Brain Network Connectivity Analysis of Different ADHD Groups Based on CNN-LSTM Classification Model

Abstract

Attention deficit hyperactivity disorder (ADHD), as a common disease of adolescents, is characterized by the inability to concentrate and moderate impulsive behavior. Since the clinical level mostly depends on the doctor's psychological and environmental analysis of the patient, there is no objective classification standard. ADHD is closely related to the signal connection in the brain and the study of its brain connection mode is of great significance. In this study, the CNN-LSTM network model was applied to process open-source EEG data to achieve high-precision classification. The model was also used to visualize the features that contributed the most, and generate high-precision feature gradient data. The results showed that the traditional processing of original data was different from that of gradient data and the latter was more reliable. The strongest connections in both ADHD and ADD patients were short-range, whereas the healthy group had long-range connections between the occipital lobe and left anterior temporal regions. This study preliminarily achieved the research purpose of finding differences among three groups of people through the features of brain network connectivity.

This work was supported in part by the National Natural Science Foundation of China (#81927804, #62101538), Shenzhen Governmental Basic Research Grant (#JCYJ20180507182241622), Science and Technology Planning Project of Shenzhen (#JSGG20210713091808027, #JSGG20211029095801002), China Postdoctoral Science Foundation (2022M710968), SIAT Innovation Program for Excellent Young Researchers (E1G027), CAS President’s International Fellowship Initiative Project (2022VEA0012). #The first two authors have equal contributions to this work.