Diagnosis and prognosis of Alzheimer's disease using 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

doi:10.1016/j.nicl.2019.101859

Title: Diagnosis and prognosis of Alzheimer's disease using brain morphometry and white matter connectomes

Journal Article · 13 May 2019 · NeuroImage: Clinical

DOI: https://doi.org/10.1016/j.nicl.2019.101859 · OSTI ID:1514489

^[1]; Xu, Chenxiao ^[2]; Park, Ji-Hwan ^[2]; Lee, Seonjoo ^[3]; Stern, Yaakov ^[4]; Yoo, Shinjae ^[5]; Kim, Jong Hun ^[6]; Kim, Hyoung Seop ^[7];

^[8]

Columbia Univ., New York, NY (United States). Medical Center. Dept. of Psychiatry
Stony Brook Univ., NY (United States). Dept. of Applied Mathematics
Columbia Univ., New York, NY (United States). Medical Center. Dept. of Biostatistics. School of Public Health
Columbia Univ., New York, NY (United States). Medical Center. Dept. of Neurology
Brookhaven National Lab. (BNL), Upton, NY (United States). Computational Science Initiative
National Health Insurance Service Ilsan Hospital, Goyang (Korea, Republic of). Dept. of Neurology
National Health Insurance Service Ilsan Hospital, Goyang (Korea, Republic of). Dept. of Physical Medicine and Rehabilitation
Columbia Univ., New York, NY (United States). Medical Center. Dept. of Psychiatry. Data Science Inst.

Accurate, reliable prediction of risk for Alzheimer's disease (AD) is essential for early, disease-modifying 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 the multimodal MRI (T1-weighted structure and diffusion MRI), combined with high-throughput brain phenotyping—morphometry and structural connectomics—and machine learning, as a diagnostic tool for AD. We used, firstly, a clinical cohort at a dementia clinic (National Health Insurance Service-Ilsan Hospital [NHIS-IH]; N = 211; 110 AD, 64 mild cognitive impairment [MCI], and 37 cognitively normal with subjective memory complaints [SMC]) to test the diagnostic models; and, secondly, Alzheimer's Disease Neuroimaging Initiative (ADNI)-2 to test the generalizability. 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) in NHIS-IH cohort, outperforming a benchmark model (FLAIR-based white matter hyperintensity volumes). In ADNI-2 data, the combined connectome and morphometry model showed similar or superior accuracies (AD/HC: 96%; MCI/HC: 70%; AD/MCI: 75% accuracy) compared with the CSF biomarker model (t-tau, p-tau, and Amyloid β, and ratios). In predicting MCI to AD progression in a smaller cohort of ADNI-2 (n = 60), the morphometry model showed similar performance with 69% accuracy compared with CSF biomarker model with 70% accuracy. Our comparisons of the classifiers trained on structural MRI, diffusion MRI, FLAIR, and CSF biomarkers showed the promising utility of the white matter structural connectomes in classifying AD and MCI in addition to the widely used structural MRI-based morphometry, when combined with machine learning.

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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States); Columbia Univ., New York, NY (United States); National Health Insurance Service Ilsan Hospital, Goyang (Korea, Republic of)

Sponsoring Organization:: USDOE; National Inst. of Health (NIH) (United States); National Health Insurance Ilsan Hospital Research Fund (Korea, Republic of)

Grant/Contract Number:: SC0012704; K01 MH109836

OSTI ID:: 1514489

Report Number(s):: BNL-211602-2019-JAAM

Journal Information:: NeuroImage: Clinical, Vol. 23; ISSN 2213-1582

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 19 works

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