Publications Home » Classification of ADNI subjects b...
Background: As research continues to focus on the development of new treatments for Alzheimer's disease (AD), and the selection of suitable subjects for clinical trials becomes increasingly important, the ability to reliably identify patients in the early or pre-symptomatic stages of the disease, and particularly those with amnestic mild cognitive impairment (aMCI) is desirable. The aim of this study is to classify subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI), based on a regional analysis of their baseline and 12-month FDG-PET scans, as healthy controls (HC), or as having either aMCI or AD.
Methods: Image data were obtained from 179 subjects (37 AD, 94 aMCI, 48 HC), whose baseline and 12-month FDG-PET scans were each realigned into the space of their corresponding baseline MRI, in which hippocampal masks were automatically generated for both timepoints. The signal intensity per cubic millimetre was determined in the hippocampus for both the baseline and 12-month FDG-PET scans, and the difference between the two calculated. Global variations in the cerebral metabolic rate of glucose between subjects were accounted for using the recently proposed "reference cluster" method, in which areas of apparent hypermetabolism between patients and controls (relatively unaffected by the disease) are extracted from the image data and used for normalisation.
Results: Consideration of both baseline and follow-up data provides increased classification accuracies, determined using linear discriminant analysis, compared with those obtained using the baseline data alone. Accuracy increased from 72% to 78% between AD patients and HC, 65% to 68% between aMCI patients and HC, and 58% to 61% between AD and aMCI patients.
Conclusions: This work-in-progress follows from the successful application of this regional analysis technique to baseline FDG-PET data from the ADNI, in which the features used for classification include not only the hippocampus, but all those extracted from a subject-specific segmentation consisting of 83 anatomical regions. These early results suggest that the group discrimination achieved using baseline data alone may be further improved by the inclusion of follow-up FDG-PET data.
Poster available at http://www.doc.ic.ac.uk/~krg03/ICAD2010_KRGray.pdf
pubs.doc.ic.ac.uk: built & maintained by Ashok Argent-Katwala.