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

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