Label-aligned multi-task feature learning for multimodal classification of Alzheimer's disease and mild cognitive impairment

Abstract

Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer's disease (AD), as well as its prodromal stage, i.e., mild cognitive impairment (MCI). However, to the best of our knowledge, most existing methods focus on mining the relationship across multiple modalities of the same subjects, while ignoring the potentially useful relationship across different subjects. Accordingly, in this paper, we propose a novel learning method for multimodal classification of AD/MCI, by fully exploring the relationships across both modalities and subjects. Specifically, our proposed method includes two subsequent components, i.e., label-aligned multi-task feature selection and multimodal classification. In the first step, the feature selection learning from multiple modalities are treated as different learning tasks and a group sparsity regularizer is imposed to jointly select a subset of relevant features. Furthermore, to utilize the discriminative information among labeled subjects, a new label-aligned regularization term is added into the objective function of standard multi-task feature selection, where label-alignment means that all multi-modality subjects with the same class labels should be closer in the new feature-reduced space. In the second step, a multi-kernel support vector machine (SVM) is adopted to fuse the selected features from multi-modality data for final classification. To validate our method, we perform experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using baseline MRI and FDG-PET imaging data. The experimental results demonstrate that our proposed method achieves better classification performance compared with several state-of-the-art methods for multimodal classification of AD/MCI.

Keywords: Alzheimer’s disease; Feature selection; Label alignment; Mild cognitive impairment; Multi-task learning; Multimodal classification.

Fig. 1

Schematic illustration of the proposed classification pipeline.

Fig. 2

Illustrations on the relationship among modalities and subjects in (a) traditional multi-modality methods and (b) proposed method in identifying subjects in class 1 and class 2. Circles and rectangles represent MRI and PET data, respectively. Red and blue denote different classes.

Fig. 3

ROC curves of four multi-modality based methods. (a) Classification of AD vs. NC, (b) Classification of MCI vs. NC.

Fig. 4

ROC curves of four multi-modality based methods for classification of MCI converters.

Fig. 5

Top 10 ROIs selected by the proposed method for MCI.

Fig. 6

The classification accuracy with regularization parameters λ₁ and λ₂. (a) AD classification, (b) MCI classification, and (c) MCI conversion classification. Each curve denotes the performance for different selected value for λ₁. X-axis represents diverse values for λ₂.

Fig. 7

The classification results on three classification tasks with respect to different combining weights of MRI and PET (Top: classification accuracy; Bottom: AUC value).