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
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