Brain Relatively Inert Network: Taking Adult Attention Deficit Hyperactivity Disorder As an Example

Resting-state functional MRI (rs-fMRI) has been increasingly applied in the research of brain cognitive science and psychiatric diseases. However, previous studies only focused on specific activation areas of the brain, and there are few studies on the inactivation areas. This may overlook much information that explains the brain's cognitive function. In this paper, we propose a relatively inert network (RIN) and try to explore its important role in understanding the cognitive mechanism of the brain and the study of mental diseases, using adult attention deficit hyperactivity disorder (ADHD) as an example. Here, we utilize methods based on group independent component analysis (GICA) and -test to identify RIN and calculate its corresponding time series. Through experiments, alterations in the RIN and the corresponding activation network (AN) in adult ADHD patients are observed. And compared with those in the left brain, the activation changes in the right brain are greater. Further, when the RIN functional connectivity is introduced as a feature to classify adult ADHD patients from healthy controls (HCs), the classification accuracy rate is 12% higher than that of the original functional connectivity feature. This was also verified by testing on an independent public dataset. These findings confirm that the RIN of the brain contains much information that will probably be neglected. Moreover, this research provides an effective new means of exploring the information integration between brain regions and the diagnosis of mental illness.