Domain Transfer Learning for MCI Conversion Prediction

Abstract

Machine learning methods have successfully been used to predict the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD), by classifying MCI converters (MCI-C) from MCI nonconverters (MCI-NC). However, most existing methods construct classifiers using data from one particular target domain (e.g., MCI), and ignore data in other related domains (e.g., AD and normal control (NC)) that may provide valuable information to improve MCI conversion prediction performance. To address is limitation, we develop a novel domain transfer learning method for MCI conversion prediction, which can use data from both the target domain (i.e., MCI) and auxiliary domains (i.e., AD and NC). Specifically, the proposed method consists of three key components: 1) a domain transfer feature selection component that selects the most informative feature-subset from both target domain and auxiliary domains from different imaging modalities; 2) a domain transfer sample selection component that selects the most informative sample-subset from the same target and auxiliary domains from different data modalities; and 3) a domain transfer support vector machine classification component that fuses the selected features and samples to separate MCI-C and MCI-NC patients. We evaluate our method on 202 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) that have MRI, FDG-PET, and CSF data. The experimental results show the proposed method can classify MCI-C patients from MCI-NC patients with an accuracy of 79.4%, with the aid of additional domain knowledge learned from AD and NC.

Fig. 1

The system diagram of our proposed classification framework.

Fig. 2

Illustration on the proposed DTFS and DTSS models. (a) Using DTFS to select discriminative brain regions. (b) Using DTSS to select informative subjects.

Fig. 3

ROC curves of different methods for MCI-C/MCI-NC classification with multi-modality and single-modality data, respectively.

Fig. 4

ROC curves of different variants of our proposed method.

Fig. 5

Selected stable brain regions by three different methods on (Top) MRI and (Bottom) PET images. Note that different colors indicate different brain regions.

Fig. 6

Classification accuracy of our proposed method in multimodal case with respect to different iterations, achieved by iterative optimization algorithm.