Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA
While functional near-infrared spectroscopy (fNIRS) had previously been suggested for major depressive disorder (MDD) diagnosis, the clinical application to predict antidepressant treatment response (ATR) is still unclear. To address this, the aim of the current study is to investigate MDD ATR at th...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10767732/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832590302516346880 |
|---|---|
| author | Lok Hua Lee Cyrus Su Hui Ho Yee Ling Chan Gabrielle Wann Nii Tay Cheng-Kai Lu Tong Boon Tang |
| author_facet | Lok Hua Lee Cyrus Su Hui Ho Yee Ling Chan Gabrielle Wann Nii Tay Cheng-Kai Lu Tong Boon Tang |
| author_sort | Lok Hua Lee |
| collection | DOAJ |
| description | While functional near-infrared spectroscopy (fNIRS) had previously been suggested for major depressive disorder (MDD) diagnosis, the clinical application to predict antidepressant treatment response (ATR) is still unclear. To address this, the aim of the current study is to investigate MDD ATR at three response levels using fNIRS and micro-ribonucleic acids (miRNAs). Our proposed algorithm includes a custom inter-subject variability reduction based on the principal component analysis (PCA). The principal components of extracted features are first identified for non-responders’ group. The first few components that sum up to 99% of explained variance are discarded to minimize inter-subject variability while the remaining projection vectors are applied on all response groups (24 non-responders, 15 partial-responders, 13 responders) to obtain their relative projections in feature space. The entire algorithm achieved a better performance through the radial basis function (RBF) support vector machine (SVM), with 82.70% accuracy, 78.44% sensitivity, 86.15% precision, and 91.02% specificity, respectively, when compared with conventional machine learning approaches that combine clinical, sociodemographic and genetic information as the predictor. The performance of the proposed custom algorithm suggests the prediction of ATR can be improved with multiple features sources, provided that the inter-subject variability is properly addressed, and can be an effective tool for clinical decision support system in MDD ATR prediction. Clinical and Translational Impact Statement—The fusion of neuroimaging fNIRS features and miRNA profiles significantly enhances the prediction accuracy of MDD ATR. The minimally required features also make the personalized medicine more practical and realizable |
| format | Article |
| id | doaj-art-03f7dba93b6f4312bd988054461e7dc2 |
| institution | Kabale University |
| issn | 2168-2372 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj-art-03f7dba93b6f4312bd988054461e7dc22025-01-24T00:01:01ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722025-01-011392210.1109/JTEHM.2024.350655610767732Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNALok Hua Lee0https://orcid.org/0000-0002-9762-7226Cyrus Su Hui Ho1https://orcid.org/0000-0002-7092-9566Yee Ling Chan2Gabrielle Wann Nii Tay3https://orcid.org/0000-0001-6578-6049Cheng-Kai Lu4https://orcid.org/0000-0002-5819-0754Tong Boon Tang5https://orcid.org/0000-0002-5721-6828Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS, Seri Iskandar, Perak, MalaysiaDepartment of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Queenstown, SingaporeCentre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS, Seri Iskandar, Perak, MalaysiaDepartment of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Queenstown, SingaporeDepartment of Electrical and Electronic Engineering, National Taiwan Normal University, Taipei, TaiwanCentre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS, Seri Iskandar, Perak, MalaysiaWhile functional near-infrared spectroscopy (fNIRS) had previously been suggested for major depressive disorder (MDD) diagnosis, the clinical application to predict antidepressant treatment response (ATR) is still unclear. To address this, the aim of the current study is to investigate MDD ATR at three response levels using fNIRS and micro-ribonucleic acids (miRNAs). Our proposed algorithm includes a custom inter-subject variability reduction based on the principal component analysis (PCA). The principal components of extracted features are first identified for non-responders’ group. The first few components that sum up to 99% of explained variance are discarded to minimize inter-subject variability while the remaining projection vectors are applied on all response groups (24 non-responders, 15 partial-responders, 13 responders) to obtain their relative projections in feature space. The entire algorithm achieved a better performance through the radial basis function (RBF) support vector machine (SVM), with 82.70% accuracy, 78.44% sensitivity, 86.15% precision, and 91.02% specificity, respectively, when compared with conventional machine learning approaches that combine clinical, sociodemographic and genetic information as the predictor. The performance of the proposed custom algorithm suggests the prediction of ATR can be improved with multiple features sources, provided that the inter-subject variability is properly addressed, and can be an effective tool for clinical decision support system in MDD ATR prediction. Clinical and Translational Impact Statement—The fusion of neuroimaging fNIRS features and miRNA profiles significantly enhances the prediction accuracy of MDD ATR. The minimally required features also make the personalized medicine more practical and realizablehttps://ieeexplore.ieee.org/document/10767732/Treatment response predictionfunctional near-infrared spectroscopymachine learningmicro-ribonucleic acid. |
| spellingShingle | Lok Hua Lee Cyrus Su Hui Ho Yee Ling Chan Gabrielle Wann Nii Tay Cheng-Kai Lu Tong Boon Tang Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA IEEE Journal of Translational Engineering in Health and Medicine Treatment response prediction functional near-infrared spectroscopy machine learning micro-ribonucleic acid. |
| title | Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA |
| title_full | Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA |
| title_fullStr | Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA |
| title_full_unstemmed | Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA |
| title_short | Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA |
| title_sort | antidepressant treatment response prediction with early assessment of functional near infrared spectroscopy and micro rna |
| topic | Treatment response prediction functional near-infrared spectroscopy machine learning micro-ribonucleic acid. |
| url | https://ieeexplore.ieee.org/document/10767732/ |
| work_keys_str_mv | AT lokhualee antidepressanttreatmentresponsepredictionwithearlyassessmentoffunctionalnearinfraredspectroscopyandmicrorna AT cyrussuhuiho antidepressanttreatmentresponsepredictionwithearlyassessmentoffunctionalnearinfraredspectroscopyandmicrorna AT yeelingchan antidepressanttreatmentresponsepredictionwithearlyassessmentoffunctionalnearinfraredspectroscopyandmicrorna AT gabriellewannniitay antidepressanttreatmentresponsepredictionwithearlyassessmentoffunctionalnearinfraredspectroscopyandmicrorna AT chengkailu antidepressanttreatmentresponsepredictionwithearlyassessmentoffunctionalnearinfraredspectroscopyandmicrorna AT tongboontang antidepressanttreatmentresponsepredictionwithearlyassessmentoffunctionalnearinfraredspectroscopyandmicrorna |