European Radiology

, Volume 22, Issue 7, pp 1385–1394 | Cite as

Do brain image databanks support understanding of normal ageing brain structure? A systematic review

  • David Alexander DickieEmail author
  • Dominic E. Job
  • Ian Poole
  • Trevor S. Ahearn
  • Roger T. Staff
  • Alison D. Murray
  • Joanna M. Wardlaw
Computer Applications



To document accessible magnetic resonance (MR) brain images, metadata and statistical results from normal older subjects that may be used to improve diagnoses of dementia.


We systematically reviewed published brain image databanks (print literature and Internet) concerned with normal ageing brain structure.


From nine eligible databanks, there appeared to be 944 normal subjects aged ≥60 years. However, many subjects were in more than one databank and not all were fully representative of normal ageing clinical characteristics. Therefore, there were approximately 343 subjects aged ≥60 years with metadata representative of normal ageing, but only 98 subjects were openly accessible. No databank had the range of MR image sequences, e.g. T2*, fluid-attenuated inversion recovery (FLAIR), required to effectively characterise the features of brain ageing. No databank supported random subject retrieval; therefore, manual selection bias and errors may occur in studies that use these subjects as controls. Finally, no databank stored results from statistical analyses of its brain image and metadata that may be validated with analyses of further data.


Brain image databanks require open access, more subjects, metadata, MR image sequences, searchability and statistical results to improve understanding of normal ageing brain structure and diagnoses of dementia.

Key Points

We reviewed databanks with structural MR brain images of normal older people.

Among these nine databanks, 98 normal subjects ≥60 years were openly accessible.

None had all the required sequences, random subject retrieval or statistical results.

More access, subjects, sequences, metadata, searchability and results are needed.

These may improve understanding of normal brain ageing and diagnoses of dementia.


Magnetic resonance imaging Normality Databanks Review Brain disease 



This work was carried out in The University of Edinburgh Brain Research Imaging Centre (BRIC; and the University of Aberdeen Biomedical Imaging Centre (—both centres are part of the Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) collaboration ( that is funded by the Scottish Funding Council, Scottish Executive Chief Scientist Office, and the six collaborator Universities—and in Toshiba Medical Visualisation Systems Europe (TMVSE; We thank the funders of this work as follows. Prof. Joanna M. Wardlaw was funded by the Scottish Funding Council and Scottish Executive Chief Scientist Office through the SINAPSE collaboration; David Alexander Dickie was funded by a SINAPSE industrial collaboration (SPIRIT) PhD scholarship with TMVSE, a Medical Research Council (MRC) scholarship, and the Tony Watson Scholarship bequest to The University of Edinburgh; Dr Dominic E. Job was funded by Wellcome Trust Grant 007393/Z/05/Z; Dr Trevor S. Ahearn was funded by SINAPSE and the University of Aberdeen; Dr Roger T. Staff was funded by NHS Grampian; Dr Alison D. Murray was funded by NHS Grampian via the University of Aberdeen.


  1. 1.
    Farrell C, Chappell F, Armitage P, Keston P, MacLullich A, Shenkin S, Wardlaw JM (2009) Development and initial testing of normal reference MR images for the brain at ages 65–70 and 75–80 years. Eur Radiol 19:177–183PubMedCrossRefGoogle Scholar
  2. 2.
    Fotenos AF, Snyder A, Girton L, Morris J, Buckner R (2005) Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology 64:1032–1039PubMedCrossRefGoogle Scholar
  3. 3.
    Good CD, Johnsrude IS, Ashburner J, Henson RNA, Friston KJ, Frackowiak RSJ (2001) A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage 14:21–36PubMedCrossRefGoogle Scholar
  4. 4.
    Manolio TA, Kronmal RA, Burke GL, Poirier V, O'Leary DH, Gardin JM, Fried LP, Steinberg EP, Bryan RN (1994) Magnetic resonance abnormalities and cardiovascular disease in older adults. The Cardiovascular Health Study. Stroke 25:318–327PubMedCrossRefGoogle Scholar
  5. 5.
    Resnick SM, Pham DL, Kraut MA, Zonderman AB, Davatzikos C (2003) Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain. J Neurosci 23:3295–3301PubMedGoogle Scholar
  6. 6.
    Sowell ER, Peterson BS, Thompson PM, Welcome SE, Henkenius AL, Toga AW (2003) Mapping cortical change across the human life span. Nat Neurosci 6:309–315PubMedCrossRefGoogle Scholar
  7. 7.
    Thompson PM, Hayashi KM, de Zubicaray GI, Janke AL, Rose SE, Semple J, Herman D, Hong MS, Dittmer SS, Doddrell DM, Toga AW (2003) Dynamics of gray matter loss in Alzheimer's disease. J Neurosci 23:994–1005PubMedGoogle Scholar
  8. 8.
    Luengo-Fernandez R, Leal J, Gray A (2010) Dementia 2010: The economic burden of dementia and associated research funding in the United Kingdom. University of Oxford for the Alzheimer’s Research TrustGoogle Scholar
  9. 9.
    Selkoe DJ (2001) Alzheimer's disease: genes, proteins, and therapy. Physiol Rev 81:741–766PubMedGoogle Scholar
  10. 10.
    Department of Health (2009) Living well with dementia: A National Dementia Strategy. LondonGoogle Scholar
  11. 11.
    Cohen J (1994) The earth is round (p < .05). Am Psychol 49:997–1003CrossRefGoogle Scholar
  12. 12.
    Freedman D (2010) Statistical Models and Causal Inference: A Dialogue with the Social Sciences. Cambridge University Press, CambridgeGoogle Scholar
  13. 13.
    Meehl PE (1978) Theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology. J Consult Clin Psychol 46:806–834CrossRefGoogle Scholar
  14. 14.
    Salthouse TA (2011) Neuroanatomical substrates of age-related cognitive decline. Psychol Bull 137:753–784PubMedCrossRefGoogle Scholar
  15. 15.
    Insel TR, Volkow ND, Landis SC, Li TK, Battey JF, Sieving P (2004) Limits to growth: why neuroscience needs large-scale science. Nat Neurosci 7:426–427PubMedCrossRefGoogle Scholar
  16. 16.
    Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K, Woods R, Paus T, Simpson G, Pike B, Holmes C, Collins L, Thompson P, MacDonald D, Iacoboni M, Schormann T, Amunts K, Palomero-Gallagher N, Geyer S, Parsons L, Narr K, Kabani N, Le Goualher G, Boomsma D, Cannon T, Kawashima R, Mazoyer B (2001) A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos Trans R Soc Lond B Biol Sci 356:1293–1322PubMedCrossRefGoogle Scholar
  17. 17.
    Toga AW (2002) Neuroimage databases: the good, the bad and the ugly. Nat Rev Neurosci 3:302–309PubMedCrossRefGoogle Scholar
  18. 18.
    Toga AW, Thompson PM, Mori S, Amunts K, Zilles K (2006) Towards multimodal atlases of the human brain. Nat Rev Neurosci 7:952–966PubMedCrossRefGoogle Scholar
  19. 19.
    Van Horn JD, Toga AW (2009) Is it time to re-prioritize neuroimaging databases and digital repositories? NeuroImage 47:1720–1734PubMedCrossRefGoogle Scholar
  20. 20.
    Wardlaw JM, Bastin ME, Valdés Hernández MC, Muñoz Maniega S, Royle NA, Morris Z, Clayden JD, Sandeman EM, Eadie E, Murray C, Starr JM, Deary IJ (2011) Brain ageing, cognition in youth and old age, and vascular disease in the Lothian Birth Cohort 1936: rationale, design and methodology of the imaging protocol. Int J Stroke 6:547–559PubMedCrossRefGoogle Scholar
  21. 21.
    Mazziotta JC, Woods R, Iacoboni M, Sicotte N, Yaden K, Tran M, Bean C, Kaplan J, Toga AW (2009) The myth of the normal, average human brain − The ICBM experience: (1) Subject screening and eligibility. NeuroImage 44:914–922PubMedCrossRefGoogle Scholar
  22. 22.
    Ellis KA, Bush AI, Darby D, De Fazio D, Foster J, Hudson P, Lautenschlager NT, Lenzo N, Martins RN, Maruff P (2009) The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer's disease. Int Psychogeriatr 21:672–687PubMedCrossRefGoogle Scholar
  23. 23.
    Marcus DS, Fotenos AF, Csernansky JG, Morris JC, Buckner RL (2010) Open access series of imaging studies (OASIS): Longitudinal MRI data in nondemented and demented older adults. J Cogn Neurosci 22:2677–2684PubMedCrossRefGoogle Scholar
  24. 24.
    Marcus DS, Wang TH, Parker J, Csernansky JG, Morris JC, Buckner RL (2007) Open access series of imaging studies (OASIS): Cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J Cogn Neurosci 19:1498–1507PubMedCrossRefGoogle Scholar
  25. 25.
    DeCarli C, Massaro J, Harvey D, Hald J, Tullberg M, Au R, Beiser A, D'Agostino R, Wolf PA (2005) Measures of brain morphology and infarction in the Framingham Heart Study: establishing what is normal. Neurobiol Aging 26:491–510PubMedCrossRefGoogle Scholar
  26. 26.
    Jernigan TL, Archibald SL, Fennema-Notestine C, Gamst AC, Stout JC, Bonner J, Hesselink JR (2001) Effects of age on tissues and regions of the cerebrum and cerebellum. Neurobiol Aging 22:581–594PubMedCrossRefGoogle Scholar
  27. 27.
    Grady CL, Springer MV, Hongwanishkul D, McIntosh AR, Winocur G (2006) Age-related changes in brain activity across the adult lifespan. J Cogn Neurosci 18:227–241PubMedCrossRefGoogle Scholar
  28. 28.
    Barkhof F, Fox NC, Bastos-Leite AJ, Scheltens P (2011) Neuroimaging in Dementia. Springer, Berlin HeidelbergCrossRefGoogle Scholar
  29. 29.
    Folstein M, Folstein S, McHugh P (1975) “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189–198PubMedCrossRefGoogle Scholar
  30. 30.
    Morris JC (1993) The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 43:2412–2414PubMedCrossRefGoogle Scholar
  31. 31.
    Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS medicine 6:e1000097PubMedCrossRefGoogle Scholar
  32. 32.
    Moher D, Liberati A, Tetzlaff J, Altman DG (2009) The PRISMA Statement. Available: Accessed 31 May 2011
  33. 33.
    EQUATOR Network (2011) About EQUATOR. Available: Accessed 31 May 2011
  34. 34.
    The Cardiovascular Health Study (2010) Data Distribution Policy. Available: Accessed 31 May 2011
  35. 35.
    The AddNeuroMed Study (2011) Data Access. Available: Accessed 07 September 2011
  36. 36.
    Simmons A, Westman E, Muehlboeck S, Mecocci P, Vellas B, Tsolaki M, Koszewska I, Wahlund LO, Soininen H, Lovestone S, Evans A, Spenger C (2011) The AddNeuroMed framework for multi centre MRI assessment of Alzheimer's disease: experience from the first 24 months. Int J Geriatr Psychiatry 26:75–82PubMedCrossRefGoogle Scholar
  37. 37.
    Neuroinformatics Research Group (2011) BrainSCAPE. Available: Accessed 10 October 2011
  38. 38.
    Johnson KA, Becker JA (1999) The Whole Brain Atlas. Available: Accessed 31 May 2011
  39. 39.
    Sato K, Taki Y, Fukuda H, Kawashima R (2003) Neuroanatomical database of normal Japanese brains. Neural Netw 16:1301–1310PubMedCrossRefGoogle Scholar
  40. 40.
    Sato K, Taki Y, Fukuda H, Kawashima R (2003) Japanese Reference Brains. Available: Accessed 31 May 2011
  41. 41.
    Allen Institute for Brain Science (2011) Allen Human Brain Atlas. Available: Accessed 31 May 2011
  42. 42.
    Evans AC (2006) The NIH MRI study of normal brain development. NeuroImage 30:184–202PubMedCrossRefGoogle Scholar
  43. 43.
    Evans AC (2006) The NIH MRI Study of Normal Brain Development Database. Available: Accessed 31 May 2011
  44. 44.
    Cocosco CA, Kollokian V, Remi KSK, Pike GB, Evans AC (1997) Brainweb: Online interface to a 3D MRI simulated brain database. NeuroImage 5:s425Google Scholar
  45. 45.
    McConnell Brain Imaging Centre of the Montreal Neurological Institute (2004) BrainWeb: Simulated MRI Volumes for Normal Brain Database. Available: Accessed 31 May 2011
  46. 46.
    The IMAGEN Consortium (2011) Imagen Europe−6th Framework Project. Available: Accessed 31 May 2011
  47. 47.
    Biomedical Informatics Research Network (BIRN) (2009) Morphometry BIRN Multi-site Multi-session Structural MRI Data. Available: Accessed 10 October 2011
  48. 48.
    Van Essen Lab (2001) Surface Management System Database. Available: Accessed 31 May 2011
  49. 49.
    Dickson J, Drury H, Van Essen DC (2001) The surface management system (SuMS) database: a surface–based database to aid cortical surface reconstruction, visualization and analysis. Philos Trans R Soc Lond B Biol Sci 356:1277–1292PubMedCrossRefGoogle Scholar
  50. 50.
    Sorensen AG, Wu O (2010) International Stroke Database. Available: Accessed 31 May 2011
  51. 51.
    Neuropsychiatric Imaging Research Laboratory (2009) NIRL Imaging Database. Available: Accessed 31 May 2011
  52. 52.
    Milham M, Buckner RL, Castellanos FX, Margulies D, Zang Y, Mennes M, Gutman D, Bangaru S, Craddock C, LaConte S, Mostofsky S, Villringer A (2011) 1000 Functional Connectomes Project. Available: Accessed October 10 2011
  53. 53.
    Biomedical Informatics Research Network (BIRN) (2011) Function BIRN Data Repository. Available: Accessed 07 October 2011
  54. 54.
    Letovsky SI, Whitehead S, Paik CH, Miller GA, Gerber J, Herskovits EH, Fulton TK, Bryan RN (1998) A brain image database for structure/function analysis. AJNR Am J Neuroradiol 19:1869–1877PubMedGoogle Scholar
  55. 55.
    Department of Radiology University of Pennsylvania (2008) Brain Image Database (BRAID). Available: Accessed 31 May 2011
  56. 56.
    Fox PT, Lancaster JL (2002) Mapping context and content: the BrainMap model. Nat Rev Neurosci 3:319–321PubMedCrossRefGoogle Scholar
  57. 57.
    Research Imaging Institute UTHSCSA (2010) BrainMap Database. Available: Accessed 31 May 2011
  58. 58.
    BRAINnet Foundation (2009) BRAINnet Database. Available: Accessed 31 May 2011
  59. 59.
    Technical University of Denmark Informatics (2009) Brede Database. Available: Accessed 31 May 2011
  60. 60.
    The Center for Morphometric Analysis MGH HMS (2002) Internet Brain Volume Database. Available: Accessed 31 May 2011
  61. 61.
    McConnell Brain Imaging Centre of the Montreal Neurological Institute (2010) Atlases. Available: Accessed 31 May 2011
  62. 62.
    Petersen R, Aisen P, Beckett L, Donohue M, Gamst A, Harvey D, Jack C, Jagust W, Shaw L, Toga A (2010) Alzheimer's Disease Neuroimaging Initiative (ADNI). Neurology 74:201PubMedCrossRefGoogle Scholar
  63. 63.
    Jack CR Jr, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, Borowski B, Britson PJ (2008) The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging 27:685–691PubMedCrossRefGoogle Scholar
  64. 64.
    Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack CR, Jagust W, Trojanowski JQ, Toga AW, Beckett L (2005) Ways toward an early diagnosis in Alzheimer's disease: The Alzheimer's Disease Neuroimaging Initiative (ADNI). Alzheimers Dement 1:55–66PubMedCrossRefGoogle Scholar
  65. 65.
    Laboratory of Neuro Imaging (2011) Alzheimer’s Disease Neuroimaging Initiative (ADNI). Available: Accessed 31 May 2011
  66. 66.
    Laboratory of Neuro Imaging (2011) LONI Image Data Archive (IDA) − Data Access. Available: Accessed 12 May 2011
  67. 67.
    Toga AW (2009) LONI Image Data Archive User Manual. Laboratory Of Neuro Imaging, UCLAGoogle Scholar
  68. 68.
    Ellis KA, Rowe CC, Villemagne VL, Martins RN, Masters CL, Salvado O, Szoeke C, Ames D (2010) Addressing population aging and Alzheimer's disease through the Australian imaging biomarkers and lifestyle study: Collaboration with the Alzheimer's disease neuroimaging initiative. Alzheimers Dement 6:291–296PubMedCrossRefGoogle Scholar
  69. 69.
    Mortamet B, Zeng D, Gerig G, Prastawa M, Bullitt E (2005) Effects of healthy aging measured by intracranial compartment volumes using a designed MR brain database. Medical Image Computing and Computer-Assisted Intervention (MICCAI) 3749:383–391CrossRefGoogle Scholar
  70. 70.
    Bullitt E, Smith JK, Lin W (2010) Designed Database of MR Brain Images of Healthy Volunteers. Available: Accessed 31 May 2011
  71. 71.
    Van Horn JD, Grethe JS, Kostelec P, Woodward JB, Aslam JA, Rus D, Rockmore D, Gazzaniga MS (2001) The functional magnetic resonance imaging data center (fMRIDC): the challenges and rewards of large-scale databasing of neuroimaging studies. Philos Trans R Soc Lond B Biol Sci 356:1323–1339PubMedCrossRefGoogle Scholar
  72. 72.
    Van Horn JD, Grethe JS, Kostelec P, Woodward JB, Aslam JA, Rus D, Rockmore D, Gazzaniga MS (2007) The fMRI Data Center. Available: Accessed 31 May 2011
  73. 73.
    Biomedical Image Analysis Group Imperial College London (2010) Information eXtraction from Images (IXI) dataset. Available: Accessed 31 May 2011
  74. 74.
    Hill DLG, Hawkes D, Williams S (2010) Information eXtraction from Images (IXI): Details of Grant. Available: Accessed 31 May 2011
  75. 75.
    Rowland A, Burns M, Hartkens T, Hajnal JV, Rueckert D, Hill DLG (2004) Information extraction from images (IXI): Image processing workflows using a grid enabled image database. Distributed Databases in Medical Image Computing Workshop MICCAI, Rennes, FranceGoogle Scholar
  76. 76.
    Marcus DS, Wang TH, Parker J, Csernansky JG, Morris JC, Buckner RL (2007) Open Access Series of Imaging Studies (OASIS). Available: Accessed 31 May 2011
  77. 77.
    Marcus DS, Olsen TR, Ramaratnam M, Buckner RL (2007) The extensible neuroimaging archive toolkit. Neuroinformatics 5:11–33PubMedGoogle Scholar
  78. 78.
    Marcus DS, Olsen TR, Ramaratnam M, Buckner RL (2011) XNAT Central. Available: Accessed 31 May 2011
  79. 79.
    Head D, Snyder AZ, Girton LE, Morris JC, Buckner RL (2005) Frontal-hippocampal double dissociation between normal aging and Alzheimer's disease. Cereb Cortex 15:732–739PubMedCrossRefGoogle Scholar
  80. 80.
    ADNI (2011) ADNI Publications. Available: Accessed 26 June 2011
  81. 81.
    Google Scholar (2011) Search within articles citing Marcus: Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Available:,5. Accessed 2 September 2011
  82. 82.
    Breteler M, Van Swieten J, Bots M, Grobbee D, Claus J, Van Den Hout J, Van Harskamp F, Tanghe H, De Jong P, Van Gijn J (1994) Cerebral white matter lesions, vascular risk factors, and cognitive function in a population-based study. Neurology 44:1246–1246PubMedCrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2012

Authors and Affiliations

  • David Alexander Dickie
    • 1
    • 3
    Email author
  • Dominic E. Job
    • 1
    • 3
  • Ian Poole
    • 4
  • Trevor S. Ahearn
    • 2
    • 3
  • Roger T. Staff
    • 2
    • 3
  • Alison D. Murray
    • 2
    • 3
  • Joanna M. Wardlaw
    • 1
    • 3
  1. 1.Division of Clinical Neurosciences, Western General Hospital, Brain Research Imaging Centre (BRIC)University of EdinburghEdinburghUK
  2. 2.Aberdeen Biomedical Imaging CentreUniversity of AberdeenAberdeenUK
  3. 3.Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) collaborationEdinburghUK
  4. 4.Toshiba Medical Visualisation Systems Europe, Ltd.EdinburghUK

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