| Full text | |
| Author(s): |
Poloni, Katia Maria
;
Ferrari, Ricardo Jose
;
Alzheimer's Dis Neuroimaging Initi
Total Authors: 3
|
| Document type: | Journal article |
| Source: | EXPERT SYSTEMS WITH APPLICATIONS; v. 195, p. 12-pg., 2022-02-11. |
| 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. (AU) | |
| FAPESP's process: | 18/06049-5 - Automatic computational scheme for the detection, identification and classification of cerebral structural changes in magnetic resonance images to aid the diagnosis of patients with mild cognitive impairment and mild Alzheimer's disease |
| Grantee: | Katia Maria Poloni |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 18/08826-9 - Development of feature engineering and deep learning techniques applied to the classification of magnetic resonance images in healthy cognitive aging, mild cognitive impairment and Alzheimer's Disease |
| Grantee: | Ricardo José Ferrari |
| Support Opportunities: | Regular Research Grants |