Generalized fused group lasso regularized multi-task feature learning for predicting cognitive outcomes in Alzheimers disease

• Published in: Computer methods and programs in biomedicine 2018-08, Vol.162, p.19-45
• Main Authors: Cao, Peng, Liu, Xiaoli, Liu, Hezi, Yang, Jinzhu, Zhao, Dazhe, Huang, Min, Zaiane, Osmar
• Format: Article
• Language: English
• Subjects: Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - pathology; Alzheimer’s disease; Biomarker identification; Biomarkers; Brain; Cognition; Cognitive Dysfunction - diagnostic imaging; Diagnosis, Computer-Assisted; Fused lasso; Group lasso; Humans; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; Models, Statistical; Multi-task learning; Neuroimaging; Pattern Recognition, Automated; Positron-Emission Tomography; Prognosis; Regression; Regression Analysis; Sparse learning
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2018.04.028

•A fused group lasso regularized multi-task learning is proposed.•The new regularization considers the underlying graph structure within the tasks and group structure among MRI features.•An efficient ADMM based optimization algorithm is derived to solve the non-smooth formulation.•Experimental results demonstrate significant performance improvements over the existing methods.•Our method is able to discover the biomarkers relevant to cognitive performance. Alzheimers disease (AD) is characterized by gradual neurodegeneration and loss of brain function, especially for memory during early stages. Regression analysis has been widely applied to AD research to relate clinical and biomarker data such as predicting cognitive outcomes from Magnetic Resonance Imaging (MRI) measures. Recently, the multi-task feature learning (MTFL) methods have been widely studied to predict cognitive outcomes and select the discriminative feature subset from MRI features by incorporating inherent correlations among multiple clinical cognitive measures. However, the existing MTFL assumes the correlation among all the tasks is uniform, and the task relatedness is modeled by encouraging a common subset of features with neglecting the inherent structure of tasks and MRI features. In this paper, we proposed a generalized fused group lasso (GFGL) regularization to model the underlying structures, involving (1) a graph structure within tasks and (2) a group structure among the image features. Then, we present a multi-task learning framework (called GFGL–MTFL), combining the ℓ2, 1-norm with the GFGL regularization, to model the flexible structures. Through empirical evaluation and comparison with different baseline methods and the state-of-the-art MTL methods on data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, we illustrate that the proposed GFGL–MTFL method outperforms other methods in terms of both Mean Squared Error (nMSE) and weighted correlation coefficient (wR). Improvements are statistically significant for most scores (tasks). The experimental results with real and synthetic data demonstrate that incorporating the two prior structures by the generalized fused group lasso norm into the multi task feature learning can improve the prediction performance over several state-of-the-art competing methods, and the estimated correlation of the cognitive functions and the identification of cognition relevant imaging markers are clinically and biologically meaningful.