Functional MRI and ApoE4 genotype for predicting cognitive decline in amyloid-positive individuals

Jun-Ding Zhu; Chi-Wei Huang; Hsin-I Chang; Shih-Jen Tsai; Shu-Hua Huang; Shih-Wei Hsu; Chen-Chang Lee; Hong-Jie Chen; Chiung-Chih Chang; Albert C Yang

doi:10.1177/17562864221138154

Functional MRI and ApoE4 genotype for predicting cognitive decline in amyloid-positive individuals

Ther Adv Neurol Disord. 2022 Nov 19:15:17562864221138154. doi: 10.1177/17562864221138154. eCollection 2022.

Authors

Jun-Ding Zhu¹, Chi-Wei Huang², Hsin-I Chang², Shih-Jen Tsai^{1

3

4}, Shu-Hua Huang⁵, Shih-Wei Hsu⁶, Chen-Chang Lee⁶, Hong-Jie Chen⁵, Chiung-Chih Chang⁷, Albert C Yang^{8

9}

Affiliations

¹ Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan.
² Department of Neurology, Cognition and Aging Center, Institute for Translational Research in Biomedicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
³ Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.
⁴ Division of Psychiatry, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
⁵ Department of Nuclear Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
⁶ Department of NeuroRadiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
⁷ Cognition and Aging Center, Institute for Translational Research in Biomedicine, Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123 Ta-Pei Road, Niau-Sung District, Kaohsiung 833, Taiwan.
⁸ Institute of Brain Science/Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong Street, Beitou District, Taipei 112, Taiwan.
⁹ Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan.

Abstract

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.

Keywords: Alzheimer’s disease; ApoE genotype; amyloid; cognition; functional connectivity; machine learning.