A digital map of the ageing brain could aid the diagnosis of Alzheimer’s disease and other neurodegenerative disorders in older people, a study suggests.
The atlas created using images from MRI scans of older people could aid diagnosis by comparing the patients’ scans with a detailed map of the healthy ageing brain.
Most existing MRI atlases are based on the brains of young and middle-aged people, which don’t reflect the normal changes that take place in the brain as we age, the team says.
Researchers at the University of Edinburgh constructed a detailed atlas of the human brain using MRI scans from more than 130 healthy people aged 60 or over.
The team used their atlas to study brain scans taken of normal older subjects and those who had been diagnosed with Alzheimer’s disease. The atlas was able to pinpoint changes in patients’ brain structure that can be an underlying sign of the condition, researchers say.
A key sign of early Alzheimer’s disease is the loss of brain tissue in a region of the brain, known as the medial temporal lobe. These changes to the structure of the brain are often subtle and can be difficult to spot, but an MRI atlas could make it easier to detect them, researchers say.
The team is continuing to develop MRI atlases of the healthy brain across the lifespan as part of a project – Brain Imaging in Normal Subjects (BRAINS) – which aims to detect brain damage in other diseases such as schizophrenia and preterm birth.
For the atlases to be useful and reliable, the team says brain imaging centres need to continue to collect scans from many healthy older people and work together to make large brain image banks.
The ultimate aim is to use digital brain atlases to support earlier diagnoses of Alzheimer’s and other neurological diseases that develop at different stages of life, the team says.
The study, published in the journal PLOS ONE, was principally supported by the Scottish Funding Council, Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), and the Medical Research Council.
Dr David Alexander Dickie, of The University of Edinburgh’s Brain Research Imaging Centre and SINAPSE, who was first author of the study, said: “We’re absolutely delighted with these preliminary results and that our brain MRI atlases may be used to support earlier diagnoses of diseases such as Alzheimer’s. Earlier diagnoses are currently our strongest defence against these devastating diseases and, while our work is preliminary and ongoing, digital brain atlases are likely to be at the core of this defence.”
Funding The research was supported by the Scottish Funding Council, SINAPSE, and the Medical Research Council.
Source: Corin Campbell – University of Edinburgh
Image Credit: Image is credited to Dickie et al./PLOS ONE
Original Research: Full open access research for “Use of Brain MRI Atlases to Determine Boundaries of Age-Related Pathology: The Importance of Statistical Method” by David Alexander Dickie, Dominic E. Job, David Rodriguez Gonzalez, Susan D. Shenkin, and Joanna M. Wardlaw in PLOS ONE. Published online May 29 2015 doi:10.1371/journal.pone.0127939
Abstract
Use of Brain MRI Atlases to Determine Boundaries of Age-Related Pathology: The Importance of Statistical Method
Introduction
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.
Discussion
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.
“Use of Brain MRI Atlases to Determine Boundaries of Age-Related Pathology: The Importance of Statistical Method” by David Alexander Dickie, Dominic E. Job, David Rodriguez Gonzalez, Susan D. Shenkin, and Joanna M. Wardlaw in PLOS ONE. Published online May 29 2015 doi:10.1371/journal.pone.0127939
