Predicting the Treatment Response of Patients With Major Depressive Disorder to Selective Serotonin Reuptake Inhibitors Using Machine Learning Techniques and EEG Functional Connectivity Features.
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| Title: | Predicting the Treatment Response of Patients With Major Depressive Disorder to Selective Serotonin Reuptake Inhibitors Using Machine Learning Techniques and EEG Functional Connectivity Features. |
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| Authors: | Wang, Fanglan (AUTHOR), You, Zifan (AUTHOR), Zhang, Tingkai (AUTHOR), Xu, Kai (AUTHOR), Wang, Liangliang (AUTHOR), He, Jingqi (AUTHOR), Tang, Jinsong (AUTHOR), Zhang, Fuquan (AUTHOR) |
| Source: | Depression & Anxiety (1091-4269). 1/22/2025, Vol. 2025, p1-10. 10p. |
| Subjects: | Serotonin uptake inhibitors, Machine learning, Escitalopram, Functional connectivity, Mental depression, Clinical prediction rules, Hamilton Depression Inventory, Sertraline |
| Abstract: | Background: Escitalopram and sertraline are first‐line medications for treating depression. They belong to selective serotonin reuptake inhibitors (SSRIs) and are widely used due to their effectiveness and fewer side effects. However, despite the significant efficacy of escitalopram and sertraline, there is a large variation among individuals. Therefore, predicting symptom improvement based on the baseline period is crucial. Methods: In this study, we conducted functional connectivity (FC) analysis of electroencephalogram (EEG) data during resting‐state with eyes closed, resting‐state with eyes open, watching neutral videos, negative videos, and comedy videos for 30 untreated depression patients over 2 weeks. Each modality yielded 18 EEG FC features. Based on the treatment response at 8 weeks, patients were divided into treatment‐effective and treatment‐ineffective groups. The dataset was randomly split into a 75% training set and a 25% independent test set. Feature selection was performed on these FC features in the training set, and the selected features were used to classify the effective and ineffective groups using the support vector machine (SVM) machine learning algorithm. Fivefold cross‐validation was conducted on the training set to obtain validation results, followed by testing on the test set. The Spearman's correlation method was used to analyze the correlation between each EEG feature value and the reduction rate of the Hamilton Depression Rating Scale for Depression (HAMD‐17) scores from baseline to 8 weeks, with Bonferroni correction applied. Results: The study found that out of all modalities, 33 features achieved classification accuracies of over 95% on the validation set, and two features achieved classification accuracies of over 85% on the independent test set. A total of 58 feature values were found to be correlated with the reduction rate of HAMD‐17 scores from baseline to 8 weeks. Conclusions: The findings from this research suggest that EEG FC features at baseline can be used to differentiate between effective and ineffective groups with high accuracy using machine learning models. Multiple feature values and HAMD‐17 scores were found to be correlated with the reduction rate of HAMD‐17 scores from baseline to 8 weeks, and these correlated feature values can be used to predict treatment efficacy. Trial Registration: ClinicalTrials.gov identifier: NCT05775809 [ABSTRACT FROM AUTHOR] |
| Copyright of Depression & Anxiety (1091-4269) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Background: Escitalopram and sertraline are first‐line medications for treating depression. They belong to selective serotonin reuptake inhibitors (SSRIs) and are widely used due to their effectiveness and fewer side effects. However, despite the significant efficacy of escitalopram and sertraline, there is a large variation among individuals. Therefore, predicting symptom improvement based on the baseline period is crucial. Methods: In this study, we conducted functional connectivity (FC) analysis of electroencephalogram (EEG) data during resting‐state with eyes closed, resting‐state with eyes open, watching neutral videos, negative videos, and comedy videos for 30 untreated depression patients over 2 weeks. Each modality yielded 18 EEG FC features. Based on the treatment response at 8 weeks, patients were divided into treatment‐effective and treatment‐ineffective groups. The dataset was randomly split into a 75% training set and a 25% independent test set. Feature selection was performed on these FC features in the training set, and the selected features were used to classify the effective and ineffective groups using the support vector machine (SVM) machine learning algorithm. Fivefold cross‐validation was conducted on the training set to obtain validation results, followed by testing on the test set. The Spearman's correlation method was used to analyze the correlation between each EEG feature value and the reduction rate of the Hamilton Depression Rating Scale for Depression (HAMD‐17) scores from baseline to 8 weeks, with Bonferroni correction applied. Results: The study found that out of all modalities, 33 features achieved classification accuracies of over 95% on the validation set, and two features achieved classification accuracies of over 85% on the independent test set. A total of 58 feature values were found to be correlated with the reduction rate of HAMD‐17 scores from baseline to 8 weeks. Conclusions: The findings from this research suggest that EEG FC features at baseline can be used to differentiate between effective and ineffective groups with high accuracy using machine learning models. Multiple feature values and HAMD‐17 scores were found to be correlated with the reduction rate of HAMD‐17 scores from baseline to 8 weeks, and these correlated feature values can be used to predict treatment efficacy. Trial Registration: ClinicalTrials.gov identifier: NCT05775809 [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10914269 |
| DOI: | 10.1155/da/9340993 |
