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

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 under...

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Bibliographic Details
Published in:Scientific reports 2018-03, Vol.8 (1), p.4161-10, Article 4161
Main Authors: Lee, Jin San, Kim, Changsoo, Shin, Jeong-Hyeon, Cho, Hanna, Shin, Dae-seock, Kim, Nakyoung, Kim, Hee Jin, Kim, Yeshin, Lockhart, Samuel N., Na, Duk L., Seo, Sang Won, Seong, Joon-Kyung
Format: Article
Language:English
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Alzheimer's disease
Artificial intelligence
Atrophy
Cognitive ability
Cortex
Dementia
Dementia disorders
Humanities and Social Sciences
Learning algorithms
Magnetic resonance imaging
Morphometry
multidisciplinary
Science
Science (multidisciplinary)
Similarity measures
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Summary: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  
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-018-22277-x