Background: Longitudinal studies using structural magnetic resonance imaging (MRI) and neuropsychological measurements (NMs) allow a noninvasive means of following the subtle anatomical changes occurring during the evolution of AD. New Method: This paper compared two approaches for the construction of longitudinal predictive models: a) two-group comparison between converter and nonconverter MCI subjects and b) longitudinal survival analysis. Predictive models combined MRI-based markers with NMs and included demographic and clinical information as covariates. Both approaches employed linear mixed effects modeling to capture the longitudinal trajectories of the markers. The two-group comparison approaches used linear discriminant analysis and the survival analysis used risk ratios obtained from the extended Cox model and logistic regression. Results: The proposed approaches were developed and evaluated using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset with a total of 1330 visits from 321 subjects. With both approaches, a very small number of features were selected. These markers are easily interpretable, generating robust, verifiable and reliable predictive models. Our best models predicted conversion with 78% accuracy at baseline (AUC = 0.860, 79% sensitivity, 76% specificity). As more visits were made, longitudinal predictive models improved their predictions with 85% accuracy (AUC = 0.944, 86% sensitivity, 85% specificity). Comparison with Existing Method: Unlike the recently published models, there was also an improvement in the prediction accuracy of the conversion to AD when considering the longitudinal trajectory of the patients. Conclusions: The survival-based predictive models showed a better balance between sensitivity and specificity with respect to the models based on the two-group comparison approach.
Background: Longitudinal studies using structural magnetic resonance imaging (MRI) and neuropsychological measurements (NMs) allow a noninvasive means of following the subtle anatomical changes occurring during the evolution of AD. New Method: This paper compared two approaches for the construction of longitudinal predictive models: a) two-group comparison between converter and nonconverter MCI subjects and b) longitudinal survival analysis. Predictive models combined MRI-based markers with NMs and included demographic and clinical information as covariates. Both approaches employed linear mixed effects modeling to capture the longitudinal trajectories of the markers. The two-group comparison approaches used linear discriminant analysis and the survival analysis used risk ratios obtained from the extended Cox model and logistic regression. Results: The proposed approaches were developed and evaluated using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset with a total of 1330 visits from 321 subjects. With both approaches, a very small number of features were selected. These markers are easily interpretable, generating robust, verifiable and reliable predictive models. Our best models predicted conversion with 78% accuracy at baseline (AUC = 0.860, 79% sensitivity, 76% specificity). As more visits were made, longitudinal predictive models improved their predictions with 85% accuracy (AUC = 0.944, 86% sensitivity, 85% specificity). Comparison with Existing Method: Unlike the recently published models, there was also an improvement in the prediction accuracy of the conversion to AD when considering the longitudinal trajectory of the patients. Conclusions: The survival-based predictive models showed a better balance between sensitivity and specificity with respect to the models based on the two-group comparison approach. Read More
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