Early MCI-to-AD Conversion Prediction Using Future Value Forecasting of Multimodal Features

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 6628036
• Main Authors: Minhas, Sidra, Khanum, Aasia, Alvi, Atif, Riaz, Farhan, Khan, Shoab A., Alsolami, Fawaz, A Khan, Muazzam
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Accuracy; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Biomarkers; Classification; Cognitive ability; Cognitive Dysfunction; Conversion; Datasets; Dementia; Discriminant analysis; Humans; Impairment; Magnetic Resonance Imaging; Medical imaging; Neurodegenerative diseases; Neuroimaging; Neuropsychology; Predictions; Regression analysis; Signs and symptoms; Support Vector Machine; Support vector machines
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2021/6628036

In Alzheimer’s disease (AD) progression, it is imperative to identify the subjects with mild cognitive impairment before clinical symptoms of AD appear. This work proposes a technique for decision support in identifying subjects who will show transition from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) in the future. We used robust predictors from multivariate MRI-derived biomarkers and neuropsychological measures and tracked their longitudinal trajectories to predict signs of AD in the MCI population. Assuming piecewise linear progression of the disease, we designed a novel weighted gradient offset-based technique to forecast the future marker value using readings from at least two previous follow-up visits. Later, the complete predictor trajectories are used as features for a standard support vector machine classifier to identify MCI-to-AD progressors amongst the MCI patients enrolled in the Alzheimer’s disease neuroimaging initiative (ADNI) cohort. We explored the performance of both unimodal and multimodal models in a 5-fold cross-validation setup. The proposed technique resulted in a high classification AUC of 91.2% and 95.7% for 6-month- and 1-year-ahead AD prediction, respectively, using multimodal markers. In the end, we discuss the efficacy of MRI markers as compared to NM for MCI-to-AD conversion prediction.