Effective connectivity signatures in major depressive disorder: fMRI study using a multi-site dataset
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911742968201216 |
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| author | Dai, Peishan Shi, Yun Xiong, Tong Zhou, Xiaoyan Liao, Shenghui Huang, Zhongchao Yi, Xiaoping Chen, Bihong T. |
| author_facet | Dai, Peishan Shi, Yun Xiong, Tong Zhou, Xiaoyan Liao, Shenghui Huang, Zhongchao Yi, Xiaoping Chen, Bihong T. |
| contents | Diagnosis of major depressive disorder (MDD) primarily relies on the patient's self-reported symptoms and a clinical evaluation. Effective connectivity (EC) from resting-state functional magnetic resonance imaging (rs-fMRI) analysis can reflect the directionality of connections between brain regions, making it a candidate method to classify MDD. This study used Granger causality analysis to extract EC features from a large multi-site MDD dataset. The ComBat algorithm and multivariate linear regression were used to harmonize site difference and to remove age and sex covariates, respectively. Two-sample t-tests and model-based feature selection methods were used to screen for highly discriminative EC features for MDD, and LightGBM was used to classify MDD. In this large-scale multi-site rs-fMRI dataset, 97 EC features deemed highly discriminative for MDD were screened. In the nested five-fold cross-validation, the best classification model with the 97 EC features achieved accuracy, sensitivity, and specificity of 94.35%, 93.52%, and 95.25%, respectively. In another independent large dataset, which tested the generalization performance of the 97 EC features, the best classification models achieved 94.74%, 90.59%, and 96.75% for accuracy, sensitivity, and specificity, respectively. This work demonstrated that EC had a reasonable discriminative ability and supported the notion for using EC to potentially assist clinical diagnosis of MDD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_20231 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Effective connectivity signatures in major depressive disorder: fMRI study using a multi-site dataset Dai, Peishan Shi, Yun Xiong, Tong Zhou, Xiaoyan Liao, Shenghui Huang, Zhongchao Yi, Xiaoping Chen, Bihong T. Neurons and Cognition Diagnosis of major depressive disorder (MDD) primarily relies on the patient's self-reported symptoms and a clinical evaluation. Effective connectivity (EC) from resting-state functional magnetic resonance imaging (rs-fMRI) analysis can reflect the directionality of connections between brain regions, making it a candidate method to classify MDD. This study used Granger causality analysis to extract EC features from a large multi-site MDD dataset. The ComBat algorithm and multivariate linear regression were used to harmonize site difference and to remove age and sex covariates, respectively. Two-sample t-tests and model-based feature selection methods were used to screen for highly discriminative EC features for MDD, and LightGBM was used to classify MDD. In this large-scale multi-site rs-fMRI dataset, 97 EC features deemed highly discriminative for MDD were screened. In the nested five-fold cross-validation, the best classification model with the 97 EC features achieved accuracy, sensitivity, and specificity of 94.35%, 93.52%, and 95.25%, respectively. In another independent large dataset, which tested the generalization performance of the 97 EC features, the best classification models achieved 94.74%, 90.59%, and 96.75% for accuracy, sensitivity, and specificity, respectively. This work demonstrated that EC had a reasonable discriminative ability and supported the notion for using EC to potentially assist clinical diagnosis of MDD. |
| title | Effective connectivity signatures in major depressive disorder: fMRI study using a multi-site dataset |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2310.20231 |