Previous studies investigating neuropsychological profiles of cognitive impairment people have found a learning curve can be a useful indicator of AD diagnosis or progression. However, the data on the relationship between amyloid β (Aβ) deposition status and the learning curve in amnestic mild cognitive impairment (aMCI) are limited. In this study, we investigate the role of the learning curve in predicting Aβ deposition status in patients with aMCI.

This is a cross-sectional study of 67 aMCI patients (N = 67; 33 aMCI with amyloid positive (Aβ-PET (+)), and 34 aMCI with amyloid negative (Aβ-PET (-))). All participants underwent Seoul Neuropsychological Screening Battery for a comprehensive neuropsychological test battery and brain MRI. To determine Aβ deposition status, each participant underwent amyloid PET scans using 18F-florbetaben. The learning curve was obtained using immediate recall of Seoul Verbal Learning Test-learning curve (SVLT- learning curve). The association of cognitive test scores and dichotomized Aβ deposition status was examined using logistic regression models in patients with aMCI. Receiver operating characteristic (ROC) curves were used to examine the predictive ability of cognitive test to detect Aβ deposition status in aMCI.

Logistic regression models showed that SVLT-learning curve and Rey Complex Figure Test- delayed recall (RCFT-delayed recall) scores were significantly associated with Aβ deposition status. In ROC analysis to assess the predictive power, SVLT-learning curve (area under the curve (AUC) = 0.734, P = 0.001) and RCFT-delayed recall (AUC = 0.739, P = 0.001) independently discriminated Aβ-PET (+) and Aβ-PET (-). The combination of these clinical markers (SVLT-learning curve and RCFT-delayed recall) improved the predictive accuracy of Aβ-PET (+) (AUC = 0.833, P < 0.001).

Our findings of association of Aβ deposition status with SVLT-learning curve and RCFT- delayed recall suggest that these cognitive tests could be a useful screening tool for Aβ deposition status among aMCI patients in resource-limited clinics.