Early identification of Alzheimer’s risk using anatomically interpretable estimation of brain age

Abstract

AbstractBackgroundNeuroanatomical aging patterns can be revealed by examining the discrepancy between brain age (BA) and chronological age (CA) – also known as age gaps (AGs). Older than expected BAs, as estimated using deep learning of magnetic resonance images (MRIs), are observed in patients with mild cognitive impairment (MCI) and Alzheimer’s Disease (AD) (Tseng et al., 2022). Our 3‐dimensional convolutional neural network (3D‐CNN) enhances the accuracy of BA estimation and employs saliency mapping to explore structural patterns of aging between persons with and without CI.MethodsMRIs and neurocognitive measures for 6,561 participants (5,851 cognitively normal ‘CN’, 351 MCI, 359 AD) were collated across online repositories (Downey & Peakman, 2008; Petersen et al., 2010; Van Essen et al., 2012). We constructed a deep learning regression model using a 3D‐CNN whose inputs are pre‐processed MRIs (Fischl, 2012) and whose outputs are estimated BAs and saliency maps (Yin et al., 2023). Spearman rank correlations between neurocognitive measures and BA and CA were calculated, then compared using Fisher’s z‐test. The ability of AGs to predict conversion from MCI to AD was examined using logistic regression.ResultsIn participants with CI, older BA (but not CA) is significantly correlated with worse neurocognitive function across all measures except delayed verbal recall and logical memory (Table 1, Fig 1). AGs are positively associated with MCI participants’ probability of conversion to AD (β = 1.417, t 343 = 2.240, p = 0.025). Saliency maps reveal that, in CI participants, the 3D‐CNN relies more on features of the cerebral white matter, the brainstem, medial aspects of the temporal lobes, and the caudal portions of the anterior cingulate when estimating age according to cognitive status (Fig 2).ConclusionsCompared to CA, our BAs are significantly more strongly associated with early signs of Alzheimer’s disease, possibly reflecting disease‐related structural alterations. Saliency maps conveying the importance of each brain region for estimating BA reveal differences in patterns of neurological aging between persons with and without CI. Our BA estimation tool enhances the interpretability of such models, potentially improving the ability to stratify people according to risk of CI.