Predicting Alzheimer's conversion in mild cognitive impairment patients using longitudinal neuroimaging and clinical markers.
Carlos PlateroM Carmen Tobarnull nullPublished in: Brain imaging and behavior (2021)
Patients with mild cognitive impairment (MCI) have a high risk for conversion to Alzheimer's disease (AD). Early diagnose of AD in MCI subjects could help to slow or halt the disease progression. Selecting a set of relevant markers from multimodal data to predict conversion from MCI to probable AD has become a challenging task. The aim of this paper 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. These models were built with longitudinal data and validated using baseline values. By using a linear mixed effects approach, we modeled the longitudinal trajectories of the markers. A set of longitudinal features potentially discriminating between MCI subjects who convert to dementia and those who remain stable over a period of 3 years was obtained. Classifier were trained using the marginal longitudinal trajectory residues from the selected features. Our best models predicted conversion with 77% accuracy at baseline (AUC = 0.855, 84% sensitivity, 70% specificity). As more visits were available, longitudinal predictive models improved their predictions with 84% accuracy (AUC = 0.912, 83% sensitivity, 84% specificity). The proposed approach was developed, trained and evaluated using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with a total of 2491 visits from 610 subjects.
Keyphrases
- mild cognitive impairment
- cognitive decline
- magnetic resonance imaging
- cross sectional
- electronic health record
- end stage renal disease
- big data
- high resolution
- ejection fraction
- chronic kidney disease
- peritoneal dialysis
- depressive symptoms
- chronic pain
- mass spectrometry
- quality improvement
- body composition
- artificial intelligence
- optical coherence tomography