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...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Neural Plasticity |
| Online Access: | http://dx.doi.org/10.1155/2024/8862647 |
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| author | Yilin Hu Junling Ran Rui Qiao Jiayang Xu Congming Tan Liangliang Hu Yin Tian |
| author_facet | Yilin Hu Junling Ran Rui Qiao Jiayang Xu Congming Tan Liangliang Hu Yin Tian |
| author_sort | Yilin Hu |
| collection | DOAJ |
| description | 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 networks within brain networks. Our objective is to investigate the neural mechanisms related to attention and explore neuroimaging biological tags that can be generalized within the attention networks. In this paper, we utilized resting-state functional magnetic resonance imaging data to examine the differential functional connectivity network between ADHD and typically developing individuals. We employed a graph convolutional neural network model to identify individuals with ADHD. After classification, we visualized brain regions with significant contributions to the classification results. Our results suggest that the frontal, temporal, parietal, and cerebellar regions are likely the primary areas of dysfunction in individuals with ADHD. We also explored the relationship between regions of interest and attention networks, as well as the connection between crucial nodes and the distribution of positively and negatively correlated connections. This analysis allowed us to pinpoint the most discriminative brain regions, including the right orbitofrontal gyrus, the left rectus gyrus and bilateral insula, the right inferior temporal gyrus and bilateral transverse temporal gyrus in the temporal region, and the lingual gyrus of the occipital lobe, multiple regions of the basal ganglia and the upper cerebellum. These regions are primarily involved in the attention executive control network and the attention orientation network. Dysfunction in the functional connectivity of these regions may contribute to the underlying causes of ADHD. |
| format | Article |
| id | doaj-art-2c007e1a882443d0bddc77bbafedb34b |
| institution | OA Journals |
| issn | 1687-5443 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Neural Plasticity |
| spelling | doaj-art-2c007e1a882443d0bddc77bbafedb34b2025-08-20T02:24:07ZengWileyNeural Plasticity1687-54432024-01-01202410.1155/2024/8862647Identifying ADHD-Related Abnormal Functional Connectivity with a Graph Convolutional Neural NetworkYilin Hu0Junling Ran1Rui Qiao2Jiayang Xu3Congming Tan4Liangliang Hu5Yin Tian6Department of Biomedical EngineeringDepartment of Biomedical EngineeringDepartment of Biomedical EngineeringDepartment of Biomedical EngineeringCollege of Computer Science and TechnologyCollege of Computer Science and TechnologyDepartment of Biomedical EngineeringAttention 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 networks within brain networks. Our objective is to investigate the neural mechanisms related to attention and explore neuroimaging biological tags that can be generalized within the attention networks. In this paper, we utilized resting-state functional magnetic resonance imaging data to examine the differential functional connectivity network between ADHD and typically developing individuals. We employed a graph convolutional neural network model to identify individuals with ADHD. After classification, we visualized brain regions with significant contributions to the classification results. Our results suggest that the frontal, temporal, parietal, and cerebellar regions are likely the primary areas of dysfunction in individuals with ADHD. We also explored the relationship between regions of interest and attention networks, as well as the connection between crucial nodes and the distribution of positively and negatively correlated connections. This analysis allowed us to pinpoint the most discriminative brain regions, including the right orbitofrontal gyrus, the left rectus gyrus and bilateral insula, the right inferior temporal gyrus and bilateral transverse temporal gyrus in the temporal region, and the lingual gyrus of the occipital lobe, multiple regions of the basal ganglia and the upper cerebellum. These regions are primarily involved in the attention executive control network and the attention orientation network. Dysfunction in the functional connectivity of these regions may contribute to the underlying causes of ADHD.http://dx.doi.org/10.1155/2024/8862647 |
| spellingShingle | Yilin Hu Junling Ran Rui Qiao Jiayang Xu Congming Tan Liangliang Hu Yin Tian Identifying ADHD-Related Abnormal Functional Connectivity with a Graph Convolutional Neural Network Neural Plasticity |
| title | Identifying ADHD-Related Abnormal Functional Connectivity with a Graph Convolutional Neural Network |
| title_full | Identifying ADHD-Related Abnormal Functional Connectivity with a Graph Convolutional Neural Network |
| title_fullStr | Identifying ADHD-Related Abnormal Functional Connectivity with a Graph Convolutional Neural Network |
| title_full_unstemmed | Identifying ADHD-Related Abnormal Functional Connectivity with a Graph Convolutional Neural Network |
| title_short | Identifying ADHD-Related Abnormal Functional Connectivity with a Graph Convolutional Neural Network |
| title_sort | identifying adhd related abnormal functional connectivity with a graph convolutional neural network |
| url | http://dx.doi.org/10.1155/2024/8862647 |
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