Health Science Technology Group Non-Commercial Software License Agreement Yes Universidad Politécnica de Madrid NITRC Predicting Alzheimer’s conversion in mild cognitive impairment patients using longitudinal neuroimaging and clinical markers Patients with mild cognitive impairment (MCI) have a high risk for conversion to Alzheimer’s disease (AD). Selecting a set of relevant markers from multimodal data to predict conversion from MCI to AD has become a challenging task. The aim of this project is to quantify the impact of longitudinal predictive models with single- or multisource data for predicting MCI-to-AD conversion and identifying a very small subset of features that are highly predictive of conversion. We developed predictive models of MCI-to-AD progression that combine magnetic resonance imaging (MRI)-based markers (cortical thickness and volume of subcortical structures) with neuropsychological tests. A set of longitudinal features potentially discriminating between MCI subjects who convert to AD and those who remain stable over a period of 3 years was obtained. The proposed approach was developed, trained and evaluated using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset 2018-11-07 Demo source matlab 2017a Predicting Alzheimer’s conversion in mild cognitive impairment patients using longitudinal neuroimaging and clinical markers Clinical Neuroinformatics, MR, Computational Neuroscience, Non-Commercial Software License Agreement http://stage.nitrcce.org/projects/predict_mci2ad/, http://http://www.nitrc.org/projects/predict_mci2ad/