Multimodal classification of Alzheimer's disease and mild cognitive impairment

Abstract

Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment (MCI)), has attracted more and more attention recently. So far, multiple biomarkers have been shown to be sensitive to the diagnosis of AD and MCI, i.e., structural MR imaging (MRI) for brain atrophy measurement, functional imaging (e.g., FDG-PET) for hypometabolism quantification, and cerebrospinal fluid (CSF) for quantification of specific proteins. However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for the diagnosis of AD and MCI. In this paper, we propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Specifically, ADNI baseline MRI, FDG-PET, and CSF data from 51AD patients, 99 MCI patients (including 43 MCI converters who had converted to AD within 18 months and 56 MCI non-converters who had not converted to AD within 18 months), and 52 healthy controls are used for development and validation of our proposed multimodal classification method. In particular, for each MR or FDG-PET image, 93 volumetric features are extracted from the 93 regions of interest (ROIs), automatically labeled by an atlas warping algorithm. For CSF biomarkers, their original values are directly used as features. Then, a linear support vector machine (SVM) is adopted to evaluate the classification accuracy, using a 10-fold cross-validation. As a result, for classifying AD from healthy controls, we achieve a classification accuracy of 93.2% (with a sensitivity of 93% and a specificity of 93.3%) when combining all three modalities of biomarkers, and only 86.5% when using even the best individual modality of biomarkers. Similarly, for classifying MCI from healthy controls, we achieve a classification accuracy of 76.4% (with a sensitivity of 81.8% and a specificity of 66%) for our combined method, and only 72% even using the best individual modality of biomarkers. Further analysis on MCI sensitivity of our combined method indicates that 91.5% of MCI converters and 73.4% of MCI non-converters are correctly classified. Moreover, we also evaluate the classification performance when employing a feature selection method to select the most discriminative MR and FDG-PET features. Again, our combined method shows considerably better performance, compared to the case of using an individual modality of biomarkers.

Fig. 1

Schematic illustration of multimodal data fusion and classification pipeline.

Fig. 2

ROC curves of different methods, for AD classification (top) and for MCI classification (bottom).

Fig. 3

AD Classification results with respect to different combining weights of MRI, PET and CSF. Only the squares in the upper triangular part have valid values, due to the constraint: β_PET+β_CSF+β_MRI=1. Note that for each plot, the top left, top right, and bottom left squares denote the individual-modality based classification results using PET (β_PET=1), CSF (β_CSF=1), and MRI (β_MRI=1), respectively.

Fig. 4

MCI Classification with respect to different combining weights of MRI, PET and CSF. Only the squares in the upper triangular part have valid values, due to the constraint: β_PET+β_CSF+β_MRI=1. Note that for each plot, the top left, top right, and bottom left squares denote the individual-modality based classification results using PET (β_PET=1), CSF (β_CSF=1) and MRI (β_MRI=1), respectively.

Fig. 5

Top 11 brain regions selected for MCI classification detected from MRI. Brain regions are overlaid on the template image, and images are displayed in radiological convention.

Fig. 6

Top 11 brain regions selected for MCI classification detected from PET. Brain regions are overlaid on the template image, and images are displayed in radiological convention.

Fig. 7

Classification accuracy of four different methods, with respect to different number of regions selected for AD classification (top) and MCI classification (bottom).