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A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD

Zhao, Kanhao ; Duka, Boris ; Xie, Hua ; Oathes, Desmond J. ; Calhoun, Vince ; Zhang, Yu

NeuroImage (Orlando, Fla.), 2022-02, Vol.246, p.118774-118774, Article 118774 [Periódico revisado por pares]

United States: Elsevier Inc

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Título:
A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD
Autor: Zhao, Kanhao ; Duka, Boris ; Xie, Hua ; Oathes, Desmond J. ; Calhoun, Vince ; Zhang, Yu
Assuntos: Adult ; Alzheimers disease ; Artificial intelligence ; Attention Deficit Disorder with Hyperactivity - diagnostic imaging ; Attention Deficit Disorder with Hyperactivity - physiopathology ; Attention deficit hyperactivity disorder ; Autism ; Biomarkers ; Brain mapping ; Brain networks ; Cerebellum ; Cerebral Cortex - diagnostic imaging ; Cerebral Cortex - physiopathology ; Clinical medicine ; Connectome - methods ; Deep learning ; Diagnosis ; Frontal lobe ; Functional magnetic resonance imaging ; Graph convolutional networks ; Graph representations ; Humans ; Learning algorithms ; Machine learning ; Magnetic Resonance Imaging ; Medical imaging ; Mental disorders ; Nerve Net - diagnostic imaging ; Nerve Net - physiopathology ; Neural networks ; Neural Networks, Computer ; Neuroimaging ; Precision diagnosis ; Resting-state fMRI ; Temporal lobe ; Time series ; Young Adult
É parte de: NeuroImage (Orlando, Fla.), 2022-02, Vol.246, p.118774-118774, Article 118774
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Descrição: •A novel approach is proposed to incorporate dynamic graph computation and 2-hop neighbor nodes feature aggregation into graph convolution for brain network modeling.•A convolutional pooling strategy is developed to readout the graph, which jointly integrates graph convolutional and readout functions.•Our model outperforms other existing methods and shows high efficacy in quantifying brain dysfunctions associated with ADHD.•Visualization of model weights shows interpretable connectomic patterns facilitating the understanding of brain functional abnormalities. The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic resonance imaging (fMRI) has emerged as a common neuroimaging technique for studying the brain functional connectome. Most existing methods that have either ignored or simply utilized graph structure, do not fully leverage the potentially important topological information which may be useful in characterizing brain disorders. There is a crucial need for designing novel and efficient approaches which can capture such information. To this end, we propose a new dynamic graph convolutional network (dGCN), which is trained with sparse brain regional connections from dynamically calculated graph features. We also develop a novel convolutional readout layer to improve graph representation. Our extensive experimental analysis demonstrates significantly improved performance of dGCN for ADHD diagnosis compared with existing machine learning and deep learning methods. Visualizations of the salient regions of interest (ROIs) and connectivity based on informative features learned by our model show that the identified functional abnormalities mainly involve brain regions in temporal pole, gyrus rectus, and cerebellar gyri from temporal lobe, frontal lobe, and cerebellum, respectively. A positive correlation was further observed between the identified connectomic abnormalities and ADHD symptom severity. The proposed dGCN model shows great promise in providing a functional network-based precision diagnosis of ADHD and is also broadly applicable to brain connectome-based study of mental disorders.
Editor: United States: Elsevier Inc
Idioma: Inglês

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A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD

Zhao, Kanhao ; Duka, Boris ; Xie, Hua ; Oathes, Desmond J. ; Calhoun, Vince ; Zhang, Yu

United States: Elsevier Inc

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