Risk Prediction of Alzheimer's Disease Conversion in Mild Cognitive Impaired Population Based on Brain Age Estimation

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 31; pp. 2468 - 2476
• Main Authors: Liu, Weijia, Dong, Qunxi, Sun, Shuting, Shen, Jian, Qian, Kun, Hu, Bin
• Format: Journal Article
• Language: English
• Published: United States IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Age determination; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Analytical models; Brain; Brain - diagnostic imaging; brain age; Brain modeling; Chronology; Cognition; Cognitive ability; Cognitive Dysfunction - diagnosis; Conversion; conversion risk prediction; cox hazard analysis; Disease Progression; Estimation; Hazard assessment; Hazards; Humans; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Modules; Neurodegenerative diseases; Neuroimaging; nomogram; Nomograms; Risk
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2023.3247590

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases in the world. To reduce the incidence of AD, it's essential to quantify the AD conversion risk of mild cognitive impaired (MCI) individuals. Here, we propose an AD conversion risk estimation system (CRES), which contains an automated MRI feature extractor, brain age estimation (BAE) module, and AD conversion risk estimation module. The CRES is trained on 634 normal controls (NC) from the public IXI and OASIS cohorts, then it is evaluated on 462 subjects (106 NC, 102 stable MCI (sMCI), 124 progressive MCI (pMCI) and 130 AD) from the ADNI dataset. Experimental results show that the MRI derived age gap (AG, chronological age subtracted from the estimated brain age) significantly distinguish NC, sMCI, pMCI and AD groups with <inline-formula> <tex-math notation="LaTeX">{p} </tex-math></inline-formula>-value <inline-formula> <tex-math notation="LaTeX">=0.000017 </tex-math></inline-formula>. Considering AG as the primary factor, incorporating gender and Minimum Mental State Examination (MMSE) for more robust Cox multi-variate hazard analysis, we concluded that each additional year in AG is associated with 4.57% greater AD conversion risk for the MCI group. Furthermore, a nomogram was drawn to describe MCI conversion risk at the individual level in the next 1 year, 3 years, 5 years and even 8 years from baseline. This work demonstrates that CRES can estimate AG based on MRI data, evaluate AD conversion risk of the MCI subjects, and identify the individuals with high AD conversion risk, which is valuable for effective intervention and diagnosis within an early period.