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Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation

Authors
 Jin San Lee  ;  Changsoo Kim  ;  Jeong-Hyeon Shin  ;  Hanna Cho  ;  Dae-Seock Shin  ;  Nakyoung Kim  ;  Hee Jin Kim  ;  Yeshin Kim  ;  Samuel N Lockhart  ;  Duk L Na  ;  Sang Won Seo  ;  Joon-Kyung Seong 
Citation
 SCIENTIFIC REPORTS, Vol.8(1) : 4161, 2018 
Journal Title
 SCIENTIFIC REPORTS 
Issue Date
2018
Abstract
To develop a new method for measuring Alzheimer's disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed an individual-level machine learning algorithm. A total of 869 cognitively normal (CN) individuals and 473 patients with probable AD dementia who underwent high-resolution 3T brain MRI were included. We propose a machine learning-based method for measuring the similarity of an individual subject's cortical atrophy pattern with that of a representative AD patient cohort. In addition, we validated this similarity measure in two longitudinal cohorts consisting of 79 patients with amnestic-mild cognitive impairment (aMCI) and 27 patients with probable AD dementia. Surface-based morphometry classifier for discriminating AD from CN showed sensitivity and specificity values of 87.1% and 93.3%, respectively. In the longitudinal validation study, aMCI-converts had higher atrophy similarity at both baseline (p < 0.001) and first year visits (p < 0.001) relative to non-converters. Similarly, AD patients with faster decline had higher atrophy similarity than slower decliners at baseline (p = 0.042), first year (p = 0.028), and third year visits (p = 0.027). The AD-specific atrophy similarity measure is a novel approach for the prediction of dementia risk and for the evaluation of AD trajectories on an individual subject level.
Files in This Item:
T201801040.pdf Download
DOI
10.1038/s41598-018-22277-x
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Preventive Medicine and Public Health (예방의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Neurology (신경과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Chang Soo(김창수) ORCID logo https://orcid.org/0000-0002-5940-5649
Cho, Hanna(조한나) ORCID logo https://orcid.org/0000-0001-5936-1546
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/162196
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