Characterizing brain age in the Alzheimer's disease connectome project using a deep neural network pre‐trained on the UK Biobank. (31st December 2021)

• Record Type: Journal Article
• Authors: Adluru, Nagesh; Nair, Veena A.; Prabhakaran, Vivek; Bashyam, Vishnu; Li, Shi‐Jiang; Okonkwo, Ozioma C.; Davidson, Richard J.; Alexander, Andrew L.; Bendlin, Barbara B.
• Is Part Of: Alzheimer's & dementia. Volume 17(2021)Supplement 5
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-31
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.057535
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Overall age of an individual is commonly measured in chronological time. With the advent of in vivo imaging and reliable predictive modeling, a finer gradation of the age into many internal biological scales such as cellular, tissue, organ and cognitive aging etc. can be estimated. Accurate estimation of the age of the in vivo brain can offer insights into its differential aging patterns and accelerated excess aging due to the burden of disease processes eventually leading to dementia. Modern machine learning algorithms have allowed for accurate estimation of "brain age" using MRI data. The purpose of this study was to evaluate the applicability of a 3D fully‐convolutional neural network (FCNN), pre‐trained on a large dataset of over ten thousand T1‐weighted (T1‐w) MR images from the UK Biobank to data from the Alzheimer's disease connectome project. Method: T1‐w data acquired from 152 participants were minimally pre‐processed (brain masking, B1 bias reduction, and linear registration to MNI) and input to the pre‐trained FCNN to predict their brain age. A linear regression between calendar age and brain age gap was used to correct the bias in each of the predicted brain age. Result: The brain age predictions before and after bias correction, mean and spread of the calendar ages, mean absolute error of prediction for each group and sex are shown in Fig. 1. The quantiles of the excess brain aging are shown in Fig. 2. Conclusion: With a minimal pre‐processing of T1w‐MRI, the UK Biobank pre‐trained deep neural network (DNN), along with a linear bias correction, provided an estimate of the age of the brain in ADCP with excellent approximation. The analysis revealed that the median gap between the brain age and calendar age is about a year in the AD group, and about half‐a‐year in the MCI group and none in the CU group, when averaged over the sexes. This provides preliminary evidence of accelerated aging of the brain in the cognitively impaired groups (MCI/AD). Future analysis involves examining excess brain and cognitive aging due to amyloid and tau burden. Future work entails developing DNNs to improve generalization, standardization, and individualized precision of such predictive models.