Application of functional near-infrared spectroscopy and machine learning to predict treatment response after six months in major depressive disorder
Abstract Depression treatment responses vary widely among individuals. Identifying objective biomarkers with predictive accuracy for therapeutic outcomes can enhance treatment efficiency and avoid ineffective therapies. This study investigates whether functional near-infrared spectroscopy (fNIRS) an...
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| Format: | Article |
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Nature Publishing Group
2025-01-01
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| Series: | Translational Psychiatry |
| Online Access: | https://doi.org/10.1038/s41398-025-03224-7 |
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| author | Cyrus Su Hui Ho Jinyuan Wang Gabrielle Wann Nii Tay Roger Ho Hai Lin Zhifei Li Nanguang Chen |
| author_facet | Cyrus Su Hui Ho Jinyuan Wang Gabrielle Wann Nii Tay Roger Ho Hai Lin Zhifei Li Nanguang Chen |
| author_sort | Cyrus Su Hui Ho |
| collection | DOAJ |
| description | Abstract Depression treatment responses vary widely among individuals. Identifying objective biomarkers with predictive accuracy for therapeutic outcomes can enhance treatment efficiency and avoid ineffective therapies. This study investigates whether functional near-infrared spectroscopy (fNIRS) and clinical assessment information can predict treatment response in major depressive disorder (MDD) through machine-learning techniques. Seventy patients with MDD were included in this 6-month longitudinal study, with the primary treatment outcome measured by changes in the Hamilton Depression Rating Scale (HAM-D) scores. fNIRS and clinical information were strictly evaluated using nested cross-validation to predict responders and non-responders based on machine-learning models, including support vector machine, random forest, XGBoost, discriminant analysis, Naïve Bayes, and transformers. The task change of total haemoglobin (HbT), defined as the difference between pre-task and post-task average HbT concentrations, in the dorsolateral prefrontal cortex (dlPFC) is significantly correlated with treatment response (p < 0.005). Leveraging a Naïve Bayes model, inner cross-validation performance (bAcc = 70% [SD = 4], AUC = 0.77 [SD = 0.04]) and outer cross-validation results (bAcc = 73% [SD = 3], AUC = 0.77 [SD = 0.02]) were yielded for predicting response using solely fNIRS data. The bimodal model combining fNIRS and clinical data showed inferior performance in outer cross-validation (bAcc = 68%, AUC = 0.70) compared to the fNIRS-only model. Collectively, fNIRS holds potential as a scalable neuroimaging modality for predicting treatment response in MDD. |
| format | Article |
| id | doaj-art-e0196edf4e834c3ea48dad141ba826fd |
| institution | Kabale University |
| issn | 2158-3188 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Publishing Group |
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| series | Translational Psychiatry |
| spelling | doaj-art-e0196edf4e834c3ea48dad141ba826fd2025-01-12T12:40:49ZengNature Publishing GroupTranslational Psychiatry2158-31882025-01-011511910.1038/s41398-025-03224-7Application of functional near-infrared spectroscopy and machine learning to predict treatment response after six months in major depressive disorderCyrus Su Hui Ho0Jinyuan Wang1Gabrielle Wann Nii Tay2Roger Ho3Hai Lin4Zhifei Li5Nanguang Chen6Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of SingaporeDepartment of Biomedical Engineering, National University of SingaporeDepartment of Psychological Medicine, Yong Loo Lin School of Medicine, National University of SingaporeDepartment of Psychological Medicine, Yong Loo Lin School of Medicine, National University of SingaporeDepartment of Neurosurgery, Shenzhen Second People’s Hospital, the First Affiliated Hospital of Shenzhen UniversityNational University of Singapore (Suzhou) Research InstituteDepartment of Biomedical Engineering, National University of SingaporeAbstract Depression treatment responses vary widely among individuals. Identifying objective biomarkers with predictive accuracy for therapeutic outcomes can enhance treatment efficiency and avoid ineffective therapies. This study investigates whether functional near-infrared spectroscopy (fNIRS) and clinical assessment information can predict treatment response in major depressive disorder (MDD) through machine-learning techniques. Seventy patients with MDD were included in this 6-month longitudinal study, with the primary treatment outcome measured by changes in the Hamilton Depression Rating Scale (HAM-D) scores. fNIRS and clinical information were strictly evaluated using nested cross-validation to predict responders and non-responders based on machine-learning models, including support vector machine, random forest, XGBoost, discriminant analysis, Naïve Bayes, and transformers. The task change of total haemoglobin (HbT), defined as the difference between pre-task and post-task average HbT concentrations, in the dorsolateral prefrontal cortex (dlPFC) is significantly correlated with treatment response (p < 0.005). Leveraging a Naïve Bayes model, inner cross-validation performance (bAcc = 70% [SD = 4], AUC = 0.77 [SD = 0.04]) and outer cross-validation results (bAcc = 73% [SD = 3], AUC = 0.77 [SD = 0.02]) were yielded for predicting response using solely fNIRS data. The bimodal model combining fNIRS and clinical data showed inferior performance in outer cross-validation (bAcc = 68%, AUC = 0.70) compared to the fNIRS-only model. Collectively, fNIRS holds potential as a scalable neuroimaging modality for predicting treatment response in MDD.https://doi.org/10.1038/s41398-025-03224-7 |
| spellingShingle | Cyrus Su Hui Ho Jinyuan Wang Gabrielle Wann Nii Tay Roger Ho Hai Lin Zhifei Li Nanguang Chen Application of functional near-infrared spectroscopy and machine learning to predict treatment response after six months in major depressive disorder Translational Psychiatry |
| title | Application of functional near-infrared spectroscopy and machine learning to predict treatment response after six months in major depressive disorder |
| title_full | Application of functional near-infrared spectroscopy and machine learning to predict treatment response after six months in major depressive disorder |
| title_fullStr | Application of functional near-infrared spectroscopy and machine learning to predict treatment response after six months in major depressive disorder |
| title_full_unstemmed | Application of functional near-infrared spectroscopy and machine learning to predict treatment response after six months in major depressive disorder |
| title_short | Application of functional near-infrared spectroscopy and machine learning to predict treatment response after six months in major depressive disorder |
| title_sort | application of functional near infrared spectroscopy and machine learning to predict treatment response after six months in major depressive disorder |
| url | https://doi.org/10.1038/s41398-025-03224-7 |
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