Random forest-based similarity measures for multi-modal classification of Alzheimer's disease

Abstract

Neurodegenerative disorders, such as Alzheimer's disease, are associated with changes in multiple neuroimaging and biological measures. These may provide complementary information for diagnosis and prognosis. We present a multi-modality classification framework in which manifolds are constructed based on pairwise similarity measures derived from random forest classifiers. Similarities from multiple modalities are combined to generate an embedding that simultaneously encodes information about all the available features. Multi-modality classification is then performed using coordinates from this joint embedding. We evaluate the proposed framework by application to neuroimaging and biological data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Features include regional MRI volumes, voxel-based FDG-PET signal intensities, CSF biomarker measures, and categorical genetic information. Classification based on the joint embedding constructed using information from all four modalities out-performs the classification based on any individual modality for comparisons between Alzheimer's disease patients and healthy controls, as well as between mild cognitive impairment patients and healthy controls. Based on the joint embedding, we achieve classification accuracies of 89% between Alzheimer's disease patients and healthy controls, and 75% between mild cognitive impairment patients and healthy controls. These results are comparable with those reported in other recent studies using multi-kernel learning. Random forests provide consistent pairwise similarity measures for multiple modalities, thus facilitating the combination of different types of feature data. We demonstrate this by application to data in which the number of features differs by several orders of magnitude between modalities. Random forest classifiers extend naturally to multi-class problems, and the framework described here could be applied to distinguish between multiple patient groups in the future.

Figure 1

Schematic overview of the proposed methodology. Each random forest (RF) step provides a classification result whose performance will be reported. Random forests are used both to derive the pairwise similarity measures for each feature set, and also to perform the single- and multi-modality classification experiments.

Figure 2

Illustration of a random forest, showing two trees in detail. Each node is partitioned based on a single feature, and each branch ends in a terminal node. Terminal nodes provide a prediction for the class of a test example based on the path taken through the tree. The colour of a terminal node indicates its class prediction. The final predicted class for a test example is obtained by combining the predictions of all individual trees.

Figure 3

Feature importances for discriminating between the three clinical group pairs using region-based MRI (top), and voxel-based FDG-PET (bottom). For MRI, regional feature importances are superimposed onto slices of a maximum probability brain atlas which has been masked in the same way as the anatomical segmentations. For FDG-PET, important voxels are overlaid onto a MNI-space average MR image.

Figure 4

Similarity matrices for each of the four modalities for the AD/HC experiment. Matrices are symmetric, and each entry represents the similarity between a pair of subjects based on the input feature data.

Figure 5

Cobweb plots showing the distribution of parameters selected over the 100 leave-25%-out runs for all three classification experiments. The four spokes of each plot represent the four modalities, and each coloured line connecting the four spokes represents a set of parameter values. The colour and weight of each line represents the percentage of runs in which the associated parameter set was selected.