Multi-task fused sparse learning for mild cognitive impairment identification

BACKGROUND: Brain functional connectivity network (BFCN) has been widely applied to identify biomarkers for the brain function understanding and brain diseases analysis. OBJECTIVE: Building a biologically meaningful brain network is a crucial work in these applications. For this task, sparse learning has been widely applied for the network construction. If multiple time-point data is added to the brain imaging application, the disease progression pattern in the longitudinal analysis can be better revealed. METHODS: A novel longitudinal analysis for MCI classification is devised based on resting-state functional magnetic resonating imaging (rs-fMRI). Specifically, this paper proposes a novel multi-task learning method to integrate fused penalty by regularization. In addition, a novel objective function is developed for fused sparse learning via smoothness constraint. RESULTS: The proposed method achieves the best classification performance with an accuracy of 95.74% for baseline and 93.64% for year 1 data. CONCLUSIONS: The experimental results show that our proposed method achieves quite promising classification performance.