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Sharma N, Sharma M, Tailor J, Chaudhari A, Joshi D, Acharya UR. Automated detection of depression using wavelet scattering networks. Med Eng Phys 2024; 124:104107. [PMID: 38418014 DOI: 10.1016/j.medengphy.2024.104107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 12/16/2023] [Accepted: 01/09/2024] [Indexed: 03/01/2024]
Abstract
Today, depression is a common problem that affects many people all over the world. It can impact a person's mood and quality of life unless identified and treated immediately. Due to the hectic and stressful modern life seems to be, depression has become a leading cause of mental health illnesses. Signals from electroencephalograms (EEG) are frequently used to detect depression. It is difficult, time-consuming, and highly skilled to manually detect depression using EEG data analysis. Hence, in the proposed study, an automated depression detection system using EEG signals is proposed. The proposed study uses a clinically available dataset and dataset provided by the Department of Psychiatry at the Government Medical College (GMC) in Kozhikode, Kerala, India which consisted of 15 depressed patients and 15 healthy subjects and a publically available Multi-modal Open Dataset (MODMA) for Mental-disorder Analysis available at UK Data service reshare that consisted of 24 depressed patients and 29 healthy subjects. In this study, we have developed a novel Deep Wavelet Scattering Network (DWSN) for the automated detection of depression EEG signals. The best-performing classifier is then chosen by feeding the features into several machine-learning algorithms. For the clinically available GMC dataset, Medium Neural Network (MNN) achieved the highest accuracy of 99.95% with a Kappa value of 0.999. Using the suggested methods, the precision, recall, and F1-score are all 1. For the MODMA dataset, Wide Neural Network (WNN) achieved the highest accuracy of 99.3% with a Kappa value of 0.987. Using the suggested methods, the precision, recall, and F1-score are all 0.99. In comparison to all current methodologies, the performance of the suggested research is superior. The proposed method can be used to automatically diagnose depression both at home and in clinical settings.
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Affiliation(s)
- Nishant Sharma
- Department of Electrical Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India.
| | - Manish Sharma
- Department of Electrical Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India.
| | - Jimit Tailor
- Department of Electrical Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India.
| | - Arth Chaudhari
- Department of Electrical Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India.
| | - Deepak Joshi
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi (IITD), Delhi, India.
| | - U Rajendra Acharya
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Toowoomba 4350, Queensland, Australia.
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2
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Korte M, Cerci D, Wehry R, Timmers R, Williamson VJ. The same but different. Multidimensional assessment of depression in students of natural science and music. Health Psychol Res 2023; 11:74879. [PMID: 37405314 PMCID: PMC10317516 DOI: 10.52965/001c.74879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/06/2023] Open
Abstract
Depression is one of the most common and debilitating health problems, however, its heterogeneity makes a diagnosis challenging. Thus far the restriction of depression variables explored within groups, the lack of comparability between groups, and the heterogeneity of depression as a concept limit a meaningful interpretation, especially in terms of predictability. Research established students in late adolescence to be particularly vulnerable, especially those with a natural science or musical study main subject. This study used a predictive design, observing the change in variables between groups as well as predicting which combinations of variables would likely determine depression prevalence. 102 under- and postgraduate students from various higher education institutions participated in an online survey. Students were allocated into three groups according to their main study subject and type of institution: natural science students, music college students and a mix of music and natural science students at university with comparable levels of musical training and professional musical identity. Natural science students showed significantly higher levels of anxiety prevalence and pain catastrophizing prevalence, while music college students showed significantly higher depression prevalence compared to the other groups. A hierarchical regression and a tree analysis found that depression for all groups was best predicted with a combination of variables: high anxiety prevalence and low burnout of students with academic staff. The use of a larger pool of depression variables and the comparison of at-risk groups provide insight into how these groups experience depression and thus allow initial steps towards personalized support structures.
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Affiliation(s)
| | - Deniz Cerci
- Klinik für Forensische Psychiatrie Universitätsmedizin Rostock, Germany
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3
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Zheng Y, Zhang L, He S, Xie Z, Zhang J, Ge C, Sun G, Huang J, Li H. Integrated Module of Multidimensional Omics for Peripheral Biomarkers (iMORE) in patients with major depressive disorder: rationale and design of a prospective multicentre cohort study. BMJ Open 2022; 12:e067447. [PMID: 36418119 PMCID: PMC9685190 DOI: 10.1136/bmjopen-2022-067447] [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] [Indexed: 11/24/2022] Open
Abstract
INTRODUCTION Major depressive disorder (MDD) represents a worldwide burden on healthcare and the response to antidepressants remains limited. Systems biology approaches have been used to explore the precision therapy. However, no reliable biomarker clinically exists for prognostic prediction at present. The objectives of the Integrated Module of Multidimensional Omics for Peripheral Biomarkers (iMORE) study are to predict the efficacy of antidepressants by integrating multidimensional omics and performing validation in a real-world setting. As secondary aims, a series of potential biomarkers are explored for biological subtypes. METHODS AND ANALYSIS iMore is an observational cohort study in patients with MDD with a multistage design in China. The study is performed by three mental health centres comprising an observation phase and a validation phase. A total of 200 patients with MDD and 100 healthy controls were enrolled. The protocol-specified antidepressants are selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors. Clinical visits (baseline, 4 and 8 weeks) include psychiatric rating scales for symptom assessment and biospecimen collection for multiomics analysis. Participants are divided into responders and non-responders based on treatment response (>50% reduction in Montgomery-Asberg Depression Rating Scale). Antidepressants' responses are predicted and biomarkers are explored using supervised learning approach by integration of metabolites, cytokines, gut microbiomes and immunophenotypic cells. The accuracy of the prediction models constructed is verified in an independent validation phase. ETHICS AND DISSEMINATION The study was approved by the ethics committee of Shanghai Mental Health Center (approval number 2020-87). All participants need to sign a written consent for the study entry. Study findings will be published in peer-reviewed journals. TRIAL REGISTRATION NUMBER NCT04518592.
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Affiliation(s)
- Yuzhen Zheng
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Linna Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shen He
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zuoquan Xie
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Jing Zhang
- Shanghai Green Valley Pharmaceutical Co Ltd, Shanghai, China
| | - Changrong Ge
- Shanghai Green Valley Pharmaceutical Co Ltd, Shanghai, China
| | - Guangqiang Sun
- Shanghai Green Valley Pharmaceutical Co Ltd, Shanghai, China
| | - Jingjing Huang
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Research Center for Mental Health, Shanghai Mental Health Center, Shanghai, China
| | - Huafang Li
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Research Center for Mental Health, Shanghai Mental Health Center, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Gerlach AR, Karim HT, Peciña M, Ajilore O, Taylor WD, Butters MA, Andreescu C. MRI predictors of pharmacotherapy response in major depressive disorder. Neuroimage Clin 2022; 36:103157. [PMID: 36027717 PMCID: PMC9420953 DOI: 10.1016/j.nicl.2022.103157] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 07/11/2022] [Accepted: 08/15/2022] [Indexed: 02/08/2023]
Abstract
Major depressive disorder is among the most prevalent psychiatric disorders, exacting a substantial personal, social, and economic toll. Antidepressant treatment typically involves an individualized trial and error approach with an inconsistent success rate. Despite a pressing need, no reliable biomarkers for predicting treatment outcome have yet been discovered. Brain MRI measures hold promise in this regard, though clinical translation remains elusive. In this review, we summarize structural MRI and functional MRI (fMRI) measures that have been investigated as predictors of treatment outcome. We broadly divide these into five categories including three structural measures: volumetric, white matter burden, and white matter integrity; and two functional measures: resting state fMRI and task fMRI. Currently, larger hippocampal volume is the most widely replicated predictor of successful treatment. Lower white matter hyperintensity burden has shown robustness in late life depression. However, both have modest discriminative power. Higher fractional anisotropy of the cingulum bundle and frontal white matter, amygdala hypoactivation and anterior cingulate cortex hyperactivation in response to negative emotional stimuli, and hyperconnectivity within the default mode network (DMN) and between the DMN and executive control network also show promise as predictors of successful treatment. Such network-focused measures may ultimately provide a higher-dimensional measure of treatment response with closer ties to the underlying neurobiology.
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Affiliation(s)
- Andrew R Gerlach
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Helmet T Karim
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Marta Peciña
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois-Chicago, Chicago, IL, USA
| | - Warren D Taylor
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Geriatric Research, Education, and Clinical Center, Veterans Affairs Tennessee Valley Health System, Nashville, TN, USA
| | - Meryl A Butters
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Carmen Andreescu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
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5
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Kamishikiryo T, Okada G, Itai E, Masuda Y, Yokoyama S, Takamura M, Fuchikami M, Yoshino A, Mawatari K, Numata S, Takahashi A, Ohmori T, Okamoto Y. Left DLPFC activity is associated with plasma kynurenine levels and can predict treatment response to escitalopram in major depressive disorder. Psychiatry Clin Neurosci 2022; 76:367-376. [PMID: 35543406 PMCID: PMC9544423 DOI: 10.1111/pcn.13373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 03/16/2022] [Accepted: 04/24/2022] [Indexed: 11/27/2022]
Abstract
AIM To establish treatment response biomarkers that reflect the pathophysiology of depression, it is important to use an integrated set of features. This study aimed to determine the relationship between regional brain activity at rest and blood metabolites related to treatment response to escitalopram to identify the characteristics of depression that respond to treatment. METHODS Blood metabolite levels and resting-state brain activity were measured in patients with moderate to severe depression (n = 65) before and after 6-8 weeks of treatment with escitalopram, and these were compared between Responders and Nonresponders to treatment. We then examined the relationship between blood metabolites and brain activity related to treatment responsiveness in patients and healthy controls (n = 36). RESULTS Thirty-two patients (49.2%) showed a clinical response (>50% reduction in the Hamilton Rating Scale for Depression score) and were classified as Responders, and the remaining 33 patients were classified as Nonresponders. The pretreatment fractional amplitude of low-frequency fluctuation (fALFF) value of the left dorsolateral prefrontal cortex (DLPFC) and plasma kynurenine levels were lower in Responders, and the rate of increase of both after treatment was correlated with an improvement in symptoms. Moreover, the fALFF value of the left DLPFC was significantly correlated with plasma kynurenine levels in pretreatment patients with depression and healthy controls. CONCLUSION Decreased resting-state regional activity of the left DLPFC and decreased plasma kynurenine levels may predict treatment response to escitalopram, suggesting that it may be involved in the pathophysiology of major depressive disorder in response to escitalopram treatment.
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Affiliation(s)
- Toshiharu Kamishikiryo
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
| | - Eri Itai
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
| | - Yoshikazu Masuda
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
| | - Satoshi Yokoyama
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
| | - Masahiro Takamura
- Department of Neurology, Faculty of Medicine, Shimane University, Izumo-shi, Japan
| | - Manabu Fuchikami
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
| | - Atsuo Yoshino
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
| | - Kazuaki Mawatari
- Department of Preventive Environment and Nutrition, Institute of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Shusuke Numata
- Department of Psychiatry, Institute of Biomedical Science, Tokushima University Graduate School, Tokushima, Japan
| | - Akira Takahashi
- Department of Preventive Environment and Nutrition, Institute of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Tetsuro Ohmori
- Department of Psychiatry, Institute of Biomedical Science, Tokushima University Graduate School, Tokushima, Japan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
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6
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Li JM, Jiang CL. Biological Diagnosis of Depression: A Biomarker Panel from Several Nonspecial Indicators Instead of the Specific Biomarker(s). Neuropsychiatr Dis Treat 2022; 18:3067-3071. [PMID: 36606185 PMCID: PMC9809399 DOI: 10.2147/ndt.s393553] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 12/06/2022] [Indexed: 12/31/2022] Open
Abstract
It is a consensus that the diagnosis efficiency of depression is rather low in clinic. The traditional way of diagnosing depression by symptomatology is flawed. Recent years, a growing body of evidence has underlined the importance of physiological indicators in the diagnosis of depression. However, the diagnosis of depression is difficult to be like some common clinical diseases, which have clear physiological indicators. A single physiological index provides limited information to clinicians and is of little help in the diagnosis of depression. Thus, it is more rational and practical to diagnose depression with a biomarker panel, which covers a few non-specific indicators, such as hormones, cytokines, and neurotrophins. This open review suggested that biomarker panel had a bright future in creating a new model of depression diagnosis or at least providing a reference to the existing depression criteria. The viewpoint is also the future of other psychiatric diagnosis.
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Affiliation(s)
- Jia-Mei Li
- Department of Stress Medicine, Faculty of Psychology, Second Military Medical University, Shanghai, People's Republic of China.,Department of Neurology, the 971st Hospital, Qingdao, People's Republic of China
| | - Chun-Lei Jiang
- Department of Stress Medicine, Faculty of Psychology, Second Military Medical University, Shanghai, People's Republic of China
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7
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Ong SK, Husain SF, Wee HN, Ching J, Kovalik JP, Cheng MS, Schwarz H, Tang TB, Ho CS. Integration of the Cortical Haemodynamic Response Measured by Functional Near-Infrared Spectroscopy and Amino Acid Analysis to Aid in the Diagnosis of Major Depressive Disorder. Diagnostics (Basel) 2021; 11:diagnostics11111978. [PMID: 34829325 PMCID: PMC8617819 DOI: 10.3390/diagnostics11111978] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 10/22/2021] [Accepted: 10/22/2021] [Indexed: 01/02/2023] Open
Abstract
Background: Major depressive disorder (MDD) is a debilitating condition with a high disease burden and medical comorbidities. There are currently few to no validated biomarkers to guide the diagnosis and treatment of MDD. In the present study, we evaluated the differences between MDD patients and healthy controls (HCs) in terms of cortical haemodynamic responses during a verbal fluency test (VFT) using functional near-infrared spectroscopy (fNIRS) and serum amino acid profiles, and ascertained if these parameters were correlated with clinical characteristics. Methods: Twenty-five (25) patients with MDD and 25 age-, gender-, and ethnicity-matched HCs were recruited for the study. Real-time monitoring of the haemodynamic response during completion of a VFT was quantified using a 52-channel NIRS system. Serum samples were analysed and quantified by liquid chromatography-mass spectrometry for amino acid profiling. Receiver-operating characteristic (ROC) curves were used to classify potential candidate biomarkers. Results: The MDD patients had lower prefrontal and temporal activation during completion of the VFT than HCs. The MDD patients had lower mean concentrations of oxy-Hb in the left orbitofrontal cortex (OFC), and lower serum histidine levels. When the oxy-haemoglobin response was combined with the histidine concentration, the sensitivity and specificity of results improved significantly from 66.7% to 73.3% and from 65.0% to 90.0% respectively, as compared to results based only on the NIRS response. Conclusions: These findings demonstrate the use of combination biomarkers to aid in the diagnosis of MDD. This technique could be a useful approach to detect MDD with greater precision, but additional studies are required to validate the methodology.
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Affiliation(s)
- Samantha K. Ong
- Department of Psychological Medicine, National University Health System, Singapore 119228, Singapore;
| | - Syeda F. Husain
- Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore 119276, Singapore;
| | - Hai Ning Wee
- Cardiovascular and Metabolic Disorders Programme, Duke-NUS Graduate Medical School, Singapore 169609, Singapore; (H.N.W.); (J.C.); (J.-P.K.)
| | - Jianhong Ching
- Cardiovascular and Metabolic Disorders Programme, Duke-NUS Graduate Medical School, Singapore 169609, Singapore; (H.N.W.); (J.C.); (J.-P.K.)
| | - Jean-Paul Kovalik
- Cardiovascular and Metabolic Disorders Programme, Duke-NUS Graduate Medical School, Singapore 169609, Singapore; (H.N.W.); (J.C.); (J.-P.K.)
| | - Man Si Cheng
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117593, Singapore; (M.S.C.); (H.S.)
| | - Herbert Schwarz
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117593, Singapore; (M.S.C.); (H.S.)
| | - Tong Boon Tang
- Centre for Intelligent Signal and Imaging Research (CISIR), University Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak, Malaysia;
| | - Cyrus S. Ho
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
- Correspondence: ; Tel.: +65-67795555
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8
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Kelley ME, Choi KS, Rajendra JK, Craighead WE, Rakofsky JJ, Dunlop BW, Mayberg HS. Establishing Evidence for Clinical Utility of a Neuroimaging Biomarker in Major Depressive Disorder: Prospective Testing and Implementation Challenges. Biol Psychiatry 2021; 90:236-242. [PMID: 33896622 PMCID: PMC8324510 DOI: 10.1016/j.biopsych.2021.02.966] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 01/25/2021] [Accepted: 02/12/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND Although a number of neuroimaging biomarkers for response have been proposed, none have been tested prospectively for direct effects on treatment outcomes. To the best of our knowledge, this is the first prospective test of the clinical utility of the use of an imaging biomarker to select treatment for patients with major depressive disorder. METHODS Eligible participants (n = 60) had a primary diagnosis of major depressive disorder and were assigned to either escitalopram or cognitive behavioral therapy based on fluorodeoxyglucose positron emission tomography activity in the right anterior insula. The overall study remission rate after 12 weeks of treatment, based on the end point Hamilton Depression Rating Scale score, was then examined for futility and benefit of the strategy. RESULTS Remission rates demonstrated lack of futility at the end of stage 1 (37%, 10/27), and the study proceeded to stage 2. After adjustment for the change in stage 2 sample size, the complete remission rate did not demonstrate evidence of benefit (37.7%, 95% confidence interval, 26.3%-51.4%, p = .38). However, total remission rates (complete and partial remission) did reach significance in post hoc analysis (49.1%, 95% confidence interval, 37.6%-60.7%, p = .020). CONCLUSIONS The study shows some evidence for a role of the right anterior insula in the clinical choice of major depressive disorder monotherapy. The effect size, however, is insufficient for the use of insula activity as a sole predictive biomarker of remission. The study also demonstrates the logistical difficulties in establishing clinical utility of biomarkers.
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Affiliation(s)
- Mary E. Kelley
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Ki Sueng Choi
- Center for Advanced Circuit Therapeutics , Icahn School of Medicine at Mount Sinai, New York, NY, USA,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Justin K. Rajendra
- Scientific and Statistical Computing Core, NIMH/NIH/DHHS, Bethesda, Maryland, USA
| | - W. Edward Craighead
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA.,Department of Psychology, Emory University, Atlanta, GA, USA
| | - Jeffrey J. Rakofsky
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Boadie W. Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Helen S. Mayberg
- Center for Advanced Circuit Therapeutics , Icahn School of Medicine at Mount Sinai, New York, NY, USA,Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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9
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Świądro M, Stelmaszczyk P, Wietecha-Posłuszny R, Dudek D. Development of a new method for drug detection based on a combination of the dried blood spot method and capillary electrophoresis. J Chromatogr B Analyt Technol Biomed Life Sci 2020; 1157:122339. [PMID: 32877802 DOI: 10.1016/j.jchromb.2020.122339] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Revised: 08/05/2020] [Accepted: 08/18/2020] [Indexed: 12/18/2022]
Abstract
The aim of this study was to develop a new approach to sample preparation of biological material based on a combination of the Dried Blood Spot (DBS) method and capillary electrophoresis coupled with mass spectrometry (CE-MS) for the analysis of blood samples collected in vivo or post-mortem. The proposed approach allowed the identification of typical drugs from different groups, such as tricyclic antidepressants (amitriptyline, imipramine), selective serotonin reuptake inhibitors (citalopram), benzodiazepines (tetrazepam) and hypnotics (zolpidem). In this study, a blood sample was spotted on FTA DMPK C cards, then dried, and 6-mm discs were cut out. The sample preparation procedure involved microwave-assisted extraction (MAE). Various extraction agents, temperatures and durations of extraction were examined in order to achieve the highest efficiency of the process. The method was subjected to a validation procedure. Limits of detection (LOD = 1.76 - 14.7 ng/mL) and quantification (LOQ = 5.25 - 49.0 ng/mL), inter- (CV = 1.31 - 9.43%) and intra- (CV = 3.26 - 18.52%) day precision of the determinations, recovery (RE = 85.0-105.4%) and matrix effect on ionization of analytes (ME = 98.6-105.5%) were determined. Furthermore, the developed DBS/MAE/CM-MS method was selective and analytes present in the blood applied on DBS cards were found to be stable after 7 and after 14 days. Moreover, the developed method was successfully applied to the analysis of both post-mortem samples and blood samples taken from patients treated with the analyzed drugs.
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Affiliation(s)
- Magdalena Świądro
- Laboratory for Forensic Chemistry, Department of Analytical Chemistry, Faculty of Chemistry, Jagiellonian University, 2, Gronostajowa St., 30-387 Kraków, Poland
| | - Paweł Stelmaszczyk
- Laboratory for Forensic Chemistry, Department of Analytical Chemistry, Faculty of Chemistry, Jagiellonian University, 2, Gronostajowa St., 30-387 Kraków, Poland
| | - Renata Wietecha-Posłuszny
- Laboratory for Forensic Chemistry, Department of Analytical Chemistry, Faculty of Chemistry, Jagiellonian University, 2, Gronostajowa St., 30-387 Kraków, Poland.
| | - Dominika Dudek
- Department of Adult Psychiatry, Jagiellonian University Medical College, 21a, Mikołaja Kopernika St., 31-000 Kraków, Poland
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10
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Dimick MK, Omrin D, MacIntosh BJ, Mitchell RHB, Riegert D, Levitt A, Schaffer A, Belo S, Iazzetta J, Detzler G, Choi M, Choi S, Orser BA, Goldstein BI. Nitrous oxide as a putative novel dual-mechanism treatment for bipolar depression: Proof-of-concept study design and methodology. Contemp Clin Trials Commun 2020; 19:100600. [PMID: 32637725 PMCID: PMC7327241 DOI: 10.1016/j.conctc.2020.100600] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 06/10/2020] [Accepted: 06/21/2020] [Indexed: 12/23/2022] Open
Abstract
Introduction Depressive symptoms predominate in the course of bipolar disorder (BD) and there is an urgent need to evaluate novel application of repurposed compounds that act on pre-specified treatment targets. Several lines of reasoning suggest that nitrous oxide (N2O) is an ideal medication to study as a potential treatment and as a strategy to identify the underlying pathophysiology of bipolar depression. N2O is a potent cerebral vasodilator and there is compelling evidence of reduced frontal cerebral blood flow (CBF; i.e. hypoperfusion) in depression. Therefore, N2O may increase CBF and thereby improve symptoms of depression. The goal of this randomized, double-blind trial is to study the effect of a single administration of N2O versus the active comparator midazolam on mood and CBF in adults with treatment-resistant bipolar depression. Methods Participants with BD-I/-II currently experiencing a major depressive episode will be randomized to one of two conditions (n = 20/group): 1) inhaled N2O plus intravenous saline, or 2) inhaled room air plus intravenous midazolam. Montgomery-Asberg Depression Rating Scale scores will serve as the primary endpoint. CBF will be measured via arterial spin labelling magnetic resonance imaging. Conclusions N2O is a potential novel treatment for bipolar depression, as it causes cerebral vasodilation. This proof-of-concept study will provide valuable information regarding the acute impact of N2O on mood and on CBF. If N2O proves to be efficacious in future larger-scale trials, its ubiquity, safety, low cost, and ease of use suggest that it has great potential to become a game-changing acute treatment for bipolar depression.
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Affiliation(s)
- Mikaela K Dimick
- Pharmacology and Toxicology Department, University of Toronto, Toronto, Ontario, Canada.,Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Danielle Omrin
- Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Bradley J MacIntosh
- Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Rachel H B Mitchell
- Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Daniel Riegert
- Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.,Department of Anesthesia, University of Toronto, Toronto, Ontario, Canada
| | - Anthony Levitt
- Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Ayal Schaffer
- Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Susan Belo
- Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.,Department of Anesthesia, University of Toronto, Toronto, Ontario, Canada
| | - John Iazzetta
- Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.,Pharmacy Department, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | | | - Mabel Choi
- Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.,Department of Anesthesia, University of Toronto, Toronto, Ontario, Canada
| | - Stephen Choi
- Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.,Department of Anesthesia, University of Toronto, Toronto, Ontario, Canada
| | - Beverley A Orser
- Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.,Department of Anesthesia, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin I Goldstein
- Pharmacology and Toxicology Department, University of Toronto, Toronto, Ontario, Canada.,Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
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11
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Chang B, Choi Y, Jeon M, Lee J, Han KM, Kim A, Ham BJ, Kang J. ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder. Genes (Basel) 2019; 10:genes10110907. [PMID: 31703457 PMCID: PMC6895829 DOI: 10.3390/genes10110907] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Revised: 10/25/2019] [Accepted: 10/29/2019] [Indexed: 12/20/2022] Open
Abstract
Treating patients with major depressive disorder is challenging because it takes several months for antidepressants prescribed for the patients to take effect. This limitation may result in increased risks and treatment costs. To address this limitation, an accurate antidepressant response prediction model is needed. Recently, several studies have proposed models that extract useful features such as neuroimaging biomarkers and genetic variants from patient data, and use them as predictors for predicting the antidepressant responses of patients. However, it is impossible to utilize all the different types of predictors when making a clinical decision on what drugs to prescribe for a patient. Although a machine learning-based antidepressant response prediction model has been proposed to overcome this problem, the model cannot find the most effective antidepressant for a patient. Based on a neural network, we propose an Antidepressant Response Prediction Network (ARPNet) model capturing high-dimensional patterns from useful features. Based on a literature survey and data-driven feature selection, we extract useful features from patient data, and use the features as predictors. In ARPNet, the patient representation layer captures patient features and the antidepressant prescription representation layer captures antidepressant features. Utilizing the patient and antidepressant prescription representation vectors, ARPNet predicts the degree of antidepressant response. The experimental evaluation results demonstrate that our proposed ARPNet model outperforms machine learning-based models in predicting antidepressant response. Moreover, we demonstrate the applicability of ARPNet in downstream applications in use case scenarios.
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Affiliation(s)
- Buru Chang
- Department of Computer Science and Engineering, Korea University, Seoul 02841, Korea; (B.C.); (Y.C.); (M.J.); (J.L.)
| | - Yonghwa Choi
- Department of Computer Science and Engineering, Korea University, Seoul 02841, Korea; (B.C.); (Y.C.); (M.J.); (J.L.)
| | - Minji Jeon
- Department of Computer Science and Engineering, Korea University, Seoul 02841, Korea; (B.C.); (Y.C.); (M.J.); (J.L.)
| | - Junhyun Lee
- Department of Computer Science and Engineering, Korea University, Seoul 02841, Korea; (B.C.); (Y.C.); (M.J.); (J.L.)
| | - Kyu-Man Han
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul 02841, Korea;
| | - Aram Kim
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul 02841, Korea;
| | - Byung-Joo Ham
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul 02841, Korea;
- Brain Convergence Research Center, Korea University Anam Hospital, Seoul 02841, Korea
- Correspondence: (B.-J.H.); (J.K.)
| | - Jaewoo Kang
- Department of Computer Science and Engineering, Korea University, Seoul 02841, Korea; (B.C.); (Y.C.); (M.J.); (J.L.)
- Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul 02841, Korea
- Correspondence: (B.-J.H.); (J.K.)
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12
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Kandilarova SS, Terziyski KV, Draganova AI, Stoyanov DS, Akabaliev VH, Kostianev SS. Response to Pharmacological Treatment in Major Depression Predicted by Electroencephalographic Alpha Power - a Pilot Naturalistic Study. Folia Med (Plovdiv) 2019; 59:318-325. [PMID: 28976896 DOI: 10.1515/folmed-2017-0040] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Accepted: 01/24/2017] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Pharmacological treatment of depression is currently led by the trial and error principle mainly because of lack of reliable biomarkers. Earlier findings suggest that baseline alpha power and asymmetry could differentiate between responders and non-responders to specific antidepressants. AIM The current study investigated quantitative electroencephalographic (QEEG) measures before and early in treatment as potential response predictors to various antidepressants in a naturalistic sample of depressed patients. We were aiming at developing markers for early prediction of treatment response based on different QEEG measures. MATERIALS AND METHODS EEG data from 25 depressed subjects were acquired at baseline and after one week of treatment. Mean and total alpha powers were calculated at eight electrode sites F3, F4, C3, C4, P3, P4, O1, O2. Response to treatment was defined as 50% decrease in MADRS score at week 4. RESULTS Mean P3 alpha predicted response with sensitivity and specificity of 80%, positive and negative predictive values of 92.31% and 71.43%, respectively. The combined model of response prediction using mean baseline P3 alpha and mean week 1 C4 alpha values correctly identified 80% of the cases with sensitivity of 84.62%, and specificity of 71.43%. CONCLUSIONS Simple QEEG measures (alpha power) acquired before initiation of antidepressant treatment could be useful in outcome prediction with an overall accuracy of about 80%. These findings add to the growing body of evidence that alpha power might be developed as a reliable biomarker for the prediction of antidepressant response.
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Affiliation(s)
- Sevdalina S Kandilarova
- Department of Psychiatry and Medical Psychology, Faculty of Medicine, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Kiril V Terziyski
- Department of Pathophysiology, Faculty of Medicine, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Aneliya I Draganova
- Department of Pathophysiology, Faculty of Medicine, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Drozdstoy S Stoyanov
- Department of Psychiatry and Medical Psychology, Faculty of Medicine, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Valentin H Akabaliev
- Department of Psychiatry and Medical Psychology, Faculty of Medicine, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Stefan S Kostianev
- Department of Pathophysiology, Faculty of Medicine, Medical University of Plovdiv, Plovdiv, Bulgaria
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13
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Bares M, Novak T, Vlcek P, Hejzlar M, Brunovsky M. Early change of prefrontal theta cordance and occipital alpha asymmetry in the prediction of responses to antidepressants. Int J Psychophysiol 2019; 143:1-8. [PMID: 31195067 DOI: 10.1016/j.ijpsycho.2019.06.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 06/07/2019] [Accepted: 06/07/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND The study evaluated the effectiveness of EEG alpha 1, alpha 2 and theta power, along with prefrontal theta cordance (PFC), frontal and occipital alpha 1, alpha 2 asymmetry (FAA1/2, OAA1/2) at baseline and their changes at week 1 in predicting response to antidepressants. METHOD Resting-state EEG data were recorded from 103 depressive patients that were treated in average for 5.1 ± 0.9 weeks with SSRIs (n = 57) and SNRIs (n = 46). RESULTS Fifty-five percent of patients (n = 56) responded to treatment (i.e.reduction of Montgomery-Åsberg Depression Rating Scale score ≥ 50%) and 45% (n = 47) of treated subjects did not reach positive treatment outcome. No differences in EEG baseline alpha and theta power or changes at week 1 for prefrontal, frontal, central, temporal and occipital regions were found between responders and non-responders. Both groups showed no differences at baseline PFC, FAA1/2 and OAA1/2 as well as change of FAA1/2 at week 1. The only parameters associated with treatment outcome were decrease of PFC in responders and increase of OAA1/2 at week 1 in non-responders. There was no influence of the used antidepressant classes on the results. The PFC change at week 1 (PFCC) (area under curve-AUC = 0.75) showed only a numerically higher predictive ability than OAA change in alpha 1 (OAA1C, AUC = 0.64)/alpha 2 (OAA2C, AUC = 0.63). A combined model, where OAA1C was added to PFCC (AUC = 0.79), did not significantly improve response prediction. CONCLUSION Besides PFCC, we found that OAA1C/OAA2C might be another candidate for EEG predictors of antidepressant response.
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Affiliation(s)
- Martin Bares
- National Institute of Mental Health Czech Republic, Topolova 748, 250 67 Klecany, Czech Republic; Department of Psychiatry and Medical Psychology of Third Medical Faculty, Charles University, Ruská 87, 100 00 Prague 10, Czech Republic.
| | - Tomas Novak
- National Institute of Mental Health Czech Republic, Topolova 748, 250 67 Klecany, Czech Republic; Department of Psychiatry and Medical Psychology of Third Medical Faculty, Charles University, Ruská 87, 100 00 Prague 10, Czech Republic.
| | - Premysl Vlcek
- National Institute of Mental Health Czech Republic, Topolova 748, 250 67 Klecany, Czech Republic; Department of Psychiatry and Medical Psychology of Third Medical Faculty, Charles University, Ruská 87, 100 00 Prague 10, Czech Republic.
| | - Martin Hejzlar
- National Institute of Mental Health Czech Republic, Topolova 748, 250 67 Klecany, Czech Republic; Department of Psychiatry and Medical Psychology of Third Medical Faculty, Charles University, Ruská 87, 100 00 Prague 10, Czech Republic.
| | - Martin Brunovsky
- National Institute of Mental Health Czech Republic, Topolova 748, 250 67 Klecany, Czech Republic; Department of Psychiatry and Medical Psychology of Third Medical Faculty, Charles University, Ruská 87, 100 00 Prague 10, Czech Republic.
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14
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Perlman K, Benrimoh D, Israel S, Rollins C, Brown E, Tunteng JF, You R, You E, Tanguay-Sela M, Snook E, Miresco M, Berlim MT. A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder. J Affect Disord 2019; 243:503-515. [PMID: 30286415 DOI: 10.1016/j.jad.2018.09.067] [Citation(s) in RCA: 97] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 07/29/2018] [Accepted: 09/16/2018] [Indexed: 12/16/2022]
Abstract
INTRODUCTION The heterogeneity of symptoms and complex etiology of depression pose a significant challenge to the personalization of treatment. Meanwhile, the current application of generic treatment approaches to patients with vastly differing biological and clinical profiles is far from optimal. Here, we conduct a meta-review to identify predictors of response to antidepressant therapy in order to select robust input features for machine learning models of treatment response. These machine learning models will allow us to learn associations between patient features and treatment response which have predictive value at the individual patient level; this learning can be optimized by selecting high-quality input features for the model. While current research is difficult to directly apply to the clinic, machine learning models built using knowledge gleaned from current research may become useful clinical tools. METHODS The EMBASE and MEDLINE/PubMed online databases were searched from January 1996 to August 2017, using a combination of MeSH terms and keywords to identify relevant literature reviews. We identified a total of 1909 articles, wherein 199 articles met our inclusion criteria. RESULTS An array of genetic, immune, endocrine, neuroimaging, sociodemographic, and symptom-based predictors of treatment response were extracted, varying widely in clinical utility. LIMITATIONS Due to heterogeneous sample sizes, effect sizes, publication biases, and methodological disparities across reviews, we could not accurately assess the strength and directionality of every predictor. CONCLUSION Notwithstanding our cautious interpretation of the results, we have identified a multitude of predictors that can be used to formulate a priori hypotheses regarding the input features for a computational model. We highlight the importance of large-scale research initiatives and clinically accessible biomarkers, as well as the need for replication studies of current findings. In addition, we provide recommendations for future improvement and standardization of research efforts in this field.
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Affiliation(s)
- Kelly Perlman
- Montreal Neurological Institute, McGill University, 3801 Rue Université, Montréal, QC H3A 2B4, Canada.
| | - David Benrimoh
- Department of Psychiatry, McGill University, Montreal, Canada; Faculty of Medicine, McGill University, Montreal, Canada
| | - Sonia Israel
- Department of Psychiatry, McGill University, Montreal, Canada; Douglas Mental Health University Institute, Montreal, Canada
| | - Colleen Rollins
- Department of Psychiatry, University of Cambridge, Cambridge, England, UK
| | - Eleanor Brown
- Montreal Neurological Institute, McGill University, 3801 Rue Université, Montréal, QC H3A 2B4, Canada
| | - Jingla-Fri Tunteng
- Montreal Children's Hospital, McGill University Health Center, Montreal, Canada
| | - Raymond You
- School of Physical and Occupational Therapy, McGill University, Montreal, Canada
| | - Eunice You
- Faculty of Medicine, McGill University, Montreal, Canada
| | - Myriam Tanguay-Sela
- Montreal Neurological Institute, McGill University, 3801 Rue Université, Montréal, QC H3A 2B4, Canada
| | - Emily Snook
- Douglas Mental Health University Institute, Montreal, Canada
| | - Marc Miresco
- Department of Psychiatry, Jewish General Hospital, Montreal, Canada
| | - Marcelo T Berlim
- Department of Psychiatry, McGill University, Montreal, Canada; Douglas Mental Health University Institute, Montreal, Canada
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15
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Peng D, Yao Z. Neuroimaging Advance in Depressive Disorder. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1180:59-83. [DOI: 10.1007/978-981-32-9271-0_3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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16
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Kelley ME, Dunlop B, Nemeroff CB, Lori A, Carrillo-Roa T, Binder EB, Kutner MH, Rivera VA, Craighead WE, Mayberg HS. Response rate profiles for major depressive disorder: Characterizing early response and longitudinal nonresponse. Depress Anxiety 2018; 35:992-1000. [PMID: 30260539 PMCID: PMC6662579 DOI: 10.1002/da.22832] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 05/23/2018] [Accepted: 07/11/2018] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Definition of response is critical when seeking to establish valid predictors of treatment success. However, response at the end of study or endpoint only provides one view of the overall clinical picture that is relevant in testing for predictors. The current study employed a classification technique designed to group subjects based on their rate of change over time, while simultaneously addressing the issue of controlling for baseline severity. METHODS A set of latent class trajectory analyses, incorporating baseline level of symptoms, were performed on a sample of 344 depressed patients from a clinical trial evaluating the efficacy of cognitive behavior therapy and two antidepressant medications (escitalopram and duloxetine) in patients with major depressive disorder. RESULTS Although very few demographic and illness-related features were associated with response rate profiles, the aggregated effect of candidate genetic variants previously identified in large pharmacogenetic studies and meta-analyses showed a significant association with early remission as well as nonresponse. These same genetic scores showed a less compelling relationship with endpoint response categories. In addition, consistent nonresponse throughout the study treatment period was shown to occur in different subjects than endpoint nonresponse, which was verified by follow-up augmentation treatment outcomes. CONCLUSIONS When defining groups based on the rate of change, controlling for baseline depression severity may help to identify the clinically relevant distinctions of early response on one end and consistent nonresponse on the other.
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Affiliation(s)
- Mary E. Kelley
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - BoadieW. Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia
| | - Charles B. Nemeroff
- Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, Florida
| | - Adriana Lori
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia
| | - Tania Carrillo-Roa
- Department of Translational Research in Psychiatry, Max-Planck Institute of Psychiatry, Munich, Germany
| | - Elisabeth B. Binder
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia,Department of Translational Research in Psychiatry, Max-Planck Institute of Psychiatry, Munich, Germany
| | - Michael H. Kutner
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Vivianne Aponte Rivera
- Departmentof Psychiatry and Behavioral Sciences, Tulane University, NewOrleans, Louisiana
| | - W. Edward Craighead
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia,Department of Psychology, Emory University, Atlanta, Georgia
| | - Helen S. Mayberg
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia,Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia
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Gonda X, Petschner P, Eszlari N, Baksa D, Edes A, Antal P, Juhasz G, Bagdy G. Genetic variants in major depressive disorder: From pathophysiology to therapy. Pharmacol Ther 2018; 194:22-43. [PMID: 30189291 DOI: 10.1016/j.pharmthera.2018.09.002] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
In spite of promising preclinical results there is a decreasing number of new registered medications in major depression. The main reason behind this fact is the lack of confirmation in clinical studies for the assumed, and in animals confirmed, therapeutic results. This suggests low predictive value of animal studies for central nervous system disorders. One solution for identifying new possible targets is the application of genetics and genomics, which may pinpoint new targets based on the effect of genetic variants in humans. The present review summarizes such research focusing on depression and its therapy. The inconsistency between most genetic studies in depression suggests, first of all, a significant role of environmental stress. Furthermore, effect of individual genes and polymorphisms is weak, therefore gene x gene interactions or complete biochemical pathways should be analyzed. Even genes encoding target proteins of currently used antidepressants remain non-significant in genome-wide case control investigations suggesting no main effect in depression, but rather an interaction with stress. The few significant genes in GWASs are related to neurogenesis, neuronal synapse, cell contact and DNA transcription and as being nonspecific for depression are difficult to harvest pharmacologically. Most candidate genes in replicable gene x environment interactions, on the other hand, are connected to the regulation of stress and the HPA axis and thus could serve as drug targets for depression subgroups characterized by stress-sensitivity and anxiety while other risk polymorphisms such as those related to prominent cognitive symptoms in depression may help to identify additional subgroups and their distinct treatment. Until these new targets find their way into therapy, the optimization of current medications can be approached by pharmacogenomics, where metabolizing enzyme polymorphisms remain prominent determinants of therapeutic success.
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Affiliation(s)
- Xenia Gonda
- Department of Psychiatry and Psychotherapy, Kutvolgyi Clinical Centre, Semmelweis University, Budapest, Hungary; NAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary; MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary.
| | - Peter Petschner
- MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary; Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary
| | - Nora Eszlari
- NAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary; Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary
| | - Daniel Baksa
- Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary; SE-NAP 2 Genetic Brain Imaging Migraine Research Group, Hungarian Academy of Sciences, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
| | - Andrea Edes
- Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary; SE-NAP 2 Genetic Brain Imaging Migraine Research Group, Hungarian Academy of Sciences, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
| | - Peter Antal
- Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
| | - Gabriella Juhasz
- Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary; SE-NAP 2 Genetic Brain Imaging Migraine Research Group, Hungarian Academy of Sciences, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary; Neuroscience and Psychiatry Unit, University of Manchester, Manchester Academic Health Sciences Centre, Manchester, UK
| | - Gyorgy Bagdy
- NAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary; MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary; Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary.
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18
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Voegeli G, Cléry-Melin ML, Ramoz N, Gorwood P. Progress in Elucidating Biomarkers of Antidepressant Pharmacological Treatment Response: A Systematic Review and Meta-analysis of the Last 15 Years. Drugs 2018; 77:1967-1986. [PMID: 29094313 DOI: 10.1007/s40265-017-0819-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
BACKGROUND Antidepressant drugs are widely prescribed, but response rates after 3 months are only around one-third, explaining the importance of the search of objectively measurable markers predicting positive treatment response. These markers are being developed in different fields, with different techniques, sample sizes, costs, and efficiency. It is therefore difficult to know which ones are the most promising. OBJECTIVE Our purpose was to compute comparable (i.e., standardized) effect sizes, at study level but also at marker level, in order to conclude on the efficacy of each technique used and all analyzed markers. METHODS We conducted a systematic search on the PubMed database to gather all articles published since 2000 using objectively measurable markers to predict antidepressant response from five domains, namely cognition, electrophysiology, imaging, genetics, and transcriptomics/proteomics/epigenetics. A manual screening of the abstracts and the reference lists of these articles completed the search process. RESULTS Executive functioning, theta activity in the rostral Anterior Cingular Cortex (rACC), and polysomnographic sleep measures could be considered as belonging to the best objectively measured markers, with a combined d around 1 and at least four positive studies. For inter-category comparisons, the approaches that showed the highest effect sizes are, in descending order, imaging (combined d between 0.703 and 1.353), electrophysiology (0.294-1.138), cognition (0.929-1.022), proteins/nucleotides (0.520-1.18), and genetics (0.021-0.515). CONCLUSION Markers of antidepressant treatment outcome are numerous, but with a discrepant level of accuracy. Many biomarkers and cognitions have sufficient predictive value (d ≥ 1) to be potentially useful for clinicians to predict outcome and personalize antidepressant treatment.
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Affiliation(s)
- G Voegeli
- CMME, Hôpital Sainte-Anne, Université Paris Descartes, 100 rue de la Santé, 75014, Paris, France.
- Centre de Psychiatrie et Neuroscience (INSERM UMR 894), 2 ter rue d'Alésia, 75014, Paris, France.
| | - M L Cléry-Melin
- CMME, Hôpital Sainte-Anne, Université Paris Descartes, 100 rue de la Santé, 75014, Paris, France
- Centre de Psychiatrie et Neuroscience (INSERM UMR 894), 2 ter rue d'Alésia, 75014, Paris, France
| | - N Ramoz
- CMME, Hôpital Sainte-Anne, Université Paris Descartes, 100 rue de la Santé, 75014, Paris, France
- Centre de Psychiatrie et Neuroscience (INSERM UMR 894), 2 ter rue d'Alésia, 75014, Paris, France
| | - P Gorwood
- CMME, Hôpital Sainte-Anne, Université Paris Descartes, 100 rue de la Santé, 75014, Paris, France
- Centre de Psychiatrie et Neuroscience (INSERM UMR 894), 2 ter rue d'Alésia, 75014, Paris, France
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19
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Arumugam V, John VS, Augustine N, Jacob T, Joy SM, Sen S, Sen T. The impact of antidepressant treatment on brain-derived neurotrophic factor level: An evidence-based approach through systematic review and meta-analysis. Indian J Pharmacol 2018; 49:236-242. [PMID: 29033483 PMCID: PMC5637134 DOI: 10.4103/ijp.ijp_700_16] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
OBJECTIVES: Antidepressant treatment alters brain-derived neurotrophic factor (BDNF) levels, but it is not well established whether BDNF can be used as a marker to prove the efficacy of antidepressant treatment. The present systematic review and meta-analysis aim at assessing the influence of antidepressant treatment on BDNF level and the Hamilton Depression Rating Scale (HDRS) score, thereby to establish the rationale of utilizing BDNF as a predictive biomarker and HDRS score as an indicator for antidepressant treatment efficacy. MATERIALS AND METHODS: Search was conducted in PubMed, Science Direct, and Cochrane databases using the key words “BDNF” and “Depression” and “Antidepressants.” On the basis of the inclusion and exclusion criteria, studies were filtered and finally 6 randomized controlled trials were shortlisted. RESULTS: Comparison of serum BDNF level before and after antidepressant treatment was performed and the result showed that antidepressant treatment does not significantly affect the BDNF levels (confidence interval [CI]: −0.483 to 0.959; standard mean difference [SMD]: 0.238, P = 0.518). Egger's regression test (P = 0.455) and heterogeneity test (I2 = 88.909%) were done. Similarly, comparison of HDRS scores before and after antidepressant treatment indicated improvement in HDRS score suggesting positive outcome (CI: 1.719 to 3.707; SMD: 2.713, P < 0.001). Egger's regression test (P = 0.1417) and heterogeneity test (I2 = 89.843%) were performed. Publication bias was observed by funnel plot. CONCLUSION: Changes in BDNF levels do not occur uniformly for all the antidepressants. Hence, to use BDNF as a biomarker, it needs to be seen whether the same is true for all antidepressants.
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Affiliation(s)
- Vijayakumar Arumugam
- Department of Pharmacy Practice, Drug and Poison Information Center, KMCH College of Pharmacy, Coimbatore, Tamil Nadu, India
| | - Vini Susan John
- Department of Pharmacy Practice, Drug and Poison Information Center, KMCH College of Pharmacy, Coimbatore, Tamil Nadu, India
| | - Nisha Augustine
- Department of Pharmacy Practice, Drug and Poison Information Center, KMCH College of Pharmacy, Coimbatore, Tamil Nadu, India
| | - Taniya Jacob
- Department of Pharmacy Practice, Drug and Poison Information Center, KMCH College of Pharmacy, Coimbatore, Tamil Nadu, India
| | - Sagar Maliakkal Joy
- Department of Pharmacy Practice, Drug and Poison Information Center, KMCH College of Pharmacy, Coimbatore, Tamil Nadu, India
| | - Suchandra Sen
- Department of Pharmacy Practice, Drug and Poison Information Center, KMCH College of Pharmacy, Coimbatore, Tamil Nadu, India
| | - Tuhinadri Sen
- Department of Pharmaceutical Technology, Division of Pharmacology, Jadavpur University, Kolkata, West Bengal, India
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20
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Hacimusalar Y, Eşel E. Suggested Biomarkers for Major Depressive Disorder. ACTA ACUST UNITED AC 2018; 55:280-290. [PMID: 30224877 DOI: 10.5152/npa.2017.19482] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Accepted: 06/08/2017] [Indexed: 12/21/2022]
Abstract
Currently, the diagnosis of major depressive disorder (MDD) mainly relies on clinical examination and subjective evaluation of depressive symptoms. There is no non-invasive, quantitative test available today for the diagnosis of MDD. In MDD, exploration of biomarkers will be helpful in diagnosing the disorder as well as in choosing a treatment, and predicting the treatment response. In this article, it is aimed to review the findings of suggested biomarkers such as growth factors, cytokines and other inflammatory markers, oxidative stress markers, endocrine markers, energy balance hormones, genetic and epigenetic features, and neuroimaging in MDD and to evaluate how these findings contribute to the pathophysiology of MDD, the prediction of treatment response, severity of the disorder, and identification of subtypes. Among these, the findings related to the brain-derived neurotrophic factor, the hypothalamo-pituitary-adrenal axis, cytokines, and neuroimaging may be strong candidates for being biomarkers MDD, and may provide critical information in understanding biological etiology of depression. Although the findings are not sufficient yet, we think that the results of epigenetic studies will also provide very important contributions to the biomarker research in MDD. The availability of biomarkers in MDD will be an advancement that will facilitate the diagnosis of the disorder, treatment choices in the early stages, and prediction of the course of the disorder.
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Affiliation(s)
- Yunus Hacimusalar
- Department of Psychiatry, Kayseri Training and Research Hospital, Kayseri, Turkey
| | - Ertuğrul Eşel
- Department of Psychiatry, Erciyes University Faculty of Medicine, Kayseri, Turkey
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21
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Distinctive pretreatment features of bilateral nucleus accumbens networks predict early response to antidepressants in major depressive disorder. Brain Imaging Behav 2017; 12:1042-1052. [DOI: 10.1007/s11682-017-9773-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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22
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Geerlings MI, Gerritsen L. Late-Life Depression, Hippocampal Volumes, and Hypothalamic-Pituitary-Adrenal Axis Regulation: A Systematic Review and Meta-analysis. Biol Psychiatry 2017; 82:339-350. [PMID: 28318491 DOI: 10.1016/j.biopsych.2016.12.032] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Revised: 12/20/2016] [Accepted: 12/21/2016] [Indexed: 12/13/2022]
Abstract
BACKGROUND We systematically reviewed and meta-analyzed the association of late-life depression (LLD) with hippocampal volume (HCV) and total brain volume (TBV), and of cortisol levels with HCV, including subgroup analyses of depression characteristics and methodological aspects. METHODS We searched PubMed and Embase for original studies that examined the cross-sectional relationship between LLD and HCV or TBV, and 46 studies fulfilled the inclusion criteria. Standardized mean differences (Hedges' g) between LLD and control subjects were calculated from crude or adjusted brain volumes using random effects. Standardized Fisher transformations of the correlations between cortisol levels and HCVs were calculated using random effects. RESULTS We included 2702 LLD patients and 11,165 control subjects from 35 studies examining HCV. Relative to control subjects, patients had significantly smaller HCVs (standardized mean difference = -0.32 [95% confidence interval, -0.44 to -0.19]). Subgroup analyses showed that late-onset depression was more strongly associated with HCV than early-onset depression. In addition, effect sizes were larger for case-control studies, studies with lower quality, and studies with small sample size, and were almost absent in cohort studies and studies with larger sample sizes. For TBV, 2523 patients and 7880 control subjects from 31 studies were included. The standardized mean difference in TBV between LLD and control subjects was -0.10 (95% confidence interval, -0.16 to -0.04). Of the 12 studies included, higher levels of cortisol were associated with smaller HCV (correlation = -0.11 [95% confidence interval, -0.18 to -0.04]). CONCLUSIONS While an overall measure of LLD may be associated with smaller HCVs, differentiating clinical aspects of LLD and examining methodological issues show that this relationship is not straightforward.
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Affiliation(s)
- Mirjam I Geerlings
- University Medical Center Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht, The Netherlands.
| | - Lotte Gerritsen
- Department of Clinical Psychology, Utrecht University, Utrecht, The Netherlands; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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23
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Mendez-David I, Boursier C, Domergue V, Colle R, Falissard B, Corruble E, Gardier AM, Guilloux JP, David DJ. Differential Peripheral Proteomic Biosignature of Fluoxetine Response in a Mouse Model of Anxiety/Depression. Front Cell Neurosci 2017; 11:237. [PMID: 28860968 PMCID: PMC5561647 DOI: 10.3389/fncel.2017.00237] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Accepted: 07/26/2017] [Indexed: 01/12/2023] Open
Abstract
The incorporation of peripheral biomarkers in the treatment of major depressive disorders (MDD) could improve the efficiency of treatments and increase remission rate. Peripheral blood mononuclear cells (PBMCs) represent an attractive biological substrate allowing the identification of a drug response signature. Using a proteomic approach with high-resolution mass spectrometry, the present study aimed to identify a biosignature of antidepressant response (fluoxetine, a Selective Serotonin Reuptake Inhibitor) in PBMCs in a mouse model of anxiety/depression. Following determination of an emotionality score, using complementary behavioral analysis of anxiety/depression across three different tests (Elevated Plus Maze, Novelty Suppressed Feeding, Splash Test), we showed that a 4-week corticosterone treatment (35 μg/ml, CORT model) in C57BL/6NTac male mice induced an anxiety/depressive-like behavior. Then, chronic fluoxetine treatment (18 mg/kg/day for 28 days in the drinking water) reduced corticosterone-induced increase in emotional behavior. However, among 46 fluoxetine-treated mice, only 30 of them presented a 50% decrease in emotionality score, defining fluoxetine responders (CORT/Flx-R). To determine a peripheral biological signature of fluoxetine response, proteomic analysis was performed from PBMCs isolated from the “most” affected corticosterone/vehicle (CORT/V), corticosterone/fluoxetine responders and non-responders (CORT/Flx-NR) animals. In comparison to CORT/V, a total of 263 proteins were differently expressed after fluoxetine exposure. Expression profile of these proteins showed a strong similarity between CORT/Flx-R and CORT/Flx-NR (R = 0.827, p < 1e-7). Direct comparison of CORT/Flx-R and CORT/Flx-NR groups revealed 100 differently expressed proteins, representing a combination of markers associated either with the maintenance of animals in a refractory state, or associated with behavioral improvement. Finally, 19 proteins showed a differential direction of expression between CORT/Flx-R and CORT/Flx-NR that drove them away from the CORT-treated profile. Among them, eight upregulated proteins (RPN2, HSPA9, NPTN, AP2B1, UQCRC2, RACK-1, TOLLIP) and one downregulated protein, TLN2, were previously associated with MDD or antidepressant drug response in the literature. Future preclinical studies will be required to validate whether proteomic changes observed in PBMCs from CORT/Flx-R mice mirror biological changes in brain tissues.
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Affiliation(s)
- Indira Mendez-David
- CESP/UMR-S 1178, Université Paris-Sud, INSERM, Université Paris-SaclayChâtenay-Malabry, France
| | - Céline Boursier
- Proteomic Facility, Institut Paris Saclay d'Innovation Thérapeutique (UMS IPSIT), Université Paris-Sud, Université Paris-SaclayChâtenay-Malabry, France
| | - Valérie Domergue
- Animal Facility, Institut Paris Saclay d'Innovation Thérapeutique (UMS IPSIT), Université Paris-Sud, Université Paris-SaclayChâtenay-Malabry, France
| | - Romain Colle
- CESP/UMR 1178, Service de Psychiatrie, Faculté de Médecine, Université Paris-Sud, INSERM, Université Paris-Saclay, Hôpital BicêtreLe Kremlin Bicêtre, France
| | - Bruno Falissard
- CESP/UMR 1178, Service de Psychiatrie, Faculté de Médecine, Université Paris-Sud, INSERM, Université Paris-Saclay, Hôpital BicêtreLe Kremlin Bicêtre, France
| | - Emmanuelle Corruble
- CESP/UMR 1178, Service de Psychiatrie, Faculté de Médecine, Université Paris-Sud, INSERM, Université Paris-Saclay, Hôpital BicêtreLe Kremlin Bicêtre, France
| | - Alain M Gardier
- CESP/UMR-S 1178, Université Paris-Sud, INSERM, Université Paris-SaclayChâtenay-Malabry, France
| | - Jean-Philippe Guilloux
- CESP/UMR-S 1178, Université Paris-Sud, INSERM, Université Paris-SaclayChâtenay-Malabry, France
| | - Denis J David
- CESP/UMR-S 1178, Université Paris-Sud, INSERM, Université Paris-SaclayChâtenay-Malabry, France
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24
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Hervé M, Bergon A, Le Guisquet AM, Leman S, Consoloni JL, Fernandez-Nunez N, Lefebvre MN, El-Hage W, Belzeaux R, Belzung C, Ibrahim EC. Translational Identification of Transcriptional Signatures of Major Depression and Antidepressant Response. Front Mol Neurosci 2017; 10:248. [PMID: 28848385 PMCID: PMC5550836 DOI: 10.3389/fnmol.2017.00248] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Accepted: 07/24/2017] [Indexed: 12/12/2022] Open
Abstract
Major depressive disorder (MDD) is a highly prevalent mental illness whose therapy management remains uncertain, with more than 20% of patients who do not achieve response to antidepressants. Therefore, identification of reliable biomarkers to predict response to treatment will greatly improve MDD patient medical care. Due to the inaccessibility and lack of brain tissues from living MDD patients to study depression, researches using animal models have been useful in improving sensitivity and specificity of identifying biomarkers. In the current study, we used the unpredictable chronic mild stress (UCMS) model and correlated stress-induced depressive-like behavior (n = 8 unstressed vs. 8 stressed mice) as well as the fluoxetine-induced recovery (n = 8 stressed and fluoxetine-treated mice vs. 8 unstressed and fluoxetine-treated mice) with transcriptional signatures obtained by genome-wide microarray profiling from whole blood, dentate gyrus (DG), and the anterior cingulate cortex (ACC). Hierarchical clustering and rank-rank hypergeometric overlap (RRHO) procedures allowed us to identify gene transcripts with variations that correlate with behavioral profiles. As a translational validation, some of those transcripts were assayed by RT-qPCR with blood samples from 10 severe major depressive episode (MDE) patients and 10 healthy controls over the course of 30 weeks and four visits. Repeated-measures ANOVAs revealed candidate trait biomarkers (ARHGEF1, CMAS, IGHMBP2, PABPN1 and TBC1D10C), whereas univariate linear regression analyses uncovered candidates state biomarkers (CENPO, FUS and NUBP1), as well as prediction biomarkers predictive of antidepressant response (CENPO, NUBP1). These data suggest that such a translational approach may offer new leads for clinically valid panels of biomarkers for MDD.
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Affiliation(s)
- Mylène Hervé
- Aix Marseille Univ, CNRS, CRN2M UMR 7286Marseille, France.,FondaMental, Fondation de Recherche et de Soins en Santé MentaleCréteil, France
| | - Aurélie Bergon
- Aix Marseille Univ, INSERM, TAGC UMR_S 1090Marseille, France
| | | | - Samuel Leman
- INSERM U930 Eq 4, UFR Sciences et Techniques, Université François RabelaisTours, France
| | - Julia-Lou Consoloni
- Aix Marseille Univ, CNRS, CRN2M UMR 7286Marseille, France.,FondaMental, Fondation de Recherche et de Soins en Santé MentaleCréteil, France.,AP-HM, Hôpital Sainte Marguerite, Pôle de Psychiatrie Universitaire SolarisMarseille, France
| | | | | | - Wissam El-Hage
- INSERM U930 Eq 4, UFR Sciences et Techniques, Université François RabelaisTours, France.,CHRU de Tours, Clinique Psychiatrique UniversitaireTours, France.,INSERM CIC 1415, Centre d'Investigation Clinique, CHRU de ToursTours, France
| | - Raoul Belzeaux
- Aix Marseille Univ, CNRS, CRN2M UMR 7286Marseille, France.,FondaMental, Fondation de Recherche et de Soins en Santé MentaleCréteil, France.,AP-HM, Hôpital Sainte Marguerite, Pôle de Psychiatrie Universitaire SolarisMarseille, France.,McGill Group for Suicide Studies, Douglas Mental Health University Institute, Department of Psychiatry, McGill UniversityMontreal, QC, Canada
| | - Catherine Belzung
- INSERM U930 Eq 4, UFR Sciences et Techniques, Université François RabelaisTours, France
| | - El Chérif Ibrahim
- Aix Marseille Univ, CNRS, CRN2M UMR 7286Marseille, France.,FondaMental, Fondation de Recherche et de Soins en Santé MentaleCréteil, France.,Aix Marseille Univ, CNRS, INT, Inst Neurosci Timone UMR 7289Marseille, France
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25
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The Comparison of Effectiveness of Various Potential Predictors of Response to Treatment With SSRIs in Patients With Depressive Disorder. J Nerv Ment Dis 2017; 205:618-626. [PMID: 27660994 DOI: 10.1097/nmd.0000000000000574] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The substantial non-response rate in depressive patients indicates a continuing need to identify predictors of treatment outcome. The aim of this 6-week, open-label study was (1) to compare the efficacy of a priori defined predictors: ≥20% reduction in MADRS score at week 1, ≥20% reduction in MADRS score at week 2 (RM ≥ 20% W2), decrease of cordance (RC), and increase of serum and plasma level of brain-derived neurotrophic factor at week 1; and (2) to assess whether their combination yields higher efficacy in the prediction of response to selective serotonin re-uptake inhibitors (SSRIs) than when used singly. Twenty-one patients (55%) achieved a response to SSRIs. The RM ≥20% W2 (areas under curve-AUC = 0.83) showed better predictive efficacy compared to all other predictors with the exception of RC. The identified combined model (RM ≥ 20% W2 + RC), which predicted response with an 84% accuracy (AUC = 0.92), may be a useful tool in the prediction of response to SSRIs.
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26
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Liao SC, Wu CT, Huang HC, Cheng WT, Liu YH. Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns. SENSORS (BASEL, SWITZERLAND) 2017; 17:E1385. [PMID: 28613237 PMCID: PMC5492453 DOI: 10.3390/s17061385] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2017] [Revised: 06/07/2017] [Accepted: 06/10/2017] [Indexed: 01/19/2023]
Abstract
Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs) are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA) to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total). Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM) classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be achieved by the KEFP-CSP feature and the SVM classifier with only several trials, and this level of accuracy seems to become stable as more trials (i.e., <7 trials) are used. These findings therefore suggest that the proposed method has a great potential for developing an efficient (required only a few 6-s EEG signals from the 8 electrodes over the temporal) and effective (~80% classification accuracy) EEG-based brain-computer interface (BCI) system which may, in the future, help psychiatrists provide individualized and effective treatments for MDD patients.
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Affiliation(s)
- Shih-Cheng Liao
- Department of Psychiatry, National Taiwan University Hospital, Taipei 10051, Taiwan.
| | - Chien-Te Wu
- Department of Psychiatry, National Taiwan University Hospital, Taipei 10051, Taiwan.
- School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei 10051, Taiwan.
| | - Hao-Chuan Huang
- Graduate Institute of Mechatronics Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
| | - Wei-Teng Cheng
- Department of Mechanical Engineering, Chung Yuan Christian University, Chungli 32023, Taiwan.
| | - Yi-Hung Liu
- Graduate Institute of Mechatronics Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
- Department of Mechanical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
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27
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Kiang M, Farzan F, Blumberger DM, Kutas M, McKinnon MC, Kansal V, Rajji TK, Daskalakis ZJ. Abnormal self-schema in semantic memory in major depressive disorder: Evidence from event-related brain potentials. Biol Psychol 2017; 126:41-47. [PMID: 28385626 DOI: 10.1016/j.biopsycho.2017.04.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Revised: 03/03/2017] [Accepted: 04/02/2017] [Indexed: 12/28/2022]
Abstract
An overly negative self-schema is a proposed cognitive mechanism of major depressive disorder (MDD). Self-schema - one's core conception of self, including how strongly one believes one possesses various characteristics - is part of semantic memory (SM), our knowledge about concepts and their relationships. We used the N400 event-related potential (ERP) - elicited by meaningful stimuli, and reduced by greater association of the stimulus with preceding context - to measure association strength between self-concept and positive, negative, and neutral characteristics in SM. ERPs were recorded from MDD patients (n=16) and controls (n=16) who viewed trials comprising a self-referential phrase followed by a positive, negative, or neutral adjective. Participants' task was to indicate via button-press whether or not they felt each adjective described themselves. Controls endorsed more positive adjectives than did MDD patients, but the opposite was true for negative adjectives. Patients had smaller N400s than controls specifically for negative adjectives, suggesting that MDD is associated with stronger than normal functional neural links between self-concept and negative characteristics in SM.
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Affiliation(s)
- Michael Kiang
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.
| | - Faranak Farzan
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Daniel M Blumberger
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Marta Kutas
- Department of Cognitive Science and Department of Neurosciences, University of California (San Diego), La Jolla, CA, USA
| | - Margaret C McKinnon
- St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
| | - Vinay Kansal
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
| | - Tarek K Rajji
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Zafiris J Daskalakis
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
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Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder. Epidemiol Psychiatr Sci 2017; 26:22-36. [PMID: 26810628 PMCID: PMC5125904 DOI: 10.1017/s2045796016000020] [Citation(s) in RCA: 97] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
BACKGROUNDS Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. METHOD We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. RESULTS Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. CONCLUSIONS Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.
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Szczepanik J, Nugent AC, Drevets WC, Khanna A, Zarate CA, Furey ML. Amygdala response to explicit sad face stimuli at baseline predicts antidepressant treatment response to scopolamine in major depressive disorder. Psychiatry Res Neuroimaging 2016; 254:67-73. [PMID: 27366831 PMCID: PMC6711385 DOI: 10.1016/j.pscychresns.2016.06.005] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Revised: 06/08/2016] [Accepted: 06/15/2016] [Indexed: 12/20/2022]
Abstract
The muscarinic antagonist scopolamine produces rapid antidepressant effects in individuals with major depressive disorder (MDD). In healthy subjects, manipulation of acetyl-cholinergic transmission modulates attention in a stimulus-dependent manner. This study tested the hypothesis that baseline amygdalar activity in response to emotional stimuli correlates with antidepressant treatment response to scopolamine and could thus potentially predict treatment outcome. MDD patients and healthy controls performed an attention shifting task involving emotional faces while undergoing functional magnetic resonance imaging (fMRI). We found that blood oxygenation level dependent (BOLD) signal in the amygdala acquired while MDD patients processed sad face stimuli correlated positively with antidepressant response to scopolamine. Amygdalar response to sad faces in MDD patients who did not respond to scopolamine did not differ from that of healthy controls. This suggests that the pre-treatment task elicited amygdalar activity that may constitute a biomarker of antidepressant treatment response to scopolamine. Furthermore, in MDD patients who responded to scopolamine, we observed a post-scopolamine stimulus processing shift towards a pattern demonstrated by healthy controls, indicating a change in stimulus-dependent neural response potentially driven by attenuated cholinergic activity in the amygdala.
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Affiliation(s)
- Joanna Szczepanik
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
| | - Allison C Nugent
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Wayne C Drevets
- Janssen Pharmaceuticals, LLC of Johnson and Johnson, Inc., Titusville, NJ, USA
| | - Ashish Khanna
- Physical Medicine and Rehabilitation, Jewish Medical Center, Brooklyn Hospital Center, Brooklyn, NY, USA
| | - Carlos A Zarate
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Maura L Furey
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA; Neuroscience Biomarkers Division, Janssen Research and Development, San Diego, CA, USA
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30
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Pathak Y, Salami O, Baillet S, Li Z, Butson CR. Longitudinal Changes in Depressive Circuitry in Response to Neuromodulation Therapy. Front Neural Circuits 2016; 10:50. [PMID: 27524960 PMCID: PMC4965463 DOI: 10.3389/fncir.2016.00050] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Accepted: 06/29/2016] [Indexed: 12/22/2022] Open
Abstract
Background: Major depressive disorder (MDD) is a public health problem worldwide. There is increasing interest in using non-invasive therapies such as repetitive transcranial magnetic stimulation (rTMS) to treat MDD. However, the changes induced by rTMS on neural circuits remain poorly characterized. The present study aims to test whether the brain regions previously targeted by deep brain stimulation (DBS) in the treatment of MDD respond to rTMS, and whether functional connectivity (FC) measures can predict clinical response. Methods: rTMS (20 sessions) was administered to five MDD patients at the left-dorsolateral prefrontal cortex (L-DLPFC) over 4 weeks. Magnetoencephalography (MEG) recordings and Montgomery-Asberg depression rating scale (MADRS) assessments were acquired before, during and after treatment. Our primary measures, obtained with MEG source imaging, were changes in power spectral density (PSD) and changes in FC as measured using coherence. Results: Of the five patients, four met the clinical response criterion (40% or greater decrease in MADRS) after 4 weeks of treatment. An increase in gamma power at the L-DLPFC was correlated with improvement in symptoms. We also found that increases in delta band connectivity between L-DLPFC/amygdala and L-DLPFC/pregenual anterior cingulate cortex (pACC), and decreases in gamma band connectivity between L-DLPFC/subgenual anterior cingulate cortex (sACC), were correlated with improvements in depressive symptoms. Conclusions: Our results suggest that non-invasive intervention techniques, such as rTMS, modulate the ongoing activity of depressive circuits targeted for DBS, and that MEG can capture these changes. Gamma oscillations may originate from GABA-mediated inhibition, which increases synchronization of large neuronal populations, possibly leading to increased long-range FC. We postulate that responses to rTMS could provide valuable insights into early evaluation of patient candidates for DBS surgery.
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Affiliation(s)
- Yagna Pathak
- Department of Biomedical Engineering, Marquette University Milwaukee, WI, USA
| | - Oludamilola Salami
- Department of Psychiatry, Medical College of Wisconsin Milwaukee, WI, USA
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal, QC, Canada
| | - Zhimin Li
- Department of Neurology, Medical College of Wisconsin Milwaukee, WI, USA
| | - Christopher R Butson
- Department of Biomedical Engineering, Marquette UniversityMilwaukee, WI, USA; Department of Psychiatry, Medical College of WisconsinMilwaukee, WI, USA; Department of Neurology, Medical College of WisconsinMilwaukee, WI, USA; Department of Bioengineering, Scientific Computing and Imaging (SCI) Institute, University of UtahSalt Lake City, UT, USA
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31
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Lin E, Tsai SJ. Genome-wide microarray analysis of gene expression profiling in major depression and antidepressant therapy. Prog Neuropsychopharmacol Biol Psychiatry 2016; 64:334-40. [PMID: 25708651 DOI: 10.1016/j.pnpbp.2015.02.008] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2015] [Revised: 02/13/2015] [Accepted: 02/15/2015] [Indexed: 12/21/2022]
Abstract
Major depressive disorder (MDD) is a serious health concern worldwide. Currently there are no predictive tests for the effectiveness of any particular antidepressant in an individual patient. Thus, doctors must prescribe antidepressants based on educated guesses. With the recent advent of scientific research, genome-wide gene expression microarray studies are widely utilized to analyze hundreds of thousands of biomarkers by high-throughput technologies. In addition to the candidate-gene approach, the genome-wide approach has recently been employed to investigate the determinants of MDD as well as antidepressant response to therapy. In this review, we mainly focused on gene expression studies with genome-wide approaches using RNA derived from peripheral blood cells. Furthermore, we reviewed their limitations and future directions with respect to the genome-wide gene expression profiling in MDD pathogenesis as well as in antidepressant therapy.
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Affiliation(s)
- Eugene Lin
- Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan; Vita Genomics, Inc., Taipei, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Division of Psychiatry, National Yang-Ming University, Taipei, Taiwan.
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Chi KF, Korgaonkar M, Grieve SM. Imaging predictors of remission to anti-depressant medications in major depressive disorder. J Affect Disord 2015; 186:134-44. [PMID: 26233324 DOI: 10.1016/j.jad.2015.07.002] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2015] [Revised: 06/17/2015] [Accepted: 07/03/2015] [Indexed: 12/20/2022]
Abstract
BACKGROUND We review what is currently known about neuroimaging predictors of remission in major depressive disorder (MDD) after antidepressant medication (ADM) treatment. METHODS A systematic literature search found a total of twenty-seven studies comparing baseline neuroimaging findings in depressed patients who achieved remission with non-remitters following treatment with ADMs. RESULTS Eighteen of these studies utilised structural magnetic resonance imaging (MRI). These studies associated larger hippocampal (four studies) and cingulate volume (two studies) with remission. Two diffusion MRI studies identified a positive relationship between the fractional anisotropy of the cingulum bundle and remission. White matter signal hyperintensities were quantified in two papers - both observing decreased remission rates with increasing lesion burden. Nine studies on functional imaging met inclusion criteria - three using functional MRI, one with single photon emission computed tomography (SPECT), and five which evaluated patients with positron emission tomography (PET). These findings were not convergent, with different regions of interest interrogated. LIMITATIONS The studies were generally underpowered. Overall these data were heterogeneous with only a small number identifying concordant findings. CONCLUSIONS At present, the data remains inconsistent. The more promising biomarker of remission to ADMs appears to be hippocampal size, although this marker also has conflicting reports. Given remission should be the primary end-point of treatment, and that ADMs are the front-line treatment type for MDD, more focussed research is required to focus specifically on the imaging correlates of remission to ADMs.
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Affiliation(s)
- Kee F Chi
- Department of Radiology, Royal Prince Alfred Hospital, Camperdown, Sydney, NSW 2006, Australia; Sydney Translational Imaging Laboratory, Charles Perkins Centre and Sydney Medical School, University of Sydney, NSW 2006, Australia
| | - Mayuresh Korgaonkar
- The Brain Dynamics Centre, Westmead Millennium Institute and Sydney Medical School, Sydney, NSW, Australia; Discipline of Psychiatry, Sydney Medical School, The University of Sydney, Westmead Hospital, Sydney, NSW, Australia
| | - Stuart M Grieve
- Department of Radiology, Royal Prince Alfred Hospital, Camperdown, Sydney, NSW 2006, Australia; Sydney Translational Imaging Laboratory, Charles Perkins Centre and Sydney Medical School, University of Sydney, NSW 2006, Australia; The Brain Dynamics Centre, Westmead Millennium Institute and Sydney Medical School, Sydney, NSW, Australia.
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Tian JS, Liu CC, Xiang H, Zheng XF, Peng GJ, Zhang X, Du GH, Qin XM. Investigation on the antidepressant effect of sea buckthorn seed oil through the GC-MS-based metabolomics approach coupled with multivariate analysis. Food Funct 2015; 6:3585-92. [PMID: 26328874 DOI: 10.1039/c5fo00695c] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Depression is one of the prevalent and serious mental disorders and the number of depressed patients has been on the rise globally during the recent decades. Sea buckthorn seed oil from traditional Chinese medicine (TCM) is edible and has been widely used for treatment of different diseases for a long time. However, there are few published reports on the antidepressant effect of sea buckthorn seed oil. With the objective of finding potential biomarkers of the therapeutic response of sea buckthorn seed oil in chronic unpredictable mild stress (CUMS) rats, urine metabolomics based on gas chromatography-mass spectrometry (GC-MS) coupled with multivariate analysis was applied. In this study, we discovered a higher level of pimelic acid as well as palmitic acid and a lower level of suberic acid, citrate, phthalic acid, cinnamic acid and Sumiki's acid in urine of rats exposed to CUMS procedures after sea buckthorn seed oil was administered. These changes of metabolites are involved in energy metabolism, fatty acid metabolism and other metabolic pathways as well as in the synthesis of neurotransmitters and it is helpful to facilitate the efficacy evaluation and mechanism elucidating the effect of sea buckthorn seed oil for depression management.
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Affiliation(s)
- Jun-sheng Tian
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan 030006, P. R. China.
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Hashimoto K. Inflammatory biomarkers as differential predictors of antidepressant response. Int J Mol Sci 2015; 16:7796-801. [PMID: 25856677 PMCID: PMC4425050 DOI: 10.3390/ijms16047796] [Citation(s) in RCA: 95] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2015] [Revised: 04/03/2015] [Accepted: 04/06/2015] [Indexed: 11/16/2022] Open
Abstract
Although antidepressants are generally effective in the treatment of major depressive disorder (MDD), it can still take weeks before patients feel the full antidepressant effects. Despite the efficacy of standard treatments, approximately two-thirds of patients with MDD fail to respond to pharmacotherapy. Therefore, the identification of blood biomarkers that can predict the treatment response to antidepressants would be highly useful in order to improve this situation. This article discusses inflammatory molecules as predictive biomarkers for antidepressant responses to several classes of antidepressants, including the N-methyl-d-aspartate (NMDA) receptor antagonist ketamine.
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Affiliation(s)
- Kenji Hashimoto
- Division of Clinical Neuroscience, Chiba University Center for Forensic Mental Health, 1-8-1 Inohana, Chiba 260-7680, Japan.
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Zheng X, Zhang X, Wang G, Hao H. Treat the brain and treat the periphery: toward a holistic approach to major depressive disorder. Drug Discov Today 2015; 20:562-8. [PMID: 25849660 DOI: 10.1016/j.drudis.2015.03.015] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Revised: 03/07/2015] [Accepted: 03/27/2015] [Indexed: 12/17/2022]
Abstract
The limited medication for major depressive disorder (MDD) against an ever-rising disease burden presents an urgent need for therapeutic innovations. During recent years, studies looking at the systems regulation of mental health and disease have shown a remarkably powerful control of MDD by systemic signals. Meanwhile, the identification of a host of targets outside the brain opens the way to treat MDD by targeting systemic signals. We examine these emerging findings and consider the implications for current thinking regarding MDD pathogenesis and treatment. We highlight the opportunities and challenges of a periphery-targeting strategy and propose its incorporation into a holistic approach.
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Affiliation(s)
- Xiao Zheng
- Department of Pharmacy, Nanjing University of Chinese Medicine Affiliated Hospital, Nanjing 210029, China.
| | - Xueli Zhang
- Department of Pharmacy, Southeast University Affiliated Zhong Da Hospital, Nanjing 210009, China
| | - Guangji Wang
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Haiping Hao
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China.
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Park HJ, Lim EJ, Zhao RJ, Oh SR, Jung JW, Ahn EM, Lee ES, Koo JS, Kim HY, Chang S, Shim HS, Kim KJ, Gwak YS, Yang CH. Effect of the fragrance inhalation of essential oil from Asarum heterotropoides on depression-like behaviors in mice. BMC COMPLEMENTARY AND ALTERNATIVE MEDICINE 2015; 15:43. [PMID: 25881143 PMCID: PMC4354743 DOI: 10.1186/s12906-015-0571-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2014] [Accepted: 02/21/2015] [Indexed: 12/16/2022]
Abstract
Background Psychological stressors may cause affective disorders, such as depression and anxiety, by altering expressions of corticotropin releasing factor (CRF), serotonin (5-HT), and tyrosine hydroxylase (TH) in the brain. This study investigated the effects of essential oil from Asarum heterotropoides (EOAH) on depression-like behaviors and brain expressions of CRF, 5-HT, and TH in mice challenged with stress. Methods Male ICR mice received fragrance inhalation of EOAH (0.25, 0.5, 1.0, and 2.0 g) for 3 h in the special cage capped with a filter paper before start of the forced swimming test (FST) and tail suspension test (TST). The duration of immobility was measured for the determination of depression-like behavior in the FST and TST. The selective serotonin reuptake inhibitor fluoxetine as positive control was administered at a dose of 15 mg/kg (i.p.) 30 min before start of behavioral testing. Immunoreactivities of CRF, 5-HT, and TH in the brain were also measured using separate groups of mice subjected to the FST. Results EOAH at higher doses (1.0 and 2.0 g) reduced immobility time in the FST and TST. In addition, EOAH at a dose of 1.0 g significantly reduced the expected increases in the expression of CRF positive neurons in the paraventricular nucleus and the expression of TH positive neurons in the locus coeruleus, and the expected decreases of the 5-HT positive neurons in the dorsal raphe nucleus. Conclusion These results provide strong evidence that EOAH effectively inhibits depression-like behavioral responses, brain CRF and TH expression increases, and brain 5-HT expression decreases in mice challenged with stress.
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Bares M, Novak T, Höschl C. Prediction of the Therapeutic Outcome. Eur Psychiatry 2015. [DOI: 10.1016/s0924-9338(15)30046-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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Abstract
Major depressive disorder (MDD) is characterized by mood, vegetative, cognitive, and even psychotic symptoms and signs that can cause substantial impairments in quality of life and functioning. Biomarkers are measurable indicators that could help diagnosing MDD or predicting treatment response. In this chapter, lipid profiles, immune/inflammation, and neurotrophic factor pathways that have long been implicated in the pathogenesis of MDD are discussed. Then, pharmacogenetics and epigenetics of serotonin transport and its metabolism pathway, brain-derived neurotrophic factor, and abnormality of hypothalamo-pituitary-adrenocortical axis also revealed new biomarkers. Lastly, new techniques, such as proteomics and metabolomics, which allow researchers to approach the studying of MDD with new directions and make new discoveries are addressed. In the future, more data are needed regarding pathophysiology of MDD, including protein levels, single nucleotide polymorphism, epigenetic regulation, and clinical data in order to better identify reliable and consistent biomarkers for diagnosis, treatment choice, and outcome prediction.
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Affiliation(s)
- Tiao-Lai Huang
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan.
| | - Chin-Chuen Lin
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
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Carvalho AF, Rocha DQC, McIntyre RS, Mesquita LM, Köhler CA, Hyphantis TN, Sales PMG, Machado-Vieira R, Berk M. Adipokines as emerging depression biomarkers: a systematic review and meta-analysis. J Psychiatr Res 2014; 59:28-37. [PMID: 25183029 DOI: 10.1016/j.jpsychires.2014.08.002] [Citation(s) in RCA: 91] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2014] [Revised: 08/06/2014] [Accepted: 08/07/2014] [Indexed: 12/15/2022]
Abstract
Adiponectin, leptin and resistin may play a role in the pathophysiology of major depressive disorder (MDD). However, differences in peripheral levels of these hormones are inconsistent across diagnostic and intervention studies. Therefore, we performed meta-analyses of diagnostic studies (i.e., MDD subjects versus healthy controls) and intervention investigations (i.e., pre-vs. post-antidepressant treatment) in MDD. Adiponectin (N = 1278; Hedge's g = -0.35; P = 0.16) and leptin (N = 893; Hedge's g = -0.018; P = 0.93) did not differ across diagnostic studies. Meta-regression analyses revealed that gender and depression severity explained the heterogeneity observed in adiponectin diagnostic studies, while BMI and the difference in BMI between MDD individuals and controls explained the heterogeneity of leptin diagnostic studies. Subgroup analyses revealed that adiponectin peripheral levels were significantly lower in MDD participants compared to controls when assayed with RIA, but not ELISA. Leptin levels were significantly higher in individuals with mild/moderate depression versus controls. Resistin serum levels were lower in MDD individuals compared to healthy controls (N = 298; Hedge's g = -0.25; P = 0.03). Leptin serum levels did not change after antidepressant treatment. However, heterogeneity was significant and sample size was low (N = 108); consequently meta-regression analysis could not be performed. Intervention meta-analyses could not be performed for adiponectin and resistin (i.e., few studies met inclusion criteria). In conclusion, this systematic review and meta-analysis underscored that relevant moderators/confounders (e.g., BMI, depression severity and type of assay) should be controlled for when considering the role of leptin and adiponectin as putative MDD diagnostic biomarkers.
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Affiliation(s)
- André F Carvalho
- Translational Psychiatry Research Group, Faculty of Medicine, Federal University of Ceara, Fortaleza, CE, Brazil.
| | - Davi Q C Rocha
- Translational Psychiatry Research Group, Faculty of Medicine, Federal University of Ceara, Fortaleza, CE, Brazil
| | - Roger S McIntyre
- Departments of Pharmacology and Psychiatry, University of Toronto, Toronto, ON, Canada; Mood Disorders Psychopharmacology Unit, University of Toronto, Toronto, ON, Canada
| | - Lucas M Mesquita
- Translational Psychiatry Research Group, Faculty of Medicine, Federal University of Ceara, Fortaleza, CE, Brazil
| | - Cristiano A Köhler
- Memory Research Laboratory, Brain Institute (ICe), Federal University of Rio Grande do Norte (UFRN), Natal, RN, Brazil
| | - Thomas N Hyphantis
- Department of Psychiatry, Medical School, University of Ioaninna, Ioaninna, Greece
| | - Paulo M G Sales
- Translational Psychiatry Research Group, Faculty of Medicine, Federal University of Ceara, Fortaleza, CE, Brazil
| | - Rodrigo Machado-Vieira
- National Institute of Mental Health (NIMH), Bethesda, USA; Laboratory of Neuroscience, LIM-27, Department and Institute of Psychiatry, University of São Paulo, USP, Brazil; Center for Interdisciplinary Research in Applied Neuroscience (NAPNA), USP, Brazil
| | - Michael Berk
- IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, Vic., Australia; Florey Institute of Neuroscience and Mental Health, Australia; Orygen Youth Health Research Centre, University of Melbourne, Parkville, Vic., Australia
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