Detecting amyloid-β positivity using regions of interest from structural magnetic resonance imaging

for the Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background and purpose: Alzheimer disease (AD) is the most common type of dementia. Amyloid-β (Aβ) positivity is the main diagnostic marker for AD. Aβ positron emission tomography and cerebrospinal fluid are widely used in the clinical diagnosis of AD. However, these methods only assess the concentrations of Aβ, and the accessibility of these methods is thus relatively limited compared with structural magnetic resonance imaging (sMRI). Methods: We investigated whether regions of interest (ROIs) in sMRIs can be used to predict Aβ positivity for samples with normal cognition (NC), mild cognitive impairment (MCI), and dementia. We obtained 846 Aβ negative (Aβ−) and 865 Aβ positive (Aβ+) samples from the Alzheimer's Disease Neuroimaging Initiative database. To predict which samples are Aβ+, we built five machine learning models using ROIs and apolipoprotein E (APOE) genotypes as features. To test the performance of the machine learning models, we constructed a new cohort containing 97 Aβ− and 81 Aβ+ samples. Results: The best performing machine learning model combining ROIs and APOE had an accuracy of 0.798, indicating that it can help predict Aβ+. Furthermore, we searched ROIs that could aid our prediction and discovered that an average left entorhinal cortical region (L-ERC) thickness is an important feature. We also noted significant differences in L-ERC thickness between the Aβ− and Aβ+ samples even in the same diagnosis of NC, MCI, and dementia. Conclusions: Our findings indicate that ROIs from sMRIs along with APOE can be used as an initial screening tool in the early diagnosis of AD.

Original languageEnglish
Pages (from-to)1574-1584
Number of pages11
JournalEuropean Journal of Neurology
Volume30
Issue number6
DOIs
StatePublished - Jun 2023

Bibliographical note

Funding Information:
This work was supported by the Bio & Medical Technology Development Program of the NRF funded by the Korean Government (MSIT; NRF‐2018M3C7A1054935) and an Institute of Information Communications Technology Planning Evaluation grant funded by the Korea government (MSIT; No. 2019–0‐01842, Artificial Intelligence Graduate School Program).

Publisher Copyright:
© 2023 European Academy of Neurology.

Keywords

  • Alzheimer disease
  • amyloid-β
  • left entorhinal cortical region
  • machine learning
  • sMRI

Fingerprint

Dive into the research topics of 'Detecting amyloid-β positivity using regions of interest from structural magnetic resonance imaging'. Together they form a unique fingerprint.

Cite this