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Research Paper|Volume 13, Issue 10|pp 13496—13514

MRI-based Alzheimer’s disease-resemblance atrophy index in the detection of preclinical and prodromal Alzheimer’s disease

Wanting Liu1,2, Lisa Wing Chi Au1,2, Jill Abrigo3, Yishan Luo4, Adrian Wong1,2, Bonnie Yin Ka Lam1,2, Xiang Fan1,2, Pauline Wing Lam Kwan1,2, Hon Wing Ma1,2, Anthea Yee Tung Ng1,2, Sirong Chen5, Eric Yim Lung Leung5, Chi Lai Ho5, Simon Ho Man Wong6, Winnie CW Chu3, Ho Ko1,2,7, Alexander Yuk Lun Lau1,2, Lin Shi3,4, Vincent Chung Tong Mok1,2, Alzheimer’s Disease Neuroimaging Initiative
  • 1Division of Neurology, Department of Medicine and Therapeutics, Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Hong Kong SAR, China
  • 2Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
  • 3Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
  • 4BrainNow Research Institute, Hong Kong Science and Technology Park, Hong Kong SAR, China
  • 5Department of Nuclear Medicine and PET, Hong Kong Sanatorium and Hospital, Hong Kong SAR, China
  • 6Medhealth Diagnostic MRI Centre, Hong Kong SAR, China
  • 7Li Ka Shing Institute of Health Sciences, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
* Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
Received: January 19, 2021Accepted: March 14, 2021Published: May 25, 2021
https://doi.org/10.18632/aging.203082

Copyright: © 2021 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Alzheimer’s Disease-resemblance atrophy index (AD-RAI) is an MRI-based machine learning derived biomarker that was developed to reflect the characteristic brain atrophy associated with AD. Recent study showed that AD-RAI (≥0.5) had the best performance in predicting conversion from mild cognitive impairment (MCI) to dementia and from cognitively unimpaired (CU) to MCI. We aimed to validate the performance of AD-RAI in detecting preclinical and prodromal AD. We recruited 128 subjects (MCI=50, CU=78) from two cohorts: CU-SEEDS and ADNI. Amyloid (A+) and tau (T+) status were confirmed by PET (11C-PIB, 18F-T807) or CSF analysis. We investigated the performance of AD-RAI in detecting preclinical and prodromal AD (i.e. A+T+) among MCI and CU subjects and compared its performance with that of hippocampal measures. AD-RAI achieved the best metrics among all subjects (sensitivity 0.74, specificity 0.91, accuracy 85.94%) and among MCI subjects (sensitivity 0.92, specificity 0.81, accuracy 86.00%) in detecting A+T+ subjects over other measures. Among CU subjects, AD-RAI yielded the best specificity (0.95) and accuracy (85.90%) over other measures, while hippocampal volume achieved a higher sensitivity (0.73) than AD-RAI (0.47) in detecting preclinical AD. These results showed the potential of AD-RAI in the detection of early AD, in particular at the prodromal stage.