Attention-deficit/hyperactivity disorder (ADHD) is increasingly being diagnosed in adults, but the neural mechanisms underlying its distinct clinical symptoms (hyperactivity and inattention) remain poorly understood. Here, we used a nested-spectral partition approach to study resting-state brain networks for ADHD patients and healthy adults and adopted hierarchical segregation and integration to predict clinical symptoms. Adult ADHD is typically characterized by an overintegrated interaction within default mode network. Limbic system is dominantly affected by ADHD and has an earlier aging functional pattern, but salient attention system is preferably affected by age and shows an opposite aging trajectory. More importantly, these two systems selectively and robustly predict distinct ADHD symptoms. Earlier-aging limbic system prefers to predict hyperactivity, and age-affected salient attention system better predicts inattention. Our findings provide a more comprehensive and deeper understanding of the neural basis of distinct ADHD symptoms and could contribute to the development of more objective clinical diagnoses.
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