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

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 Alzhei...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2021, Vol.2021 (1), p.6628036-6628036
Main Authors: Minhas, Sidra, Khanum, Aasia, Alvi, Atif, Riaz, Farhan, Khan, Shoab A., Alsolami, Fawaz, A Khan, Muazzam
Format: Article
Language:English
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
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Summary: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.
ISSN:1687-5265
1687-5273
DOI:10.1155/2021/6628036