Brain Age Assessment of Patients with Major Depressive Disorder Based on Convolutional Neural Network

Abstract

Brain age has become an important analysis object in the diagnosis and mechanism research of neurodegenerative diseases. There is no consistent conclusion on whether major depression increases the brain age of patients, and few studies in this direction have been conducted in the Chinese population. In this paper, a REST-meta-MDD (resting-state functional magnetic resonance imaging dataset of major depressive disorder) dataset collected from 25 hospitals in China was used to construct a convolutional neural network model based on high-resolution T1-weighted three-dimensional magnetic resonance images of brain structures to predict the brain age of patients and calculate the difference from the actual age. The mean absolute error and correlation coefficients of the final results were 3.16 and 0.93, and the mean brain age of the patients with major depression increased by 3.94 years compared with the healthy group, further confirming that major depression accelerates brain aging, and the severity of the disease is related to the gender, age, and education of the patients. Compared with the traditional machine learning algorithms, the average absolute error of the results obtained by this model is smaller and the correlation coefficient is higher.

Keywords

• brain age
• major depression
• convolutional neural networks
• brain aging