Brain age prediction in generalized anxiety disorder using a convolutional neural network

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 3511 controls, and the testing dataset included 1595 individuals with GAD and 4552 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 from the predicted 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 compared to individuals without GAD, though we did find evidence for greater variation in PAD for individuals with GAD (Levene's test: W = 442.98, p < 0.001) and evidence for greater variability in PAD of those with GAD over 25 years of age. In several exploratory analyses, we found that symptom severity related significantly to PAD, even after controlling for medication and comorbid diagnoses, echoing previous brain age research. These findings underscore the need for consideration of heterogeneity and dimensionality of psychopathology when examining brain age predicted differences.