Brain Age Prediction in Generalized Anxiety Disorder using a Convolutional Neural Network.

Abstract

Higher predicted brain age difference has been associated with several psychiatric disorders. Generalized anxiety disorder (GAD) is associated with markers of accelerated aging. In this study, we determined brain predicted age difference (PAD) in individuals with GAD and healthy controls (HC) as well as group differences in PAD variability using voxel-wise structural MRI. The training dataset included 3,511 controls, and the testing dataset included 1,595 individuals with GAD and 4,552 HC from the ENIGMA-Anxiety GAD Working Group. A convolutional neural network model using four input modalities per subject and a model ensemble approach was used to predict brain age. The PAD was then calculated by subtracting chronological age. Model performance was consistent with other image-based brain age prediction models with similar accuracy across the training set (mean absolute error (MAE) = 2.95 years) and HC in the testing set (MAE = 2.94). We found no evidence of accelerated brain aging in individuals with GAD, though we did find evidence for greater variation in PAD for individuals with GAD (Levene's test: W = 442.98, p < .001) and evidence for greater variability in PAD of those with GAD over 25 years of age. No relationships between PAD and clinical or demographic measures were found. To conclude, using large training and testing samples, the study found no significant association between GAD and PAD, although individuals with GAD had greater heterogeneity in brain-predicted age.