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Default mode network dissociation linking cerebral beta amyloid retention and depression in cognitively normal older adults


Cerebral beta amyloid (Aβ) deposition and late-life depression (LLD) are known to be associated with the trajectory of Alzheimer’s disease (AD). However, their neurobiological link is not clear. Previous studies showed aberrant functional connectivity (FC) changes in the default mode network (DMN) in early Aβ deposition and LLD, but its mediating role has not been elucidated. This study was performed to investigate the distinctive association pattern of DMN FC linking LLD and Aβ retention in cognitively normal older adults. A total of 235 cognitively normal older adults with (n = 118) and without depression (n = 117) underwent resting-state functional magnetic resonance imaging and 18F-flutemetamol positron emission tomography to investigate the associations between Aβ burden, depression, and DMN FC. Independent component analysis showed increased anterior DMN FC and decreased posterior DMN FC in the depression group compared with the no depression group. Global cerebral Aβ retention was positively correlated with anterior and negatively correlated with posterior DMN FC. Anterior DMN FC was positively correlated with severity of depression, whereas posterior DMN FC was negatively correlated with cognitive function. In addition, the effects of global cerebral Aβ retention on severity of depression were mediated by subgenual anterior cingulate FC. Our results of anterior and posterior DMN FC dissociation pattern may be pivotal in linking cerebral Aβ pathology and LLD in the course of AD progression. Further longitudinal studies are needed to confirm the causal relationships between cerebral Aβ retention and LLD.

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Fig. 1: Group difference of functional connectivity.
Fig. 2: Effect of depression on the relationships between Aß retention and FC.
Fig. 3: Direction of the association between FC and Aß retention.
Fig. 4: Mediation analysis.


  1. 1.

    Byers AL, Yaffe K. Depression and risk of developing dementia. Nat Rev Neurol. 2011;7:323–31.

    PubMed  PubMed Central  CAS  Google Scholar 

  2. 2.

    Kessing LV. Depression and the risk for dementia. Curr Opin Psychiatry. 2012;25:457–61.

    PubMed  Google Scholar 

  3. 3.

    Ownby RL, Crocco E, Acevedo A, John V, Loewenstein D. Depression and risk for Alzheimer disease: systematic review, meta-analysis, and metaregression analysis. Arch Gen Psychiatry. 2006;63:530–8.

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Peters ME, Lyketsos CG. Beyond memory: a focus on the other neuropsychiatric symptoms of dementia. Am J Geriatr Psychiatry. 2015;23:115–8.

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    Sapolsky RM. Glucocorticoids and hippocampal atrophy in neuropsychiatric disorders. Arch Gen Psychiatry. 2000;57:925–35.

    PubMed  CAS  Google Scholar 

  6. 6.

    Butters MA, Young JB, Lopez O, Aizenstein HJ, Mulsant BH, Reynolds CF, et al. Pathways linking late-life depression to persistent cognitive impairment and dementia. Dialogues Clin Neurosci. 2008;10:345–57.

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Sheline YI, Price JL, Vaishnavi SN, Mintun MA, Barch DM, Epstein AA, et al. Regional white matter hyperintensity burden in automated segmentation distinguishes late-life depressed subjects from comparison subjects matched for vascular risk factors. Am J Psychiatry. 2008;165:524–32.

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Qiu WQ, Zhu H, Dean M, Liu Z, Vu L, Fan G, et al. Amyloid-associated depression and ApoE4 allele: longitudinal follow-up for the development of Alzheimer’s disease. Int J Geriatr Psychiatry. 2016;31:316–22.

    PubMed  Google Scholar 

  9. 9.

    Pomara N, Bruno D, Sarreal AS, Hernando RT, Nierenberg J, Petkova E, et al. Lower CSF amyloid beta peptides and higher F2-isoprostanes in cognitively intact elderly individuals with major depressive disorder. Am J Psychiatry. 2012;169:523–30.

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Yasuno F, Kazui H, Morita N, Kajimoto K, Ihara M, Taguchi A, et al. High amyloid-beta deposition related to depressive symptoms in older individuals with normal cognition: a pilot study. Int J Geriatr Psychiatry. 2016;31:920–8.

    PubMed  Google Scholar 

  11. 11.

    Blasko I, Kemmler G, Jungwirth S, Wichart I, Krampla W, Weissgram S, et al. Plasma amyloid beta-42 independently predicts both late-onset depression and Alzheimer disease. Am J Geriatr Psychiatry. 2010;18:973–82.

    PubMed  Google Scholar 

  12. 12.

    Donovan NJ, Locascio JJ, Marshall GA, Gatchel J, Hanseeuw BJ, Rentz DM, et al. Longitudinal association of amyloid beta and anxious-depressive symptoms in cognitively normal older adults. Am J Psychiatry. 2018;175:530–37.

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Alexopoulos GS. Mechanisms and treatment of late-life depression. Transl Psychiatry. 2019;9:188.

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    De Winter FL, Emsell L, Bouckaert F, Claes L, Jain S, Farrar G, et al. No association of lower hippocampal volume with Alzheimer’s disease pathology in late-life depression. Am J Psychiatry. 2017;174:237–45.

    PubMed  Google Scholar 

  15. 15.

    McCutcheon ST, Han D, Troncoso J, Koliatsos VE, Albert M, Lyketsos CG, et al. Clinicopathological correlates of depression in early Alzheimer’s disease in the NACC. Int J Geriatr Psychiatry. 2016;31:1301–11.

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    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;7:280–92.

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Jack CR Jr, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, et al. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 2010;9:119–28.

    PubMed  PubMed Central  CAS  Google Scholar 

  18. 18.

    Buckner RL, Snyder AZ, Shannon BJ, LaRossa G, Sachs R, Fotenos AF, et al. Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci. 2005;25:7709–17.

    PubMed  PubMed Central  CAS  Google Scholar 

  19. 19.

    Hedden T, Van Dijk KR, Becker JA, Mehta A, Sperling RA, Johnson KA, et al. Disruption of functional connectivity in clinically normal older adults harboring amyloid burden. J Neurosci. 2009;29:12686–94.

    PubMed  PubMed Central  CAS  Google Scholar 

  20. 20.

    Sheline YI, Raichle ME, Snyder AZ, Morris JC, Head D, Wang S, et al. Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly. Biol Psychiatry. 2010;67:584–7.

    PubMed  CAS  Google Scholar 

  21. 21.

    Mormino EC, Smiljic A, Hayenga AO, Onami SH, Greicius MD, Rabinovici GD, et al. Relationships between beta-amyloid and functional connectivity in different components of the default mode network in aging. Cereb Cortex. 2011;21:2399–407.

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Andrews-Hanna JR, Reidler JS, Sepulcre J, Poulin R, Buckner RL. Functional-anatomic fractionation of the brain’s default network. Neuron. 2010;65:550–62.

    PubMed  PubMed Central  CAS  Google Scholar 

  23. 23.

    Zhu X, Wang X, Xiao J, Liao J, Zhong M, Wang W, et al. Evidence of a dissociation pattern in resting-state default mode network connectivity in first-episode, treatment-naive major depression patients. Biol Psychiatry. 2012;71:611–7.

    PubMed  Google Scholar 

  24. 24.

    Coutinho JF, Fernandesl SV, Soares JM, Maia L, Goncalves OF, Sampaio A. Default mode network dissociation in depressive and anxiety states. Brain Imaging Behav. 2016;10:147–57.

    PubMed  Google Scholar 

  25. 25.

    Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry. 1998;59:22–33. quiz 34-57

    PubMed  Google Scholar 

  26. 26.

    Thurfjell L, Lilja J, Lundqvist R, Buckley C, Smith A, Vandenberghe R, et al. Automated quantification of 18F-flutemetamol PET activity for categorizing scans as negative or positive for brain amyloid: concordance with visual image reads. J Nucl Med. 2014;55:1623–8.

    PubMed  CAS  Google Scholar 

  27. 27.

    Calhoun VD, Liu J, Adali T. A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. Neuroimage. 2009;45:S163–72.

    PubMed  Google Scholar 

  28. 28.

    Shirer WR, Ryali S, Rykhlevskaia E, Menon V, Greicius MD. Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb Cortex. 2012;22:158–65.

    PubMed  CAS  Google Scholar 

  29. 29.

    Wu M, Rosano C, Butters M, Whyte E, Nable M, Crooks R, et al. A fully automated method for quantifying and localizing white matter hyperintensities on MR images. Psychiatry Res. 2006;148:133–42.

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Udupa JK, Wei L, Samarasekera S, Miki Y, van Buchem MA, Grossman RI. Multiple sclerosis lesion quantification using fuzzy-connectedness principles. IEEE Trans Med Imaging. 1997;16:598–609.

    PubMed  CAS  Google Scholar 

  31. 31.

    Genovese C, Lazar N, Nichols T. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. NeuroImage. 2002;15:870–78.

    PubMed  Google Scholar 

  32. 32.

    Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008;40:879–91.

    PubMed  Google Scholar 

  33. 33.

    Charlton RA, Lamar M, Zhang A, Yang S, Ajilore O, Kumar A. White-matter tract integrity in late-life depression: associations with severity and cognition. Psychol Med. 2014;44:1427–37.

    PubMed  CAS  Google Scholar 

  34. 34.

    Lim HK, Nebes R, Snitz B, Cohen A, Mathis C, Price J, et al. Regional amyloid burden and intrinsic connectivity networks in cognitively normal elderly subjects. Brain. 2014;137:3327–38.

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Khemka VK, Ganguly A, Bagchi D, Ghosh A, Bir A, Biswas A, et al. Raised serum proinflammatory cytokines in Alzheimer’s disease with depression. Aging Dis. 2014;5:170–6.

    PubMed  PubMed Central  Google Scholar 

  36. 36.

    Sheline YI, Barch DM, Price JL, Rundle MM, Vaishnavi SN, Snyder AZ, et al. The default mode network and self-referential processes in depression. Proc Natl Acad Sci USA. 2009;106:1942–7.

    PubMed  PubMed Central  CAS  Google Scholar 

  37. 37.

    Nejad AB, Fossati P, Lemogne C. Self-referential processing, rumination, and cortical midline structures in major depression. Front Hum Neurosci. 2013;7:666.

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Dickerson BC, Eichenbaum H. The episodic memory system: neurocircuitry and disorders. Neuropsychopharmacology. 2010;35:86–104.

    PubMed  Google Scholar 

  39. 39.

    Nellessen N, Rottschy C, Eickhoff SB, Ketteler ST, Kuhn H, Shah NJ, et al. Specific and disease stage-dependent episodic memory-related brain activation patterns in Alzheimer’s disease: a coordinate-based meta-analysis. Brain Struct Funct. 2015;220:1555–71.

    PubMed  CAS  Google Scholar 

  40. 40.

    Ma D, Fetahu IS, Wang M, Fang R, Li J, Liu H, et al. The fusiform gyrus exhibits an epigenetic signature for Alzheimer’s disease. Clin Epigenetics. 2020;12:129.

    PubMed  PubMed Central  CAS  Google Scholar 

  41. 41.

    Bokde AL, Lopez-Bayo P, Meindl T, Pechler S, Born C, Faltraco F, et al. Functional connectivity of the fusiform gyrus during a face-matching task in subjects with mild cognitive impairment. Brain. 2006;129:1113–24.

    PubMed  CAS  Google Scholar 

  42. 42.

    Alexopoulos GS, Meyers BS, Young RC, Campbell S, Silbersweig D, Charlson M. ‘Vascular depression’ hypothesis. Arch Gen Psychiatry. 1997;54:915–22.

    PubMed  CAS  Google Scholar 

  43. 43.

    Chen X, Huang L, Ye Q, Yang D, Qin R, Luo C, et al. Disrupted functional and structural connectivity within default mode network contribute to WMH-related cognitive impairment. Neuroimage Clin. 2019;24:102088.

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Wu M, Andreescu C, Butters MA, Tamburo R, Reynolds CF 3rd, Aizenstein H. Default-mode network connectivity and white matter burden in late-life depression. Psychiatry Res. 2011;194:39–46.

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Cole DM, Smith SM, Beckmann CF. Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Front Syst Neurosci. 2010;4:8.

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Cai S, Peng Y, Chong T, Zhang Y, von Deneen KM, Huang L, et al. Differentiated effective connectivity patterns of the executive control network in progressive MCI: a potential biomarker for predicting AD. Curr Alzheimer Res. 2017;14:937–50.

    PubMed  CAS  Google Scholar 

  47. 47.

    Gandelman JA, Albert K, Boyd BD, Park JW, Riddle M, Woodward ND, et al. Intrinsic functional network connectivity is associated with clinical symptoms and cognition in late-life depression. Biol Psychiatry Cogn Neurosci Neuroimaging 2019;4:160–70.

    PubMed  Google Scholar 

  48. 48.

    Wang P, Zhou B, Yao H, Zhan Y, Zhang Z, Cui Y, et al. Aberrant intra- and inter-network connectivity architectures in Alzheimer’s disease and mild cognitive impairment. Sci Rep. 2015;5:14824.

    PubMed  PubMed Central  CAS  Google Scholar 

  49. 49.

    Balthazar ML, Pereira FR, Lopes TM, da Silva EL, Coan AC, Campos BM, et al. Neuropsychiatric symptoms in Alzheimer’s disease are related to functional connectivity alterations in the salience network. Hum Brain Mapp. 2014;35:1237–46.

    PubMed  Google Scholar 

  50. 50.

    Fredericks CA, Sturm VE, Brown JA, Hua AY, Bilgel M, Wong DF, et al. Early affective changes and increased connectivity in preclinical Alzheimer’s disease. Alzheimers Dement. 2018;10:471–79.

    Google Scholar 

  51. 51.

    Li W, Wang Y, Ward BD, Antuono PG, Li SJ, Goveas JS. Intrinsic inter-network brain dysfunction correlates with symptom dimensions in late-life depression. J Psychiatr Res. 2017;87:71–80.

    PubMed  Google Scholar 

  52. 52.

    Alexopoulos GS, Hoptman MJ, Kanellopoulos D, Murphy CF, Lim KO, Gunning FM. Functional connectivity in the cognitive control network and the default mode network in late-life depression. J Affect Disord. 2012;139:56–65.

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Karim HT, Andreescu C, Tudorascu D, Smagula SF, Butters MA, Karp JF, et al. Intrinsic functional connectivity in late-life depression: trajectories over the course of pharmacotherapy in remitters and non-remitters. Mol Psychiatry. 2017;22:450–57.

    PubMed  CAS  Google Scholar 

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Author information




S-MW and HKL drafted the manuscript and contributed to project design, data collection, management, analysis, and interpretation. N-YK and DWK contributed to project design, data collection, and management. YHU, H-RN, and CUL contributed to data management and revision of the manuscript.

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Correspondence to Hyun Kook Lim.

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Wang, SM., Kim, NY., Um, Y.H. et al. Default mode network dissociation linking cerebral beta amyloid retention and depression in cognitively normal older adults. Neuropsychopharmacol. 46, 2180–2187 (2021).

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