Skip to main content

Advertisement

Log in

Differences in structural and functional default mode network connectivity in amyloid positive mild cognitive impairment: a longitudinal study

  • Functional Neuroradiology
  • Published:
Neuroradiology Aims and scope Submit manuscript

Abstract

Purpose

Default mode network (DMN) has emerged as a potential biomarker of Alzheimer’s disease (AD); however, it is not clear whether it can differentiate amnestic mild cognitive impairment with altered amyloid (aMCI-Aβ +) who will evolve to AD. We evaluated if structural and functional connectivity (FC), hippocampal volumes (HV), and cerebrospinal fluid biomarkers (CSF—Aβ42, p-Tau, and t-Tau) can differentiate aMCI-Aβ + converters from non-converters.

Methods

Forty-eight individuals (18 normal controls and 30 aMCI subjects in the AD continuum — with altered Aβ42 in the CSF) were followed up for an average of 13 months. We used MultiAtlas, UF2C, and Freesurfer software to evaluate diffusion tensor imaging, FC, and HV, respectively, INNOTEST® kits to measure CSF proteins, and neuropsychological tests. Besides, we performed different MANOVAs with further univariate analyses to differentiate groups.

Results

During follow-up, 8/30 aMCI-Aβ + converted (26.6%) to AD dementia. There were no differences in multivariate analysis between groups in CSF biomarkers (p = 0.092) or at DMN functional connectivity (p = 0.814). aMCI-Aβ + converters had smaller right HV than controls (p = 0.013), and greater right cingulum parahippocampal bundle radial diffusivity than controls (p < 0.001) and non-converters (p = 0.036).

Conclusion

In this exploratory study, structural, but not functional, DMN connectivity alterations may differentiate aMCI-Aβ + subjects who converted to AD dementia.

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.

Fig. 1

Similar content being viewed by others

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. van den Heuvel MP, Sporns O (2019) A cross-disorder connectome landscape of brain dysconnectivity. Nat Rev Neurosci 20(7):435–446. https://doi.org/10.1038/s41583-019-0177-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Jones DT, Graff-Radford J, Lowe VJ, Wiste HJ, Gunter JL, Senjem ML et al (2017) Tau, amyloid, and cascading network failure across the Alzheimer’s disease spectrum. Cortex 97:143–159. https://doi.org/10.1016/j.cortex.2017.09.018

    Article  PubMed  PubMed Central  Google Scholar 

  3. Seeley WW, Crawford RK, Zhou J, Miller BL, Greicius MD (2009) Neurodegenerative diseases target large-scale human brain networks. Neuron 62(1):42–52. https://doi.org/10.1016/j.neuron.2009.03.024

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Raj A, Powell F (2018) Models of network spread and network degeneration in brain disorders. Biol Psychiatry Cogn Neurosci Neuroimaging 3(9):788–797. https://doi.org/10.1016/j.bpsc.2018.07.012

    Article  PubMed  Google Scholar 

  5. Franzmeier N, Rubinski A, Neitzel J, Kim Y, Damm A, Na DL et al (2019) Functional connectivity associated with tau levels in ageing, Alzheimer’s, and small vessel disease. Brain 142(4):1093–1107. https://doi.org/10.1093/brain/awz026

    Article  PubMed  PubMed Central  Google Scholar 

  6. Franzmeier N, Neitzel J, Rubinski A, Smith R, Strandberg O, Ossenkoppele R et al (2020) Functional brain architecture is associated with the rate of tau accumulation in Alzheimer’s disease. Nat Commun 11(1):347. https://doi.org/10.1038/s41467-019-14159-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Agosta F, Pievani M, Geroldi C, Copetti M, Frisoni GB, Filippi M (2012) Resting state fMRI in Alzheimer’s disease: beyond the default mode network. Neurobiol Aging 33(8):1564–1578. https://doi.org/10.1016/j.neurobiolaging.2011.06.007

    Article  PubMed  Google Scholar 

  8. Banks SJ, Zhuang X, Bayram E, Bird C, Cordes D, Caldwell JZK et al (2018) Default mode network lateralization and memory in healthy aging and Alzheimer’s disease. J Alzheimers Dis 66(3):1223–1234. https://doi.org/10.3233/jad-180541

    Article  PubMed  PubMed Central  Google Scholar 

  9. Balthazar ML, de Campos BM, Franco AR, Damasceno BP, Cendes F (2014) Whole cortical and default mode network mean functional connectivity as potential biomarkers for mild Alzheimer’s disease. Psychiatry Res 221(1):37–42. https://doi.org/10.1016/j.pscychresns.2013.10.010

    Article  PubMed  Google Scholar 

  10. Matura S, Kohler J, Reif A, Fusser F, Karakaya T, Scheibe M, et al (2019) Intrinsic functional connectivity, CSF biomarker profiles and their relation to cognitive function in mild cognitive impairment. Acta Neuropsychiatr, 1–24. https://doi.org/10.1017/neu.2019.49

  11. Vaquer-Alicea J, Diamond MI (2019) Propagation of protein aggregation in neurodegenerative diseases. Annu Rev Biochem 88:785–810. https://doi.org/10.1146/annurev-biochem-061516-045049

    Article  CAS  PubMed  Google Scholar 

  12. Mayo CD, Garcia-Barrera MA, Mazerolle EL, Ritchie LJ, Fisk JD, Gawryluk JR (2018) Relationship between DTI metrics and cognitive function in Alzheimer’s disease. Front Aging Neurosci 10:436. https://doi.org/10.3389/fnagi.2018.00436

    Article  PubMed  Google Scholar 

  13. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186–198. https://doi.org/10.1038/nrn2575

    Article  CAS  PubMed  Google Scholar 

  14. De Vogelaere F, Santens P, Achten E, Boon P, Vingerhoets G (2012) Altered default-mode network activation in mild cognitive impairment compared with healthy aging. Neuroradiology 54(11):1195–1206. https://doi.org/10.1007/s00234-012-1036-6

    Article  PubMed  Google Scholar 

  15. Petrella JR, Sheldon FC, Prince SE, Calhoun VD, Doraiswamy PM (2011) Default mode network connectivity in stable vs progressive mild cognitive impairment. Neurology 76(6):511–517. https://doi.org/10.1212/WNL.0b013e31820af94e

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Yi D, Choe YM, Byun MS, Sohn BK, Seo EH, Han J et al (2015) Differences in functional brain connectivity alterations associated with cerebral amyloid deposition in amnestic mild cognitive impairment. Front Aging Neurosci 7:15. https://doi.org/10.3389/fnagi.2015.00015

    Article  PubMed  PubMed Central  Google Scholar 

  17. Skouras S, Falcon C, Tucholka A, Rami L, Sanchez-Valle R, Lladó A et al (2019) Mechanisms of functional compensation, delineated by eigenvector centrality mapping, across the pathophysiological continuum of Alzheimer’s disease. Neuroimage Clin 22:101777. https://doi.org/10.1016/j.nicl.2019.101777

    Article  PubMed  PubMed Central  Google Scholar 

  18. Koch K, Myers NE, Göttler J, Pasquini L, Grimmer T, Förster S et al (2015) Disrupted intrinsic networks link amyloid-β pathology and impaired cognition in prodromal Alzheimer’s disease. Cereb Cortex 25(12):4678–4688. https://doi.org/10.1093/cercor/bhu151

    Article  PubMed  Google Scholar 

  19. Mayo CD, Mazerolle EL, Ritchie L, Fisk JD, Gawryluk JR (2017) Longitudinal changes in microstructural white matter metrics in Alzheimer’s disease. Neuroimage Clin 13:330–338. https://doi.org/10.1016/j.nicl.2016.12.012

    Article  PubMed  Google Scholar 

  20. Bharath S, Joshi H, John JP, Balachandar R, Sadanand S, Saini J et al (2017) A multimodal structural and functional neuroimaging study of amnestic mild cognitive impairment. Am J Geriatr Psychiatry 25(2):158–169. https://doi.org/10.1016/j.jagp.2016.05.001

    Article  PubMed  Google Scholar 

  21. Chiesa PA, Cavedo E, Vergallo A, Lista S, Potier MC, Habert MO et al (2019) Differential default mode network trajectories in asymptomatic individuals at risk for Alzheimer’s disease. Alzheimers Dement 15(7):940–950. https://doi.org/10.1016/j.jalz.2019.03.006

    Article  PubMed  Google Scholar 

  22. Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC et al (2011) 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 7(3):270–279. https://doi.org/10.1016/j.jalz.2011.03.008

    Article  PubMed  PubMed Central  Google Scholar 

  23. Forlenza OV, Radanovic M, Talib LL, Aprahamian I, Diniz BS, Zetterberg H et al (2015) Cerebrospinal fluid biomarkers in Alzheimer’s disease: diagnostic accuracy and prediction of dementia. Alzheimers Dement (Amst) 1(4):455–463. https://doi.org/10.1016/j.dadm.2015.09.003

    Article  Google Scholar 

  24. Struyfs H, Molinuevo JL, Martin JJ, De Deyn PP, Engelborghs S (2014) Validation of the AD-CSF-index in autopsy-confirmed Alzheimer’s disease patients and healthy controls. J Alzheimers Dis 41(3):903–909. https://doi.org/10.3233/JAD-131085

    Article  CAS  PubMed  Google Scholar 

  25. Mo JA, Lim JH, Sul AR, Lee M, Youn YC, Kim HJ (2015) Cerebrospinal fluid β-amyloid1-42 levels in the differential diagnosis of Alzheimer’s disease–systematic review and meta-analysis. PLoS One 10(2):e0116802. https://doi.org/10.1371/journal.pone.0116802

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ritchie C, Smailagic N, Noel-Storr AH, Takwoingi Y, Flicker L, Mason SE et al (2014) Plasma and cerebrospinal fluid amyloid beta for the diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev 6:CD008782. https://doi.org/10.1002/14651858.CD008782.pub4

    Article  Google Scholar 

  27. Jack CR Jr, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB et al (2018) NIA-AA Research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement 14(4):535–562. https://doi.org/10.1016/j.jalz.2018.02.018

    Article  PubMed  PubMed Central  Google Scholar 

  28. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH et al (2011) 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 7(3):263–269. https://doi.org/10.1016/j.jalz.2011.03.005

    Article  PubMed  PubMed Central  Google Scholar 

  29. Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA (1987) MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. AJR Am J Roentgenol 149(2):351–356. https://doi.org/10.2214/ajr.149.2.351

    Article  CAS  PubMed  Google Scholar 

  30. Hachinski V, Iadecola C, Petersen RC, Breteler MM, Nyenhuis DL, Black SE et al (2006) National institute of neurological disorders and stroke-Canadian stroke network vascular cognitive impairment harmonization standards. Stroke 37(9):2220–2241. https://doi.org/10.1161/01.str.0000237236.88823.47

    Article  PubMed  Google Scholar 

  31. Folstein MF, Folstein SE, McHugh PR (1975) “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12(3):189–98

    Article  CAS  PubMed  Google Scholar 

  32. Malloy-Diniz LF, Lasmar VA, Gazinelli Lde S, Fuentes D, Salgado JV (2007) The rey auditory-verbal learning test: applicability for the Brazilian elderly population. Rev Bras Psiquiatr 29(4):324–329

    Article  PubMed  Google Scholar 

  33. Osterrieth PA (1944) Le Test de copie d’une figure complexe : contribution à l’étude de la perception et de la mémoire. Delachaux & Niestlé, Neuchâtel

    Google Scholar 

  34. Norton JC (1978) The trail making test and bender background interference procedure as screening devices. J Clin Psychol 34(4):916–922. https://doi.org/10.1002/1097-4679(197810)34:4%3c916::aid-jclp2270340418%3e3.0.co;2-4

    Article  CAS  PubMed  Google Scholar 

  35. Scarpina F, Tagini S (2017) The stroop color and word test. Front Psychol 8:557. https://doi.org/10.3389/fpsyg.2017.00557

    Article  PubMed  PubMed Central  Google Scholar 

  36. Williams BW, Mack W, Henderson VW (1989) Boston naming test in Alzheimer’s disease. Neuropsychologia 27(8):1073–1079. https://doi.org/10.1016/0028-3932(89)90186-3

    Article  CAS  PubMed  Google Scholar 

  37. Morris JC (1993) The clinical dementia rating (CDR): current version and scoring rules. Neurology 43(11):2412–2414

    Article  CAS  PubMed  Google Scholar 

  38. Magalhães TNC, Weiler M, Teixeira CVL, Hayata T, Moraes AS, Boldrini VO et al (2018) Systemic Inflammation and multimodal biomarkers in amnestic mild cognitive impairment and Alzheimer’s disease. Mol Neurobiol 55(7):5689–5697. https://doi.org/10.1007/s12035-017-0795-9

    Article  CAS  PubMed  Google Scholar 

  39. Weiler M, de Campos BM, Nogueira MH, Pereira Damasceno B, Cendes F, Balthazar ML (2014) Structural connectivity of the default mode network and cognition in Alzheimer׳s disease. Psychiatry Res 223(1):15–22. https://doi.org/10.1016/j.pscychresns.2014.04.008

    Article  PubMed  Google Scholar 

  40. de Campos BM, Coan AC, Lin Yasuda C, Casseb RF, Cendes F (2016) Large-scale brain networks are distinctly affected in right and left mesial temporal lobe epilepsy. Hum Brain Mapp 37(9):3137–3152. https://doi.org/10.1002/hbm.23231

    Article  PubMed  PubMed Central  Google Scholar 

  41. Tang X, Crocetti D, Kutten K, Ceritoglu C, Albert MS, Mori S et al (2015) Segmentation of brain magnetic resonance images based on multi-atlas likelihood fusion: testing using data with a broad range of anatomical and photometric profiles. Front Neurosci 9:61. https://doi.org/10.3389/fnins.2015.00061

    Article  PubMed  PubMed Central  Google Scholar 

  42. Zhan Y, Ma J, Alexander-Bloch AF, Xu K, Cui Y, Feng Q et al (2016) Longitudinal study of impaired intra- and inter-network brain connectivity in subjects at high risk for Alzheimer’s disease. J Alzheimers Dis 52(3):913–927. https://doi.org/10.3233/JAD-160008

    Article  CAS  PubMed  Google Scholar 

  43. Winklewski PJ, Sabisz A, Naumczyk P, Jodzio K, Szurowska E, Szarmach A (2018) Understanding the physiopathology behind axial and radial diffusivity changes-what do we know? Front Neurol 9:92. https://doi.org/10.3389/fneur.2018.00092

    Article  PubMed  PubMed Central  Google Scholar 

  44. Stricker NH, Salat DH, Kuhn TP, Foley JM, Price JS, Westlye LT et al (2016) Mild cognitive impairment is associated with white matter integrity changes in late-myelinating regions within the corpus callosum. Am J Alzheimers Dis Other Demen 31(1):68–75. https://doi.org/10.1177/1533317515578257

    Article  PubMed  Google Scholar 

  45. Alm KH, Bakker A (2019) Relationships between diffusion tensor imaging and cerebrospinal fluid metrics in early stages of the Alzheimer’s disease continuum. J Alzheimers Dis 70(4):965–981. https://doi.org/10.3233/JAD-181210

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Davatzikos C, Xu F, An Y, Fan Y, Resnick SM (2009) Longitudinal progression of Alzheimer’s-like patterns of atrophy in normal older adults: the SPARE-AD index. Brain 132(Pt 8):2026–2035. https://doi.org/10.1093/brain/awp091

    Article  PubMed  PubMed Central  Google Scholar 

  47. Garcés P, Angel Pineda-Pardo J, Canuet L, Aurtenetxe S, López ME, Marcos A et al (2014) The Default Mode Network is functionally and structurally disrupted in amnestic mild cognitive impairment - a bimodal MEG-DTI study. Neuroimage Clin 6:214–221. https://doi.org/10.1016/j.nicl.2014.09.004

    Article  PubMed  PubMed Central  Google Scholar 

  48. Pineda-Pardo JA, Bruña R, Woolrich M, Marcos A, Nobre AC, Maestú F et al (2014) Guiding functional connectivity estimation by structural connectivity in MEG: an application to discrimination of conditions of mild cognitive impairment. Neuroimage 101:765–777. https://doi.org/10.1016/j.neuroimage.2014.08.002

    Article  PubMed  Google Scholar 

  49. Devanand DP, Pradhaban G, Liu X, Khandji A, De Santi S, Segal S et al (2007) Hippocampal and entorhinal atrophy in mild cognitive impairment: prediction of Alzheimer disease. Neurology 68(11):828–836. https://doi.org/10.1212/01.wnl.0000256697.20968.d7

    Article  CAS  PubMed  Google Scholar 

  50. Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC et al (2017) Recent publications from the Alzheimer’s disease neuroimaging initiative: reviewing progress toward improved AD clinical trials. Alzheimers Dement 13(4):e1–e85. https://doi.org/10.1016/j.jalz.2016.11.007

    Article  PubMed  PubMed Central  Google Scholar 

  51. den Heijer T, van der Lijn F, Koudstaal PJ, Hofman A, van der Lugt A, Krestin GP et al (2010) A 10-year follow-up of hippocampal volume on magnetic resonance imaging in early dementia and cognitive decline. Brain 133(Pt 4):1163–1172. https://doi.org/10.1093/brain/awq048

    Article  Google Scholar 

  52. Fellgiebel A, Müller MJ, Wille P, Dellani PR, Scheurich A, Schmidt LG et al (2005) Color-coded diffusion-tensor-imaging of posterior cingulate fiber tracts in mild cognitive impairment. Neurobiol Aging 26(8):1193–1198. https://doi.org/10.1016/j.neurobiolaging.2004.11.006

    Article  PubMed  Google Scholar 

  53. Fellgiebel A, Dellani PR, Greverus D, Scheurich A, Stoeter P, Müller MJ (2006) Predicting conversion to dementia in mild cognitive impairment by volumetric and diffusivity measurements of the hippocampus. Psychiatry Res 146(3):283–287. https://doi.org/10.1016/j.pscychresns.2006.01.006

    Article  PubMed  Google Scholar 

  54. Choo IH, Lee DY, Oh JS, Lee JS, Lee DS, Song IC et al (2010) Posterior cingulate cortex atrophy and regional cingulum disruption in mild cognitive impairment and Alzheimer’s disease. Neurobiol Aging 31(5):772–779. https://doi.org/10.1016/j.neurobiolaging.2008.06.015

    Article  PubMed  Google Scholar 

  55. Yu J, Lam CLM, Lee TMC (2017) White matter microstructural abnormalities in amnestic mild cognitive impairment: a meta-analysis of whole-brain and ROI-based studies. Neurosci Biobehav Rev 83:405–416. https://doi.org/10.1016/j.neubiorev.2017.10.026

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank Dr. Annamaria Rudderow for English editing and grants from the São Paulo Research Foundation (FAPESP).

Funding

The study was financed by the São Paulo Research Foundation (FAPESP). Grant number: 2017/01286–6, 2017/13906–9, 2011/17092–0, and 2018/15571–7, São Paulo Research Foundation (FAPESP).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Thamires Naela Cardoso Magalhães.

Ethics declarations

Competing interests

The authors have no conflict of interest to report.

Ethics approval and consent to participate

The Medical Research Ethics Committee of UNICAMP Hospital approved this study (CAAE: 09,634,412.5.0000.5404). Written informed consent (either from the subjects or from their responsible caretakers, if incapable) was obtained from all participants before the commencement of the study, following the Declaration of Helsinki.

Consent to publication

This study does not contain any person’s data.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Magalhães, T.N.C., Gerbelli, C.L.B., Pimentel-Silva, L.R. et al. Differences in structural and functional default mode network connectivity in amyloid positive mild cognitive impairment: a longitudinal study. Neuroradiology 64, 141–150 (2022). https://doi.org/10.1007/s00234-021-02760-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00234-021-02760-5

Keywords

Navigation