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Chan YL, Ho CSH, Tay GWN, Tan TWK, Tang TB. MicroRNA classification and discovery for major depressive disorder diagnosis: Towards a robust and interpretable machine learning approach. J Affect Disord 2024; 360:326-335. [PMID: 38788856 DOI: 10.1016/j.jad.2024.05.066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/08/2024] [Accepted: 05/15/2024] [Indexed: 05/26/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) is notably underdiagnosed and undertreated due to its complex nature and subjective diagnostic methods. Biomarker identification would help provide a clearer understanding of MDD aetiology. Although machine learning (ML) has been implemented in previous studies to study the alteration of microRNA (miRNA) levels in MDD cases, clinical translation has not been feasible due to the lack of interpretability (i.e. too many miRNAs for consideration) and stability. METHODS This study applied logistic regression (LR) model to the blood miRNA expression profile to differentiate patients with MDD (n = 60) from healthy controls (HCs, n = 60). Embedded (L1-regularised logistic regression) feature selector was utilised to extract clinically relevant miRNAs, and optimized for clinical application. RESULTS Patients with MDD could be differentiated from HCs with the area under the receiver operating characteristic curve (AUC) of 0.81 on testing data when all available miRNAs were considered (which served as a benchmark). Our LR model selected miRNAs up to 5 (known as LR-5 model) emerged as the best model because it achieved a moderate classification ability (AUC = 0.75), relatively high interpretability (feature number = 5) and stability (ϕ̂Z=0.55) compared to the benchmark. The top-ranking miRNAs identified by our model have demonstrated associations with MDD pathways involving cytokine signalling in the immune system, the reelin signalling pathway, programmed cell death and cellular responses to stress. CONCLUSION The LR-5 model, which is optimised based on ML design factors, may lead to a robust and clinically usable MDD diagnostic tool.
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Affiliation(s)
- Yee Ling Chan
- Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS (UTP), Bandar Seri Iskandar 32610, Perak, Malaysia
| | - Cyrus S H Ho
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117543, Singapore
| | - Gabrielle W N Tay
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117543, Singapore
| | - Trevor W K Tan
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117543, Singapore; Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117543, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore; N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore 117456, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore 119077, Singapore
| | - Tong Boon Tang
- Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS (UTP), Bandar Seri Iskandar 32610, Perak, Malaysia.
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Eken A, Nassehi F, Eroğul O. Diagnostic machine learning applications on clinical populations using functional near infrared spectroscopy: a review. Rev Neurosci 2024; 35:421-449. [PMID: 38308531 DOI: 10.1515/revneuro-2023-0117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 01/12/2024] [Indexed: 02/04/2024]
Abstract
Functional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classification of clinical disorders due to the lack of robust and objective biomarkers. This review provides an overview of research on psychiatric diseases by using fNIRS and ML. Article search was carried out and 45 studies were evaluated by considering their sample sizes, used features, ML methodology, and reported accuracy. To our best knowledge, this is the first review that reports diagnostic ML applications using fNIRS. We found that there has been an increasing trend to perform ML applications on fNIRS-based biomarker research since 2010. The most studied populations are schizophrenia (n = 12), attention deficit and hyperactivity disorder (n = 7), and autism spectrum disorder (n = 6) are the most studied populations. There is a significant negative correlation between sample size (>21) and accuracy values. Support vector machine (SVM) and deep learning (DL) approaches were the most popular classifier approaches (SVM = 20) (DL = 10). Eight of these studies recruited a number of participants more than 100 for classification. Concentration changes in oxy-hemoglobin (ΔHbO) based features were used more than concentration changes in deoxy-hemoglobin (ΔHb) based ones and the most popular ΔHbO-based features were mean ΔHbO (n = 11) and ΔHbO-based functional connections (n = 11). Using ML on fNIRS data might be a promising approach to reveal specific biomarkers for diagnostic classification.
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Affiliation(s)
- Aykut Eken
- Department of Biomedical Engineering, Faculty of Engineering, TOBB University of Economics and Technology, Sogutozu, 06510, Ankara, Türkiye
| | - Farhad Nassehi
- Department of Biomedical Engineering, Faculty of Engineering, TOBB University of Economics and Technology, Sogutozu, 06510, Ankara, Türkiye
| | - Osman Eroğul
- Department of Biomedical Engineering, Faculty of Engineering, TOBB University of Economics and Technology, Sogutozu, 06510, Ankara, Türkiye
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Park Y, Park S, Lee M. Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review. J Affect Disord 2024; 361:445-456. [PMID: 38889858 DOI: 10.1016/j.jad.2024.06.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 11/27/2023] [Accepted: 06/14/2024] [Indexed: 06/20/2024]
Abstract
OBJECTIVES This study underscores the importance of exploring AI's creative applications in treating depressive disorders to revolutionize mental health care. Through innovative integration of AI technologies, the research confirms their positive effects on preventing, diagnosing, and treating depression. The systematic review establishes an evidence base for AI in depression management, offering directions for effective interventions. METHODS This systematic literature review investigates the effectiveness of AI in depression management by analyzing studies from January 1, 2017, to May 31, 2022. Utilizing search engines like IEEE Xplore, PubMed, and Web of Science, the review focused on keywords such as Depression/Mental Health, Machine Learning/Artificial Intelligence, and Prediction/Diagnosis. The analysis of 95 documents involved classification based on use, data type, and algorithm type. RESULTS The study revealed that AI in depression management excelled in accuracy, particularly in monitoring and prediction. Biomarker-derived data demonstrated the highest accuracy, with the CNN algorithm proving most effective. The findings affirm the therapeutic benefits of AI, including treatment, detection, and disease prediction, highlighting its potential in analyzing monitored data for depression management. LIMITATIONS This study exclusively examined the application of AI in individuals with depressive disorders. Interpretation should be cautious due to the limited scope of subjects to this specific population. CONCLUSIONS To introduce digital healthcare and therapies for ongoing depression management, it's crucial to present empirical evidence on the medical fee payment system, safety, and efficacy. These findings support enhanced medical accessibility through digital healthcare, offering personalized disease management for patients seeking non-face-to-face treatment.
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Affiliation(s)
- Yoonseo Park
- Department of Convergence Healthcare Medicine, Ajou University, Suwon, South Korea.
| | - Sewon Park
- Department of Medical Science, Ajou University School of Medicine, Suwon, South Korea.
| | - Munjae Lee
- Department of Medical Science, Ajou University School of Medicine, Suwon, South Korea.
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Chen H, Lu M, Lyu Q, Shi L, Zhou C, Li M, Feng S, Liang X, Zhou X, Ren L. Mitochondrial dynamics dysfunction: Unraveling the hidden link to depression. Biomed Pharmacother 2024; 175:116656. [PMID: 38678964 DOI: 10.1016/j.biopha.2024.116656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 04/08/2024] [Accepted: 04/24/2024] [Indexed: 05/01/2024] Open
Abstract
Depression is a common mental disorder and its pathogenesis is not fully understood. However, more and more evidence shows that mitochondrial dynamics dysfunction may play an important role in the occurrence and development of depression. Mitochondria are the centre of energy production in cells, and are also involved in important processes such as apoptosis and oxidative stress. Studies have found that there are abnormalities in mitochondrial function in patients with depression, including mitochondrial morphological changes, mitochondrial dynamics disorders, mitochondrial DNA damage, and impaired mitochondrial respiratory chain function. These abnormalities may cause excessive free radicals and oxidative stress in mitochondria, which further damage cells and affect the balance of neurotransmitters, causing or aggravating depressive symptoms. Studies have shown that mitochondrial dynamics dysfunction may participate in the occurrence and development of depression by affecting neuroplasticity, inflammation and neurotransmitters. This article reviews the effects of mitochondrial dynamics dysfunction on the pathogenesis of depression and its potential molecular pathway. The restorers for the treatment of depression by regulating the function of mitochondrial dynamics were summarized and the possibility of using mitochondrial dynamics as a biomarker of depression was discussed.
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Affiliation(s)
- Haiyang Chen
- Department of Acupuncture and Moxibustion, Liaoning University of Traditional Chinese Medicine, Shenyang 110847, China
| | - Mei Lu
- Department of Acupuncture and Moxibustion, Liaoning University of Traditional Chinese Medicine, Shenyang 110847, China
| | - Qin Lyu
- Graduate School, Liaoning University of Traditional Chinese Medicine, Shenyang, 110847, China
| | - Liuqing Shi
- Graduate School, Liaoning University of Traditional Chinese Medicine, Shenyang, 110847, China
| | - Chuntong Zhou
- Department of Acupuncture and Moxibustion, Liaoning University of Traditional Chinese Medicine, Shenyang 110847, China
| | - Mingjie Li
- Department of Acupuncture and Moxibustion, Liaoning University of Traditional Chinese Medicine, Shenyang 110847, China
| | - Shiyu Feng
- Graduate School, Liaoning University of Traditional Chinese Medicine, Shenyang, 110847, China
| | - Xicai Liang
- Experimental Animal Center of Liaoning University of traditional Chinese Medicine, Shenyang 110847, China
| | - Xin Zhou
- Department of Acupuncture and Moxibustion, Liaoning University of Traditional Chinese Medicine, Shenyang 110847, China.
| | - Lu Ren
- Graduate School, Liaoning University of Traditional Chinese Medicine, Shenyang, 110847, China; Mental disorders research laboratory, Liaoning University of Traditional Chinese Medicine, Shenyang 110847, China.
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Xu Y, Wang Y, Hu N, Yang L, Yu Z, Han L, Xu Q, Zhou J, Chen J, Mao H, Pan Y. Intrinsic Organization of Occipital Hubs Predicts Depression: A Resting-State fNIRS Study. Brain Sci 2022; 12:brainsci12111562. [PMID: 36421888 PMCID: PMC9688420 DOI: 10.3390/brainsci12111562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/11/2022] [Accepted: 11/15/2022] [Indexed: 11/19/2022] Open
Abstract
Dysfunctional brain networks have been found in patients with major depressive disorder (MDD). In this study, to verify this in a more straightforward way, we investigated the intrinsic organization of brain networks in MDD by leveraging the resting-state functional near-infrared spectroscopy (rs-fNIRS). Thirty-four MDD patients (24 females, 38.41 ± 13.14 years old) and thirty healthy controls (22 females, 34.43 ± 5.03 years old) underwent a 10 min rest while their brain activity was recorded via fNIRS. The results showed that MDD patients and healthy controls exhibited similar resting-state functional connectivity. Moreover, the depression group showed lower small-world Lambda (1.12 ± 0.04 vs. 1.16 ± 0.10, p = 0.04) but higher global efficiency (0.51 ± 0.03 vs. 0.48 ± 0.05, p = 0.03) than the control group. Importantly, MDD patients, as opposed to healthy controls, showed a significantly lower nodal local efficiency at the left middle occipital gyrus (0.56 ± 0.36 vs. 0.81 ± 0.20, pFDR < 0.05), which predicted the level of depression in MDD (r = 0.45, p = 0.01, R2 = 0.15). In sum, we found a more integrated brain network in MDD patients with a lower nodal local efficiency at the occipital hub, which could predict depressive symptoms.
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Affiliation(s)
- You Xu
- Department of Sleep Medicine, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Tianmushan Road 305, Hangzhou 310013, China
| | - Yajie Wang
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310058, China
| | - Nannan Hu
- Department of Psychiatry, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou 310013, China
| | - Lili Yang
- Department of Sleep Medicine, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Tianmushan Road 305, Hangzhou 310013, China
| | - Zhenghe Yu
- Department of Sleep Medicine, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Tianmushan Road 305, Hangzhou 310013, China
| | - Li Han
- Department of Sleep Medicine, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Tianmushan Road 305, Hangzhou 310013, China
| | - Qianqian Xu
- Department of Sleep Medicine, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Tianmushan Road 305, Hangzhou 310013, China
| | - Jingjing Zhou
- Department of Sleep Medicine, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Tianmushan Road 305, Hangzhou 310013, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hongjing Mao
- Department of Sleep Medicine, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Tianmushan Road 305, Hangzhou 310013, China
- Correspondence: (H.M.); (Y.P.)
| | - Yafeng Pan
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310058, China
- Correspondence: (H.M.); (Y.P.)
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