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A quantitative diagnostic method incorporating brain images and clinical measures

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This is a project in collaboration with the Brain Research Imaging Centre under the
Edinburgh Imaging Prize Studentships (Centre for In Vivo Imaging Science).
and the form in


Brain structure and clinical measures, e.g. blood pressure, change with age. Although
associations have been found between brain structure and clinical measures, there is no
standard, quantitative method for determining “how normal” individual patient brain scans
and associated clinical measures are, e.g. what are the normal levels and limits of grey
matter volume given different blood pressures at different ages?
Templates of normal brain structure exist but they require qualitative assessment by
clinicians. Further, these templates have been developed with relatively few subjects mostly
at younger ages and have not incorporated clinical data. Given that: i) normal ageing is
associated with a wide range of brain structure; ii) values of clinical measures may affect the
“normality” of brain structure; and iii) the experience of different clinicians (radiologists,
neuroradiologists, general physicians) may lead to different interpretations of images; a
quantitative method for diagnosing brain scans and clinical measures together is required.
This would then need to be evaluated to assess whether it is clinically useful. This method
may also be used by other researchers to ensure their controls are appropriate, i.e. not
skewed to one side of the normative range.
This work will build on the Brain Images of Normal Subjects (BraINS) bank that is compiling
the required data, and collaborate with all members of this team, and potentially with other
brain banks internationally. The aims of the proposed work are to: 1) combine and
summarise large volumes of normal brain image and clinical data; 2) develop a system to
read new patient data and produce a single “rank of normality”; 3) test whether this system is
clinically useful; and 4) determine the significance of normality ranks, e.g. whether or not they
predict disease.

Project status: 
Still available
Degree level: 
The project would suit a student with strong statistical skills and a background in Neuroinformatics, Neuroscience, or Psychology but also potentially a student from a pure Statistical, Mathematical, or Engineering background and an interest in brain ageing and pathology. It requires understanding of statistical analyses and summaries (e.g. hypotheses testing, means, and percentiles), clinical and brain image data, the sensitivities and management of these data; and the ability to work as part of an interdisciplinary group of researchers. There will be additional mentorship from Prof Joanna Wardlaw (expertise in neuroimaging).
Supervisors @ NeSC: 
Other supervisors: 
Dr Dominic Job Dr Susan Shenkin
Subject areas: 
1. Dickie, D.A., et al. (2012). Do brain image databanks support understanding of normal ageing brain structure? A systematic review. Eur Radiol. 22, 1385-1394. 2. Farrell, C., et al. (2009). Development and initial testing of normal reference MR images for the brain at ages 65–70 and 75–80 years. Eur Radiol. 19, 177–183. 3. Mazziotta, J.C., et al. (2009). The myth of the normal, average human brain - the ICBM experience: (1) Subject screening and eligibility. Neuroimage. 44, 914-922. 4. Freedman, D. (2010). Statistical Models and Causal Inference: A Dialogue with the Social Sciences. Cambridge University Press, Cambridge. 5. Breteler, M., et al. (1994). Cerebral white matter lesions, vascular risk factors, and cognitive function in a population-based study. Neurology. 44,1246-1252.
Student project type: