Comparison of deep learning methods for brain age prediction: Neuroimaging / Normal brain aging. (7th December 2020)

• Record Type: Journal Article
• Authors: Lam, Pradeep; Zhu, Alyssa; Salminen, Lauren; Thomopoulos, Sophia I; Bright, Joanna; Jahanshad, Neda; Thompson, Paul M
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 5
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• 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.046763
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: BrainAge ‐ an estimate of a person's age predicted from their structural MRI ‐ is a biomarker used to quantify age‐related neurodegeneration. The deviation of BrainAge from chronological age (Predicted Age Difference ‐ PAD) is of interest for prognostic research. Here we evaluated the accuracy of several deep learning methods (convolutional neural networks; CNNs) in predicting brain age. Method: All experiments were conducted with a subset of 10, 801 neurologically healthy participants from the UK Biobank. Of these, 9, 301 were scanned at a single site; an additional 1, 500 were from auxiliary sites with the same calibration standards. Participants' age range was 48.9‐76.6 years. The primary site data had a mean age of 62.4 (SD, 7.5; 47% women/53% men). The auxiliary site scanned subjects with a mean age of 63.9 (SD, 7.3; 55% women/45% men). From the 9, 301 single‐site scans, 7, 153 were used to train the models; the rest were split into validation and testing sets. All auxiliary site data was used for out‐of‐site testing. Each model was trained using a different divergence penalty. In addition, a single model was evaluated on 25, 756 participants from the UK Biobank dataset. K ‐fold Cross‐Validation was used to prevent overlap between train, validation, and test sets. For each training instance, 12, 365 subjects were used for the training set; the rest were split evenly between validation and test sets. For this experiment, subjects were not filtered out based on neurological health. Result: All models performed well on both the in‐site and out‐of‐site test sets with an average mean absolute error (MAE) of 2.98 and 2.94 years, respectively. The model evaluated on 25, 756 subjects received a MAE of 2.85 (R 0.845) with a residual correlation of 0.432 between PAD and true age. Conclusion: Good accuracy achieved by all models suggests the value of BrainAge as a holistic biomarker of brain aging.