Frontiers in psychiatry · 2026
A connectome-based neural correlate of pediatric ADHD hyperactivity-impulsivity symptoms.
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Tao Jie, Wu Yue, Liu Ping, Wang Rong, Gao Ranran, Zhang Dai, Zhang Qing, Geng Feng
Аннотация
BACKGROUND: This study examined the brain networks related to hyperactivity and impulsivity in children with Attention-Deficit/Hyperactivity Disorder (ADHD) and aimed to develop a resting-state functional connectivity marker that can predict symptom severity.
METHODS: A total of 44 children with ADHD (31 boys and 13 girls; mean age = 8.45 ± 1.52 years; range = 6-12 years) who met DSM-5 criteria were included. Resting-state fMRI data and clinical symptom scores were collected. Connectome-based predictive modeling (CPM) was used to predict hyperactivity-impulsivity symptoms based on whole-brain functional connectivity matrices. Symptoms were assessed using the SNAP-IV Parent and Teacher Rating Scales and the Conners Comprehensive Behavior Rating Scale. Model performance was tested with leave-one-out cross-validation and permutation testing.
RESULTS: The CPM model based on interregional functional connectivity successfully predicted hyperactivity-impulsivity symptoms (SNAP-IV parent and teacher ratings: r = 0.48, p = 0.001). The model also generalized well within the same dataset, as the selected functional connections significantly predicted hyperactivity-impulsivity scores on the Conners Parent Rating Scale (r = 0.49, p = 0.0009). Further analysis showed that stronger connectivity between the frontoparietal control network (FPN) and the dorsal attention network (DAN), and weaker connectivity between the FPN and the ventral attention network (VAN) and between the FPN and the somatomotor network (SMN), were related to symptom severity.
CONCLUSIONS: Whole-brain functional connectivity is associated with hyperactivity-impulsivity symptoms in children with ADHD. The findings highlight the key role of FPN-related networks in these symptoms and provide a neural correlate that, pending further validation, represents a preliminary candidate for a neuroimaging marker.
METHODS: A total of 44 children with ADHD (31 boys and 13 girls; mean age = 8.45 ± 1.52 years; range = 6-12 years) who met DSM-5 criteria were included. Resting-state fMRI data and clinical symptom scores were collected. Connectome-based predictive modeling (CPM) was used to predict hyperactivity-impulsivity symptoms based on whole-brain functional connectivity matrices. Symptoms were assessed using the SNAP-IV Parent and Teacher Rating Scales and the Conners Comprehensive Behavior Rating Scale. Model performance was tested with leave-one-out cross-validation and permutation testing.
RESULTS: The CPM model based on interregional functional connectivity successfully predicted hyperactivity-impulsivity symptoms (SNAP-IV parent and teacher ratings: r = 0.48, p = 0.001). The model also generalized well within the same dataset, as the selected functional connections significantly predicted hyperactivity-impulsivity scores on the Conners Parent Rating Scale (r = 0.49, p = 0.0009). Further analysis showed that stronger connectivity between the frontoparietal control network (FPN) and the dorsal attention network (DAN), and weaker connectivity between the FPN and the ventral attention network (VAN) and between the FPN and the somatomotor network (SMN), were related to symptom severity.
CONCLUSIONS: Whole-brain functional connectivity is associated with hyperactivity-impulsivity symptoms in children with ADHD. The findings highlight the key role of FPN-related networks in these symptoms and provide a neural correlate that, pending further validation, represents a preliminary candidate for a neuroimaging marker.