Low Rank Self-Calibrated Brain Network Estimation and Auto-Weighted Centralized Multi-Task Learning for Early Mild Cognitive Impairment Diagnosis

Abstract

Detection of mild cognitive impairment (MCI) is important, and appropriate interventions can be taken to delay or prevent its progression to Alzheimer's disease (AD). The construction of brain networks based on brain image data to depict the interaction of brain functions or structures at the level of brain connections has been widely used to identify individuals with MCI/AD from the normal control (NC). Exploring the structural and functional connections and interactions between brain regions is beneficial to detect MCI. For this reason, we propose a new model for automatic MCI diagnosis based on this information. Firstly, a new functional brain network estimation method is proposed. Self-calibration is introduced using quality indicators, and functional brain network estimation is performed at the same time. Then we integrate the functional and structural connected neuroimaging patterns into our multi-task learning model to select informative feature. By identifying synergies and differences between different tasks, the most discriminative features are determined. Finally, the most relevant features are sent to the support vector machine classifier for diagnosis and identification of MCI. The experimental results based on the public Alzheimer’s disease neuroimaging (ADNI) show that our method can effectively diagnose different stages of MCI and assist the physician to improve the MCI diagnostic accuracy. At the same time, compared with the existing classification methods, the proposed method achieves relatively high classification accuracy. In addition, it can identify the most discriminative brain regions. These findings suggest that our approach not only improves classification performance, but also successfully identifies important biomarkers associated with disease.