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

Background and purpose: Alzheimer disease (AD) is the most common type of dementia. Amyloid-beta (A beta) positivity is the main diagnostic marker for AD. A beta positron emission tomography and cerebrospinal fluid are widely used in the clinical diagnosis of AD. However, these methods only assess the concentrations of A beta, 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 A beta positivity for samples with normal cognition (NC), mild cognitive impairment (MCI), and dementia. We obtained 846 A beta negative (A beta-) and 865 A beta positive (A beta+) samples from the Alzheimer's Disease Neuroimaging Initiative database. To predict which samples are A beta+, 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 A beta- and 81 A beta+ samples. Results: The best performing machine learning model combining ROIs and APOE had an accuracy of 0.798, indicating that it can help predict A beta+. 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 A beta- and A beta+ 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.

Automated summary

This study investigated the use of regions of interest (ROIs) from structural magnetic resonance imaging (sMRI) and apolipoprotein E (APOE) genotypes as features to predict amyloid-beta (A beta) positivity. The machine learning model combining ROIs and APOE had an accuracy of 0.798 and identified the average left entorhinal cortical region (L-ERC) thickness as an important feature for prediction. These findings suggest that ROIs from sMRIs, along with APOE, can be used as a screening tool for early diagnosis of Alzheimer’s disease.