title: Use of brain MRI atlases to determine boundaries of age-related pathology: the importance of statistical method.
abstract: BACKGROUND
  Neurodegenerative disease diagnoses may be supported by the comparison of an individual patient's brain magnetic resonance image (MRI) with a voxel-based atlas of normal brain MRI. Most current brain MRI atlases are of young to middle-aged adults and parametric, e.g., mean ± standard deviation (SD); these atlases require data to be Gaussian. Brain MRI data, e.g., grey matter (GM) proportion images, from normal older subjects are apparently not Gaussian. We created a nonparametric and a parametric atlas of the normal limits of GM proportions in older subjects and compared their classifications of GM proportions in Alzheimer's disease (AD) patients.
  
  
  METHODS
  Using publicly available brain MRI from 138 normal subjects and 138 subjects diagnosed with AD (all 55-90 years), we created: a mean ± SD atlas to estimate parametrically the percentile ranks and limits of normal ageing GM; and, separately, a nonparametric, rank order-based GM atlas from the same normal ageing subjects. GM images from AD patients were then classified with respect to each atlas to determine the effect statistical distributions had on classifications of proportions of GM in AD patients.
  
  
  RESULTS
  The parametric atlas often defined the lower normal limit of the proportion of GM to be negative (which does not make sense physiologically as the lowest possible proportion is zero). Because of this, for approximately half of the AD subjects, 25-45% of voxels were classified as normal when compared to the parametric atlas; but were classified as abnormal when compared to the nonparametric atlas. These voxels were mainly concentrated in the frontal and occipital lobes.
  
  
  CONCLUSIONS
  To our knowledge, we have presented the first nonparametric brain MRI atlas. In conditions where there is increasing variability in brain structure, such as in old age, nonparametric brain MRI atlases may represent the limits of normal brain structure more accurately than parametric approaches. Therefore, we conclude that the statistical method used for construction of brain MRI atlases should be selected taking into account the population and aim under study. Parametric methods are generally robust for defining central tendencies, e.g., means, of brain structure. Nonparametric methods are advisable when studying the limits of brain structure in ageing and neurodegenerative disease.
authors.affiliation: Neuroimaging Sciences, Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh Medical School, Edinburgh, United Kingdom; Geriatric Medicine Unit, The University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.
authors.forename: David Alexander
authors.initials: DA
authors.lastname: Dickie
authors.affiliation: Neuroimaging Sciences, Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh Medical School, Edinburgh, United Kingdom; Geriatric Medicine Unit, The University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.
authors.forename: Dominic E
authors.initials: DE
authors.lastname: Job
authors.affiliation: Neuroimaging Sciences, Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh Medical School, Edinburgh, United Kingdom; Geriatric Medicine Unit, The University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.
authors.forename: David Rodriguez
authors.initials: DR
authors.lastname: Gonzalez
authors.affiliation: Geriatric Medicine Unit, The University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) collaboration, Glasgow, United Kingdom.
authors.forename: Susan D
authors.initials: SD
authors.lastname: Shenkin
authors.affiliation: Neuroimaging Sciences, Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh Medical School, Edinburgh, United Kingdom; Geriatric Medicine Unit, The University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.
authors.forename: Joanna M
authors.initials: JM
authors.lastname: Wardlaw
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000473:pathology*; D000544:Alzheimer Disease / Q000473:pathology*; D001921:Brain / Q000473:pathology* / Q000502:physiology; D016022:Case-Control Studies; D003627:Data Interpretation, Statistical; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008875:Middle Aged; D059906:Neuroimaging / Q000706:statistics & numerical data; D016011:Normal Distribution; D012016:Reference Values