Brain Imaging and Behavior

, Volume 11, Issue 2, pp 368–382 | Cite as

Resting-state global functional connectivity as a biomarker of cognitive reserve in mild cognitive impairment

  • N. Franzmeier
  • M. Á. Araque Caballero
  • A. N. W. Taylor
  • L. Simon-Vermot
  • K. Buerger
  • B. Ertl-Wagner
  • C. Mueller
  • C. Catak
  • D. Janowitz
  • E. Baykara
  • B. Gesierich
  • M. Duering
  • M. Ewers
  • for the Alzheimer’s Disease Neuroimaging Initiative
SI: Resilience/Reserve in AD

Abstract

Cognitive reserve (CR) shows protective effects in Alzheimer’s disease (AD) and reduces the risk of dementia. Despite the clinical significance of CR, a clinically useful diagnostic biomarker of brain changes underlying CR in AD is not available yet. Our aim was to develop a fully-automated approach applied to fMRI to produce a biomarker associated with CR in subjects at increased risk of AD. We computed resting-state global functional connectivity (GFC), i.e. the average connectivity strength, for each voxel within the cognitive control network, which may sustain CR due to its central role in higher cognitive function. In a training sample including 43 mild cognitive impairment (MCI) subjects and 24 healthy controls (HC), we found that MCI subjects with high CR (> median of years of education, CR+) showed increased frequency of high GFC values compared to MCI-CR- and HC. A summary index capturing such a surplus frequency of high GFC was computed (called GFC reserve (GFC-R) index). GFC-R discriminated MCI-CR+ vs. MCI-CR-, with the area under the ROC = 0.84. Cross-validation in an independently recruited test sample of 23 MCI subjects showed that higher levels of the GFC-R index predicted higher years of education and an alternative questionnaire-based proxy of CR, controlled for memory performance, gray matter of the cognitive control network, white matter hyperintensities, age, and gender. In conclusion, the GFC-R index that captures GFC changes within the cognitive control network provides a biomarker candidate of functional brain changes of CR in patients at increased risk of AD.

Keywords

Cognitive reserve Biomarker Mild cognitive impairment Alzheimer’s disease Global functional connectivity Resting-state fMRI 

Notes

Compliance with ethical standards

The study at the ISD was approved by the ethics committee of the Ludwig Maximilian University of Munich. For the ADNI-sample ethical approval was obtained by the ADNI investigators. All procedures performed were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. All study participants provided written, informed consent to the study.

Funding

The research was funded by grants of the LMUexcellent Initiative and the European Commission (ERC, PCIG12-GA-2012-334259), Alzheimer’s Forschung Initiative (AFI, DE-15035). Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Conflict of interest

The authors declare that they have no conflict of interest

Supplementary material

11682_2016_9599_Fig6_ESM.gif (14 kb)
Supplementary Fig. 1

Shown is the distribution of GFC voxels averaged across subjects within CR (CR+ vs. CR-) and diagnostic (MCI vs. HC) groups for the test sample. The graphs are equivalent to Fig. 2b of the main manuscript. (GIF 14 kb)

11682_2016_9599_MOESM1_ESM.tif (8.1 mb)
High Resolution image (TIF 8269 kb)

References

  1. Achard, S., Salvador, R., Whitcher, B., Suckling, J., & Bullmore, E. (2006). A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. Journal of Neuroscience, 26(1), 63–72. doi: 10.1523/JNEUROSCI.3874-05.2006.PubMedCrossRefGoogle Scholar
  2. Arenaza-Urquijo, E. M., Landeau, B., La Joie, R., Mevel, K., Mezenge, F., Perrotin, A., & Chetelat, G. (2013). Relationships between years of education and gray matter volume, metabolism and functional connectivity in healthy elders. NeuroImage, 83, 450–457. doi: 10.1016/j.neuroimage.2013.06.053.PubMedCrossRefGoogle Scholar
  3. Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. NeuroImage, 38(1), 95–113. doi: 10.1016/j.neuroimage.2007.07.007.PubMedCrossRefGoogle Scholar
  4. Ashburner, J., & Friston, K. J. (2005). Unified segmentation. NeuroImage, 26(3), 839–851. doi: 10.1016/j.neuroimage.2005.02.018.PubMedCrossRefGoogle Scholar
  5. Barulli, D., & Stern, Y. (2013). Efficiency, capacity, compensation, maintenance, plasticity: emerging concepts in cognitive reserve. Trends in Cognitive Sciences, 17(10), 502–509. doi: 10.1016/j.tics.2013.08.012.PubMedCrossRefGoogle Scholar
  6. Bastin, C., Yakushev, I., Bahri, M. A., Fellgiebel, A., Eustache, F., Landeau, B., & Salmon, E. (2012). Cognitive reserve impacts on inter-individual variability in resting-state cerebral metabolism in normal aging. NeuroImage, 63(2), 713–722. doi: 10.1016/j.neuroimage.2012.06.074.PubMedCrossRefGoogle Scholar
  7. Boots, E. A., Schultz, S. A., Almeida, R. P., Oh, J. M., Koscik, R. L., Dowling, M. N., & Okonkwo, O. C. (2015). Occupational complexity and cognitive reserve in a middle-aged cohort at risk for Alzheimer’s disease. Archives of Clinical Neuropsychology. doi: 10.1093/arclin/acv041.PubMedPubMedCentralGoogle Scholar
  8. Bosch, B., Bartres-Faz, D., Rami, L., Arenaza-Urquijo, E. M., Fernandez-Espejo, D., Junque, C., & Molinuevo, J. L. (2010). Cognitive reserve modulates task-induced activations and deactivations in healthy elders, amnestic mild cognitive impairment and mild Alzheimer’s disease. Cortex, 46(4), 451–461. doi: 10.1016/j.cortex.2009.05.006.PubMedCrossRefGoogle Scholar
  9. Bozzali, M., Dowling, C., Serra, L., Spano, B., Torso, M., Marra, C., & Cercignani, M. (2015). The impact of cognitive reserve on brain functional connectivity in Alzheimer’s disease. Journal of Alzheimer’s Disease, 44(1), 243–250. doi: 10.3233/JAD-141824.PubMedGoogle Scholar
  10. Brickman, A. M., Siedlecki, K. L., Muraskin, J., Manly, J. J., Luchsinger, J. A., Yeung, L. K., & Stern, Y. (2011). White matter hyperintensities and cognition: testing the reserve hypothesis. Neurobiology of Aging, 32(9), 1588–1598. doi: 10.1016/j.neurobiolaging.2009.10.013.PubMedCrossRefGoogle Scholar
  11. Buschert, V. C., Friese, U., Teipel, S. J., Schneider, P., Merensky, W., Rujescu, D., & Buerger, K. (2011). Effects of a newly developed cognitive intervention in amnestic mild cognitive impairment and mild Alzheimer’s disease: a pilot study. Journal of Alzheimer’s Disease, 25(4), 679–694. doi: 10.3233/JAD-2011-100999.PubMedGoogle Scholar
  12. Cabeza, R., Anderson, N. D., Locantore, J. K., & McIntosh, A. R. (2002). Aging gracefully: compensatory brain activity in high-performing older adults. Neuroimage, 17(3), 1394–1402. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12414279 http://ac.els-cdn.com/S1053811902912802/1-s2.0-S1053811902912802-main.pdf?_tid=47765b2a-94f7-11e3-b99e-00000aacb35d&acdnat=1392327732_9b8b93ea71e8f450a6a07dc8e9dace8c.
  13. Cole, M. W., & Schneider, W. (2007). The cognitive control network: integrated cortical regions with dissociable functions. NeuroImage, 37(1), 343–360. doi: 10.1016/j.neuroimage.2007.03.071.PubMedCrossRefGoogle Scholar
  14. Cole, M. W., Pathak, S., & Schneider, W. (2010). Identifying the brain’s most globally connected regions. NeuroImage, 49(4), 3132–3148. doi: 10.1016/j.neuroimage.2009.11.001.PubMedCrossRefGoogle Scholar
  15. Cole, M. W., Yarkoni, T., Repovs, G., Anticevic, A., & Braver, T. S. (2012). Global connectivity of prefrontal cortex predicts cognitive control and intelligence. Journal of Neuroscience, 32(26), 8988–8999. doi: 10.1523/JNEUROSCI.0536-12.2012.PubMedPubMedCentralCrossRefGoogle Scholar
  16. Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic, A., & Braver, T. S. (2013). Multi-task connectivity reveals flexible hubs for adaptive task control. Nature Neuroscience, 16(9), 1348–1355. doi: 10.1038/nn.3470.PubMedPubMedCentralCrossRefGoogle Scholar
  17. Cole, M. W., Bassett, D. S., Power, J. D., Braver, T. S., & Petersen, S. E. (2014a). Intrinsic and task-evoked network architectures of the human brain. Neuron, 83(1), 238–251. doi: 10.1016/j.neuron.2014.05.014.PubMedPubMedCentralCrossRefGoogle Scholar
  18. Cole, M. W., Repovs, G., & Anticevic, A. (2014b). The frontoparietal control system: a central role in mental health. The Neuroscientist, 20(6), 652–664. doi: 10.1177/1073858414525995.PubMedPubMedCentralCrossRefGoogle Scholar
  19. Elman, J. A., Oh, H., Madison, C. M., Baker, S. L., Vogel, J. W., Marks, S. M., & Jagust, W. J. (2014). Neural compensation in older people with brain amyloid-beta deposition. Nature Neuroscience, 17(10), 1316–1318. doi: 10.1038/nn.3806.PubMedPubMedCentralCrossRefGoogle Scholar
  20. Ewers, M., Teipel, S. J., Dietrich, O., Schonberg, S. O., Jessen, F., Heun, R., & Hampel, H. (2006). Multicenter assessment of reliability of cranial MRI. Neurobiology of Aging, 27(8), 1051–1059. doi: 10.1016/j.neurobiolaging.2005.05.032.PubMedCrossRefGoogle Scholar
  21. Ewers, M., Brendel, M., Rizk-Jackson, A., Rominger, A., Bartenstein, P., Schuff, N., & Alzheimer’s Disease Neuroimaging I. (2014). Reduced FDG-PET brain metabolism and executive function predict clinical progression in elderly healthy subjects. Neuroimage Clinical, 4, 45–52. doi: 10.1016/j.nicl.2013.10.018.PubMedCrossRefGoogle Scholar
  22. Feis, R. A., Smith, S. M., Filippini, N., Douaud, G., Dopper, E. G., Heise, V., & Mackay, C. E. (2015). ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI. Frontiers in Neuroscience, 9, 395. doi: 10.3389/fnins.2015.00395.PubMedPubMedCentralCrossRefGoogle Scholar
  23. 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. Proceedings of the National Academy of Sciences of the United States of America, 101(13), 4637–4642. doi: 10.1073/pnas.0308627101.PubMedPubMedCentralCrossRefGoogle Scholar
  24. Hall, C. B., Derby, C., LeValley, A., Katz, M. J., Verghese, J., & Lipton, R. B. (2007). Education delays accelerated decline on a memory test in persons who develop dementia. Neurology, 69(17), 1657–1664. doi: 10.1212/01.wnl.0000278163.82636.30.PubMedCrossRefGoogle Scholar
  25. Jones, D. T., Machulda, M. M., Vemuri, P., McDade, E. M., Zeng, G., Senjem, M. L., & Jack, C. R., Jr. (2011). Age-related changes in the default mode network are more advanced in Alzheimer disease. Neurology, 77(16), 1524–1531. doi: 10.1212/WNL.0b013e318233b33d.PubMedPubMedCentralCrossRefGoogle Scholar
  26. Landau, S. M., Breault, C., Joshi, A. D., Pontecorvo, M., Mathis, C. A., Jagust, W. J., & Alzheimer’s Disease Neuroimaging I. (2013). Amyloid-beta imaging with Pittsburgh compound B and florbetapir: comparing radiotracers and quantification methods. Journal of Nuclear Medicine, 54(1), 70–77. doi: 10.2967/jnumed.112.109009.PubMedCrossRefGoogle Scholar
  27. 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. Human Brain Mapping, 36(3), 1217–1232. doi: 10.1002/hbm.22689.PubMedCrossRefGoogle Scholar
  28. Liao, X. H., Xia, M. R., Xu, T., Dai, Z. J., Cao, X. Y., Niu, H. J., & He, Y. (2013). Functional brain hubs and their test-retest reliability: a multiband resting-state functional MRI study. NeuroImage, 83, 969–982. doi: 10.1016/j.neuroimage.2013.07.058.PubMedCrossRefGoogle Scholar
  29. Luck, T., Riedel-Heller, S., & Wiese, B. (2009). CERAD-NP-Testbatterie: Alters-, geschlechts- und bildungsspezifischen Normen ausgewählter Subtests. Zeitschrift für Gerontologie und Geriatrie, 42, 372–384.PubMedCrossRefGoogle Scholar
  30. Matarazzo, J. D., & Hermann, D. O. (1984). The relationship of education and IQ in the WAIS--R standardization sample. Journal of Consulting and Clinical Psychology, 52(4), 631–634.CrossRefGoogle Scholar
  31. Members, E. C. C., Brayne, C., Ince, P. G., Keage, H. A., McKeith, I. G., Matthews, F. E., & Sulkava, R. (2010). Education, the brain and dementia: neuroprotection or compensation? Brain, 133(Pt 8), 2210–2216. doi: 10.1093/brain/awq185.Google Scholar
  32. Meng, X., & D’Arcy, C. (2012). Education and dementia in the context of the cognitive reserve hypothesis: a systematic review with meta-analyses and qualitative analyses. PLoS ONE, 7(6), e38268. doi: 10.1371/journal.pone.0038268.PubMedPubMedCentralCrossRefGoogle Scholar
  33. Mevel, K., Chetelat, G., Eustache, F., & Desgranges, B. (2011). The default mode network in healthy aging and Alzheimer’s disease. International Journal of Alzheimer’s Disease, 2011, 535816. doi: 10.4061/2011/535816.PubMedPubMedCentralGoogle Scholar
  34. Nucci, M., Mapelli, D., & Mondini, S. (2012). Cognitive reserve index questionnaire (CRIq): a new instrument for measuring cognitive reserve. Aging Clinical and Experimental Research, 24(3), 218–226. doi: 10.3275/7800.PubMedGoogle Scholar
  35. Oh, H., Steffener, J., Razlighi, Q. R., Habeck, C., Liu, D., Gazes, Y., & Stern, Y. (2015). Abeta-related hyperactivation in frontoparietal control regions in cognitively normal elderly. Neurobiology of Aging, 36(12), 3247–3254. doi: 10.1016/j.neurobiolaging.2015.08.016.PubMedPubMedCentralCrossRefGoogle Scholar
  36. Otsu, N. (1979). A thresholding selection method from gray-level histogram. IEEE Transactions on Systems, Man, and Cybernetics, 9.Google Scholar
  37. Petersen, R. C. (2004). Mild cognitive impairment as a diagnostic entity. Journal of Internal Medicine, 256(3), 183–194. doi: 10.1111/j.1365-2796.2004.01388.x.PubMedCrossRefGoogle Scholar
  38. R Development Core Team. (2013). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.Google Scholar
  39. Reed, B. R., Mungas, D., Farias, S. T., Harvey, D., Beckett, L., Widaman, K., & DeCarli, C. (2010). Measuring cognitive reserve based on the decomposition of episodic memory variance. Brain, 133(Pt 8), 2196–2209. doi: 10.1093/brain/awq154.PubMedPubMedCentralCrossRefGoogle Scholar
  40. Reijnders, J., van Heugten, C., & van Boxtel, M. (2013). Cognitive interventions in healthy older adults and people with mild cognitive impairment: a systematic review. Ageing Research Reviews, 12(1), 263–275. doi: 10.1016/j.arr.2012.07.003.PubMedCrossRefGoogle Scholar
  41. Rentz, D. M., Locascio, J. J., Becker, J. A., Moran, E. K., Eng, E., Buckner, R. L., & Johnson, K. A. (2010). Cognition, reserve, and amyloid deposition in normal aging. Annals of Neurology, 67(3), 353–364. doi: 10.1002/ana.21904.PubMedGoogle Scholar
  42. Sando, S. B., Melquist, S., Cannon, A., Hutton, M., Sletvold, O., Saltvedt, I., & Aasly, J. (2008). Risk-reducing effect of education in Alzheimer’s disease. International Journal of Geriatric Psychiatry, 23(11), 1156–1162. doi: 10.1002/gps.2043.PubMedCrossRefGoogle Scholar
  43. Scarmeas, N., Zarahn, E., Anderson, K. E., Habeck, C. G., Hilton, J., Flynn, J., & Stern, Y. (2003). Association of life activities with cerebral blood flow in Alzheimer disease: implications for the cognitive reserve hypothesis. Arch Neurol, 60(3), 359–365. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12633147.
  44. Schoenberg, M. R., Dawson, K. A., Duff, K., Patton, D., Scott, J. G., & Adams, R. L. (2006). Test performance and classification statistics for the Rey auditory verbal learning test in selected clinical samples. Archives of Clinical Neuropsychology, 21(7), 693–703. doi: 10.1016/j.acn.2006.06.010.PubMedCrossRefGoogle Scholar
  45. Schultz, S. A., Larson, J., Oh, J., Koscik, R., Dowling, M. N., Gallagher, C. L., & Okonkwo, O. C. (2015). Participation in cognitively-stimulating activities is associated with brain structure and cognitive function in preclinical Alzheimer’s disease. Brain Imaging and Behavior, 9(4), 729–736. doi: 10.1007/s11682-014-9329-5.PubMedPubMedCentralCrossRefGoogle Scholar
  46. Soldan, A., Pettigrew, C., Lu, Y., Wang, M. C., Selnes, O., Albert, M., & Team, B. R. (2015). Relationship of medial temporal lobe atrophy, APOE genotype, and cognitive reserve in preclinical Alzheimer’s disease. Human Brain Mapping, 36(7), 2826–2841. doi: 10.1002/hbm.22810.PubMedPubMedCentralCrossRefGoogle Scholar
  47. Solé-Padullés, C., Bartrés-Faz, D., Junqué, C., Vendrell, P., Rami, L., Clemente, I. C., & Molinuevo, J. L. (2009). Brain structure and function related to cognitive reserve variables in normal aging, mild cognitive impairment and Alzheimer’s disease. Neurobiology of Aging, 30(7), 1114–1124. Retrieved from http://www.sciencedirect.com/science/article/pii/S0197458007004083.
  48. Stern, Y. (2002). What is cognitive reserve? Theory and research application of the reserve concept. J Int Neuropsychol Soc, 8(3), 448–460. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11939702.
  49. Stern, Y. (2009). Cognitive reserve. Neuropsychologia, 47(10), 2015–2028. doi: 10.1016/j.neuropsychologia.2009.03.004.PubMedPubMedCentralCrossRefGoogle Scholar
  50. Stern, Y. (2012). Cognitive reserve in ageing and Alzheimer’s disease. Lancet Neurology, 11(11), 1006–1012. doi: 10.1016/s1474-4422(12)70191-6.PubMedPubMedCentralCrossRefGoogle Scholar
  51. Stern, Y., Alexander, G. E., Prohovnik, I., & Mayeux, R. (1992). Inverse relationship between education and parietotemporal perfusion deficit in Alzheimer’s disease. Annals of Neurology, 32(3), 371–375. doi: 10.1002/ana.410320311.PubMedCrossRefGoogle Scholar
  52. Stern, Y., Gurland, B., Tatemichi, T. K., Tang, M. X., Wilder, D., & Mayeux, R. (1994). Influence of education and occupation on the incidence of Alzheimer’s disease. Jama, 271(13), 1004–1010. Retrieved from http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8139057.
  53. Stern, Y., Alexander, G. E., Prohovnik, I., Stricks, L., Link, B., Lennon, M. C., & Mayeux, R. (1995). Relationship between lifetime occupation and parietal flow: implications for a reserve against Alzheimer’s disease pathology. Neurology, 45(1), 55–60. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/7824135.
  54. Stern, Y., Habeck, C., Moeller, J., Scarmeas, N., Anderson, K. E., Hilton, H. J., & van Heertum, R. (2005). Brain networks associated with cognitive reserve in healthy young and old adults. Cerebral Cortex, 15(4), 394–402. doi: 10.1093/cercor/bhh142.PubMedPubMedCentralCrossRefGoogle Scholar
  55. Stern, Y., Zarahn, E., Habeck, C., Holtzer, R., Rakitin, B. C., Kumar, A., & Brown, T. (2008). A common neural network for cognitive reserve in verbal and object working memory in young but not old. Cerebral Cortex, 18(4), 959–967. doi: 10.1093/cercor/bhm134.PubMedCrossRefGoogle Scholar
  56. Suo, C., Singh, M. F., Gates, N., Wen, W., Sachdev, P., Brodaty, H., & Valenzuela, M. J. (2016). Therapeutically relevant structural and functional mechanisms triggered by physical and cognitive exercise. Molecular Psychiatry. doi: 10.1038/mp.2016.19.PubMedCentralGoogle Scholar
  57. Valenzuela, M. J., & Sachdev, P. (2006). Brain reserve and dementia: a systematic review. Psychological Medicine, 36(4), 441–454. doi: 10.1017/S0033291705006264.PubMedCrossRefGoogle Scholar
  58. Vemuri, P., Weigand, S. D., Przybelski, S. A., Knopman, D. S., Smith, G. E., Trojanowski, J. Q., & Alzheimer’s Disease Neuroimaging I. (2011). Cognitive reserve and Alzheimer’s disease biomarkers are independent determinants of cognition. Brain, 134(Pt 5), 1479–1492. doi: 10.1093/brain/awr049.PubMedPubMedCentralCrossRefGoogle Scholar
  59. Vemuri, P., Lesnick, T. G., Przybelski, S. A., Knopman, D. S., Preboske, G. M., Kantarci, K., & Jack, C. R., Jr. (2015). Vascular and amyloid pathologies are independent predictors of cognitive decline in normal elderly. Brain, 138(Pt 3), 761–771. doi: 10.1093/brain/awu393.PubMedPubMedCentralCrossRefGoogle Scholar
  60. Vossel, S., Geng, J. J., & Fink, G. R. (2014). Dorsal and ventral attention systems: distinct neural circuits but collaborative roles. The Neuroscientist, 20(2), 150–159. doi: 10.1177/1073858413494269.PubMedPubMedCentralCrossRefGoogle Scholar
  61. Wang, K., Liang, M., Wang, L., Tian, L., Zhang, X., Li, K., & Jiang, T. (2007). Altered functional connectivity in early Alzheimer’s disease: a resting-state fMRI study. Human Brain Mapping, 28(10), 967–978. doi: 10.1002/hbm.20324.PubMedCrossRefGoogle Scholar
  62. Wang, J. H., Zuo, X. N., Gohel, S., Milham, M. P., Biswal, B. B., & He, Y. (2011). Graph theoretical analysis of functional brain networks: test-retest evaluation on short- and long-term resting-state functional MRI data. PLoS ONE, 6(7), e21976. doi: 10.1371/journal.pone.0021976.PubMedPubMedCentralCrossRefGoogle Scholar
  63. Wang, J., Zuo, X., Dai, Z., Xia, M., Zhao, Z., Zhao, X., & He, Y. (2013). Disrupted functional brain connectome in individuals at risk for Alzheimer’s disease. Biological Psychiatry, 73(5), 472–481. doi: 10.1016/j.biopsych.2012.03.026.PubMedCrossRefGoogle Scholar
  64. Wells, R. E., Yeh, G. Y., Kerr, C. E., Wolkin, J., Davis, R. B., Tan, Y., & Kong, J. (2013). Meditation’s impact on default mode network and hippocampus in mild cognitive impairment: a pilot study. Neuroscience Letters, 556, 15–19. doi: 10.1016/j.neulet.2013.10.001.PubMedPubMedCentralCrossRefGoogle Scholar
  65. Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature Methods, 8(8), 665–670. doi: 10.1038/nmeth.1635.PubMedPubMedCentralCrossRefGoogle Scholar
  66. Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165. doi: 10.1152/jn.00338.2011.PubMedCrossRefGoogle Scholar
  67. Zahodne, L. B., Manly, J. J., Brickman, A. M., Siedlecki, K. L., Decarli, C., & Stern, Y. (2013). Quantifying cognitive reserve in older adults by decomposing episodic memory variance: replication and extension. Journal of International Neuropsychological Society, 19(8), 854–862. doi: 10.1017/S1355617713000738.CrossRefGoogle Scholar
  68. Zahodne, L. B., Manly, J. J., Brickman, A. M., Narkhede, A., Griffith, E. Y., Guzman, V. A., & Stern, Y. (2015). Is residual memory variance a valid method for quantifying cognitive reserve? A longitudinal application. Neuropsychologia, 77, 260–266. doi: 10.1016/j.neuropsychologia.2015.09.009.PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • N. Franzmeier
    • 1
  • M. Á. Araque Caballero
    • 1
  • A. N. W. Taylor
    • 1
  • L. Simon-Vermot
    • 1
  • K. Buerger
    • 1
    • 2
  • B. Ertl-Wagner
    • 3
  • C. Mueller
    • 1
  • C. Catak
    • 1
  • D. Janowitz
    • 1
  • E. Baykara
    • 1
  • B. Gesierich
    • 1
  • M. Duering
    • 1
  • M. Ewers
    • 1
  • for the Alzheimer’s Disease Neuroimaging Initiative
  1. 1.Institut für Schlaganfall-und Demenzforschung (ISD), Ludwig-Maximilians-Universität LMUKlinikum der Universität MünchenMunichGermany
  2. 2.German Center for Neurodegenerative Diseases (DZNE, Munich)MunichGermany
  3. 3.Institute for Clinical RadiologyKlinikum der Universität München, Ludwig-Maximilian UniversityMunichGermany

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