A new method for identifying and evaluating depressive disorders in young people based on cognitive neurocomputing: an exploratory study

Jiakang Liu ¹ Kai Li ^2,3* Shuwu Li ^1* Shangjun Liu ^4* Chen Wang ¹ Shouqiang Huang ¹ Yuting Tu ¹ Bo Wang ¹ Pengpeng Zhang ² Yuntian Luo ² Guanqun Sun ^2* Tong Chen ^5*

• ¹ School of Medical Technology and lnformation Engineering, Zhejiang Chinese Medical University, Hangzhou, Jiangsu Province, China
• ² School of Information Engineering, Hangzhou Medical College, Hangzhou, Jiangsu Province, China
• ³ Zhejiang Engineering Research Center for Brain Cognition and Brain Diseases Digital Medical Instruments, Hangzhou Medical College, Hangzhou, Jiangsu Province, China
• ⁴ Department of Medical Psychology, First Medical Center of Chinese PLA General Hospital, Beijing, China
• ⁵ Department of Neurology, Second Medical Center of Chinese PLA General Hospital, Beijing, China

Background: Depressive disorders are one of the most common mental disorders among young people. However, there is still a lack of objective means to identify and evaluate young people with depressive disorders quickly. Cognitive impairment is one of the core characteristics of depressive disorders, which is of great value in the identification and evaluation of young people with depressive disorders. Methods: This study proposes a new method for identifying and evaluating depressive disorders in young people based on cognitive neurocomputing. The method evaluates cognitive impairments such as reduced attention, executive dysfunction, and slowed information processing speed that may exist in the youth depressive disorder population through an independently designed digital evaluation paradigm. It also mines digital biomarkers that can effectively identify these cognitive impairments. A total of 50 young patients with depressive disorders and 47 healthy controls were included in this study to validate the method's identification and evaluation capability. Results: The differences analysis results showed that the digital biomarkers of cognitive function on attention, executive function, and information processing speed extracted in this study were significantly different between young depressive disorder patients and healthy controls. Through stepwise regression analysis, four digital biomarkers of cognitive function were finally screened. The area under the curve for them to jointly distinguish patients with depressive disorders from healthy controls was 0.927. Conclusions: This new method rapidly characterizes and quantifies cognitive impairment in young people with depressive disorders. It provides a new way for organizations, such as schools, to quickly identify and evaluate the population of young people with depressive disorders based on human-computer interaction.