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
- 1 Division 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
- 2 Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
- 3 Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
- 4 BrainNow Research Institute, Hong Kong Science and Technology Park, Hong Kong SAR, China
- 5 Department of Nuclear Medicine and PET, Hong Kong Sanatorium and Hospital, Hong Kong SAR, China
- 6 Medhealth Diagnostic MRI Centre, Hong Kong SAR, China
- 7 Li Ka Shing Institute of Health Sciences, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
Received: January 19, 2021 Accepted: March 14, 2021 Published: May 25, 2021
https://doi.org/10.18632/aging.203082How to Cite
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.
Abbreviations
AD: Alzheimer’s disease; AD-RAI: Alzheimer’s disease- resemblance atrophy index; MCI: mild cognitive impairment; CU: cognitively unimpaired; NIA-AA: National Institute on Aging and Alzheimer’s Association; A+: amyloid-βpositive; T+: neurofibrillary tau positive; PET: positron emission tomography; CSF: cerebrospinal fluid; FDG: Fluorodeoxyglucose; MRI: magnetic resonance imaging; MTA: medial temporal lobe atrophy; HV: hippocampal volume; ADNI: Alzheimer’s Disease Neuroimaging Initiatives; CU-SEEDS: The Chinese University of Hong Kong - Screening for Early AlzhEimer’s DiseaSe; CAMI: Chinese Abbreviated Memory Inventory; HKLLT: Hong Kong List Learning Test; HK-MoCA: Hong Kong version of Montreal Cognitive Assessment; CDR: clinical dementia rating scale; MMSE: Mini Mental State Examination; SUVR: Standard Uptake Value ratio; HF: hippocampal fraction; ROC: receiving operating curve; CI: confidence intervals; PPV: positive prediction values; NPV: negative prediction values.