STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data
<b>Background</b>: Early diagnosis of depression is crucial for effective treatment and suicide prevention. Traditional methods rely on self-report questionnaires and clinical assessments, lacking objective biomarkers. Combining functional magnetic resonance imaging (fMRI) with artificia...
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MDPI AG
2024-11-01
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| Series: | Tomography |
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| Online Access: | https://www.mdpi.com/2379-139X/10/12/138 |
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| author | Wei Zhang Weiming Zeng Hongyu Chen Jie Liu Hongjie Yan Kaile Zhang Ran Tao Wai Ting Siok Nizhuan Wang |
| author_facet | Wei Zhang Weiming Zeng Hongyu Chen Jie Liu Hongjie Yan Kaile Zhang Ran Tao Wai Ting Siok Nizhuan Wang |
| author_sort | Wei Zhang |
| collection | DOAJ |
| description | <b>Background</b>: Early diagnosis of depression is crucial for effective treatment and suicide prevention. Traditional methods rely on self-report questionnaires and clinical assessments, lacking objective biomarkers. Combining functional magnetic resonance imaging (fMRI) with artificial intelligence can enhance depression diagnosis using neuroimaging indicators, but depression-specific fMRI datasets are often small and imbalanced, posing challenges for classification models. <b>New Method</b>: We propose the Spatio-Temporal Aggregation Network (STANet) for diagnosing depression by integrating convolutional neural networks (CNN) and recurrent neural networks (RNN) to capture both temporal and spatial features of brain activity. STANet comprises the following steps: (1) Aggregate spatio-temporal information via independent component analysis (ICA). (2) Utilize multi-scale deep convolution to capture detailed features. (3) Balance data using the synthetic minority over-sampling technique (SMOTE) to generate new samples for minority classes. (4) Employ the attention-Fourier gate recurrent unit (AFGRU) classifier to capture long-term dependencies, with an adaptive weight assignment mechanism to enhance model generalization. <b>Results</b>: STANet achieves superior depression diagnostic performance, with 82.38% accuracy and a 90.72% AUC. The Spatio-Temporal Feature Aggregation module enhances classification by capturing deeper features at multiple scales. The AFGRU classifier, with adaptive weights and a stacked Gated Recurrent Unit (GRU), attains higher accuracy and AUC. SMOTE outperforms other oversampling methods. Additionally, spatio-temporal aggregated features achieve better performance compared to using only temporal or spatial features. <b>Comparison with existing methods</b>: STANet significantly outperforms traditional classifiers, deep learning classifiers, and functional connectivity-based classifiers. <b>Conclusions</b>: The successful performance of STANet contributes to enhancing the diagnosis and treatment assessment of depression in clinical settings on imbalanced and small fMRI. |
| format | Article |
| id | doaj-art-e5083feb3b514c12b1756e6a4e578659 |
| institution | DOAJ |
| issn | 2379-1381 2379-139X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Tomography |
| spelling | doaj-art-e5083feb3b514c12b1756e6a4e5786592025-08-20T02:43:41ZengMDPI AGTomography2379-13812379-139X2024-11-0110121895191410.3390/tomography10120138STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI DataWei Zhang0Weiming Zeng1Hongyu Chen2Jie Liu3Hongjie Yan4Kaile Zhang5Ran Tao6Wai Ting Siok7Nizhuan Wang8Lab of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, ChinaLab of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, ChinaLab of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, ChinaLab of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, ChinaDepartment of Neurology, Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang 222002, ChinaDepartment of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China<b>Background</b>: Early diagnosis of depression is crucial for effective treatment and suicide prevention. Traditional methods rely on self-report questionnaires and clinical assessments, lacking objective biomarkers. Combining functional magnetic resonance imaging (fMRI) with artificial intelligence can enhance depression diagnosis using neuroimaging indicators, but depression-specific fMRI datasets are often small and imbalanced, posing challenges for classification models. <b>New Method</b>: We propose the Spatio-Temporal Aggregation Network (STANet) for diagnosing depression by integrating convolutional neural networks (CNN) and recurrent neural networks (RNN) to capture both temporal and spatial features of brain activity. STANet comprises the following steps: (1) Aggregate spatio-temporal information via independent component analysis (ICA). (2) Utilize multi-scale deep convolution to capture detailed features. (3) Balance data using the synthetic minority over-sampling technique (SMOTE) to generate new samples for minority classes. (4) Employ the attention-Fourier gate recurrent unit (AFGRU) classifier to capture long-term dependencies, with an adaptive weight assignment mechanism to enhance model generalization. <b>Results</b>: STANet achieves superior depression diagnostic performance, with 82.38% accuracy and a 90.72% AUC. The Spatio-Temporal Feature Aggregation module enhances classification by capturing deeper features at multiple scales. The AFGRU classifier, with adaptive weights and a stacked Gated Recurrent Unit (GRU), attains higher accuracy and AUC. SMOTE outperforms other oversampling methods. Additionally, spatio-temporal aggregated features achieve better performance compared to using only temporal or spatial features. <b>Comparison with existing methods</b>: STANet significantly outperforms traditional classifiers, deep learning classifiers, and functional connectivity-based classifiers. <b>Conclusions</b>: The successful performance of STANet contributes to enhancing the diagnosis and treatment assessment of depression in clinical settings on imbalanced and small fMRI.https://www.mdpi.com/2379-139X/10/12/138depressionfMRIindependent component analysis (ICA)GRUsynthetic minority over-sampling technique (SMOTE)adaptive fusion weight |
| spellingShingle | Wei Zhang Weiming Zeng Hongyu Chen Jie Liu Hongjie Yan Kaile Zhang Ran Tao Wai Ting Siok Nizhuan Wang STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data Tomography depression fMRI independent component analysis (ICA) GRU synthetic minority over-sampling technique (SMOTE) adaptive fusion weight |
| title | STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data |
| title_full | STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data |
| title_fullStr | STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data |
| title_full_unstemmed | STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data |
| title_short | STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data |
| title_sort | stanet a novel spatio temporal aggregation network for depression classification with small and unbalanced fmri data |
| topic | depression fMRI independent component analysis (ICA) GRU synthetic minority over-sampling technique (SMOTE) adaptive fusion weight |
| url | https://www.mdpi.com/2379-139X/10/12/138 |
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