Featureaware Multitask feature learning for Predicting Cognitive Outcomes in Alzheimers disease Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimeru0027s disease (AD) research in recent years. Predicting cognitive performance of subjects from neuroimage measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimeru0027s disease. Multitask based feature learning (MTFL) have been widely studied to select a discriminative feature subset from MRI features, and improve the performance by incorporating inherent correlations among multiple clinical cognitive measures. It is known that the brain imaging measures are often correlated with each other, and AD is closely related to the intercorrelation among different brain regions. However, the multitask based feature learning (MTFL) method neglects the inherent correlation among brain imaging measures. We present a novel regularized multitask learning approach via a joint sparsityinducing regularization to effectively incorporate both a relatedness among multiple cognitive score prediction tasks and a useful inherent correlation between brain imaging measures by exploiting correlations among features. It allows the simultaneous selection of a common set of biomarkers for all tasks and the preservation of the inherent structure of imaging measures. The reported experiments on the ADNI dataset show that the proposed method is effective and promising.

Deep and Joint Learning of Longitudinal Data for Alzheimers Disease Prediction Abstract   Alzheimeru0027s disease (AD) is an irreversible and progressive neurodegenerative disease. The close AD monitoring of this disease is essential for the patient treatment plan adjustment. For AD monitoring, clinical score prediction via neuroimaging data is highly desirable since it is able to reveal the disease status, adequately. For this task, most previous studies are focused on a single time point without considering relationship between neuroimaging data (e.g., Magnetic Resonance Imaging (MRI)) and clinical scores at multiple time points. Differing from these studies, we propose to build a framework based on longitudinal multiple time points data to predict clinical scores. Specifically, the proposed framework consists of three parts, feature selection based on correntropy regularized joint learning, feature encoding based on deep polynomial network, and ensemble learning for regression via the support vector regression method. Two scenarios are designed for scores prediction. Namely, scenario 1 uses the baseline data to achieve the longitudinal scores prediction, while scenario 2 utilizes all the previous time points data to obtain the predicted scores at the next time point, which can improve the score predictionu0027s accuracy. Meanwhile, the missing clinical scores at longitudinal multiple time points are imputated to solve the incompleteness of the data. Extensive experiments on the public database of Alzheimeru0027s Disease Neuroimaging Initiative (ADNI) demonstrate that our proposed framework can effectively reveal the relationship between clinical score and MRI data and outperforms the stateoftheart methods in scores prediction.