Hippocampal subfield volumes and pre-clinical Alzheimer’s disease in 408 cognitively normal adults born in 1946


Autoři: Thomas D. Parker aff001;  David M. Cash aff001;  Christopher A. S. Lane aff001;  Kirsty Lu aff001;  Ian B. Malone aff001;  Jennifer M. Nicholas aff001;  Sarah-Naomi James aff003;  Ashvini Keshavan aff001;  Heidi Murray-Smith aff001;  Andrew Wong aff003;  Sarah M. Buchanan aff001;  Sarah E. Keuss aff001;  Carole H. Sudre aff001;  Marc Modat aff001;  David L. Thomas aff006;  Sebastian J. Crutch aff001;  Marcus Richards aff003;  Nick C. Fox aff001;  Jonathan M. Schott aff001
Působiště autorů: The Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom aff001;  Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom aff002;  MRC Unit for Lifelong Health and Ageing at University College London, London, United Kingdom aff003;  School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom aff004;  Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom aff005;  Leonard Wolfson Experimental Neurology Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom aff006;  Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, University College London, London, United Kingdom aff007
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224030

Souhrn

Background

The human hippocampus comprises a number of interconnected histologically and functionally distinct subfields, which may be differentially influenced by cerebral pathology. Automated techniques are now available that estimate hippocampal subfield volumes using in vivo structural MRI data. To date, research investigating the influence of cerebral β-amyloid deposition—one of the earliest hypothesised changes in the pathophysiological continuum of Alzheimer’s disease—on hippocampal subfield volumes in cognitively normal older individuals, has been limited.

Methods

Using cross-sectional data from 408 cognitively normal individuals born in mainland Britain (age range at time of assessment = 69.2–71.9 years) who underwent cognitive assessment, 18F-Florbetapir PET and structural MRI on the same 3 Tesla PET/MR unit (spatial resolution 1.1 x 1.1 x 1.1. mm), we investigated the influences of β-amyloid status, age at scan, and global white matter hyperintensity volume on: CA1, CA2/3, CA4, dentate gyrus, presubiculum and subiculum volumes, adjusting for sex and total intracranial volume.

Results

Compared to β-amyloid negative participants (n = 334), β-amyloid positive participants (n = 74) had lower volume of the presubiculum (3.4% smaller, p = 0.012). Despite an age range at scanning of just 2.7 years, older age at time of scanning was associated with lower CA1 (p = 0.007), CA4 (p = 0.004), dentate gyrus (p = 0.002), and subiculum (p = 0.035) volumes. There was no evidence that white matter hyperintensity volume was associated with any subfield volumes.

Conclusion

These data provide evidence of differential associations in cognitively normal older adults between hippocampal subfield volumes and β-amyloid deposition and, increasing age at time of scan. The relatively selective effect of lower presubiculum volume in the β-amyloid positive group potentially suggest that the presubiculum may be an area of early and relatively specific volume loss in the pathophysiological continuum of Alzheimer’s disease. Future work using higher resolution imaging will be key to exploring these findings further.

Klíčová slova:

Alzheimer's disease – Central nervous system – Cognitive impairment – Dentate gyrus – Elderly – Magnetic resonance imaging – Memory recall – Positron emission tomography


Zdroje

1. Seab JP, Jagust WJ, Wong ST, Roos MS, Reed BR, Budinger TF. Quantitative NMR measurements of hippocampal atrophy in Alzheimer’s disease. Magn Reson Med. 1988 Oct;8(2):200–8. doi: 10.1002/mrm.1910080210 3210957

2. Kesslak JP, Nalcioglu O, Cotman CW. Quantification of magnetic resonance scans for hippocampal and parahippocampal atrophy in Alzheimer’s disease. Neurology. 1991 Jan;41(1):51–4. doi: 10.1212/wnl.41.1.51 1985296

3. Jack CR, Petersen RC, O’Brien PC, Tangalos EG. MR-based hippocampal volumetry in the diagnosis of Alzheimer’s disease. Neurology. 1992 Jan;42(1):183–8. doi: 10.1212/wnl.42.1.183 1734300

4. Scheltens P, Leys D, Barkhof F, Huglo D, Weinstein HC, Vermersch P, et al. Atrophy of medial temporal lobes on MRI in "probable" Alzheimer’s disease and normal ageing: diagnostic value and neuropsychological correlates. J Neurol Neurosurg Psychiatry. 1992 Oct;55(10):967–72.

5. Jack CR, Petersen RC, Xu Y, O’Brien PC, Smith GE, Ivnik RJ, et al. Rates of hippocampal atrophy correlate with change in clinical status in aging and AD. Neurology. 2000 Aug 22;55(4):484–9. doi: 10.1212/wnl.55.4.484 10953178

6. Bobinski M, de Leon MJ, Wegiel J, Desanti S, Convit A, Saint Louis LA, et al. The histological validation of post mortem magnetic resonance imaging-determined hippocampal volume in Alzheimer’s disease. Neuroscience. 2000;95(3):721–5. doi: 10.1016/s0306-4522(99)00476-5 10670438

7. Jack CR, Dickson DW, Parisi JE, Xu YC, Cha RH, O’Brien PC, et al. Antemortem MRI findings correlate with hippocampal neuropathology in typical aging and dementia. Neurology. 2002 Mar 12;58(5):750–7. doi: 10.1212/wnl.58.5.750 11889239

8. Cash DM, Frost C, Iheme LO, Ünay D, Kandemir M, Fripp J, et al. Assessing atrophy measurement techniques in dementia: Results from the MIRIAD atrophy challenge. Neuroimage. 2015 Dec;123:149–64. doi: 10.1016/j.neuroimage.2015.07.087 26275383

9. Iglesias JE, Augustinack JC, Nguyen K, Player CM, Player A, Wright M, et al. A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI. Neuroimage. 2015 Jul 15;115:117–37. doi: 10.1016/j.neuroimage.2015.04.042 25936807

10. Apostolova LG, Mosconi L, Thompson PM, Green AE, Hwang KS, Ramirez A, et al. Subregional hippocampal atrophy predicts Alzheimer’s dementia in the cognitively normal. Neurobiol Aging. 2010 Jul;31(7):1077–88. doi: 10.1016/j.neurobiolaging.2008.08.008 18814937

11. Mueller SG, Schuff N, Yaffe K, Madison C, Miller B, Weiner MW. Hippocampal atrophy patterns in mild cognitive impairment and Alzheimer’s disease. Hum Brain Mapp. 2010 Sep;31(9):1339–47. doi: 10.1002/hbm.20934 20839293

12. La Joie R, Perrotin A, De La Sayette V, Egret S, Doeuvre L, Belliard S, et al. Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer’s disease and semantic dementia. NeuroImage Clin. 2013;3:155–62. doi: 10.1016/j.nicl.2013.08.007 24179859

13. Pini L, Pievani M, Bocchetta M, Altomare D, Bosco P, Cavedo E, et al. Brain atrophy in Alzheimer’s Disease and aging. Ageing Res Rev. 2016;30:25–48. doi: 10.1016/j.arr.2016.01.002 26827786

14. Blanken AE, Hurtz S, Zarow C, Biado K, Honarpisheh H, Somme J, et al. Associations between hippocampal morphometry and neuropathologic markers of Alzheimer’s disease using 7 T MRI. NeuroImage Clin. 2017;15:56–61. doi: 10.1016/j.nicl.2017.04.020 28491492

15. Carlesimo GA, Piras F, Orfei MD, Iorio M, Caltagirone C, Spalletta G. Atrophy of presubiculum and subiculum is the earliest hippocampal anatomical marker of Alzheimer’s disease. Alzheimer’s Dement (Amsterdam, Netherlands). 2015 Mar;1(1):24–32.

16. Mak E, Gabel S, Su L, Williams GB, Arnold R, Passamonti L, et al. Multi-modal MRI investigation of volumetric and microstructural changes in the hippocampus and its subfields in mild cognitive impairment, Alzheimer’s disease, and dementia with Lewy bodies. Int psychogeriatrics. 2017;29(4):545–55.

17. Apostolova LG, Dutton RA, Dinov ID, Hayashi KM, Toga AW, Cummings JL, et al. Conversion of Mild Cognitive Impairment to Alzheimer Disease Predicted by Hippocampal Atrophy Maps. Arch Neurol. 2006 May 1;63(5):693. doi: 10.1001/archneur.63.5.693 16682538

18. Wisse LEM, Biessels GJ, Heringa SM, Kuijf HJ, Koek DL, Luijten PR, et al. Hippocampal subfield volumes at 7T in early Alzheimer’s disease and normal aging. Neurobiol Aging. 2014;

19. Small SA, Schobel SA, Buxton RB, Witter MP, Barnes CA. A pathophysiological framework of hippocampal dysfunction in ageing and disease. Nat Rev Neurosci. 2011 Oct 7;12(10):585–601. doi: 10.1038/nrn3085 21897434

20. Hsu PJ, Shou H, Benzinger T, Marcus D, Durbin T, Morris JC, et al. Amyloid burden in cognitively normal elderly is associated with preferential hippocampal subfield volume loss. J Alzheimers Dis. 2015;45(1):27–33. doi: 10.3233/JAD-141743 25428255

21. Tardif CL, Devenyi GA, Amaral RSC, Pelleieux S, Poirier J, Rosa-Neto P, et al. Regionally specific changes in the hippocampal circuitry accompany progression of cerebrospinal fluid biomarkers in preclinical Alzheimer’s disease. Hum Brain Mapp. 2018 Nov 21;

22. Wu W, Brickman AM, Luchsinger J, Ferrazzano P, Pichiule P, Yoshita M, et al. The brain in the age of old: the hippocampal formation is targeted differentially by diseases of late life. Ann Neurol. 2008 Dec;64(6):698–706. doi: 10.1002/ana.21557 19107993

23. de Flores R, La Joie R, Chételat G. Structural imaging of hippocampal subfields in healthy aging and Alzheimer’s disease. Neuroscience. 2015.

24. Shing YL, Rodrigue KM, Kennedy KM, Fandakova Y, Bodammer N, Werkle-Bergner M, et al. Hippocampal subfield volumes: Age, vascular risk, and correlation with associative memory. Front Aging Neurosci. 2011;

25. Bender AR, Daugherty AM, Raz N. Vascular risk moderates associations between hippocampal subfield volumes and memory. J Cogn Neurosci. 2013;

26. Hsu PJ, Shou H, Benzinger T, Marcus D, Durbin T, Morris JC, et al. Amyloid burden in cognitively normal elderly is associated with preferential hippocampal subfield volume loss. J Alzheimer’s Dis. 2015;45(1):27–33.

27. Wisse LEM, Biessels GJ, Geerlings MI. A Critical Appraisal of the Hippocampal Subfield Segmentation Package in FreeSurfer. Front Aging Neurosci. 2014;6:261. doi: 10.3389/fnagi.2014.00261 25309437

28. Stafford M, Black S, Shah I, Hardy R, Pierce M, Richards M, et al. Using a birth cohort to study ageing: representativeness and response rates in the National Survey of Health and Development. Eur J Ageing. 2013;10(2):145–57. doi: 10.1007/s10433-013-0258-8 23637643

29. Kuh D, Wong A, Shah I, Moore A, Popham M, Curran P, et al. The MRC National Survey of Health and Development reaches age 70: maintaining participation at older ages in a birth cohort study. Eur J Epidemiol. 2016 Nov;31(11):1135–47. doi: 10.1007/s10654-016-0217-8 27995394

30. Wadsworth M, Kuh D, Richards M, Hardy R. Cohort profile: The 1946 National Birth Cohort (MRC National Survey of Health and Development). Int J Epidemiol. 2006;35(1):49–54. doi: 10.1093/ije/dyi201 16204333

31. Lane CA, Parker TD, Cash DM, Macpherson K, Donnachie E, Murray-Smith H, et al. Study protocol: Insight 46 –a neuroscience sub-study of the MRC National Survey of Health and Development. BMC Neurol. 2017 Dec 18;17(1):75. doi: 10.1186/s12883-017-0846-x 28420323

32. James S-N, Lane CA, Parker TD, Lu K, Collins JD, Murray-Smith H, et al. Using a birth cohort to study brain health and preclinical dementia: Recruitment and participation rates in Insight 46. BMC Res Notes. 2018;11(1).

33. Galvin JE, Roe CM, Powlishta KK, Coats MA, Muich SJ, Grant E, et al. The AD8: a brief informant interview to detect dementia. Neurology. 2005 Aug 23;65(4):559–64. doi: 10.1212/01.wnl.0000172958.95282.2a 16116116

34. Galvin JE, Roe CM, Xiong C, Morris JC. Validity and reliability of the AD8 informant interview in dementia. Neurology. 2006 Dec 12;67(11):1942–8. doi: 10.1212/01.wnl.0000247042.15547.eb 17159098

35. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state.” J Psychiatr Res. 1975 Nov;12(3):189–98. doi: 10.1016/0022-3956(75)90026-6 1202204

36. Wechsler D. Wechsler Adult Intelligence Scale–Revised. 1981.

37. Wechsler D. Wechsler Memory Scale—Revised Edition. 1987.

38. Wechsler D. The Wechsler abbreviated scale of intelligence. San Antonio, TX: The Psychological Corporation; 1999.

39. Papp K V, Amariglio RE, Dekhtyar M, Roy K, Wigman S, Bamfo R, et al. Development of a psychometrically equivalent short form of the Face-Name Associative Memory Exam for use along the early Alzheimer’s disease trajectory. Clin Neuropsychol. 2014/05/13. 2014;28(5):771–85. doi: 10.1080/13854046.2014.911351 24815535

40. Burgos N, Cardoso MJ, Thielemans K, Modat M, Dickson J, Schott JM, et al. Multi-contrast attenuation map synthesis for PET/MR scanners: assessment on FDG and Florbetapir PET tracers. Eur J Nucl Med Mol Imaging. 2015 Aug;42(9):1447–58. doi: 10.1007/s00259-015-3082-x 26105119

41. Modat M, Cash DM, Daga P, Winston GP, Duncan JS, Ourselin S. Global image registration using a symmetric block-matching approach. J Med Imaging. 2014 Sep 19;1(2):24003-1–24003–6.

42. Fleisher AS, Chen K, Liu X, Roontiva A, Thiyyagura P, Ayutyanont N, et al. Using positron emission tomography and florbetapir F18 to image cortical amyloid in patients with mild cognitive impairment or dementia due to Alzheimer disease. Arch Neurol. 2011;

43. Landau SM, Marks SM, Mormino EC, Rabinovici GD, Oh H, O’Neil JP, et al. Association of lifetime cognitive engagement and low β-amyloid deposition. Arch Neurol. 2012;

44. Landau SM, Fero A, Baker SL, Koeppe R, Mintun M, Chen K, et al. Measurement of Longitudinal -Amyloid Change with 18F-Florbetapir PET and Standardized Uptake Value Ratios. J Nucl Med. 2015;

45. Landau SM, Breault C, Joshi AD, Pontecorvo M, Mathis CA, Jagust WJ, et al. Amyloid- Imaging with Pittsburgh Compound B and Florbetapir: Comparing Radiotracers and Quantification Methods. J Nucl Med. 2013;

46. Egan MF, Kost J, Tariot PN, Aisen PS, Cummings JL, Vellas B, et al. Randomized Trial of Verubecestat for Mild-to-Moderate Alzheimer’s Disease. N Engl J Med. 2018 May 3;378(18):1691–703. doi: 10.1056/NEJMoa1706441 29719179

47. Ottoy J, Verhaeghe J, Niemantsverdriet E, Wyffels L, Somers C, De Roeck E, et al. Validation of the Semiquantitative Static SUVR Method for 18 F-AV45 PET by Pharmacokinetic Modeling with an Arterial Input Function. J Nucl Med. 2017;

48. Jovicich J, Czanner S, Greve D, Haley E, van der Kouwe A, Gollub R, et al. Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data. Neuroimage. 2006 Apr;30(2):436–43. doi: 10.1016/j.neuroimage.2005.09.046 16300968

49. Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging. 2010 Jun;29(6):1310–20. doi: 10.1109/TMI.2010.2046908 20378467

50. Parker TD, Slattery CF, Yong KXX, Nicholas JM, Paterson RW, Foulkes AJM, et al. Differences in hippocampal subfield volume are seen in phenotypic variants of early onset Alzheimer’s disease. NeuroImage Clin. 2018;

51. Sudre CH, Cardoso MJ, Bouvy WH, Biessels GJ, Barnes J, Ourselin S. Bayesian Model Selection for Pathological Neuroimaging Data Applied to White Matter Lesion Segmentation. IEEE Trans Med Imaging. 2015;

52. Malone IB, Leung KK, Clegg S, Barnes J, Whitwell JL, Ashburner J, et al. Accurate automatic estimation of total intracranial volume: a nuisance variable with less nuisance. Neuroimage. 2015 Jan 1;104:366–72. doi: 10.1016/j.neuroimage.2014.09.034 25255942

53. Jansen WJ, Ossenkoppele R, Knol DL, Tijms BM, Scheltens P, Verhey FRJ, et al. Prevalence of Cerebral Amyloid Pathology in Persons Without Dementia: A Meta-analysis. JAMA. 2015 May 19;313(19):1924–38. doi: 10.1001/jama.2015.4668 25988462

54. Pigeon D. Tests used in the 1954 and 1957 surveys. Douglas J, editor. In the Home and the School (Appendix 1). Macgibbon & Kee; 1964.

55. Department of Education and Science. Burnham further education committee grading courses. London; 1972.

56. Guralnik JM, Butterworth S, Wadsworth MEJ, Kuh D. Childhood socioeconomic status predicts physical functioning a half century later. J Gerontol A Biol Sci Med Sci. 2006;

57. Mormino EC, Kluth JT, Madison CM, Rabinovici GD, Baker SL, Miller BL, et al. Episodic memory loss is related to hippocampal-mediated β-amyloid deposition in elderly subjects. Brain. 2008;132(5):1310–23.

58. Storandt M, Mintun M a, Head D, Morris JC. Cognitive decline and brain volume loss as signatures of cerebral amyloid-beta peptide deposition identified with Pittsburgh compound B: cognitive decline associated with Abeta deposition. Arch Neurol. 2009;66(12):1476–81. doi: 10.1001/archneurol.2009.272 20008651

59. Bourgeat P, Chételat G, Villemagne VL, Fripp J, Raniga P, Pike K, et al. β-Amyloid burden in the temporal neocortex is related to hippocampal atrophy in elderly subjects without dementia. Neurology. 2010;74(2):121–7. doi: 10.1212/WNL.0b013e3181c918b5 20065247

60. Doré V, Villemagne VL, Bourgeat P, Fripp J, Acosta O, Chetélat G, et al. Cross-sectional and longitudinal analysis of the relationship between Aβ deposition, cortical thickness, and memory in cognitively unimpaired individuals and in Alzheimer disease. JAMA Neurol. 2013;70(7):903–11. doi: 10.1001/jamaneurol.2013.1062 23712469

61. Petersen RC, Wiste HJ, Weigand SD, Rocca WA, Roberts RO, Mielke MM, et al. Association of Elevated Amyloid Levels With Cognition and Biomarkers in Cognitively Normal People From the Community. JAMA Neurol. 2016 Jan;73(1):85–92. doi: 10.1001/jamaneurol.2015.3098 26595683

62. Dubois B, Epelbaum S, Nyasse F, Bakardjian H, Gagliardi G, Uspenskaya O, et al. Cognitive and neuroimaging features and brain β-amyloidosis in individuals at risk of Alzheimer’s disease (INSIGHT-preAD): a longitudinal observational study. Lancet Neurol. 2018 Feb;

63. Mattsson N, Aisen PS, Jagust W, Mackin S, Weiner M. Brain structure and function as mediators of the effects of amyloid on memory. Neurology. 2015;84:1136–44. doi: 10.1212/WNL.0000000000001375 25681451

64. Andrews KA, Modat M, Macdonald KE, Yeatman T, Cardoso MJ, Leung KK, et al. Atrophy Rates in Asymptomatic Amyloidosis: Implications for Alzheimer Prevention Trials. PLoS One. 2013;8(3).

65. Mueller SG, Weiner MW. Selective effect of age, Apo e4, and Alzheimer’s disease on hippocampal subfields. Hippocampus. 2009 Jun;19(6):558–64. doi: 10.1002/hipo.20614 19405132

66. Malykhin N V., Huang Y, Hrybouski S, Olsen F. Differential vulnerability of hippocampal subfields and anteroposterior hippocampal subregions in healthy cognitive aging. Neurobiol Aging. 2017;

67. Daugherty AM, Bender AR, Raz N, Ofen N. Age differences in hippocampal subfield volumes from childhood to late adulthood. Hippocampus. 2016;

68. Wisniewski HM, Sadowski M, Jakubowska-Sadowska K, Tarnawski M, Wegiel J. Diffuse, lake-like amyloid-β deposits in the parvopyramidal layer of the presubiculum in Alzheimer disease. J Neuropathol Exp Neurol. 1998;

69. Kalus P, Braak H, Braak E, Bohl J. The presubicular region in Alzheimer’s disease: topography of amyloid deposits and neurofibrillary changes. Brain Res. 1989;

70. Murray CE, Gami-Patel P, Gkanatsiou E, Brinkman G, Portelius E, Wirths O, et al. The presubiculum is preserved from neurodegenerative changes in Alzheimer’s disease. Acta Neuropathol Commun. 2018;6:62. doi: 10.1186/s40478-018-0563-8 30029687

71. Josephs KA, Murray ME, Tosakulwong N, Whitwell JL, Knopman DS, Machulda MM, et al. Tau aggregation influences cognition and hippocampal atrophy in the absence of beta-amyloid: a clinico-imaging-pathological study of primary age-related tauopathy (PART). Acta Neuropathol. 2017 Feb 3;

72. Crary JF, Trojanowski JQ, Schneider JA, Abisambra JF, Abner EL, Alafuzoff I, et al. Primary age-related tauopathy (PART): a common pathology associated with human aging. Acta Neuropathol. 2014;

73. Fiford CM, Manning EN, Bartlett JW, Cash DM, Malone IB, Ridgway GR, et al. White matter hyperintensities are associated with disproportionate progressive hippocampal atrophy. Hippocampus. 2017;

74. Kerchner GA. Ultra-High Field 7T MRI: A New Tool for Studying Alzheimer’s Disease. J Alzheimer’s Dis. 2011;26(s3):91–5.

75. Carr VA, Bernstein JD, Favila SE, Rutt BK, Kerchner GA, Wagner AD. Individual differences in associative memory among older adults explained by hippocampal subfield structure and function. Proc Natl Acad Sci. 2017;

76. Johnson KA, Schultz A, Betensky RA, Becker JA, Sepulcre J, Rentz D, et al. Tau positron emission tomographic imaging in aging and early Alzheimer disease. Ann Neurol. 2016;

77. Jacobs HIL, Hedden T, Schultz AP, Sepulcre J, Perea RD, Amariglio RE, et al. Structural tract alterations predict downstream tau accumulation in amyloid-positive older individuals. Nat Neurosci. 2018 Feb 5;

78. Iglesias JE, Van Leemput K, Augustinack J, Insausti R, Fischl B, Reuter M, et al. Bayesian longitudinal segmentation of hippocampal substructures in brain MRI using subject-specific atlases. Neuroimage. 2016 Nov 1;141:542–55. doi: 10.1016/j.neuroimage.2016.07.020 27426838


Článek vyšel v časopise

PLOS One


2019 Číslo 10