MRI quantified enlarged perivascular space volumes as imaging biomarkers correlating with severity of anxiety and depression in young adults with long-time mobile phone use

Li Li ^1* Yalan Wu ^1* Jiaojiao Wu ² Bin Li ¹ Rui Hua ^2* Feng Shi ² Lizhou Chen ^3* Yeke Wu ^1*

• ¹ Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
• ² Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China
• ³ West China Hospital, Sichuan University, Chengdu, Sichuan Province, China

Long-time mobile phone use (LTMPU) has been linked to emotional issues such as anxiety and depression while the enlarged perivascular spaces (EPVS), as marker of neuroinflammation, is closely related with mental disorders. In the current study, we aim to develop a predictive model utilizing MRI-quantified EPVS metrics and machine learning algorithms to assess the severity of anxiety and depression symptoms in patients with LTMPU. Eighty-two participants with LTMPU were included, with 37 suffering from anxiety and 44 suffering from depression. Deep learning algorithms were used to segment EPVS lesions and extract quantitative metrics. Comparison and correlation analyses were performed to investigate the relationship between EPVS and self-reported mood states. Training and testing datasets were randomly assigned in the ratio of 8:2 to perform radiomics analysis, where EPVS metrics combined with sex and age were used to select the most valuable features to construct machine learning models for predicting the severity of anxiety and depression. Several EPVS features were significantly different between the two comparisons. For classifying anxiety status, eight features were selected to construct a logistic regression model, with an AUC of 0.819 (95%CI 0.573-1.000) in the testing dataset. For classifying depression status, eight features were selected to construct a K nearest neighbors model with an AUC value of 0.931 (95%CI 0.814-1.000) in the testing dataset. The utilization of MRI-quantified EPVS metrics combined with machine-learning algorithms presents a promising method for evaluating severity of anxiety and depression symptoms in patients with LTMPU, which might introduce a non-invasive, objective, and quantitative approach to enhance diagnostic efficiency and guide personalized treatment strategies.