GNN-Based Depression Recognition Using Spatio-Temporal Information: A fNIRS Study

In recent years, depression has become an increasingly serious problem globally. Previous studies of automatic depression recognition based on functional near-Infrared spectroscopy (fNIRS) or other brain imaging techniques have shown potential to serve as auxiliary diagnosis methods that provide assistance to medical professionals. Recently, some studies have found that, besides directly using the data themselves (temporal data), the use of functional connectivity among channels (spatial data) also can be effective. In this paper, we propose a method based on Graph Neural Network (GNN) that combines both temporal and spatial features of fNIRS data for automatic depression recognition. Specifically, fNIRS data of 96 subjects were collected and pre-processed. Basic statistical metrics of each channel were extracted as temporal features, and channel connectivity (coherence and correlation) were calculated as spatial features. Point-biserial analysis was conducted on these features and depression labels as a data-driven motivation. For classification, we considered data of each subject as a graph, with temporal features as node features and spatial features as edge weights. The graphs were fed into GNNs for training and testing. Experimental results showed that our GNN-based methods realized the best depression recognition performance compared with classical machine-learning methods regarding accuracy, F1 score, and precision, especially in F1 score for over 10%.

Authors

• Qiao Yu Huazhong University of Science and Technology
• Rui Wang National Key Laboratory of Space Integrated Information System, Institute of Software Chinese Academy of Sciences
• Jia Liu Xi'an Jiaotong University, Xi'an, China
• Long Hu School of Computer Science and Technology, Huazhong University of Science and Technology
• Min Chen School of Computer Science and Engineering, South China University of Technology
• Zhongchun Liu

Keywords

• Depression
• Functional near-infrared spectroscopy
• Task analysis
• Brain modeling
• Feature extraction
• Spatial databases
• Electroencephalography
• Recognition Of Depression
• Neural Network
• Neuroimaging
• F1 Score
• Spatial Features
• Temporal Features
• Edge Weights
• Temporal Data
• Node Features
• Graph Neural Networks
• Statistical Metrics
• fNIRS Data
• Convolutional Neural Network
• Spatial Information
• Precision And Recall
• Symmetric Matrix
• Baseline Methods
• Graph Convolutional Network
• Triangular Matrix
• Graph Convolution
• Upper Triangular
• Silent Period
• Highest F1 Score
• Graph Attention Network
• Point-biserial Correlation
• Matrix Layer
• Node Embeddings
• Verbal Fluency Test
• Matrix For Each Subject
• Depression Recognition
• fNIRS
• GNN
• Spatio-temporal Feature
• Functional Connectivity
• Humans
• Machine Learning
• Neural Networks, Computer
• Spectroscopy, Near-Infrared