2018 Reports

Diagnosis and Prognosis Using Machine Learning Trained on Brain Morphometry and White Matter Connectomes

Wang, Yun; Xu, Chenxiao; Park, Ji-Hwan; Lee, Seonjoo; Stern, Yaakov; Yoo, Shinjae; Kim, Jong Hun; Kim, Hyoung Seop; Cha, Jiook

Accurate, reliable prediction of risk for Alzheimer’s disease (AD) is essential for early, diseasemodifying
therapeutics. Multimodal MRI, such as structural and diffusion MRI, is likely to contain
complementary information of neurodegenerative processes in AD. Here we tested the utility of
commonly available multimodal MRI (T1-weighted structure and diffusion MRI), combined with
high-throughput brain phenotyping—morphometry and connectomics—and machine learning,
as a diagnostic tool for AD. We used, firstly, a clinical cohort at a dementia clinic (study 1: Ilsan
Dementia Cohort; N=211; 110 AD, 64 mild cognitive impairment [MCI], and 37 subjective
memory complaints [SMC]) to test and validate the diagnostic models; and, secondly,
Alzheimer’s Disease Neuroimaging Initiative (ADNI)-2 (study 2) to test the generalizability of the
approach and the prognostic models with longitudinal follow up data. Our machine learning
models trained on the morphometric and connectome estimates (number of features=34,646)
showed optimal classification accuracy (AD/SMC: 97% accuracy, MCI/SMC: 83% accuracy;
AD/MCI: 97% accuracy) with iterative nested cross-validation in a single-site study,
outperforming the benchmark model (FLAIR-based white matter hyperintensity volumes). In a
generalizability study using ADNI-2, the combined connectome and morphometry model
showed similar or superior accuracies (AD/HC: 96%; MCI/HC: 70%; AD/MCI: 75% accuracy) as
CSF biomarker model (t-tau, p-tau, and Amyloid β, and ratios). We also predicted MCI to AD
progression with 69% accuracy, compared with the 70% accuracy using CSF biomarker model.
The optimal classification accuracy in a single-site dataset and the reproduced results in multisite
dataset show the feasibility of the high-throughput imaging analysis of multimodal MRI and
data-driven machine learning for predictive modeling in AD.