Identifying ADHD-Related Abnormal Functional Connectivity with a Graph Convolutional Neural Network
Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that is characterized by inattention, hyperactivity, and impulsivity. The neural mechanisms underlying ADHD remain inadequately understood, and current approaches do not well link neural networks and attention ne...
Saved in:
| Main Authors: | Yilin Hu, Junling Ran, Rui Qiao, Jiayang Xu, Congming Tan, Liangliang Hu, Yin Tian |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | Neural Plasticity |
| Online Access: | http://dx.doi.org/10.1155/2024/8862647 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Adaptive dual-graph learning joint feature selection for EEG emotion recognition
by: Liangliang Hu, et al.
Published: (2025-06-01) -
Chromosomal Abnormalities in ADHD
by: J Gordon Millichap
Published: (2002-07-01) -
Diffusion Tensor Imaging Abnormalities in the Cerebellum of Children with ADHD and Epilepsy/ADHD
by: J Gordon Millichap
Published: (2009-08-01) -
Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting
by: Na Hu, et al.
Published: (2022-12-01) -
Identifying T cell antigen at the atomic level with graph convolutional network
by: Jinhao Que, et al.
Published: (2025-06-01)