholar 

  5. 5.

    Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10:186–98.

    CAS  Google Scholar 

  6. 6.

    Fornito A, Zalesky A, Breakspear M. The connectomics of brain disorders. Nat Rev Neurosci. 2015;16:159–72.

    CAS  Google Scholar 

  7. 7.

    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;29:1860–73.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Seeley WW, Crawford RK, Zhou J, Miller BL, Greicius MD. Neurodegenerative diseases target large-scale human brain networks. Neuron. 2009;62:42–52.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Sepulcre J, Sabuncu MR, Li Q, El Fakhri G, Sperling R, Johnson KA. Tau and amyloid-beta proteins distinctively associate to functional network changes in the aging brain. Alzheimer’s & Dement: J Alzheimer’s Assoc. 2017;13:1261–9.

    Google Scholar 

  10. 10.

    Mandelli ML, Vilaplana E, Brown JA, Hubbard HI, Binney RJ, Attygalle S, et al. Healthy brain connectivity predicts atrophy progression in non-fluent variant of primary progressive aphasia. Brain. 2016;139(Pt 10):2778–91.

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Raj A, Kuceyeski A, Weiner M. A network diffusion model of disease progression in dementia. Neuron. 2012;73:1204–15.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Collins JA, Montal V, Hochberg D, Quimby M, Mandelli ML, Makris N, et al. Focal temporal pole atrophy and network degeneration in semantic variant primary progressive aphasia. Brain. 2017;140(Pt 2):457–71.

    PubMed  Google Scholar 

  13. 13.

    Zhou J, Gennatas ED, Kramer JH, Miller BL, Seeley WW. Predicting regional neurodegeneration from the healthy brain functional connectome. Neuron. 2012;73:1216–27.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Mallio CA, Schmidt R, de Reus MA, Vernieri F, Quintiliani L, Curcio G, et al. Epicentral disruption of structural connectivity in Alzheimer’s disease. CNS Neurosci Ther. 2015;21:837–45.

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, 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;7:263–9.

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    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;7:270–9.

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189–98.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Novelli G, Papagno C, Capitani E, Laiacona N, Vallar G, Cappa SF. Tre test clinici di ricerca e produzione lessicale. Taratura su soggetti Norm Arch Psicol Neurol Psichiatr. 1986;47:477–506.

    Google Scholar 

  19. 19.

    Rey A. L’examen Clinique en Psychologie. Paris: Presses Universitaires; 1964.

  20. 20.

    Orsini A, Grossi D, Capitani E, Laiacona M, Papagno C, Vallar G. Verbal and spatial immediate memory span: normative data from 1355 adults and 1112 children. Ital J Neurol Sci. 1987;8:539–48.

    CAS  PubMed  Google Scholar 

  21. 21.

    Caffarra P, Vezzadini G, Dieci F, Zonato F, Venneri A. Rey-Osterrieth complex figure: normative values in an Italian population sample. Neurol Sci. 2002;22:443–7.

    CAS  PubMed  Google Scholar 

  22. 22.

    Spinnler H, Tognoni G. Standardizzazione e taratura italiana di test neuropsicologici. Ital J Neurol Sci. 1987;6:1–120.

    Google Scholar 

  23. 23.

    Basso A, Capitani E, Laiacona M. Raven’s coloured progressive matrices: normative values on 305 adult normal controls. Funct Neurol. 1987;2:189–94.

    CAS  PubMed  Google Scholar 

  24. 24.

    Manos PJ. Ten-point clock test sensitivity for Alzheimer’s disease in patients with MMSE scores greater than 23. Int J Geriatr Psychiatry. 1999;14:454–8.

    CAS  PubMed  Google Scholar 

  25. 25.

    De Renzi E, Vignolo LA. The token test: a sensitive test to detect receptive disturbances in aphasics. Brain. 1962;85:665–78.

    Google Scholar 

  26. 26.

    Filippi M, Basaia S, Canu E, Imperiale F, Meani A, Caso F, et al. Brain network connectivity differs in early-onset neurodegenerative dementia. Neurology. 2017;89:1764–72.

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Sporns O, Chialvo DR, Kaiser M, Hilgetag CC. Organization, development and function of complex brain networks. Trends Cogn Sci. 2004;8:418–25.

    PubMed  Google Scholar 

  28. 28.

    Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’ networks. Nature. 1998;393:440–2.

    CAS  PubMed  Google Scholar 

  29. 29.

    Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differences in brain networks. Neuroimage. 2010;53:1197–207.

    PubMed  Google Scholar 

  30. 30.

    Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 2002;17:825–41.

    PubMed  Google Scholar 

  31. 31.

    Andersson JL, Jenkinson M, Smith S. Non-linear registration, aka spatial normalisation. FMRIB technical report TR07JA2 2007.

  32. 32.

    Schmidt R, de Reus MA, Scholtens LH, van den Berg LH, van den Heuvel MP. Simulating disease propagation across white matter connectome reveals anatomical substrate for neuropathology staging in amyotrophic lateral sclerosis. Neuroimage. 2016;124(Pt A):762–9.

    PubMed  Google Scholar 

  33. 33.

    Zeileis A, Hothorn T, Hornik K. Model-based recursive partitioning. J Comput Graph Stat. 2008;17:492–514.

    Google Scholar 

  34. 34.

    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;34:2023–36.

    PubMed  Google Scholar 

  35. 35.

    Dipasquale O, Cercignani M. Network functional connectivity and whole-brain functional connectomics to investigate cognitive decline in neurodegenerative conditions. Funct Neurol. 2016;31:191–203.

    CAS  PubMed  Google Scholar 

  36. 36.

    Prescott JW, Guidon A, Doraiswamy PM, Roy Choudhury K, Liu C, Petrella JR. The Alzheimer structural connectome: changes in cortical network topology with increased amyloid plaque burden. Radiology. 2014;273:175–84.

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Raj A, LoCastro E, Kuceyeski A, Tosun D, Relkin N, Weiner M. Network diffusion model of progression predicts longitudinal patterns of atrophy and metabolism in Alzheimer’s disease. Cell Rep. 2015. [Epub ahead of print;]

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Schmidt R, Verstraete E, de Reus MA, Veldink JH, van den Berg LH, van den Heuvel MP. Correlation between structural and functional connectivity impairment in amyotrophic lateral sclerosis. Hum Brain Mapp. 2014;35:4386–95.

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Filippi M, Agosta F. Structural and functional network connectivity breakdown in Alzheimer’s disease studied with magnetic resonance imaging techniques. J Alzheimer’s Dis: JAD. 2011;24:455–74.

    Google Scholar 

  40. 40.

    Honey CJ, Sporns O, Cammoun L, Gigandet X, Thiran JP, Meuli R, et al. Predicting human resting-state functional connectivity from structural connectivity. Proc Natl Acad Sci USA. 2009;106:2035–40.

    CAS  PubMed  Google Scholar 

  41. 41.

    van den Heuvel MP, Mandl RC, Kahn RS, Hulshoff Pol HE. Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain. Hum Brain Mapp. 2009;30:3127–41.

    PubMed  Google Scholar 

  42. 42.

    Sun Y, Yin Q, Fang R, Yan X, Wang Y, Bezerianos A, et al. Disrupted functional brain connectivity and its association to structural connectivity in amnestic mild cognitive impairment and Alzheimer’s disease. PLoS ONE. 2014;9:e96505.

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Braak H, Braak E. Staging of Alzheimer’s disease-related neurofibrillary changes. Neurobiol Aging. 1995;16:271–8. discussion 278−284

    CAS  PubMed  Google Scholar 

  44. 44.

    Whitwell JL, Josephs KA, Murray ME, Kantarci K, Przybelski SA, Weigand SD, et al. MRI correlates of neurofibrillary tangle pathology at autopsy: a voxel-based morphometry study. Neurology. 2008;71:743–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, et al. Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol. 2014;13:614–29.

    PubMed  Google Scholar 

  46. 46.

    Azeez AK, Biswal BB. A review of resting-state analysis methods. Neuroimaging Clin N Am. 2017;27:581–92.

    PubMed  Google Scholar 

  47. 47.

    Johansen-Berg H, Rushworth MF. Using diffusion imaging to study human connectional anatomy. Annu Rev Neurosci. 2009;32:75–94.

    CAS  PubMed  Google Scholar 

  48. 48.

    Zalesky A, Fornito A, Harding IH, Cocchi L, Yucel M, Pantelis C, et al. Whole-brain anatomical networks: does the choice of nodes matter? Neuroimage. 2010;50:970–83.

    PubMed  Google Scholar 

  49. 49.

    de Reus MA, van den Heuvel MP. Estimating false positives and negatives in brain networks. Neuroimage. 2013;70:402–9.

    PubMed  Google Scholar 

  50. 50.

    van den Heuvel MP, de Lange SC, Zalesky A, Seguin C, Yeo BTT, Schmidt R. Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: issues and recommendations. Neuroimage. 2017;152:437–49.

    PubMed  Google Scholar 

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The study was supported by the Italian Ministry of Health (grant number GR-2010-2303035) and Alzheimer’s Drug Discovery Foundation (grant number 20131211). The authors thank the patients and their families for the time and effort they dedicated to the research, and are grateful to Dr. Andrea Fontana for his useful statistical advice.

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Correspondence to Massimo Filippi.

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Conflict of interest

M. Filippi is Editor-in-Chief of the Journal of Neurology; has received compensation for consulting services and/or speaking activities from Biogen Idec, ExceMED, Novartis, and Teva Pharmaceutical Industries; and receives research support from Biogen Idec, Teva Pharmaceutical Industries, Novartis, Italian Ministry of Health, Fondazione Italiana Sclerosi Multipla, Cure PSP, Alzheimer’s Drug Discovery Foundation (ADDF), the Jacques and Gloria Gossweiler Foundation (Switzerland), and ARiSLA (Fondazione Italiana di Ricerca per la SLA). E. Canu has received research supports from the Italian Ministry of Health. G. Comi has received consulting fees for participating on advisory boards from Novartis, Teva Pharmaceutical Ind. Ltd, Sanofi, Genzyme, Merck Serono, Bayer, Actelion and honorarium for speaking activities for Novartis, Teva Pharmaceutical Ind. Ltd, Sanofi, Genzyme, Merck Serono, Bayer, Biogen, ExceMED. F. Agosta is Section Editor of NeuroImage: Clinical; has received speaker honoraria from Biogen Idec, Novartis, and ExceMED—Excellence in Medical Education; and receives or has received research supports from the Italian Ministry of Health, AriSLA (Fondazione Italiana di Ricerca per la SLA), and the European Research Council. The remaining authors declare that they have no conflicts of interests.

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Filippi, M., Basaia, S., Canu, E. et al. Changes in functional and structural brain connectome along the Alzheimer’s disease continuum. Mol Psychiatry 25, 230–239 (2020).

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