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Diao Y, Wang H, Wang X, Qiu C, Wang Z, Ji Z, Wang C, Gu J, Liu C, Wu K, Wang C. Discriminative analysis of schizophrenia and major depressive disorder using fNIRS. J Affect Disord 2024; 361:256-267. [PMID: 38862077 DOI: 10.1016/j.jad.2024.06.013] [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: 02/09/2024] [Revised: 05/27/2024] [Accepted: 06/03/2024] [Indexed: 06/13/2024]
Abstract
BACKGROUND Research into the shared and distinct brain dysfunctions in patients with schizophrenia (SCZ) and major depressive disorder (MDD) has been increasing. However, few studies have explored the application of functional near-infrared spectroscopy (fNIRS) in investigating brain dysfunction and enhancing diagnostic methodologies in these two conditions. METHODS A general linear model was used for analysis of brain activation following task-state fNIRS from 131 patients with SCZ, 132 patients with MDD and 130 healthy controls (HCs). Subsequently, seventy-seven time-frequency analysis methods were used to construct new features of fNIRS, followed by the implementation of five machine learning algorithms to develop a differential diagnosis model for the three groups. This model was evaluated by comparing it to both a diagnostic model relying on traditional fNIRS features and assessments made by two psychiatrists. RESULTS Brain activation analysis revealed significantly lower activation in Broca's area, the dorsolateral prefrontal cortex, and the middle temporal gyrus for both the SCZ and MDD groups compared to HCs. Additionally, the SCZ group exhibited notably lower activation in the superior temporal gyrus and the subcentral gyrus compared to the MDD group. When distinguishing among the three groups using independent validation datasets, the models utilizing new fNIRS features achieved an accuracy of 85.90 % (AUC = 0.95). In contrast, models based on traditional fNIRS features reached an accuracy of 52.56 % (AUC = 0.66). The accuracies of the two psychiatrists were 42.00 % (AUC = 0.60) and 38.00 % (AUC = 0.50), respectively. CONCLUSION This investigation brings to light the shared and distinct neurobiological abnormalities present in SCZ and MDD, offering potential enhancements for extant diagnostic systems.
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Affiliation(s)
- Yunheng Diao
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, PR China; The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China
| | - Huiying Wang
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China; Henan Collaborative Innovation Center of Prevention and treatment of mental disorder, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China; The Second Clinical College, Xinxiang Medical University, Xinxiang, Henan 453003, PR China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, Henan 453002, PR China; Brain Institute, Henan Academy of Innovations in Medical Science, Zhengzhou 451163, PR China
| | - Xinyu Wang
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China; The Second Clinical College, Xinxiang Medical University, Xinxiang, Henan 453003, PR China
| | - Chen Qiu
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China; The Second Clinical College, Xinxiang Medical University, Xinxiang, Henan 453003, PR China
| | - Zitian Wang
- School of Future Technology, Xi'an JiaoTong University, Xi'an, Shanxi 710049, PR China
| | - Ziyang Ji
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China
| | - Chao Wang
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China
| | - Jingyang Gu
- Department of Psychiatry, Chaohu Hospital of Anhui Medical University, Hefei, PR China; Department of Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, PR China
| | - Cong Liu
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, PR China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, PR China; Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai 980-8575, Japan.
| | - Changhong Wang
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China; Henan Collaborative Innovation Center of Prevention and treatment of mental disorder, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, PR China; Henan Cloud Platform and Application Research Center for Psychological Assistance, Xinxiang, Henan 453002, PR China; Henan Key Laboratory for Sleep Medicine, Xinxiang, Henan 453002, PR China.
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Mao L, Hong X, Hu M. Identifying neuroimaging biomarkers in major depressive disorder using machine learning algorithms and functional near-infrared spectroscopy (fNIRS) during verbal fluency task. J Affect Disord 2024; 365:9-20. [PMID: 39151759 DOI: 10.1016/j.jad.2024.08.082] [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: 05/09/2024] [Revised: 08/12/2024] [Accepted: 08/13/2024] [Indexed: 08/19/2024]
Abstract
One of the most prevalent psychiatric disorders is major depressive disorder (MDD), which increases the probability of suicidal ideation or untimely demise. Abnormal frontal hemodynamic changes detected by functional near-infrared spectroscopy (fNIRS) during verbal fluency task (VFT) have the potential to be used as an objective indicator for assessing clinical symptoms. However, comprehensive quantitative and objective assessment instruments for individuals who exhibit symptoms suggestive of depression remain undeveloped. Drawing from a total of 467 samples in a large-scale dataset comprising 289 MDD patients and 178 healthy controls, fNIRS measurements were obtained throughout the VFT. To identify unique MDD biomarkers, this research introduced a data representation approach for extracting spatiotemporal features from fNIRS signals, which were subsequently utilized as potential predictors. Machine learning classifiers (e.g., Gradient Boosted Decision Trees (GBDT) and Multilayer Perceptron) were implemented to assess the ability to predict selected features. The mean and standard deviation of the cross-validation indicated that the GBDT model, when combined with the 180-feature pattern, distinguishes patients with MDD from healthy controls in the most effective manner. The accuracy of correct classification for the test set was 0.829 ± 0.053, with an AUC of 0.895 (95 % CI: 0.864-0.925) and a sensitivity of 0.914 ± 0.051. Channels that made the most important contribution to the identification of MDD were identified using Shapley Additive Explanations method, located in the frontopolar area and the dorsolateral prefrontal cortex, as well as pars triangularis Broca's area. Assessment of abnormal prefrontal activity during the VFT in MDD serves as an objectively measurable biomarker that could be utilized to evaluate cognitive deficits and facilitate early screening for MDD. The model suggested in this research could be applied to large-scale case-control fNIRS datasets to detect unique characteristics of MDD and offer clinicians an objective biomarker-based analytical instrument to assist in the evaluation of suspicious cases.
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Affiliation(s)
- Lingyun Mao
- Department of Psychology, School of Social and Behavioral Sciences, Nanjing University, Nanjing, China; Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China.
| | - Xin Hong
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Maorong Hu
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China.
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Yang J, Ma S, Cheng A, Zhang L, Li B, Cui W, Li K, Zhang Y, Liu X. Analysis of functional network asymmetry in major depressive disorder under four fNIRS tasks. J Affect Disord 2024:S0165-0327(24)01235-7. [PMID: 39137836 DOI: 10.1016/j.jad.2024.08.022] [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: 03/29/2024] [Revised: 07/10/2024] [Accepted: 08/09/2024] [Indexed: 08/15/2024]
Abstract
BACKGROUND Research in functional asymmetry of Major Depressive Disorder (MDD) under different tasks is crucial for clinical diagnose. METHODS Fifty individuals with MDD and twenty healthy controls (HCS) were recruited for hemodynamic data collection under four fNIRS tasks (Emotional picture, Verbal fluency, Fingering and Negative emotional picture description task). Integral values and functional connectivity strength were employed to probe neural activation and functional connectivity in frontal and temporal lobes in MDD. Following, asymmetry characteristic of the frontal cortex between MDD and HCS under four tasks were carefully analyzed and compared. RESULTS Individuals with MDD demonstrated heightened connectivity between the frontal and right temporal lobes and reduced connectivity between the frontal and left temporal lobes compared to HCS in all tasks. Additionally, MDD exhibited attenuated activation in the left frontal lobes and exaggerated activation in the right frontal lobes, diverging from HCS. Furthermore, the disparities in left-right asymmetry characteristic of frontal cortex activation between MDD and HCS were more pronounced during the combined task. LIMITATIONS Further research is required to grasp the neurophysiological mechanisms governing left-right asymmetry across various tasks and the influence of task-induced brain fatigue on cerebral cortex hemodynamics in MDD. CONCLUSION The left-right asymmetry feature provides valuable neurophysiological insights for diagnosing MDD clinically. Variations in activation patterns and functional connectivity features between MDD and HCS are closely tied to the task chosen. Thus, in clinical practice, carefully selecting appropriate fNIRS tasks and relevant features can significantly improve the diagnostic accuracy of MDD.
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Affiliation(s)
- Jianli Yang
- College of Electronic and Information Engineering, Hebei University, Baoding 071002, China; Key Laboratory of Digital Medical Engineering of Heibei Province, Baoding 071002, China
| | - Shuwei Ma
- College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
| | - Aobo Cheng
- Hebei Provincial Mental Health Center, Baoding 071000, China; Hebei Key Laboratory of Major Mental and Behavioral Disorders, Baoding 071000, China; The Sixth Clinical Medical College of Hebei University, Baoding 071000, China
| | - Lili Zhang
- Hebei Provincial Mental Health Center, Baoding 071000, China; Hebei Key Laboratory of Major Mental and Behavioral Disorders, Baoding 071000, China; The Sixth Clinical Medical College of Hebei University, Baoding 071000, China
| | - Bing Li
- Hebei Provincial Mental Health Center, Baoding 071000, China; Hebei Key Laboratory of Major Mental and Behavioral Disorders, Baoding 071000, China; The Sixth Clinical Medical College of Hebei University, Baoding 071000, China
| | - Wei Cui
- Hebei Provincial Mental Health Center, Baoding 071000, China
| | - Keqing Li
- Hebei Provincial Mental Health Center, Baoding 071000, China; Hebei Key Laboratory of Major Mental and Behavioral Disorders, Baoding 071000, China; The Sixth Clinical Medical College of Hebei University, Baoding 071000, China
| | - Yunshu Zhang
- Hebei Provincial Mental Health Center, Baoding 071000, China; Hebei Key Laboratory of Major Mental and Behavioral Disorders, Baoding 071000, China; The Sixth Clinical Medical College of Hebei University, Baoding 071000, China.
| | - Xiuling Liu
- College of Electronic and Information Engineering, Hebei University, Baoding 071002, China; Key Laboratory of Digital Medical Engineering of Heibei Province, Baoding 071002, China.
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Dagnew TM, Tseng CEJ, Yoo CH, Makary MM, Goodheart AE, Striar R, Meyer TN, Rattray AK, Kang L, Wolf KA, Fiedler SA, Tocci D, Shapiro H, Provost S, Sultana E, Liu Y, Ding W, Chen P, Kubicki M, Shen S, Catana C, Zürcher NR, Wey HY, Hooker JM, Weiss RD, Wang C. Toward AI-driven neuroepigenetic imaging biomarker for alcohol use disorder: A proof-of-concept study. iScience 2024; 27:110159. [PMID: 39021792 PMCID: PMC11253155 DOI: 10.1016/j.isci.2024.110159] [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: 11/27/2023] [Revised: 04/13/2024] [Accepted: 05/29/2024] [Indexed: 07/20/2024] Open
Abstract
Alcohol use disorder (AUD) is a disorder of clinical and public health significance requiring novel and improved therapeutic solutions. Both environmental and genetic factors play a significant role in its pathophysiology. However, the underlying epigenetic molecular mechanisms that link the gene-environment interaction in AUD remain largely unknown. In this proof-of-concept study, we showed, for the first time, the neuroepigenetic biomarker capability of non-invasive imaging of class I histone deacetylase (HDAC) epigenetic enzymes in the in vivo brain for classifying AUD patients from healthy controls using a machine learning approach in the context of precision diagnosis. Eleven AUD patients and 16 age- and sex-matched healthy controls completed a simultaneous positron emission tomography-magnetic resonance (PET/MR) scan with the HDAC-binding radiotracer [11C]Martinostat. Our results showed lower HDAC expression in the anterior cingulate region in AUD. Furthermore, by applying a genetic algorithm feature selection, we identified five particular brain regions whose combined [11C]Martinostat relative standard uptake value (SUVR) features could reliably classify AUD vs. controls. We validate their promising classification reliability using a support vector machine classifier. These findings inform the potential of in vivo HDAC imaging biomarkers coupled with machine learning tools in the objective diagnosis and molecular translation of AUD that could complement the current diagnostic and statistical manual of mental disorders (DSM)-based intervention to propel precision medicine forward.
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Affiliation(s)
- Tewodros Mulugeta Dagnew
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Chieh-En J. Tseng
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Chi-Hyeon Yoo
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Meena M. Makary
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Systems and Biomedical Engineering Department, Cairo University, Giza, Egypt
| | - Anna E. Goodheart
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Robin Striar
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tyler N. Meyer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Anna K. Rattray
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Leyi Kang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Kendall A. Wolf
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Stephanie A. Fiedler
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Darcy Tocci
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hannah Shapiro
- Division of Alcohol, Drugs, and Addiction, McLean Hospital, Belmont, MA, USA
| | - Scott Provost
- Division of Alcohol, Drugs, and Addiction, McLean Hospital, Belmont, MA, USA
| | - Eleanor Sultana
- Division of Alcohol, Drugs, and Addiction, McLean Hospital, Belmont, MA, USA
| | - Yan Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Wei Ding
- Department of Computer Science, University of Massachusetts Boston, Boston, MA, USA
| | - Ping Chen
- Department of Engineering, University of Massachusetts Boston, Boston, MA, USA
| | - Marek Kubicki
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Psychiatry Neuroimaging Laboratory, Departments of Psychiatry and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Shiqian Shen
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ciprian Catana
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Nicole R. Zürcher
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hsiao-Ying Wey
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jacob M. Hooker
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Roger D. Weiss
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Division of Alcohol, Drugs, and Addiction, McLean Hospital, Belmont, MA, USA
| | - Changning Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Xiao S, Yang Z, Yan H, Chen G, Zhong S, Chen P, Zhong H, Yang H, Jia Y, Yin Z, Gong J, Huang L, Wang Y. Gut proinflammatory bacteria is associated with abnormal functional connectivity of hippocampus in unmedicated patients with major depressive disorder. Transl Psychiatry 2024; 14:292. [PMID: 39013880 PMCID: PMC11253007 DOI: 10.1038/s41398-024-03012-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 06/27/2024] [Accepted: 07/04/2024] [Indexed: 07/18/2024] Open
Abstract
Accumulating evidence has revealed the gut bacteria dysbiosis and brain hippocampal functional and structural alterations in major depressive disorder (MDD). However, the potential relationship between the gut microbiota and hippocampal function alterations in patients with MDD is still very limited. Data of resting-state functional magnetic resonance imaging were acquired from 44 unmedicated MDD patients and 42 demographically matched healthy controls (HCs). Severn pairs of hippocampus subregions (the bilateral cornu ammonis [CA1-CA3], dentate gyrus (DG), entorhinal cortex, hippocampal-amygdaloid transition area, and subiculum) were selected as the seeds in the functional connectivity (FC) analysis. Additionally, fecal samples of participants were collected and 16S rDNA amplicon sequencing was used to identify the altered relative abundance of gut microbiota. Then, association analysis was conducted to investigate the potential relationships between the abnormal hippocampal subregions FC and microbiome features. Also, the altered hippocampal subregion FC values and gut microbiota levels were used as features separately or together in the support vector machine models distinguishing the MDD patients and HCs. Compared with HCs, patients with MDD exhibited increased FC between the left hippocampus (CA2, CA3 and DG) and right hippocampus (CA2 and CA3), and decreased FC between the right hippocampal CA3 and bilateral posterior cingulate cortex. In addition, we found that the level of proinflammatory bacteria (i.e., Enterobacteriaceae) was significantly increased, whereas the level of short-chain fatty acids producing-bacteria (i.e., Prevotellaceae, Agathobacter and Clostridium) were significantly decreased in MDD patients. Furthermore, FC values of the left hippocampal CA3- right hippocampus (CA2 and CA3) was positively correlated with the relative abundance of Enterobacteriaceae in patients with MDD. Moreover, altered hippocampal FC patterns and gut microbiota level were considered in combination, the best discrimination was obtained (AUC = 0.92). These findings may provide insights into the potential role of gut microbiota in the underlying neuropathology of MDD patients.
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Affiliation(s)
- Shu Xiao
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China
| | - Zibin Yang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China
| | - Hong Yan
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China
| | - Guanmao Chen
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China
| | - Shuming Zhong
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Pan Chen
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China
| | - Hui Zhong
- Biomedical Translational Research Institute, Jinan University, 510630, Guangzhou, China
| | - Hengwen Yang
- Biomedical Translational Research Institute, Jinan University, 510630, Guangzhou, China
| | - Yanbin Jia
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Zhinan Yin
- Biomedical Translational Research Institute, Jinan University, 510630, Guangzhou, China
| | - Jiaying Gong
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China
- Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Li Huang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China
| | - Ying Wang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China.
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China.
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Da H, Xiang N, Qiu M, Abbas S, Xiao Q, Zhang Y. Characteristics of oxyhemoglobin during the verbal fluency task in subthreshold depression: A multi-channel near-infrared spectroscopy study. J Affect Disord 2024; 356:88-96. [PMID: 38588729 DOI: 10.1016/j.jad.2024.04.005] [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: 11/23/2023] [Revised: 03/29/2024] [Accepted: 04/01/2024] [Indexed: 04/10/2024]
Abstract
OBJECTIVE Subthreshold depression is an essential precursor and risk factor for major depressive disorder, and its accurate identification and timely intervention are important for reducing the prevalence of major depressive disorder. Therefore, we used functional near-infrared spectroscopic imaging (fNIRS) to explore the characteristics of the brain neural activity of college students with subthreshold depression in the verbal fluency task. METHODS A total of 72 subthreshold depressed college students (SDs) and 67 healthy college students (HCs) were recruited, and all subjects were subjected to a verbal fluency task (VFT) while a 53-channel fNIRS device was used to collect the subjects' cerebral blood oxygenation signals. RESULTS The results of the independent samples t-test showed that the mean oxyhemoglobin in the right dorsolateral prefrontal (ch34, ch42, ch45) and Broca's area (ch51, ch53) of SDs was lower than that of HCs. The peak oxygenated hemoglobin of SDs was lower in the right dorsolateral prefrontal (ch34) and Broca's area (ch51, ch53).The brain functional connectivity strength was lower than that of HCs. Correlation analysis showed that the left DLPFC and Broca's area were significantly negatively correlated with the depression level. CONCLUSION SDs showed abnormally low, inadequate levels of brain activation and weak frontotemporal brain functional connectivity. The right DLPFC has a higher sensitivity for the differentiation of depressive symptoms and is suitable as a biomarker for the presence of depressive symptoms. Dysfunction in Broca's area can be used both as a marker of depressive symptoms and as a biomarker, indicating the severity of depressive symptoms.
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Affiliation(s)
- Hui Da
- School of Education, Huazhong University of Science and Technology, Wuhan, China.
| | - Nian Xiang
- Hospital of Huazhong University of Science and Technology, Wuhan, China.
| | - Min Qiu
- Hospital of Huazhong University of Science and Technology, Wuhan, China.
| | - Sadia Abbas
- School of Education, Huazhong University of Science and Technology, Wuhan, China.
| | - Qiang Xiao
- Hospital of Huazhong University of Science and Technology, Wuhan, China.
| | - Yan Zhang
- School of Education, Huazhong University of Science and Technology, Wuhan, China; Research Center for Innovative Education and Critical Thinking, Huazhong University of Science and Technology, Wuhan, China.
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7
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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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Murphy E, Svob C, van Dijk MT, Gameroff MJ, Yangchen T, Wickramaratne PJ, Weissman MM, Talati A. Helpful activities during the COVID-19 pandemic: Perceived benefits and actual association with psychiatric symptoms and psychological wellbeing. J Affect Disord 2023; 339:172-179. [PMID: 37442441 PMCID: PMC10529101 DOI: 10.1016/j.jad.2023.07.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/04/2023] [Accepted: 07/08/2023] [Indexed: 07/15/2023]
Abstract
BACKGROUND Few studies have rigorously examined the effectiveness of commonly reported coping activities during the COVID-19 pandemic. This study was designed to assess perceived helpful activities during the pandemic and to investigate the extent to which these activities were associated with psychological outcomes. METHOD Adults living in the US (N = 204), who were part of a longitudinal family study of depression responded to an online survey. They reported on their perceived helpful activities during the pandemic. General linear regression models (GLM) were used to evaluate the association between perceived helpful activities and current psychiatric symptoms, controlling for demographic factors, and pre-pandemic psychiatric history and symptoms. RESULTS The top perceived helpful activity during COVID-19 was communicating with friends/family via telephone text or video (75.5 %). However, of the top five activities endorsed, cooking/baking was associated with the most clinical outcomes, including lower anxiety/depression and greater psychological wellbeing (all ps < 0.05). These relationships were most prominent among younger individuals < age 40 years, females, and those with recent psychiatric history, although they extended to younger males, and individuals at high or low depression risk. LIMITATIONS Close ended items limited variability in coping activities reported. The study lacked data on substance use. The sample was racially and ethnically homogenous. CONCLUSIONS These findings move beyond anecdotal evidence that cooking/baking as a coping activity yields protection against psychopathology. Its ready accessibility and ability to confer benefits across a range of individual characteristics, make it a useful adjunct in therapeutic interventions for people confined to their homes.
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Affiliation(s)
- Eleanor Murphy
- Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, United States of America; Manhattan Psychiatric Center, New York, NY 10035, United States of America.
| | - Connie Svob
- Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, United States of America; New York State Psychiatric Institute, Division of Translational Epidemiology, New York, NY 10032, United States of America
| | - Milenna T van Dijk
- Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, United States of America; New York State Psychiatric Institute, Division of Translational Epidemiology, New York, NY 10032, United States of America
| | - Marc J Gameroff
- Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, United States of America; New York State Psychiatric Institute, Division of Translational Epidemiology, New York, NY 10032, United States of America
| | - Tenzin Yangchen
- New York State Psychiatric Institute, Division of Translational Epidemiology, New York, NY 10032, United States of America
| | - Priya J Wickramaratne
- Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, United States of America; New York State Psychiatric Institute, Division of Translational Epidemiology, New York, NY 10032, United States of America
| | - Myrna M Weissman
- Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, United States of America; New York State Psychiatric Institute, Division of Translational Epidemiology, New York, NY 10032, United States of America; Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, United States of America
| | - Ardesheer Talati
- Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, United States of America; New York State Psychiatric Institute, Division of Translational Epidemiology, New York, NY 10032, United States of America
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9
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Pettorruso M, Guidotti R, d'Andrea G, De Risio L, D'Andrea A, Chiappini S, Carullo R, Barlati S, Zanardi R, Rosso G, De Filippis S, Di Nicola M, Andriola I, Marcatili M, Nicolò G, Martiadis V, Bassetti R, Nucifora D, De Fazio P, Rosenblat JD, Clerici M, Maria Dell'Osso B, Vita A, Marzetti L, Sensi SL, Di Lorenzo G, McIntyre RS, Martinotti G. Predicting outcome with Intranasal Esketamine treatment: A machine-learning, three-month study in Treatment-Resistant Depression (ESK-LEARNING). Psychiatry Res 2023; 327:115378. [PMID: 37574600 DOI: 10.1016/j.psychres.2023.115378] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 08/15/2023]
Abstract
Treatment-resistant depression (TRD) represents a severe clinical condition with high social and economic costs. Esketamine Nasal Spray (ESK-NS) has recently been approved for TRD by EMA and FDA, but data about predictors of response are still lacking. Thus, a tool that can predict the individual patients' probability of response to ESK-NS is needed. This study investigates sociodemographic and clinical features predicting responses to ESK-NS in TRD patients using machine learning techniques. In a retrospective, multicentric, real-world study involving 149 TRD subjects, psychometric data (Montgomery-Asberg-Depression-Rating-Scale/MADRS, Brief-Psychiatric-Rating-Scale/BPRS, Hamilton-Anxiety-Rating-Scale/HAM-A, Hamilton-Depression-Rating-Scale/HAMD-17) were collected at baseline and at one month/T1 and three months/T2 post-treatment initiation. We trained three different random forest classifiers, able to predict responses to ESK-NS with accuracies of 68.53% at T1 and 66.26% at T2 and remission at T2 with 68.60% of accuracy. Features like severe anhedonia, anxious distress, mixed symptoms as well as bipolarity were found to positively predict response and remission. At the same time, benzodiazepine usage and depression severity were linked to delayed responses. Despite some limitations (i.e., retrospective study, lack of biomarkers, lack of a correct interrater-reliability across the different centers), these findings suggest the potential of machine learning in personalized intervention for TRD.
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Affiliation(s)
- Mauro Pettorruso
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D'Annunzio, Chieti, Italy
| | - Roberto Guidotti
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D'Annunzio, Chieti, Italy
| | - Giacomo d'Andrea
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D'Annunzio, Chieti, Italy.
| | - Luisa De Risio
- Department of Mental Health and Addiction, ASL Roma 5, Rome, Italy
| | - Antea D'Andrea
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D'Annunzio, Chieti, Italy
| | - Stefania Chiappini
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D'Annunzio, Chieti, Italy
| | - Rosalba Carullo
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D'Annunzio, Chieti, Italy
| | - Stefano Barlati
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy; Department of Mental Health and Addiction Services, ASST Spedali Civili of Brescia, Brescia, Italy
| | - Raffaella Zanardi
- Mood Disorder Unit, Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy; Department of Clinical Neurosciences, University Vita-Salute San Raffaele, Milan, Italy
| | - Gianluca Rosso
- Department of Neurosciences Rita Levi Montalcini, University of Torino, Turin, Italy
| | | | - Marco Di Nicola
- Department of Neurosciences, Section of Psychiatry, Università Cattolica del Sacro Cuore, Rome; Department of Psychiatry, Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome
| | | | - Matteo Marcatili
- Department of Mental Health and Addiction, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
| | - Giuseppe Nicolò
- Department of Mental Health and Addiction, ASL Roma 5, Rome, Italy
| | | | - Roberta Bassetti
- Department of Mental Health and Addiction Services, Niguarda Hospital, Milan, Italy
| | | | - Pasquale De Fazio
- Psychiatry Unit, Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Joshua D Rosenblat
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada; Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Braxia Health, Canadian Centre for Rapid Treatment Excellence (CRTCE), Mississauga, ON, Canada
| | - Massimo Clerici
- Department of Mental Health and Addiction, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy; School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Bernardo Maria Dell'Osso
- Department of Biomedical and Clinical Sciences Luigi Sacco and Aldo Ravelli Center for Neurotechnology and Brain Therapeutic, University of Milan, Milano, Italy
| | - Antonio Vita
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy; Department of Mental Health and Addiction Services, ASST Spedali Civili of Brescia, Brescia, Italy
| | - Laura Marzetti
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D'Annunzio, Chieti, Italy
| | - Stefano L Sensi
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D'Annunzio, Chieti, Italy
| | - Giorgio Di Lorenzo
- Chair of Psychiatry, Department of Systems Medicine, Tor Vergata University of Rome, Rome, Italy; IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Roger S McIntyre
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada; Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Braxia Health, Canadian Centre for Rapid Treatment Excellence (CRTCE), Mississauga, ON, Canada; Brain and Cognition Discovery Foundation, Toronto, ON, Canada
| | - Giovanni Martinotti
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D'Annunzio, Chieti, Italy; Psychopharmacology, Drug Misuse and Novel Psychoactive Substances Research Unit, School of Life and Medical Sciences, University of Hertfordshire, Hatfield AL10 9AB, UK
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10
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Sabé M, Sulstarova A, Chen C, Hyde J, Poulet E, Aleman A, Downar J, Brandt V, Mallet L, Sentissi O, Nitsche MA, Bikson M, Brunoni AR, Cortese S, Solmi M. A century of research on neuromodulation interventions: A scientometric analysis of trends and knowledge maps. Neurosci Biobehav Rev 2023; 152:105300. [PMID: 37392815 DOI: 10.1016/j.neubiorev.2023.105300] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 05/24/2023] [Accepted: 06/27/2023] [Indexed: 07/03/2023]
Abstract
Interest in neurostimulation interventions has significantly grown in recent decades, yet a scientometric analysis objectively mapping scientific knowledge and recent trends remains unpublished. Using relevant keywords, we conducted a search in the Web of Science Core Collection on September 23, 2022, retrieving a total of 47,681 documents with 987,979 references. We identified two prominent research trends: 'noninvasive brain stimulation' and 'invasive brain stimulation.' These methods have interconnected over time, forming a cluster focused on evidence synthesis. Noteworthy emerging research trends encompassed 'transcutaneous auricular vagus nerve stimulation,' 'DBS/epilepsy in the pediatric population,' 'spinal cord stimulation,' and 'brain-machine interface.' While progress has been made for various neurostimulation interventions, their approval as adjuvant treatments remains limited, and optimal stimulation parameters lack consensus. Enhancing communication between experts of both neurostimulation types and encouraging novel translational research could foster further development. These findings offer valuable insights for funding agencies and research groups, guiding future directions in the field.
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Affiliation(s)
- Michel Sabé
- Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, 2, Chemin du Petit-Bel-Air, CH-1226 Thonex, Switzerland.
| | - Adi Sulstarova
- Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, 2, Chemin du Petit-Bel-Air, CH-1226 Thonex, Switzerland
| | - Chaomei Chen
- College of Computing & Informatics, Drexel University, Philadelphia, PA, USA
| | - Joshua Hyde
- Centre for Innovation in Mental Health, School of Psychology, University of Southampton, Southampton, UK
| | - Emmanuel Poulet
- Centre Hospitalier Le Vinatier, Bron, France; INSERM, U1028, CNRS, UMR5292, France; University Lyon 1, F-69000 Villeurbanne, France; Lyon Neuroscience Research Center, PSYR2 Team, F-69000 Lyon, France; Université Jean Monnet Saint Etienne, F-42000, France; Psychiatric Emergency Service, Hospices Civils de Lyon, F-69005 Lyon, France
| | - André Aleman
- University of Groningen, Department of Biomedical Sciences of Cells & Systems, Cognitive Neuroscience Center, University Medical Center Groningen, Groningen, the Netherlands
| | - Jonathan Downar
- Krembil Research Institute, University Health Network, Toronto, ON, Canada; MRI-Guided rTMS Clinic, University Health Network, Toronto, ON, Canada; Centre for Mental Health, University Health Network, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Valerie Brandt
- Centre for Innovation in Mental Health, School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK; Clinic of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hanover, Germany
| | - Luc Mallet
- Univ Paris-Est Créteil, DMU IMPACT, Département Médical-Universitaire de Psychiatrie et d'Addictologie, Hôpitaux Universitaires Henri Mondor, Albert Chenevier, Assistance Publique-Hôpitaux de Paris, Créteil, France; Sorbonne Université, Institut du Cerveau, Paris Brain Institute, ICM, Inserm, CNRS, Paris, France; Department of Mental Health and Psychiatry, Global Health Institute, University of Geneva, Geneva, Switzerland
| | - Othman Sentissi
- Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, 2, Chemin du Petit-Bel-Air, CH-1226 Thonex, Switzerland
| | - Michael A Nitsche
- Dept. Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund, Germany; Bielefeld University, University Hospital OWL, Protestant Hospital of Bethel Foundation, University Clinic of Psychiatry and Psychotherapy and University Clinic of Child and Adolescent Psychiatry and Psychotherapy, Germany
| | - Marom Bikson
- Department of Biomedical Engineering, City College of New York, New York, NY, USA
| | - André Russowsky Brunoni
- Departamento de Clínica Médica da Faculdade de Medicina da Universidade de São Paulo, Universidade de São Paulo, São Paulo, Brazil; Instituto de Psiquiatria do Hospital das Clínicas da Faculdade de Medicina da USP, São Paulo, Brazil
| | - Samuele Cortese
- Centre for Innovation in Mental Health, School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK; Clinical and Experimental Sciences (CNS and Psychiatry), Faculty of Medicine, University of Southampton, Southampton, UK; Solent NHS Trust, Southampton, UK; Hassenfeld Children's Hospital at NYU Langone, New York University Child Study Center, New York, NY, USA; Division of Psychiatry and Applied Psychology, School of Medicine, University of Nottingham, Nottingham, UK
| | - Marco Solmi
- Department of Psychiatry, University of Ottawa, Ontario, Canada; Department of Mental Health, The Ottawa Hospital, Ontario, Canada; Ottawa Hospital Research Institute (OHRI) Clinical Epidemiology Program University of Ottawa, Ottawa, Ontario, Canada; School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada; Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany
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11
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Fukumoto T, Amitani H, Nishi R, Wada M, Oishi N, Asakawa A. Correlation between trait emotional intelligence and prefrontal activation during a verbal fluency task: A functional near-infrared spectroscopy study. Medicine (Baltimore) 2023; 102:e34418. [PMID: 37478224 PMCID: PMC10662857 DOI: 10.1097/md.0000000000034418] [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: 05/09/2023] [Accepted: 06/29/2023] [Indexed: 07/23/2023] Open
Abstract
Stress is inevitable in humans and stress changes our physical and mental states. Stress has been studied epidemiologically, biologically, and psychologically. First defined in 1990, emotional intelligence (EI) affects psychological stress management. In contrast, the prefrontal cortex (PFC) is suggested to play a vital role in stress management. Human PFC activity can be inferred from the balance of oxygenated and deoxygenated hemoglobin in cerebral blood flow, which can be measured and calculated using functional near-infrared spectroscopy (fNIRS). An important cognitive activation task to activate the PFC is the verbal fluency task (VFT). Therefore, if the PFC is activated by the VFT and monitored by fNIRS, and the activity correlates with EI, fNIRS can be used to measure EI. In this study, Psychological tests using the self-rating depression scale, state-trait anxiety inventory (STAI), and trait emotional intelligence questionnaire-short form (TEIQue-SF) were conducted to evaluate the correlation with VFT performance. Relative oxygenated and deoxygenated hemoglobin concentrations were measured using an fNIRS device, and their correlation with VFT performance was tested. Spearman correlation coefficient was used to determine correlations. Results were as follows. Although VFT performance did not correlate with the oxygenated hemoglobin concentration ([Oxy-Hb]) changes, [Oxy-Hb] was elevated in all channels. VFT performance was significantly negatively correlated with the Zung self-rating depression scale (ρ = 0.063, P = .759), trait anxiety or anxiety level as a personal characteristic of STAI (ρ = 0.243, P = .232), and state anxiety or anxiety about an event of STAI (ρ = -0.138, P = .500), whereas no correlation was found with the TEIQue-SF (ρ = 0.303, P = .132). Healthy individuals PFC activity is not severely affected by their mental state and cognitive activation successfully activates the PFC, supporting the hypothesis that EI is correlated with frontal cortical activation during the VFT in a nonclinical population. EI may play a vital role in reducing stress associated with depression and anxiety in our social lives. Although we failed to show a statistical correlation between TEIQue-SF and [Oxy-Hb] due to a sample size shortage, our preliminary study was the first to attempt to show the PFC activity of EI through a hemodynamic response. Future research may elucidate the role of EI in reducing psychological stress in social life.
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Affiliation(s)
- Takamasa Fukumoto
- Department of Psychosomatic Internal Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Haruka Amitani
- Department of Psychosomatic Internal Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Ryusei Nishi
- Department of Psychosomatic Internal Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Midori Wada
- Department of Psychosomatic Internal Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Naoya Oishi
- Medical Innovation Center, Kyoto University Graduate School of Medicine, Sakyo-ku, Kyoto, Japan
| | - Akihiro Asakawa
- Department of Psychosomatic Internal Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
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12
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Tan Y, Zhang C, Tang C, Li Z, Chen W, Jing H, Liang W, Li X, Xie G, Liang J, Guo H. Differences and correlations of biochemical index levels in patients with bipolar disorder and major depressive disorder during a stable period. Medicine (Baltimore) 2023; 102:e34172. [PMID: 37352030 PMCID: PMC10289778 DOI: 10.1097/md.0000000000034172] [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: 03/09/2023] [Revised: 06/10/2023] [Accepted: 06/12/2023] [Indexed: 06/25/2023] Open
Abstract
The differences and correlation of biochemical indexes between bipolar disorder (BPD) and major depressive disorder (MDD) in stable stage were analyzed and discussed. Patients diagnosed with BPD and MDD in the Third People's Hospital of Foshan from January 2019 to December 2021 were selected as the research subjects, with 200 cases in each. Fasting serum was collected from patients and then detected regarding TC, TG, high-density lipoprotein, low-density lipoprotein (LDL), aspartate aminotransferase, lactic dehydrogenase, creatine kinase, creatine kinase-MB, urea, creatinine, uric acid, alanine aminotransferase, glucose (GLU), hemoglobin A1c, prolactin, high-sensitivity C-reactive protein, homocysteine. The results showed that the mean age and serum LDL, GLU, and HbAc1 levels of the MDD group were significantly higher than those of the BPD group (P < .05), while there was no significant difference in other indexes (P > .05). The prevalence of BPD was significantly negatively correlated with patient age (r = -0.164, P = .020), LDL (r = -0.150, P = .034), GLU (r = -0.140, P = .048), and HbAc1 (r = -0.215, P = .002) (P < .05). There were no significant differences in serum Hcy and high-sensitivity C-reactive protein levels between the BPD and MDD groups. The age, fasting blood glucose, glycosylated hemoglobin, and LDL of BPD patients were negatively correlated with their incidence.
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Affiliation(s)
- Yukang Tan
- Department of Psychiatry, The Third People’s Hospital of Foshan, Guangdong, People’s Republic of China
| | - Chunguo Zhang
- Department of Psychiatry, The Third People’s Hospital of Foshan, Guangdong, People’s Republic of China
| | - Chaohua Tang
- Department of Psychiatry, The Third People’s Hospital of Foshan, Guangdong, People’s Republic of China
| | - Zhijian Li
- Department of Psychiatry, The Third People’s Hospital of Foshan, Guangdong, People’s Republic of China
| | - Wensheng Chen
- Department of Psychiatry, The Third People’s Hospital of Foshan, Guangdong, People’s Republic of China
| | - Huan Jing
- Department of Psychiatry, The Third People’s Hospital of Foshan, Guangdong, People’s Republic of China
| | - Wenting Liang
- Department of Psychiatry, The Third People’s Hospital of Foshan, Guangdong, People’s Republic of China
| | - Xiaoling Li
- Department of Psychiatry, The Third People’s Hospital of Foshan, Guangdong, People’s Republic of China
| | - Guojun Xie
- Department of Psychiatry, The Third People’s Hospital of Foshan, Guangdong, People’s Republic of China
| | - Jiaquan Liang
- Department of Psychiatry, The Third People’s Hospital of Foshan, Guangdong, People’s Republic of China
| | - Huagui Guo
- Department of Psychiatry, The Third People’s Hospital of Foshan, Guangdong, People’s Republic of China
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13
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Hernández-Posadas A, Lommen MJJ, de la Rosa Gómez A, Bouman TK, Mancilla-Díaz JM, del Palacio González A. Transdiagnostic factors in symptoms of depression and post-traumatic stress: a systematic review. CURRENT PSYCHOLOGY 2023:1-16. [PMID: 37359653 PMCID: PMC10226442 DOI: 10.1007/s12144-023-04792-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/17/2023] [Indexed: 06/28/2023]
Abstract
The current systematic review sought to identify quantitative empirical studies that focused on the transdiagnostic factors of intolerance of uncertainty, emotional dysregulation and rumination, and their relation with depression and post-traumatic stress disorder (PTSD). The overall research aim was to examine the relationship between these transdiagnostic factors and their relation with depression and PTSD symptoms. The systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. Out of the 768 articles initially identified, 55 met the inclusion criteria for the current review. The results determined that intolerance of uncertainty is indirectly related to depression and PTSD symptoms, mainly through other factors including emotion dysregulation and rumination. Additionally, emotional dysregulation is a significant predictor of both depression and PTSD symptoms. Rumination is a robust factor related to depression and PTSD symptoms, this relationship was significant in cross-sectional and longitudinal studies. This review provides evidence on the transdiagnostic factors of intolerance of uncertainty, emotional dysregulation and rumination in the relationship with depression and PTSD symptoms.
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Affiliation(s)
- Alejandrina Hernández-Posadas
- Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, Netherlands
- Faculty of Higher Studies Iztacala, National Autonomous University of Mexico, Av. De los Barrios Núm. 1, Los Reyes Iztacala, Torre de Tutorías, 2do. Piso, Cubículo 22, Edo. de Mexico 54090 Tlalnepantla, Mexico
| | - Miriam J. J. Lommen
- Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, Netherlands
| | - Anabel de la Rosa Gómez
- Faculty of Higher Studies Iztacala, National Autonomous University of Mexico, Av. De los Barrios Núm. 1, Los Reyes Iztacala, Torre de Tutorías, 2do. Piso, Cubículo 22, Edo. de Mexico 54090 Tlalnepantla, Mexico
| | - Theo K. Bouman
- Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, Netherlands
| | - Juan Manuel Mancilla-Díaz
- Faculty of Higher Studies Iztacala, National Autonomous University of Mexico, Av. De los Barrios Núm. 1, Los Reyes Iztacala, Torre de Tutorías, 2do. Piso, Cubículo 22, Edo. de Mexico 54090 Tlalnepantla, Mexico
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14
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Vural Keleş Ö, Yıldırım E. Depression affects working memory performance: A Functional Near Infrared Spectroscopy (fNIRS) Study. Psychiatry Res Neuroimaging 2023; 329:111581. [PMID: 36587535 DOI: 10.1016/j.pscychresns.2022.111581] [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: 09/16/2022] [Revised: 10/30/2022] [Accepted: 12/11/2022] [Indexed: 12/24/2022]
Abstract
Depression is a complex disorder that can be caused by psychosocial and biological conditions, and it not only affects to emotional life, but also cognitive functions, specifically the executive functions, attention, psychomotor speed, and memory. Some results of the studies in the literature show that depressed individuals perform worse on cognitive tasks than healthy individuals, while others indicate that there is no difference. Moreover, there is also no consensus about the depressed people brain functionalities. We aimed to compare the people who has high and low depression score measured with Beck Depression Inventory in terms of their working memory performance by using n-back paradigm and their brain activity by using optical imaging with this study. The age of lower BDI group (n = 11) is 23,9 ± 3,04 and higher BDI group (n = 23) is 22,2 + 2,28. The fNIRS were recorded from each subject while neutral words-faces and emotional words-faces are given to the subjects in the visuospatial 2-back WM task. There are no significant differences between the two groups behaviorally during the working memory performance, however, the high BDI group's PFC activation in right hemisphere is founded to be higher than the lower group. Our findings support the literature who is claiming the difference brain activity in depressed brain but not cognitive performance. Though, the small group size and the task difficulty (easy) could be the explanation of the behavioral results.
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Affiliation(s)
- Özge Vural Keleş
- Department of Psychology, Faculty of Literature, Bartin University, Turkey.
| | - Erol Yıldırım
- Department of Psychology, Faculty of Humanities and Social Sciences, Istanbul Medipol University, Turkey; Regenerative and Restorative Medical Research Center, Istanbul Medipol University, Istanbul, Turkey
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15
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Ho CSH, Soh MWT, Tay GWN. The diagnostic utility of miRNA and elucidation of pathological mechanisms in major depressive disorder. Compr Psychiatry 2023; 121:152363. [PMID: 36580691 DOI: 10.1016/j.comppsych.2022.152363] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/20/2022] [Accepted: 12/21/2022] [Indexed: 12/24/2022] Open
Abstract
AIMS Our study aims to explore how miRNAs can elucidate the molecular mechanisms of major depressive disorder (MDD) by comparing the miRNA levels in the blood serum of patients with depression and healthy individuals. It also explores the potential of miRNAs to differentiate between depressed patients and healthy controls. METHODS 60 healthy controls (n = 45 females) were matched to 60 depressed patients (n = 10 unmedicated) for age (±7), sex, ethnicity, and years of education. Depression severity was measured using the Hamilton Depression Rating Scale, and venous blood was collected using PAXgene Blood RNA tubes for miRNA profiling. To further identify the depression-related biological pathways that are influenced by differentially expressed miRNAs, networks were constructed using QIAGEN Ingenuity Pathway Analysis. Receiver operating characteristic (ROC) analyses were also conducted to examine the discriminative ability of miRNAs to distinguish between depressed and healthy individuals. RESULTS Six miRNAs (miR-542-3p, miR-181b-3p, miR-190a-5p, miR-33a-3p, miR-3690 and miR-6895-3p) showed to be considerably down-regulated in unmedicated depressed patients relative to healthy controls. miR-542-3p, in particular, also has experimentally verified mRNA targets that are predicted to be associated with MDD. ROC analyses found that a panel combining miR-542-3p, miR-181b-3p and miR-3690 produced an area under the curve value of 0.67 in distinguishing between depressed and healthy individuals. CONCLUSIONS miRNAs - most notably, miR-542-3p, miR-181b-3p and miR-3690 - may be biomarkers with targets that are implicated in the pathophysiology of depression. They could also be used to distinguish between depressed and healthy individuals with reasonable accuracy.
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Affiliation(s)
- Cyrus Su Hui Ho
- Department of Psychological Medicine, National University Health System, Singapore, Singapore; Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
| | - Michelle Wei Ting Soh
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Gabrielle Wann Nii Tay
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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Hou Y, Zhang L, Chen X, Wang Y, Jiang T, Qi Q, Zhang C, Shi C. Study on brain function of the frontal lobe in patients with functional gastroduodenal disease by near-infrared functional imaging. Biomed Pharmacother 2023; 158:114182. [PMID: 36916402 DOI: 10.1016/j.biopha.2022.114182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/27/2022] [Accepted: 12/28/2022] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVE functional gastroduodenal disease is the main type of functional gastrointestinal disease in the clinical department of Gastroenterology and psychosomatic medicine at present, which accounts for a large proportion of outpatients in gastroenterology. The main manifestations are epigastric pain, dyspepsia, belching, chronic nausea, and vomiting. The purpose of this study is to explore the changes in brain function in patients with functional gastroduodenal diseases through experiments to reveal the possible central etiology and development process. METHODS the functional changes of the prefrontal lobe in patients with functional gastroduodenal diseases and normal controls were detected and analyzed by near-infrared brain imaging. At the same time, SCL-90 was used to evaluate the mental health status of patients with functional gastroduodenal diseases and normal controls. The changes in the autonomic nerve system in patients and normal controls were detected and compared by heart rate variability trend chart. RESULTS the activity of left prefrontal lobe areas s8-d8, s10-d4, s10-d10 and s10-d15 in patients with functional gastroduodenal disease was significantly lower than normal controls (p < 0.05). The SCL-90 scale showed that there were significant differences between patients with functional gastroduodenal disease and normal controls, especially in depression, compulsion, anxiety, somatization, interpersonal sensitivity and hostility (p < 0.05). There was no significant difference in lf/hf values detected by the HRV trend chart (p > 0.05). CONCLUSION the function of the left frontal lobe is decreased in patients with functional gastroduodenal disease. The autonomic nervous system may be related to the connection system between the brain center and internal organs.
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Affiliation(s)
- Yanhong Hou
- Department of medical psychology and Department of Gastroenterology, the eighth medical center of the General Hospital of the Chinese people's Liberation Army, Beijing 100091, China
| | - Lin Zhang
- Department of medical psychology and Department of Gastroenterology, the eighth medical center of the General Hospital of the Chinese people's Liberation Army, Beijing 100091, China.
| | - Xiaofei Chen
- Department of medical psychology and Department of Gastroenterology, the eighth medical center of the General Hospital of the Chinese people's Liberation Army, Beijing 100091, China
| | - Yujing Wang
- Department of medical psychology and Department of Gastroenterology, the eighth medical center of the General Hospital of the Chinese people's Liberation Army, Beijing 100091, China
| | - Tong Jiang
- Department of medical psychology and Department of Gastroenterology, the eighth medical center of the General Hospital of the Chinese people's Liberation Army, Beijing 100091, China
| | - Qinjiazi Qi
- Department of medical psychology and Department of Gastroenterology, the eighth medical center of the General Hospital of the Chinese people's Liberation Army, Beijing 100091, China
| | - Chuanxiao Zhang
- Department of medical psychology and Department of Gastroenterology, the eighth medical center of the General Hospital of the Chinese people's Liberation Army, Beijing 100091, China
| | - Chao Shi
- Department of medical psychology and Department of Gastroenterology, the eighth medical center of the General Hospital of the Chinese people's Liberation Army, Beijing 100091, China
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Chen Y, Stewart JW, Ge J, Cheng B, Chekroud A, Hellerstein DJ. Personalized Symptom Clusters that Predict Depression Treatment Outcomes: A Replication of Machine Learning Methods. JOURNAL OF AFFECTIVE DISORDERS REPORTS 2023. [DOI: 10.1016/j.jadr.2023.100470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
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18
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Zheng Z, Zhan S, Zhou Y, Huang G, Chen P, Li B. Pediatric Crohn's disease diagnosis aid via genomic analysis and machine learning. Front Pediatr 2023; 11:991247. [PMID: 37033178 PMCID: PMC10076664 DOI: 10.3389/fped.2023.991247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 03/10/2023] [Indexed: 04/11/2023] Open
Abstract
Introduction Determination of pediatric Crohn's disease (CD) remains a major diagnostic challenge. However, the rapidly emerging field of artificial intelligence has demonstrated promise in developing diagnostic models for intractable diseases. Methods We propose an artificial neural network model of 8 gene markers identified by 4 classification algorithms based on Gene Expression Omnibus database for diagnostic of pediatric CD. Results The model achieved over 85% accuracy and area under ROC curve value in both training set and testing set for diagnosing pediatric CD. Additionally, immune infiltration analysis was performed to address why these markers can be integrated to develop a diagnostic model. Conclusion This study supports further clinical facilitation of precise disease diagnosis by integrating genomics and machine learning algorithms in open-access database.
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Affiliation(s)
- Zhiwei Zheng
- Department of Pediatrics, Zhongshan Hospital of Traditional Chinese Medicine Affiliated to Guangzhou University of Traditional Chinese Medicine, Zhongshan, China
- Correspondence: Zhiwei Zheng
| | - Sha Zhan
- School of Chinese Medicine, Jinan University, Guangzhou, China
| | - Yongmao Zhou
- Department of Pediatrics, Zhongshan Hospital of Traditional Chinese Medicine Affiliated to Guangzhou University of Traditional Chinese Medicine, Zhongshan, China
| | - Ganghua Huang
- Department of Pediatrics, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, China
| | - Pan Chen
- Department of Pediatrics, Zhongshan Hospital of Traditional Chinese Medicine Affiliated to Guangzhou University of Traditional Chinese Medicine, Zhongshan, China
| | - Baofei Li
- Department of Pediatrics, Zhongshan Hospital of Traditional Chinese Medicine Affiliated to Guangzhou University of Traditional Chinese Medicine, Zhongshan, China
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Liu Y, Zhao W, Lu Y, Zhao Y, Zhang Y, Dai M, Hai S, Ge N, Zhang S, Huang M, Liu X, Li S, Yue J, Lei P, Dong B, Dai L, Dong B. Systematic metabolic characterization of mental disorders reveals age‐related metabolic disturbances as potential risk factors for depression in older adults. MedComm (Beijing) 2022; 3:e165. [PMID: 36204590 PMCID: PMC9523679 DOI: 10.1002/mco2.165] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/01/2022] [Accepted: 07/14/2022] [Indexed: 11/13/2022] Open
Abstract
Mental disorders are associated with dysregulated metabolism, but comprehensive investigations of their metabolic similarities and differences and their clinical relevance are few. Here, based on the plasma metabolome and lipidome of subcohort1, comprising 100 healthy participants, 55 cases with anxiety, 52 persons with depression, and 41 individuals with comorbidity, which are from WCHAT, a perspective cohort study of community‐dwelling older adults aged over 50, multiple metabolites as potential risk factors of mental disorders were identified. Furthermore, participants with mental illnesses were classified into three subtypes (S1, S2, and S3) by unsupervised classification with lipidomic data. Among them, S1 showed higher triacylglycerol and lower sphingomyelin, while S2 displayed opposite features. The metabolic profile of S3 was like that of the normal group. Compared with S3, individuals in S1 and S2 had worse quality of life, and suffered more from sleep and cognitive disorders. Notably, an assessment of 6,467 individuals from the WCHAT showed an age‐related increase in the incidence of depression. Seventeen depression‐related metabolites were significantly correlated with age, which were validated in an independent subcohort2. Collectively, this work highlights the clinical relevance of metabolic perturbation in mental disorders, and age‐related metabolic disturbances may be a bridge‐linking aging and depressive.
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Affiliation(s)
- Yu Liu
- National Clinical Research Center for Geriatrics and Department of General Practice State Key Laboratory of Biotherapy West China Hospital Sichuan University Chengdu China
| | - Wanyu Zhao
- National Clinical Research Center for Geriatrics and Department of General Practice State Key Laboratory of Biotherapy West China Hospital Sichuan University Chengdu China
| | - Ying Lu
- National Clinical Research Center for Geriatrics and Department of General Practice State Key Laboratory of Biotherapy West China Hospital Sichuan University Chengdu China
| | - Yunli Zhao
- Department of Health Research Methods, Evidence, and Impact McMaster University Hamilton Ontario Canada
| | - Yan Zhang
- National Clinical Research Center for Geriatrics and Department of General Practice State Key Laboratory of Biotherapy West China Hospital Sichuan University Chengdu China
| | - Miao Dai
- National Clinical Research Center for Geriatrics and Department of General Practice State Key Laboratory of Biotherapy West China Hospital Sichuan University Chengdu China
| | - Shan Hai
- National Clinical Research Center for Geriatrics and Department of General Practice State Key Laboratory of Biotherapy West China Hospital Sichuan University Chengdu China
| | - Ning Ge
- National Clinical Research Center for Geriatrics and Department of General Practice State Key Laboratory of Biotherapy West China Hospital Sichuan University Chengdu China
| | - Shuting Zhang
- Department of Neurology, West China Hospital Sichuan University Chengdu China
| | - Mingjin Huang
- The Third Hospital of Mianyang Sichuan Mental Health Center Mianyang China
| | - Xiaohui Liu
- School of Life Sciences Tsinghua University Beijing China
| | - Shuangqing Li
- National Clinical Research Center for Geriatrics and Department of General Practice State Key Laboratory of Biotherapy West China Hospital Sichuan University Chengdu China
| | - Jirong Yue
- National Clinical Research Center for Geriatrics and Department of General Practice State Key Laboratory of Biotherapy West China Hospital Sichuan University Chengdu China
| | - Peng Lei
- National Clinical Research Center for Geriatrics and Department of General Practice State Key Laboratory of Biotherapy West China Hospital Sichuan University Chengdu China
| | - Biao Dong
- National Clinical Research Center for Geriatrics and Department of General Practice State Key Laboratory of Biotherapy West China Hospital Sichuan University Chengdu China
| | - Lunzhi Dai
- National Clinical Research Center for Geriatrics and Department of General Practice State Key Laboratory of Biotherapy West China Hospital Sichuan University Chengdu China
| | - Birong Dong
- National Clinical Research Center for Geriatrics and Department of General Practice State Key Laboratory of Biotherapy West China Hospital Sichuan University Chengdu China
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20
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Effects of long-term COVID-19 confinement and music stimulation on mental state and brain activity of young people. Neurosci Lett 2022; 791:136922. [PMID: 36272556 PMCID: PMC9580244 DOI: 10.1016/j.neulet.2022.136922] [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: 09/08/2022] [Revised: 10/06/2022] [Accepted: 10/15/2022] [Indexed: 11/23/2022]
Abstract
The Corona Virus Disease 2019 (COVID-19) pandemic may have had a negative emotional impact on individuals. This study investigated the effect of long-term lockdown and music on young people’s mood and neurophysiological responses in the prefrontal cortex (PFC). Fifteen healthy young adults were recruited and PFC activation was acquired using functional near-infrared spectroscopy during the conditions of resting, Stroop and music stimulation. The Depression Anxiety Stress Scales mental scale scores were simultaneously recorded. Mixed effect models, paired t-tests, one-way ANOVAs and Spearman analyses were adopted to analyse the experimental parameters. Stress, anxiety and depression levels increased significantly from Day 30 to Day 40. In terms of reaction time, both Stroop1 and Stroop2 were faster on Day 40 than on Day 30 (P = 0.01, P = 0.003). The relative concentration changes of oxyhemoglobin were significantly higher during premusic conditions than music stimulation and postmusic Stroop. The intensity of functional connectivity shifted from inter- to intracerebral over time. In conclusion, the reduced hemodynamic response of the PFC in healthy young adults is associated with negative emotions, especially anxiety, during lockdown. Immediate music stimulation appears to improve efficiency by altering the pattern of connections in PFC.
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Wu H, Li T, Peng C, Yang C, Bian Y, Li X, Xiao Q, Wang P, Zhang Z, Zhang Y. The right prefrontal cortex (PFC) can distinguish anxious depression from non-anxious depression: A promising functional near infrared spectroscopy study (fNIRS). J Affect Disord 2022; 317:319-328. [PMID: 36007594 DOI: 10.1016/j.jad.2022.08.024] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 07/11/2022] [Accepted: 08/10/2022] [Indexed: 10/31/2022]
Abstract
BACKGROUND Anxious depression is a serious mental disorder characterized by comorbidity of anxiety and depression, and its symptoms are similar to those of non-anxious depression. This study aimed to use functional near-infrared spectroscopy (fNIRS) as a tool to distinguish between patients with anxious and non-anxious depression based on differences in hemodynamic changes in the right prefrontal cortex during the verbal fluency task. It is helpful to improve the diagnostic accuracy of the two disorders to further promote their therapeutic effect and prognosis. METHODS A total of 105 subjects, comprising 39 patients with anxious depression, 32 patients with non-anxious depression, and 32 healthy controls, were evaluated using 53-channel fNIRS and the Depression and Anxiety Clinical Scale. RESULTS Hemodynamic activation was significantly enhanced in the right dorsolateral prefrontal cortex (DLPFC) and right frontopole cortex (FPC) in the anxious depressed group compared with the non-anxious depressed and healthy groups. LIMITATIONS First, Hospital Anxiety and Depression Scale (HADS) was used to evaluate the scores of anxiety and depression among the three groups in our study. Different scales may result in different research results. Therefore, other scales (HAM, the Montgomery Asberg Depression Rating Scale, or the Beck Depression Inventory) should be used for further verification. Second, although all the samples we have chosen were patients with the diagnosis of anxious depression or no-anxious depression, we did not distinguish between different severity of anxious depression or no-anxious depression. Third, pure anxiety was not included as the control condition in our study. CONCLUSIONS There are significant differences in activation patterns of the right DLPFC and right FPC areas between patients with and without anxious depression. Moreover, the right FPC area is promising as a brain region to assess the severity of anxious depression. fNIRS may be a potential tool to improve diagnostic accuracy for both disorders.
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Affiliation(s)
- Huifen Wu
- School of Education, Huazhong University of Science and Technology, Wuhan, China; School of Education, Hubei Engineering University, Xiaogan, China
| | - Taiping Li
- School of Education, Huazhong University of Science and Technology, Wuhan, China
| | - Cong Peng
- School of Education, Huazhong University of Science and Technology, Wuhan, China
| | - Caihong Yang
- School of Psychology, Central China Normal University, Wuhan, China
| | - Yueran Bian
- School of Education, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoqin Li
- School of Education, Huazhong University of Science and Technology, Wuhan, China
| | - Qiang Xiao
- Department of Psychiatry, Huazhong University of Science and Technology Hospital, Wuhan, China
| | - Pu Wang
- Department of Rehabilitation Medicine in The Seventh Affiliated Hospital (Shenzhen), Sun Yat-Sen University, Shenzhen, China
| | - Zhe Zhang
- Department of Humanities and Social Sciences, Huazhong University of Science and Technology, Wuhan, China.
| | - Yan Zhang
- School of Education, Huazhong University of Science and Technology, Wuhan, China.
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22
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Matsubara T, Chen C, Hirotsu M, Watanuki T, Harada K, Watanabe Y, Matsuo K, Nakagawa S. Prefrontal cortex activities during verbal fluency and emotional words tasks in major depressive, adjustment, and bipolar disorders with depressive states. J Affect Disord 2022; 316:109-117. [PMID: 35973508 DOI: 10.1016/j.jad.2022.08.025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 08/06/2022] [Accepted: 08/11/2022] [Indexed: 01/14/2023]
Abstract
BACKGROUND It can be difficult to differentiate psychiatric disorders from depressive states, with little knowledge on how to differentiate them. This study aimed to evaluate changes in brain activity during cognitive and emotional tasks in patients with depressive state to help with differential diagnoses. METHODS Sixty-two patients with depressive states [17 with adjustment disorder (AD), 27 with major depressive disorder (MDD), and 18 with bipolar disorder (BD)] and 34 healthy controls (HC) were recruited. We used a verbal fluency task (VFT) and emotional word tasks with happy and threat words. Functional near-infrared spectroscopy measured the relative change in oxygenated hemoglobin in the frontotemporal areas. RESULTS During the VFT, patients with AD or MDD showed significantly reduced activation in the bilateral frontotemporal region (all p < 0.01), whereas patients with BD demonstrated significantly reduced activation in the right frontotemporal areas compared to HC (p < 0.01). During the emotional words task with happy words, patients with MDD showed significantly increased activity in the frontopolar area compared to HC (p = 0.023). Binary logistic regression analysis showed that MDD or BD was significantly associated with brain activity during the happy word task. In distinguishing MDD or BD from HC, the happy words task performed equally well, with an area under the curve of 0.70. LIMITATIONS All study patients were taking psychotropic drugs. CONCLUSIONS Brain activation in response to a combination of cognitive or emotional stimuli could assist in distinguishing patients with depressive states from healthy controls.
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Affiliation(s)
- Toshio Matsubara
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Ube, Japan.
| | - Chong Chen
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Ube, Japan
| | - Masako Hirotsu
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Ube, Japan
| | | | - Kenichiro Harada
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Ube, Japan
| | | | - Koji Matsuo
- Department of Psychiatry, Faculty of Medicine, Saitama Medical University, Saitama, Japan
| | - Shin Nakagawa
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Ube, Japan
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Korous KM, Bradley RH, Luthar SS, Li L, Levy R, Cahill KM, Rogers CR. Socioeconomic status and depressive symptoms: An individual-participant data meta-analysis on range restriction and measurement in the United States. J Affect Disord 2022; 314:50-58. [PMID: 35798179 PMCID: PMC10947555 DOI: 10.1016/j.jad.2022.06.090] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 06/22/2022] [Accepted: 06/27/2022] [Indexed: 10/17/2022]
Abstract
INTRODUCTION The association between socioeconomic status (SES) and depressive symptoms is well documented, yet less attention has been paid to the methodological factors contributing to between-study variability. We examined the moderating role of range restriction and the depressive-symptom measurement instrument used in estimating the correlation between components of SES and depressive symptoms. METHODS We conducted an individual participant data meta-analysis of nationally-representative, public-access datasets in the United States. We identified 123 individual datasets with a total of 1,655,991 participants (56.8 % female, mean age = 40.33). RESULTS The presence of range restriction was associated with larger correlations between income and depressive symptoms and with smaller correlations between years of education and depressive symptoms. The measurement instrument of depressive symptoms moderated the association for income, years of education, and occupational status/prestige. The Center for Epidemiological Studies-Depression scale consistently produced larger correlations. Higher measurement reliability was also associated with larger correlations. LIMITATIONS This study was not a comprehensive review of all measurement instruments of depressive symptoms, focused on datasets from the United States, and did not examine the moderating role of sample characteristics. DISCUSSION Methodological characteristics, including range restriction of SES and instrument of depressive symptoms, meaningfully influence the observed magnitude of association between SES and depressive symptoms. Clinicians and researchers designing future studies should consider which instrument of depressive symptoms is suitable for their purpose and population.
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Affiliation(s)
- Kevin M Korous
- Department of Family and Preventive Medicine, University of Utah School of Medicine, 375 Chipeta Way, Suite A, Salt Lake City, UT, USA.
| | - Robert H Bradley
- T Denny Sanford School of Social and Family Dynamics, Arizona State University, 951 S. Cady Mall, Tempe, AZ, USA
| | - Suniya S Luthar
- AC Groups, Tempe, AZ, USA; Teachers College, Columbia University-EMERITA, 525 West 120th Street, New York, NY, USA
| | - Longfeng Li
- T Denny Sanford School of Social and Family Dynamics, Arizona State University, 951 S. Cady Mall, Tempe, AZ, USA
| | - Roy Levy
- T Denny Sanford School of Social and Family Dynamics, Arizona State University, 951 S. Cady Mall, Tempe, AZ, USA
| | - Karina M Cahill
- T Denny Sanford School of Social and Family Dynamics, Arizona State University, 951 S. Cady Mall, Tempe, AZ, USA
| | - Charles R Rogers
- Department of Family and Preventive Medicine, University of Utah School of Medicine, 375 Chipeta Way, Suite A, Salt Lake City, UT, USA
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Arenas E, Teruel G, Gaitán-Rossi P. Time and gender measurement invariance in the modified Calderon depression scale. Health Qual Life Outcomes 2022; 20:100. [PMID: 35752851 PMCID: PMC9233789 DOI: 10.1186/s12955-022-02007-8] [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: 04/07/2022] [Accepted: 06/08/2022] [Indexed: 11/21/2022] Open
Abstract
Background Assessing change and comparing groups requires high quality and invariant scales. However, there is limited evidence of simultaneous longitudinal and gender measurement invariance for depression scales. This evidence is even more scant with long-established panel studies from low and middle-income countries. Methods In this paper, we used three waves (years 2002, 2005, and 2009) of a nationally representative panel study to examine the psychometric properties of the modified Calderon Depression Scale (CAL-DM)—a one-item exclusion of a depression scale designed for a population residing in a middle-income country (i.e., Mexico). Our analytical sample included 16,868 participants: 7,696 men and 9,172 women. Using Confirmatory Factor Analysis (CFA), we first examined overall fit in each wave, and then we tested time, gender, and time-gender measurement invariance across three waves. We also estimated and compared depression score means by gender and time. Finally, we examined the association between depression scores and self-rated health. Results Our analyses indicated the CAL-DM is a robust scale, suitable for time, gender, and time by gender comparisons. Mean comparisons exemplified how the scale can be used as a latent variable or a summative score. Women have higher depression scores than men and the gap is narrowing from 3.4 in 2002 to 2.5 in 2009. Conclusions The CAL-DM is a reliable instrument to measure depression in the Mexican general population that can be used for epidemiological research. Our results will contribute to a burgeoning line of research that examines the social determinants of depression, and the risk factors associated with different individuals’ depression trajectories over the life course. Supplementary Information The online version contains supplementary material available at 10.1186/s12955-022-02007-8.
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Affiliation(s)
- Erika Arenas
- Department of Sociology, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Graciela Teruel
- Instituto de Investigaciones Para El Desarrollo Con Equidad (EQUIDE), Universidad Iberoamericana, Mexico City, Mexico
| | - Pablo Gaitán-Rossi
- Instituto de Investigaciones Para El Desarrollo Con Equidad (EQUIDE), Universidad Iberoamericana, Mexico City, Mexico.
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