Spatio‐temporal graph convolutional network for diagnosis and treatment response prediction of major depressive disorder from functional connectivity
Autor: | Shuwen Gao, Huazhong Shu, Z. Hou, Yingying Yue, Yonggui Yuan, Zhijun Zhang, Chunming Xie, Youyong Kong |
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Rok vydání: | 2021 |
Předmět: |
Adult
Computer science Pooling Machine learning computer.software_genre 050105 experimental psychology Convolution Reduction (complexity) 03 medical and health sciences Deep Learning 0302 clinical medicine Discriminative model Connectome medicine Humans 0501 psychology and cognitive sciences Radiology Nuclear Medicine and imaging Research Articles individual diagnosis treatment response prediction Depressive Disorder Major major depressive disorder Radiological and Ultrasound Technology business.industry functional connectivity 05 social sciences Brain Prognosis medicine.disease Magnetic Resonance Imaging Feature Dimension Neurology graph convolutional network Major depressive disorder Graph (abstract data type) Neurology (clinical) Artificial intelligence Nerve Net Anatomy business Feature learning computer 030217 neurology & neurosurgery Research Article |
Zdroj: | Human Brain Mapping |
ISSN: | 1097-0193 1065-9471 |
DOI: | 10.1002/hbm.25529 |
Popis: | The pathophysiology of major depressive disorder (MDD) has been explored to be highly associated with the dysfunctional integration of brain networks. It is therefore imperative to explore neuroimaging biomarkers to aid diagnosis and treatment. In this study, we developed a spatiotemporal graph convolutional network (STGCN) framework to learn discriminative features from functional connectivity for automatic diagnosis and treatment response prediction of MDD. Briefly, dynamic functional networks were first obtained from the resting‐state fMRI with the sliding temporal window method. Secondly, a novel STGCN approach was proposed by introducing the modules of spatial graph attention convolution (SGAC) and temporal fusion. A novel SGAC was proposed to improve the feature learning ability and special anatomy prior guided pooling was developed to enable the feature dimension reduction. A temporal fusion module was proposed to capture the dynamic features of functional connectivity between adjacent sliding windows. Finally, the STGCN proposed approach was utilized to the tasks of diagnosis and antidepressant treatment response prediction for MDD. Performances of the framework were comprehensively examined with large cohorts of clinical data, which demonstrated its effectiveness in classifying MDD patients and predicting the treatment response. The sound performance suggests the potential of the STGCN for the clinical use in diagnosis and treatment prediction. In this study, we developed a spatiotemporal graph convolutional network framework to learn discriminative features from functional connectivity for automatic diagnosis and treatment response prediction of major depressive disorder. Performances of the framework were comprehensively examined with large cohorts of clinical data, which demonstrated its effectiveness in classifying major depressive disorder patients and predicting the treatment response. |
Databáze: | OpenAIRE |
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