In Therapeutic advances in neurological disorders
BACKGROUND : In light of advancements in machine learning techniques, many studies have implemented machine learning approaches combined with data measures to predict and classify Alzheimer's disease. Studies that predicted cognitive status with longitudinal follow-up of amyloid-positive individuals remain scarce, however.
OBJECTIVE : We developed models based on voxel-wise functional connectivity (FC) density mapping and the presence of the ApoE4 genotype to predict whether amyloid-positive individuals would experience cognitive decline after 1 year.
METHODS : We divided 122 participants into cognitive decline and stable cognition groups based on the participants' change rates in Mini-Mental State Examination scores. In addition, we included 68 participants from Alzheimer's Disease Neuroimaging Initiative (ADNI) database as an external validation data set. Subsequently, we developed two classification models: the first model included 99 voxels, and the second model included 99 voxels and the ApoE4 genotype as features to train the models by Wide Neural Network algorithm with fivefold cross-validation and to predict the classes in the hold-out test and ADNI data sets.
RESULTS : The results revealed that both models demonstrated high accuracy in classifying the two groups in the hold-out test data set. The model for FC demonstrated good performance, with a mean F 1-score of 0.86. The model for FC combined with the ApoE4 genotype achieved superior performance, with a mean F 1-score of 0.90. In the ADNI data set, the two models demonstrated stable performances, with mean F 1-scores of 0.77 in the first and second models.
CONCLUSION : Our findings suggest that the proposed models exhibited promising accuracy for predicting cognitive status after 1 year in amyloid-positive individuals. Notably, the combination of FC and the ApoE4 genotype increased prediction accuracy. These findings can assist clinicians in predicting changes in cognitive status in individuals with a high risk of Alzheimer's disease and can assist future studies in developing precise treatment and prevention strategies.
Zhu Jun-Ding, Huang Chi-Wei, Chang Hsin-I, Tsai Shih-Jen, Huang Shu-Hua, Hsu Shih-Wei, Lee Chen-Chang, Chen Hong-Jie, Chang Chiung-Chih, Yang Albert C
2022
Alzheimer’s disease, ApoE genotype, amyloid, cognition, functional connectivity, machine learning
