This is a project in collaboration with the Brain Research Imaging Centre under the
Edinburgh Imaging Prize Studentships (Centre for In Vivo Imaging Science).
See http://www.edinburghimaging.com/studentships/advertising.html
and the form in http://www.edinburghimaging.com/documents/CIVIS%20PhDOct2013/application...
Description:
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.
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