Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Journal of Alzheimer's disease : JAD

BACKGROUND : Amyloid (Aβ) evaluation in amnestic mild cognitive impairment (aMCI) patients is important for predicting conversion to Alzheimer's disease. However, Aβ evaluation through amyloid positron emission tomography (PET) is limited due to high cost and safety issues.

OBJECTIVE : We therefore aimed to develop and validate prediction models of Aβ positivity for aMCI using optimal interpretable machine learning (ML) approaches utilizing multimodal markers.

METHODS : We recruited 529 aMCI patients from multiple centers who underwent Aβ PET. We trained ML algorithms using a training cohort (324 aMCI from Samsung medical center) with two-phase modelling: model 1 included age, gender, education, diabetes, hypertension, apolipoprotein E genotype, and neuropsychological test scores; model 2 included the same variables as model 1 with additional MRI features. We used four-fold cross-validation during the modelling and evaluated the models on an external validation cohort (187 aMCI from the other centers).

RESULTS : Model 1 showed good accuracy (area under the receiver operating characteristic curve [AUROC] 0.837) in cross-validation, and fair accuracy (AUROC 0.765) in external validation. Model 2 led to improvement in the prediction performance with good accuracy (AUROC 0.892) in cross validation compared to model 1. Apolipoprotein E genotype, delayed recall task scores, and interaction between cortical thickness in the temporal region and hippocampal volume were the most important predictors of Aβ positivity.

CONCLUSION : Our results suggest that ML models are effective in predicting Aβ positivity at the individual level and could help the biomarker-guided diagnosis of prodromal AD.

Kang Sung Hoon, Cheon Bo Kyoung, Kim Ji-Sun, Jang Hyemin, Kim Hee Jin, Park Kyung Won, Noh Young, Lee Jin San, Ye Byoung Seok, Na Duk L, Lee Hyejoo, Seo Sang Won


Aβ PET, amnestic mild cognitive impairment, amyloid-β positivity, machine learning, magnetic resonance imaging features, neuropsychological tests, prediction model