Diagnosis of schizophrenia with functional connectome data: a graph-based convolutional neural network approach

Autor:	Kang-Han Oh, Il-Seok Oh, Uyanga Tsogt, Jie Shen, Woo-Sung Kim, Congcong Liu, Nam-In Kang, Keon-Hak Lee, Jing Sui, Sung-Wan Kim, Young-Chul Chung
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	Brain network Functional connectome Convolutional neural network Global covariance pooling Self-attention mechanism Schizophrenia Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurophysiology and neuropsychology QP351-495
Zdroj:	BMC Neuroscience, Vol 23, Iss 1, Pp 1-11 (2022)
Druh dokumentu:	article
ISSN:	1471-2202
DOI:	10.1186/s12868-021-00682-9
Popis:	Abstract Previous deep learning methods have not captured graph or network representations of brain structural or functional connectome data. To address this, we developed the BrainNet-Global Covariance Pooling-Attention Convolutional Neural Network (BrainNet-GA CNN) by incorporating BrainNetCNN and global covariance pooling into the self-attention mechanism. Resting-state functional magnetic resonance imaging data were obtained from 171 patients with schizophrenia spectrum disorders (SSDs) and 161 healthy controls (HCs). We conducted an ablation analysis of the proposed BrainNet-GA CNN and quantitative performance comparisons with competing methods using the nested tenfold cross validation strategy. The performance of our model was compared with competing methods. Discriminative connections were visualized using the gradient-based explanation method and compared with the results obtained using functional connectivity analysis. The BrainNet-GA CNN showed an accuracy of 83.13%, outperforming other competing methods. Among the top 10 discriminative connections, some were associated with the default mode network and auditory network. Interestingly, these regions were also significant in the functional connectivity analysis. Our findings suggest that the proposed BrainNet-GA CNN can classify patients with SSDs and HCs with higher accuracy than other models. Visualization of salient regions provides important clinical information. These results highlight the potential use of the BrainNet-GA CNN in the diagnosis of schizophrenia.
Databáze:	Directory of Open Access Journals
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