Do brain image databanks support understanding of normal ageing brain structure? A systematic review
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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.
• 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.
KeywordsMagnetic resonance imaging Normality Databanks Review Brain disease
This work was carried out in The University of Edinburgh Brain Research Imaging Centre (BRIC; http://www.bric.ed.ac.uk/) and the University of Aberdeen Biomedical Imaging Centre (http://www.abdn.ac.uk/ims/imaging/)—both centres are part of the Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) collaboration (http://www.sinapse.ac.uk/) 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; http://www.tmvse.com/). 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.
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