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MRI-Based Alzheimer’s Disease-Resemblance Atrophy Index in the Detection of Preclinical and Prodromal Alzheimer’s Disease
Alexander Yuk Lun Lau
Chinese University of Hong Kong
Corresponding Author
ORCiD: https://orcid.org/0000-0002-5933-9290
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This work is licensed under a CC BY 4.0 License. Read Full License
Background: We aimed to validate the performance of an MRI-based machine learning derived Alzheimer’s Disease-resemblance atrophy index (AD-RAI) in detecting preclinical and prodromal AD.
Methods: A total of 62 subjects (mild cognitive impairment [MCI]=25, cognitively unimpaired [CU]=37) underwent MRI, 11C- PIB, and 18F-T807 PET. We investigated the performance of AD-RAI at the pre-specified cutoff of ≥ 0.5 in detecting preclinical and prodromal AD and compared its performance with that of visual and volumetric hippocampal measures.
Results: AD-RAI achieved the best metrics among all subjects (sensitivity 0.73, specificity 0.91, accuracy 87.10%) and among MCI subgroup (sensitivity 0.91, specificity 0.79, accuracy 84.00%) in detecting A+T+ subjects over other measures. Among CU subgroup, hippocampal volume (sensitivity 0.75, specificity 0.88, accuracy 86.49%) achieved a higher sensitivity than AD-RAI (sensitivity 0.25, specificity 0.97, accuracy 89.19%) in detecting preclinical AD.
Conclusions: AD-RAI aids the detection of early AD, in particular at the prodromal stage.
Keywords
preclinical Alzheimer’s Disease, prodromal Alzheimer’s Disease, volumetric segmentation tool, MRI, amyloid PET, tau PET
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This is a preprint. It has not completed peer review.
Research
MRI-Based Alzheimer’s Disease-Resemblance Atrophy Index in the Detection of Preclinical and Prodromal Alzheimer’s Disease
Alexander Yuk Lun Lau
Chinese University of Hong Kong
Corresponding Author
ORCiD: https://orcid.org/0000-0002-5933-9290
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 12 Aug, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Cognitive Neuroscience
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: We aimed to validate the performance of an MRI-based machine learning derived Alzheimer’s Disease-resemblance atrophy index (AD-RAI) in detecting preclinical and prodromal AD.
Methods: A total of 62 subjects (mild cognitive impairment [MCI]=25, cognitively unimpaired [CU]=37) underwent MRI, 11C- PIB, and 18F-T807 PET. We investigated the performance of AD-RAI at the pre-specified cutoff of ≥ 0.5 in detecting preclinical and prodromal AD and compared its performance with that of visual and volumetric hippocampal measures.
Results: AD-RAI achieved the best metrics among all subjects (sensitivity 0.73, specificity 0.91, accuracy 87.10%) and among MCI subgroup (sensitivity 0.91, specificity 0.79, accuracy 84.00%) in detecting A+T+ subjects over other measures. Among CU subgroup, hippocampal volume (sensitivity 0.75, specificity 0.88, accuracy 86.49%) achieved a higher sensitivity than AD-RAI (sensitivity 0.25, specificity 0.97, accuracy 89.19%) in detecting preclinical AD.
Conclusions: AD-RAI aids the detection of early AD, in particular at the prodromal stage.
Figure 1
Figure 2