Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease

Abstract

Imaging biomarkers for Alzheimer's disease are desirable for improved diagnosis and monitoring, as well as drug discovery. Automated image-based classification of individual patients could provide valuable diagnostic support for clinicians, when considered alongside cognitive assessment scores. We investigate the value of combining cross-sectional and longitudinal multi-region FDG-PET information for classification, using clinical and imaging data from the Alzheimer's Disease Neuroimaging Initiative. Whole-brain segmentations into 83 anatomically defined regions were automatically generated for baseline and 12-month FDG-PET images. Regional signal intensities were extracted at each timepoint, as well as changes in signal intensity over the follow-up period. Features were provided to a support vector machine classifier. By combining 12-month signal intensities and changes over 12 months, we achieve significantly increased classification performance compared with using any of the three feature sets independently. Based on this combined feature set, we report classification accuracies of 88% between patients with Alzheimer's disease and elderly healthy controls, and 65% between patients with stable mild cognitive impairment and those who subsequently progressed to Alzheimer's disease. We demonstrate that information extracted from serial FDG-PET through regional analysis can be used to achieve state-of-the-art classification of diagnostic groups in a realistic multi-centre setting. This finding may be usefully applied in the diagnosis of Alzheimer's disease, predicting disease course in individuals with mild cognitive impairment, and in the selection of participants for clinical trials.

Figure 1

Image processing pipeline, illustrating the images required for regional feature extraction from the baseline and 12-month FDG-PET images. Horizontal arrows indicate image registration and re-slicing steps. Vertical arrows indicate images used for feature extraction.

Figure 2

Typical examples of the images required for regional feature extraction from the baseline images of a HC subject. From left to right: baseline MRI overlaid with baseline FDG-PET; masked anatomical segmentation; baseline FDG-PET overlaid with normalisation cluster. The regional colour map for the segmentation is as used in Gousias et al. (2008).

Figure 3

Classification accuracies for the four clinical group pairs based on the five feature sets studied. From left to right for each boxplot: (a) baseline signal intensities, (b) 12-month signal intensities, (c) change over 12 months, (d) combined baseline intensities and change, (e) combined 12-month intensities and change. In each boxplot, the central red line represents the median, the edges of the blue box represent the 25th and 75th percentiles, and the black whiskers extend to the most extreme data points not considered outliers. Outliers are plotted individually in red for points lying outside of the range ±2.7σ.

Figure 4

ROC curves for the combined feature set of relative changes concatenated with 12-month signal intensities. AUC values for each clinical group pair are provided in brackets.

Figure 5

Regional t-values for comparisons between AD patients (n = 50) and HC (n = 54) superimposed onto sagittal (top row) and coronal (bottom row) slices of a maximum probability brain atlas, which has been masked according to the same procedure as the anatomical segmentations. The feature sets tested are, from left to right: baseline signal intensities; 12-month signal intensities; changes in signal intensity over 12 months. To allow all three feature sets to be visualised using the same colour scale, so that their spatial patterns may be compared, all t-values greater than 5.5 have been scaled to the maximum value.