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

Abstract

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 pvalue = 0.000017. 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.

Brain age as a biomarker for pathological versus healthy ageing – a REMEMBER study

“…Furthermore, no significant effects were seen for higher BPAD in relation to conversion time from MCI to ADD, which is contradictory to earlier findings. However, the previously conducted studies were analyzed using ADNI data rather than in the context of a real-world clinical setting [ 15 , 35 , 36 , 59 ].…”

“…3). To illustrate the diagnostic subgroup distributions, the min-max ranges of the MMSE scores were reported: CN [27][28][29][30], SCD [23][24][25][26][27][28][29][30], MCI [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], and ADD . Significant (positive) correlations for brain age to BPAD (r = 0.908, p < 0.001) and Age (r = 0.452, p < 0.001) were found, as well as inverse (negative) correlations with MMSE (meaning an increase in brain age is correlated to a decline in MMSE (r = -0.382, p < 0.001)).…”

“…A brain age estimation higher than that of healthy agematched peers has already been linked to AD [13][14][15][16], Parkinson's disease [17], Schizophrenia [16,[18][19][20], Multiple Sclerosis [21][22][23], as well as to life expectancy [24]. Various brain age models exist [25], but tend to be rather complex in their interpretation.…”

“…Various brain age models exist [25], but tend to be rather complex in their interpretation. In relation to AD brain age research, previous literature mainly focused on publicly available neuroimaging datasets [14,15,[26][27][28], symptomatic AD [8,13,14,[29][30][31], had limited generalizability of findings to more diverse populations due to population bias [14,32,33], were constricted to the use of 1.5T MRI scans [34], or exclusively conducted crosssectional analysis [28]. In addition, there is a notable scarcity of studies exploring BPAD using a longitudinal approach [35,36] and if conducted in a real-world clinical context, sample sizes are small [37].…”

Tau Protein Accumulation Trajectory-Based Brain Age Prediction in the Alzheimer’s Disease Continuum

“…In addition, other types of biomarkers, such as plasma and cerebrospinal fluid markers [ 45 ], need to be incorporated to complement the ATN framework and achieve a more complete model in the future. Given the previous research on brain age [ 46 , 47 , 48 ], we believe this model could be utilized as a sensitive biomarker for cognitive function decline during the symptomatic stage.…”