A deep ensemble hippocampal CNN model for brain age estimation applied to Alzheimer’s diagnosis

Abstract

Age-associated diseases rise as life expectancy increases. The brain presents age-related structural changes across life, with different extends between subjects and groups. During the development of neurodegenerative diseases, these changes are more intense and accentuated. As Alzheimer’s disease (AD) develops, the brain reflects accelerated aging with minor extends associated with mild cognitive impairment (MCI), i.e., the prodromal stage of AD. Therefore, it is crucial to understand a healthy brain aging process to predict a cognitive decline. This study produced an efficient age estimation framework using only the hippocampal regions that explores the associations of the brain age prediction error of age-matched cognitively normal (CN) subjects with AD and MCI subjects. For this, we have developed two convolutional neural networks. The first achieved very competitive state-of-the-art metrics, i.e., mean absolute error (MAE) of 3.31 and root mean square error (RMSE) of 4.65. The second has also achieved competitive metrics, but more importantly, we founded a statistically significant analysis of our delta estimation error between the compared groups. Further, we correlated our results with clinical measurements, e.g., Mini-Mental State Examination (MMSE) score, and obtained a significant negative correlation. In addition, we compared our results with other published studies. Therefore, our findings suggest that our delta could become a biomarker to support AD and MCI diagnosis.