Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Interaction of APOE, cerebral blood flow, and cortical thickness in the entorhinal cortex predicts memory decline

Abstract

The ε4 allele of the apolipoprotein E (APOE) gene, a risk factor for cognitive decline, is associated with alterations in medial temporal lobe (MTL) structure and function, yet little research has been dedicated to understanding how these alterations might interact to negatively impact cognition. To bridge this gap, the present study employed linear regression models to determine the extent to which APOE genotype (ε4+, ε4-) modifies interactive effects of baseline arterial spin labeling MRI-measured cerebral blood flow (CBF) and FreeSurfer-derived cortical thickness/volume (CT/Vo) in two MTL regions of interest (entorhinal cortex, hippocampus) on memory change in 98 older adults who were cognitively normal at baseline. Baseline entorhinal CBF was positively associated with memory change, but only among ε4 carriers with lower entorhinal CT. Similarly, baseline entorhinal CT was positively associated with memory change, but only among ε4 carriers with lower entorhinal CBF. Findings suggest that APOE ε4 carriers may experience concomitant alterations in neurovascular function and morphology in the MTL that interact to negatively affect cognition prior to the onset of overt clinical symptoms. Results also suggest the presence of distinct multimodal neural signatures in the entorhinal cortex that may signal relative risk for cognitive decline among this group, perhaps reflecting different stages of cerebrovascular compensation (early effective vs. later ineffective).

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

Fig. 1
Fig. 2

Data availability

The data that support the findings of this study may be available on request from the corresponding author [CEW]. The data are not publicly available due to them containing information that could compromise research participant privacy/consent.

References

  1. Adamson, M. M., Landy, K. M., Duong, S., Fox-Bosetti, S., Ashford, J. W., Murphy, G. M., et al. (2010). Apolipoprotein E epsilon4 influences on episodic recall and brain structures in aging pilots. Neurobiology of Aging, 31(6), 1059–1063. https://doi.org/10.1016/j.neurobiolaging.2008.07.017.

  2. Alsop, D. C., Detre, J. A., Golay, X., Günther, M., Hendrikse, J., Hernandez-Garcia, L., Lu, H., MacIntosh, B., Parkes, L. M., Smits, M., van Osch, M., Wang, D. J., Wong, E. C., & Zaharchuk, G. (2015). Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: A consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. Magnetic Resonance in Medicine, 73(1), 102–116. https://doi.org/10.1002/mrm.25197.

  3. Asllani, I., Habeck, C., Scarmeas, N., Borogovac, A., Brown, T. R., & Stern, Y. (2008). Multivariate and univariate analysis of continuous arterial spin labeling perfusion MRI in Alzheimer’s disease. Journal of Cerebral Blood Flow and Metabolism: Official Journal of the International Society of Cerebral Blood Flow and Metabolism, 28(4), 725–736. https://doi.org/10.1038/sj.jcbfm.9600570.

  4. Bangen, K. J., Restom, K., Liu, T. T., Jak, A. J., Wierenga, C. E., Salmon, D. P., & Bondi, M. W. (2009). Differential age effects on cerebral blood flow and BOLD response to encoding: Associations with cognition and stroke risk. Neurobiology of Aging, 30(8), 1276–1287. https://doi.org/10.1016/j.neurobiolaging.2007.11.012.

  5. Bangen, K. J., Restom, K., Liu, T. T., Wierenga, C. E., Jak, A. J., Salmon, D. P., & Bondi, M. W. (2012). Assessment of Alzheimer’s disease risk with functional magnetic resonance imaging: An arterial spin labeling study. Journal of Alzheimer’s Disease: JAD, 31(Suppl 3), S59–S74. https://doi.org/10.3233/JAD-2012-120292.

  6. Braak, H., & Braak, E. (1991). Demonstration of amyloid deposits and neurofibrillary changes in whole brain sections. Brain Pathology (Zurich, Switzerland), 1(3), 213–216.

  7. Bretsky, P., Guralnik, J. M., Launer, L., Albert, M., Seeman, T. E., & MacArthur Studies of Successful Aging. (2003). The role of APOE-epsilon4 in longitudinal cognitive decline: MacArthur studies of successful aging. Neurology, 60(7), 1077–1081.

  8. Burggren, A. C., Zeineh, M. M., Ekstrom, A. D., Braskie, M. N., Thompson, P. M., Small, G. W., & Bookheimer, S. Y. (2008). Reduced cortical thickness in hippocampal subregions among cognitively normal apolipoprotein E e4 carriers. NeuroImage, 41(4), 1177–1183. https://doi.org/10.1016/j.neuroimage.2008.03.039.

  9. Caselli, R. J., Dueck, A. C., Osborne, D., Sabbagh, M. N., Connor, D. J., Ahern, G. L., Baxter, L. C., Rapcsak, S. Z., Shi, J., Woodruff, B. K., Locke, D. E., Snyder, C. H., Alexander, G. E., Rademakers, R., & Reiman, E. M. (2009). Longitudinal modeling of age-related memory decline and the APOE epsilon4 effect. The New England Journal of Medicine, 361(3), 255–263. https://doi.org/10.1056/NEJMoa0809437.

  10. Caselli, R. J., Reiman, E. M., Osborne, D., Hentz, J. G., Baxter, L. C., Hernandez, J. L., & Alexander, G. G. (2004). Longitudinal changes in cognition and behavior in asymptomatic carriers of the APOE e4 allele. Neurology, 62(11), 1990–1995.

  11. Chalela, J. A., Alsop, D. C., Gonzalez-Atavales, J. B., Maldjian, J. A., Kasner, S. E., & Detre, J. A. (2000). Magnetic resonance perfusion imaging in acute ischemic stroke using continuous arterial spin labeling. Stroke, 31(3), 680–687. https://doi.org/10.1161/01.STR.31.3.680.

  12. Chappell, M. A., Groves, A. R., Whitcher, B., & Woolrich, M. W. (2009). Variational Bayesian inference for a nonlinear forward model. IEEE Transactions on Signal Processing, 57(1), 223–236. https://doi.org/10.1109/TSP.2008.2005752.

  13. Cohen, R. M., Small, C., Lalonde, F., Friz, J., & Sunderland, T. (2001). Effect of apolipoprotein E genotype on hippocampal volume loss in aging healthy women. Neurology, 57(12), 2223–2228.

  14. Cox, R. W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, an International Journal, 29(3), 162–173.

  15. Cysique, L. A., Vaida, F., Letendre, S., Gibson, S., Cherner, M., Woods, S. P., et al. (2009). Dynamics of cognitive change in impaired HIV-positive patients initiating antiretroviral therapy. Neurology, 73(5), 342–348. https://doi.org/10.1212/WNL.0b013e3181ab2b3b.

  16. Dai, W., Lopez, O. L., Carmichael, O. T., Becker, J. T., Kuller, L. H., & Gach, H. M. (2009). Mild cognitive impairment and Alzheimer disease: Patterns of altered cerebral blood flow at MR imaging. Radiology, 250(3), 856–866. https://doi.org/10.1148/radiol.2503080751.

  17. Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis. I. Segmentation and surface reconstruction. NeuroImage, 9(2), 179–194. https://doi.org/10.1006/nimg.1998.0395.

  18. Dale, A. M., & Sereno, M. I. (1993). Improved Localizadon of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: A linear approach. Journal of Cognitive Neuroscience, 5(2), 162–176. https://doi.org/10.1162/jocn.1993.5.2.162.

  19. De Blasi, S., Montesanto, A., Martino, C., Dato, S., De Rango, F., Bruni, A. C., et al. (2009). APOE polymorphism affects episodic memory among non demented elderly subjects. Experimental Gerontology, 44(3), 224–227. https://doi.org/10.1016/j.exger.2008.11.005.

  20. de la Torre, J. C. (2010). The vascular hypothesis of Alzheimer’s disease: Bench to bedside and beyond. Neurodegenerative Diseases, 7(1–3), 116–121. https://doi.org/10.1159/000285520.

  21. Delis, D. C., Kaplan, E., & Kramer, J. H. (2001). Delis-Kaplan executive function system (D-KEFS). San Antonia: The Psychological Corporation.

  22. Delis, D. C., Kramer, J. H., & Ober, B. A. (2000). The California verbal learning test (Second ed.). San Antonia: The Psychological Corporation.

  23. den Heijer, T., Oudkerk, M., Launer, L. J., van Duijn, C. M., Hofman, A., & Breteler, M. M. B. (2002). Hippocampal, amygdalar, and global brain atrophy in different apolipoprotein E genotypes. Neurology, 59(5), 746–748.

  24. Donix, M., Burggren, A. C., Scharf, M., Marschner, K., Suthana, N. A., Siddarth, P., Krupa, A. K., Jones, M., Martin-Harris, L., Ercoli, L. M., Miller, K. J., Werner, A., von Kummer, R., Sauer, C., Small, G. W., Holthoff, V. A., & Bookheimer, S. Y. (2013). APOE associated hemispheric asymmetry of entorhinal cortical thickness in aging and Alzheimer’s disease. Psychiatry Research, 214(3), 212–220. https://doi.org/10.1016/j.pscychresns.2013.09.006.

  25. Donix, M., Burggren, A. C., Suthana, N. A., Siddarth, P., Ekstrom, A. D., Krupa, A. K., Jones, M., Rao, A., Martin-Harris, L., Ercoli, L. M., Miller, K. J., Small, G. W., & Bookheimer, S. Y. (2010). Longitudinal changes in medial temporal cortical thickness in normal subjects with the APOE-4 polymorphism. NeuroImage, 53(1), 37–43. https://doi.org/10.1016/j.neuroimage.2010.06.009.

  26. Filippini, N., Ebmeier, K. P., MacIntosh, B. J., Trachtenberg, A. J., Frisoni, G. B., Wilcock, G. K., et al. (2011). Differential effects of the APOE genotype on brain function across the lifespan. NeuroImage, 54(1), 602–610. https://doi.org/10.1016/j.neuroimage.2010.08.009.

  27. Fischl, B., & Dale, A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences of the United States of America, 97(20), 11050–11055. https://doi.org/10.1073/pnas.200033797.

  28. Fischl, B., Liu, A., & Dale, A. M. (2001). Automated manifold surgery: Constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Transactions on Medical Imaging, 20(1), 70–80. https://doi.org/10.1109/42.906426.

  29. Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., et al. (2002). Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3), 341–355.

  30. Fischl, B., Salat, D. H., van der Kouwe, A. J. W., Makris, N., Ségonne, F., Quinn, B. T., & Dale, A. M. (2004a). Sequence-independent segmentation of magnetic resonance images. NeuroImage, 23(Suppl 1), S69–S84. https://doi.org/10.1016/j.neuroimage.2004.07.016.

  31. Fischl, B., Sereno, M. I., Tootell, R. B., & Dale, A. M. (1999). High-resolution intersubject averaging and a coordinate system for the cortical surface. Human Brain Mapping, 8(4), 272–284.

  32. Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Ségonne, F., Salat, D. H., et al. (2004b). Automatically parcellating the human cerebral cortex. Cerebral Cortex (New York, N.Y.: 1991), 14(1), 11–22.

  33. Fjell, A. M., Walhovd, K. B., Fennema-Notestine, C., McEvoy, L. K., Hagler, D. J., Holland, D., et al. (2009). One year brain atrophy evident in healthy aging. The Journal of neuroscience : the official journal of the Society for Neuroscience, 29(48), 15223–15231. https://doi.org/10.1523/JNEUROSCI.3252-09.2009.

  34. Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N., Friston, K. J., & Frackowiak, R. S. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage, 14(1 Pt 1), 21–36. https://doi.org/10.1006/nimg.2001.0786.

  35. Hayden, K. M., Zandi, P. P., West, N. A., Tschanz, J. T., Norton, M. C., Corcoran, C., et al. (2009). Effects of family history and Apolipoprotein E ε4 status on cognitive decline in the absence of Alzheimer dementia: The Cache County study. Archives of Neurology, 66(11), 1378–1383. https://doi.org/10.1001/archneurol.2009.237.

  36. Hays, C. C., Zlatar, Z. Z., Meloy, M. J., Bondi, M. W., Gilbert, P. E., Liu, T. T., Helm, J. L., & Wierenga, C. E. (2019). APOE modifies the interaction of entorhinal cerebral blood flow and cortical thickness on memory function in cognitively normal older adults. NeuroImage, 202, 116162. https://doi.org/10.1016/j.neuroimage.2019.116162.

  37. Hays, C. C., Zlatar, Z. Z., & Wierenga, C. E. (2016). The utility of cerebral blood flow as a biomarker of preclinical Alzheimer’s disease. Cellular and Molecular Neurobiology, 36(2), 167–179. https://doi.org/10.1007/s10571-015-0261-z.

  38. Heaton, R. K., & Psychological Assessment Resources, I. (2004). Revised Comprehensive Norms for an Expanded Halstead-Reitan Battery: Demographically Adjusted Neuropsychological Norms for African American and Caucasian Adults, Professional Manual. Retrieved from https://books.google.com/books?id=u5rfnAEACAAJ

  39. Heaton, R. K., Temkin, N., Dikmen, S., Avitable, N., Taylor, M. J., Marcotte, T. D., & Grant, I. (2001). Detecting change: A comparison of three neuropsychological methods, using normal and clinical samples. Archives of Clinical Neuropsychology: The Official Journal of the National Academy of Neuropsychologists, 16(1), 75–91.

  40. Honea, R. A., Vidoni, E., Harsha, A., & Burns, J. M. (2009). Impact of APOE on the healthy aging brain: A voxel-based MRI and DTI study. Journal of Alzheimer’s Disease : JAD, 18(3), 553–564. https://doi.org/10.3233/JAD-2009-1163.

  41. Iadecola, C. (2004). Neurovascular regulation in the normal brain and in Alzheimer’s disease. Nature Reviews. Neuroscience, 5(5), 347–360.

  42. Iturria-Medina, Y., Sotero, R. C., Toussaint, P. J., Mateos-Pérez, J. M., Evans, A. C., & Initiative, A.’s. D. N. (2016). Early role of vascular dysregulation on late-onset Alzheimer’s disease based on multifactorial data-driven analysis. Nature Communications, 7, 11934. https://doi.org/10.1038/ncomms11934.

  43. Ivanova, I., Salmon, D. P., & Gollan, T. H. (2013). The multilingual naming test in Alzheimer’s disease: Clues to the origin of naming impairments. Journal of the International Neuropsychological Society: JINS, 19(3), 272–283. https://doi.org/10.1017/S1355617712001282.

  44. Ivnik, R. J., Malec, J. F., Smith, G. E., Tangalos, E. G., Petersen, R. C., Kokmen, E., & Kurland, L. T. (1992). Mayo’s older americans normative studies: WMS-R norms for ages 56 to 94. Clinical Neuropsychologist, 6(sup001), 49–82. https://doi.org/10.1080/13854049208401879.

  45. Jak, A. J., Bondi, M. W., Delano-Wood, L., Wierenga, C., Corey-Bloom, J., Salmon, D. P., & Delis, D. C. (2009). Quantification of five neuropsychological approaches to defining mild cognitive impairment. The American Journal of Geriatric Psychiatry : Official Journal of the American Association for Geriatric Psychiatry, 17(5), 368–375. https://doi.org/10.1097/JGP.0b013e31819431d5.

  46. Jak, A. J., Houston, W. S., Nagel, B. J., Corey-Bloom, J., & Bondi, M. W. (2007). Differential cross-sectional and longitudinal impact of APOE genotype on hippocampal volumes in nondemented older adults. Dementia and Geriatric Cognitive Disorders, 23(6), 382–389. https://doi.org/10.1159/000101340.

  47. Jespersen, S. N., & Østergaard, L. (2012). The roles of cerebral blood flow, capillary transit time heterogeneity, and oxygen tension in brain oxygenation and metabolism. Journal of Cerebral Blood Flow & Metabolism, 32(2), 264–277. https://doi.org/10.1038/jcbfm.2011.153.

  48. Jung, Y., Wong, E. C., & Liu, T. T. (2010). Multiphase pseudocontinuous arterial spin labeling (MP-PCASL) for robust quantification of cerebral blood flow. Magnetic Resonance in Medicine, 64(3), 799–810. https://doi.org/10.1002/mrm.22465.

  49. Kim, J. S., Singh, V., Lee, J. K., Lerch, J., Ad-Dab’bagh, Y., MacDonald, D., et al. (2005). Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. NeuroImage, 27(1), 210–221. https://doi.org/10.1016/j.neuroimage.2005.03.036.

  50. Koizumi, K., Hattori, Y., Ahn, S. J., Buendia, I., Ciacciarelli, A., Uekawa, K., Wang, G., Hiller, A., Zhao, L., Voss, H. U., Paul, S. M., Schaffer, C., Park, L., & Iadecola, C. (2018). Apoε4 disrupts neurovascular regulation and undermines white matter integrity and cognitive function. Nature Communications, 9(1), 3816. https://doi.org/10.1038/s41467-018-06301-2.

  51. Kukolja, J., Thiel, C. M., Eggermann, T., Zerres, K., & Fink, G. R. (2010). Medial temporal lobe dysfunction during encoding and retrieval of episodic memory in non-demented APOE ε4 carriers. Neuroscience, 168(2), 487–497. https://doi.org/10.1016/j.neuroscience.2010.03.044.

  52. Lind, J., Larsson, A., Persson, J., Ingvar, M., Nilsson, L.-G., Bäckman, L., Adolfsson, R., Cruts, M., Sleegers, K., van Broeckhoven, C., & Nyberg, L. (2006). Reduced hippocampal volume in non-demented carriers of the apolipoprotein E ɛ4: Relation to chronological age and recognition memory. Neuroscience Letters, 396(1), 23–27. https://doi.org/10.1016/j.neulet.2005.11.070.

  53. Lineweaver, T. T., Bond, M. W., Thomas, R. G., & Salmon, D. P. (1999). A normative study of Nelson’s (1976) modified version of the Wisconsin card sorting test in healthy older adults. The Clinical Neuropsychologist, 13(3), 328–347. https://doi.org/10.1076/clin.13.3.328.1745.

  54. Liu, C.-C., Kanekiyo, T., Xu, H., & Bu, G. (2013). Apolipoprotein E and Alzheimer disease: Risk, mechanisms, and therapy. Nature Reviews. Neurology, 9(2), 106–118. https://doi.org/10.1038/nrneurol.2012.263.

  55. Liu, T. T., & Wong, E. C. (2005). A signal processing model for arterial spin labeling functional MRI. NeuroImage, 24(1), 207–215. https://doi.org/10.1016/j.neuroimage.2004.09.047.

  56. Luckhaus, C., Flüß, M. O., Wittsack, H.-J., Grass-Kapanke, B., Jänner, M., Khalili-Amiri, R., Friedrich, W., Supprian, T., Gaebel, W., Mödder, U., & Cohnen, M. (2008). Detection of changed regional cerebral blood flow in mild cognitive impairment and early Alzheimer’s dementia by perfusion-weighted magnetic resonance imaging. NeuroImage, 40(2), 495–503. https://doi.org/10.1016/j.neuroimage.2007.11.053.

  57. Mattis, S. (1988). Dementia rating scale: DRS: Professional manual. Odessa, FL: PAR.

  58. Ostergaard, L., Aamand, R., Gutierrez-Jimenez, E., Ho, Y. C., Blicher, J. U., Madsen, S. M., et al. (2013). The capillary dysfunction hypothesis of Alzheimer’s disease. Neurobiology of Aging, 34(4), 1018–1031.

  59. Pacheco, J., Goh, J. O., Kraut, M. A., Ferrucci, L., & Resnick, S. M. (2015). Greater cortical thinning in normal older adults predicts later cognitive impairment. Neurobiology of Aging, 36(2), 903–908. https://doi.org/10.1016/j.neurobiolaging.2014.08.031.

  60. Raz, N., Lindenberger, U., Rodrigue, K. M., Kennedy, K. M., Head, D., Williamson, A., Dahle, C., Gerstorf, D., & Acker, J. D. (2005). Regional brain changes in aging healthy adults: General trends, individual differences and modifiers. Cerebral Cortex, 15(11), 1676–1689. https://doi.org/10.1093/cercor/bhi044.

  61. Raz, N., Rodrigue, K. M., & Haacke, E. M. (2007). Brain aging and its modifiers: Insights from in vivo neuromorphometry and susceptibility weighted imaging. Annals of the New York Academy of Sciences, 1097, 84–93. https://doi.org/10.1196/annals.1379.018.

  62. Reuter, M., Rosas, H. D., & Fischl, B. (2010). Highly accurate inverse consistent registration: A robust approach. NeuroImage, 53(4), 1181–1196. https://doi.org/10.1016/j.neuroimage.2010.07.020.

  63. Reuter, M., Schmansky, N. J., Rosas, H. D., & Fischl, B. (2012). Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage, 61(4), 1402–1418. https://doi.org/10.1016/j.neuroimage.2012.02.084.

  64. Schiepers, O. J. G., Harris, S. E., Gow, A. J., Pattie, A., Brett, C. E., Starr, J. M., & Deary, I. J. (2012). APOE E4 status predicts age-related cognitive decline in the ninth decade: Longitudinal follow-up of the Lothian birth cohort 1921. Molecular Psychiatry, 17(3), 315–324. https://doi.org/10.1038/mp.2010.137.

  65. Soldan, A., Pettigrew, C., Lu, Y., Wang, M.-C., Selnes, O., Albert, M., et al. (2015). Relationship of medial temporal lobe atrophy, APOE genotype, and cognitive reserve in preclinical Alzheimer’s disease. Human Brain Mapping, 36(7), 2826–2841. https://doi.org/10.1002/hbm.22810.

  66. Suri, S., Heise, V., Trachtenberg, A. J., & Mackay, C. E. (2013). The forgotten APOE allele: A review of the evidence and suggested mechanisms for the protective effect of APOE ɛ2. Neuroscience & Biobehavioral Reviews, 37(10, part 2), 2878–2886. https://doi.org/10.1016/j.neubiorev.2013.10.010.

  67. Tai, L. M., Thomas, R., Marottoli, F. M., Koster, K. P., Kanekiyo, T., Morris, A. W., & Bu, G. (2016). The role of APOE in cerebrovascular dysfunction. Acta Neuropathologica, 131(5), 709–723. https://doi.org/10.1007/s00401-016-1547-z.

  68. Thambisetty, M., Beason-Held, L., An, Y., Kraut, M. A., & Resnick, S. M. (2010). APOE epsilon4 genotype and longitudinal changes in cerebral blood flow in normal aging. Archives of Neurology, 67(1), 93–98. https://doi.org/10.1001/archneurol.2009.913.

  69. Tohgi, H., Takahashi, S., Kato, E., Homma, A., Niina, R., Sasaki, K., Yonezawa, H., & Sasaki, M. (1997). Reduced size of right hippocampus in 39- to 80-year-old normal subjects carrying the apolipoprotein E epsilon4 allele. Neuroscience Letters, 236(1), 21–24.

  70. Tsuang, D., Leverenz, J. B., Lopez, O. L., Hamilton, R. L., Bennett, D. A., Schneider, J. A., Buchman, A. S., Larson, E. B., Crane, P. K., Kaye, J. A., Kramer, P., Woltjer, R., Trojanowski, J. Q., Weintraub, D., Chen-Plotkin, A. S., Irwin, D. J., Rick, J., Schellenberg, G. D., Watson, G. S., Kukull, W., Nelson, P. T., Jicha, G. A., Neltner, J. H., Galasko, D., Masliah, E., Quinn, J. F., Chung, K. A., Yearout, D., Mata, I. F., Wan, J. Y., Edwards, K. L., Montine, T. J., & Zabetian, C. P. (2013). APOE ϵ4 increases risk for dementia in pure Synucleinopathies. JAMA Neurology, 70(2), 223–228. https://doi.org/10.1001/jamaneurol.2013.600.

  71. Tuminello, E. R., & Han, S. D. (2011). The Apolipoprotein E antagonistic Pleiotropy hypothesis: Review and recommendations [research article]. https://doi.org/10.4061/2011/726197.

  72. Wang, J., Qiu, M., & Constable, R. T. (2005). In vivo method for correcting transmit/receive nonuniformities with phased array coils. Magnetic Resonance in Medicine, 53(3), 666–674. https://doi.org/10.1002/mrm.20377.

  73. Whitehair, D. C., Sherzai, A., Emond, J., Raman, R., Aisen, P. S., Petersen, R. C., et al. (2010). Influence of apolipoprotein E varepsilon4 on rates of cognitive and functional decline in mild cognitive impairment. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 6(5), 412–419. https://doi.org/10.1016/j.jalz.2009.12.003.

  74. Wierenga, C. E., Clark, L. R., Dev, S. I., Shin, D. D., Jurick, S. M., Rissman, R. A., et al. (2013). Interaction of age and APOE genotype on cerebral blood flow at rest. Journal of Alzheimer’s Disease: JAD, 34(4), 921–935. https://doi.org/10.3233/JAD-121897.

  75. Wierenga, C. E., Dev, S. I., Shin, D. D., Clark, L. R., Bangen, K. J., Jak, A. J., et al. (2012). Effect of mild cognitive impairment and APOE genotype on resting cerebral blood flow and its association with cognition. Journal of Cerebral Blood Flow & Metabolism, 32(8), 1589–1599. https://doi.org/10.1038/jcbfm.2012.58.

  76. Wierenga, C. E., Hays, C. C., & Zlatar, Z. Z. (2014). Cerebral blood flow measured by arterial spin labeling MRI as a preclinical marker of Alzheimer’s disease. Journal of Alzheimer’s Disease: JAD, 42(Suppl 4), S411–S419. https://doi.org/10.3233/JAD-141467.

  77. Zhao, M. Y., Mezue, M., Segerdahl, A. R., Okell, T. W., Tracey, I., Xiao, Y., & Chappell, M. A. (2017). A systematic study of the sensitivity of partial volume correction methods for the quantification of perfusion from pseudo-continuous arterial spin labeling MRI. NeuroImage, 162, 384–397. https://doi.org/10.1016/j.neuroimage.2017.08.072.

  78. Zlatar, Z. Z., Bischoff-Grethe, A., Hays, C. C., Liu, T. T., Meloy, M. J., Rissman, R. A., et al. (2016). Higher brain perfusion may not support memory functions in cognitively Normal carriers of the ApoE ε4 allele compared to non-carriers. Frontiers in Aging Neuroscience, 151. https://doi.org/10.3389/fnagi.2016.00151.

  79. Zlokovic, B. V. (2011). Neurovascular pathways to neurodegeneration in Alzheimer’s disease and other disorders. Nature Reviews. Neuroscience, 12(12), 723–738. https://doi.org/10.1038/nrn3114.

Download references

Acknowledgements

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by VA CSR&D Merit Award [5I01CX000565 C.E.W.], the National Science Foundation Graduate Research Fellowship Program [2015207525 C.C.H.], and the National Institute on Aging of the National Institutes of Health [K23AG049906 Z.Z.Z.], [K24AG026431 M.W.B], [R01AG054049 M.W.B]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the VA, National Science Foundation, or National Institutes of Health. We would also like to acknowledge Dr. Robert Rissman, Associate Professor of Neurosciences at the University of California San Diego for his contributions to this project.

Author information

Correspondence to Christina E. Wierenga.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Informed consent

Informed consent was obtained from all individual participants included in the study and all procedures were in accordance with the ethical standards of the UC San Diego and VA San Diego Healthcare System institutional review board and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Table 3 Cognitive tests, domains, and normative data

Regression-based cognitive change score

As an alternative approach to account for practice effect and regression toward the mean, standardized change scores were also calculate to estimate neuropsychological change in individuals (Cysique et al., 2009; Heaton et al., 2001). To develop the mean scaled score regression change score (MSR-CS), the first step consisted of defining a reference group for which no neuropsychological change is expected beyond practice effect: we used a sample of 77 cognitively normal older adults who were also assessed serially at one-year intervals. The demographic characteristics of the reference group were as follows: mean age of 73.3 years (SD = 8.0); with a mean level of education of 16.3 years (SD = 2.1); 36% were men. These demographic characteristics were not statistically different from the target sample. All subjects in the reference group were cognitively normal (using the same criteria described above for the target sample) at baseline and remained cognitively normal at all follow-up timepoints. The second step of the MSR-CS development was as follows: the reference group yielded a set of regression equations (see Table 4 in the Appendix) from baseline to first follow-up (mean time between baseline to first follow-up in reference sample = 1.3 years, SD = 0.77), statistically adjusting for age, sex, education, and the follow-up interval, which was then used to derive predicted follow-up scores in the target sample. Subtracting the predicted follow-up score from individual’s observed follow-up score and dividing that result by the standard deviation of the residuals from the reference group regression models provided a standard score of cognitive change. This score was then used as a continuous variable; the MSR-CS (i.e., significant neuropsychological decline as: MSR-CS ≤ -1.04 based on a two-tailed 70% confidence interval, MSR-CS ≤ -1.28 based on a two-tailed 80% confidence interval, MSR-CS ≤ -1.645 based on a two-tailed 90% confidence interval). The methods above were applied to the following cognitive test scores: trials 1–5, short delay free-recall, and long delay free-recall raw scores from the California Verbal Learning Test – Second Edition (CVLT-II), measuring word list recall, and the Logical Memory immediate and delayed recall subtests of the Wechsler Memory Scale-Revised (WMS-R), measuring story recall. These tests were selected based on results from a principal component analysis previously reported by our group on a similar sample of older adults (Wierenga et al., 2012). A verbal memory change score composite (Memory-CS-Composite) was calculated by averaging the MSR-CS’s for the five verbal memory tests.

Table 4 Regression equations for predicting follow-up scores on CVLT-II and WMS-R Logical Memory variables
Table 5 Effects of CBF, CT/Vo, and APOE, on regression-based memory change
Table 6 Post hoc analysis of EC CBF, CT/Vo, and APOE, on baseline memory

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hays, C.C., Zlatar, Z.Z., Meloy, M. et al. Interaction of APOE, cerebral blood flow, and cortical thickness in the entorhinal cortex predicts memory decline. Brain Imaging and Behavior (2020). https://doi.org/10.1007/s11682-019-00245-x

Download citation

Keywords

  • Aging
  • APOE ε4
  • Cerebral blood flow
  • Cognitive decline
  • Cortical thickness