Journal of Radioanalytical and Nuclear Chemistry

, Volume 249, Issue 2, pp 455–460 | Cite as

Hierarchical clustering of Alzheimer and “normal” brains using elemental concentrations and glucose metabolism determined by PIXE, INAA and PET

  • D. A. CuttsEmail author
  • N. M. Spyrou
  • R. P. MaguireEmail author
  • K. L. Leenders


Brain tissue samples, obtained from the Alzheimer Disease Brain Bank,Institute of Psychiatry, London, were taken from both left and right hemispheresof three regions of the cerebrum, namely the frontal, parietal and occipitallobes for both Alzheimer and 'normal' subjects. Trace elementconcentrations in the frontal lobe were determined for twenty six Alzheimer(15 male, 11 female) and twenty six 'normal' (8 male, 18 female)brain tissue samples. In the parietal lobe ten Alzheimer (2 male, 8 female)and ten 'normal' (8 male, 2 female) samples were taken along withten Alzheimer (4 male, 6 female) and ten 'normal' (6 male, 4 female)from the occipital lobe. For the frontal lobe trace element concentrationswere determined using proton induced X-ray emission (PIXE) analysis whilein parietal and occipital regions instrumental neutron activation analysis(INAA) was used. Additionally eighteen Alzheimer (9 male, 9 female) and eighteenage matched 'normal' (8 male, 10 female) living subjects wereexamined using positron emission tomography (PET) in order to determine regionalcerebral metabolic rates of glucose (rCMRGlu). The rCMRGlu of 36 regions ofthe brain was investigated including frontal, occipital and parietal lobesas in the trace element study. Hierarchical cluster analysis was applied tothe trace element and glucose metabolism data to discover which variablesin the resulting dendrograms displayed the most significant separation betweenAlzheimer and 'normal' subjects.


Positron Emission Tomography Glucose Metabolism Hierarchical Cluster Alzheimer Disease Frontal Lobe 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Kluwer Academic Publishers/Akadémiai Kiadó 2001

Authors and Affiliations

  1. 1.Physics DepartmentUniversity of SurreyGuildford, SurreyUK
  2. 2.Academic Hospital GroningenGroningenThe Netherlands

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