Relevant bibliographies by topics / Major depressive disorder

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Major depressive disorder'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 March 2023

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Journal articles on the topic "Major depressive disorder"

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Hirschfeld, Robert M. A. "Major Depression, Dysthymia and Depressive Personality Disorder." British Journal of Psychiatry 165, S26 (December 1994): 23–30. http://dx.doi.org/10.1192/s0007125000293252.

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The separation of persistent depression into meaningful and useful subcategories, including major depression, dysthymia, recurrent brief depression, and depressive personality disorder, is the subject of much debate. Depressions can be grouped on the basis of their type and severity of symptoms, aetiology, clinical course, or their association with other psychiatric illnesses. Several investigators have conducted epidemiologic and family studies to evaluate the prevalence of depressive disorders, their diagnostic stability over time, and the amount of overlap among the disorders. Although progress has been made toward a better understanding of the different disorders, insufficient evidence exists to support the hypothesis that these disorders are separate and distinct from one another. However, preliminary data suggest that depressive personality disorder is separate from the other disorders. Additionally, several questions have been raised, particularly the extent to which differentiation between the depressive disorders, specifically major depression and dysthymia, has an impact on treatment decisions.
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Grobler, G. "Major Depressive Disorder." South African Journal of Psychiatry 19, no. 3 (August 30, 2013): 7. http://dx.doi.org/10.4102/sajpsychiatry.v19i3.946.

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The treatment guideline draws on several international guidelines: (<div style="left: 477.479px; top: 324.52px; font-size: 15.45px; font-family: serif;">i</div><div style="left: 481.424px; top: 324.72px; font-size: 15.45px; font-family: serif; transform: scaleX(0.955);" data-canvas-width="8.595">)</div><div style="left: 70.8662px; top: 344.72px; font-size: 15.45px; font-family: serif; transform: scaleX(1.00294);" data-canvas-width="422.63399999999984">Practice Guidelines of the American Psychiatric Association (APA)</div><div style="left: 70.8662px; top: 364.72px; font-size: 15.45px; font-family: serif; transform: scaleX(0.962449);" data-canvas-width="420.28950000000003">for the Treatment of Patients with Major Depressive Disorder, Second</div><div style="left: 70.8662px; top: 384.72px; font-size: 15.45px; font-family: serif; transform: scaleX(0.945309);" data-canvas-width="47.068499999999986">Edition;</div><div style="left: 117.785px; top: 385.947px; font-size: 9.00733px; font-family: serif; 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Belmaker, R. H., and Galila Agam. "Major Depressive Disorder." New England Journal of Medicine 358, no. 1 (January 3, 2008): 55–68. http://dx.doi.org/10.1056/nejmra073096.

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Giannelli, Frank R. "Major depressive disorder." Journal of the American Academy of Physician Assistants 33, no. 4 (April 2020): 19–20. http://dx.doi.org/10.1097/01.jaa.0000657208.70820.ab.

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Lai, Chien-Han. "Major Depressive Disorder." Journal of Clinical Psychopharmacology 31, no. 1 (February 2011): 39–44. http://dx.doi.org/10.1097/jcp.0b013e318205a670.

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Kinyanda, Eugene, Jonathan Levin, Noeline Nakasujja, Harriet Birabwa, Juliet Nakku, Richard Mpango, Heiner Grosskurth, et al. "Major Depressive Disorder." JAIDS Journal of Acquired Immune Deficiency Syndromes 78, no. 2 (June 2018): 136–43. http://dx.doi.org/10.1097/qai.0000000000001647.

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FRIEDLANDER, ARTHUR H., and MICHAEL E. MAHLER. "Major depressive disorder." Journal of the American Dental Association 132, no. 5 (May 2001): 629–38. http://dx.doi.org/10.14219/jada.archive.2001.0240.

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Fava, Maurizio, and Kenneth S. Kendler. "Major Depressive Disorder." Neuron 28, no. 2 (November 2000): 335–41. http://dx.doi.org/10.1016/s0896-6273(00)00112-4.

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Spaner, D., R. C. Bland, and S. C. Newman. "Major Depressive Disorder." Acta Psychiatrica Scandinavica 89, s376 (January 1994): 7–15. http://dx.doi.org/10.1111/j.1600-0447.1994.tb05786.x.

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Brent, David A. "Major depressive disorder." New Directions for Mental Health Services 1992, no. 54 (1992): 39–44. http://dx.doi.org/10.1002/yd.23319925409.

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More sources

Dissertations / Theses on the topic "Major depressive disorder"

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Quiring, Jason Matthew. "Early intervention and major depressive disorder /." view abstract or download file of text, 2002. http://wwwlib.umi.com/cr/uoregon/fullcit?p3055704.

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Thesis (Ph. D.)--University of Oregon, 2002.
Typescript. Includes vita and abstract. Includes bibliographical references (leaves 114-123). Also available for download via the World Wide Web; free to University of Oregon users.
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Sheppard, Leyland Curtis. "Processing of the depressive schema in major depressive disorder." Thesis, University of Cambridge, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621919.

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Mullins, Niamh Aine. "Dissecting the genetics of major depressive disorder." Thesis, King's College London (University of London), 2017. https://kclpure.kcl.ac.uk/portal/en/theses/dissecting-the-genetics-of-major-depressive-disorder(cc98809b-0603-4dd8-b88c-ea9c8a6afbbd).html.

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Major depressive disorder (MDD) is a leading cause of disability worldwide and a major contributor to early mortality from suicide. It is a common psychiatric illness with a wellestablished heritability. MDD is an extremely heterogeneous disorder in terms of symptoms, genetics and environmental risk factors. This thesis uses genetic and environmental data from case-control, clinical trial and population samples to dissect the heterogeneity of MDD. Gene-environment interactions were tested in the Radiant UK recurrent depression sample using polygenic risk scores (PRS), which reflect genetic liability for MDD based on many common variants. No interactions were found between PRS and adult adverse events. Interactions between PRS and childhood trauma showed an inverse association with MDD status, as cases who experienced more severe trauma tended to have a lower PRS. The current selection pressures on genetic variants associated with MDD and other psychiatric disorders were investigated in the Icelandic population by testing whether PRS are associated with number of offspring. There was no evidence that risk alleles for depression are under selection, whereas higher PRS for autism were associated with fewer children and higher PRS for ADHD were associated with having more children. Genome-wide association studies on suicide attempt were conducted comparing 6,569 attempters versus 17,232 non-attempters from MDD, bipolar disorder and schizophrenia cases in the Psychiatric Genomics Consortium. This identified three genome-wide significant loci for suicide attempt in mood disorders, which will be replicated in independent samples. Finally, blood mRNA levels of SAT1, PTEN, MAP3K3 and MARCKS have been reported as biomarkers for suicidality and here, an independent test in the Genome-Based Therapeutic Drugs for Depression (GENDEP) study did not support the validity of the proposed biomarkers. The availability of phenotypic, genetic and environmental data provides abundant opportunities to leverage the heterogeneity of depression to better understand its complex aetiology.
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Williamson, Sarah Elisabeth. "STin2 RISK GENOTYPES FOR MAJOR DEPRESSIVE DISORDER." Thesis, The University of Arizona, 2009. http://hdl.handle.net/10150/197262.

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Hung, Chi-Fa. "Medical diseases and obesity in major depressive disorder." Thesis, King's College London (University of London), 2015. https://kclpure.kcl.ac.uk/portal/en/theses/medical-diseases-and-obesity-in-major-depressive-disorder(07dc80a9-c3cf-448a-83e0-ad2644640a4d).html.

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The association between major depressive disorder (MDD) and various physical diseases is well recognized. However, previous studies have mainly focused on depression in physically ill individuals. My thesis aims to investigate the reverse direction, i.e. the occurrence of physical diseases including obesity in individuals with MDD. First, I found evidence for higher prevalence rates for eight out of the 16 common physical diseases studied here in depressed people compared to controls with no history of MDD. Affected and unaffected siblings of the depressed subjects showed a similar pattern. The diseases that were significantly more common in depressed subjects were hypertension, hypercholesterolemia, myocardial infarction, asthma, allergic rhinitis/ hay fever, gastric ulcer, osteoarthritis and thyroid disease. In addition, factor and correlational analyses showed that groups of physical diseases tended to cluster together in families where one or more individuals suffered from depression. The most striking of these was a ‘metabolic syndrome’ cluster, which was associated with high body mass index (BMI). Second, I explored personality factors as possible mediators of the link between MDD and physical diseases. Neuroticism, as measured by the Eysenck Personality Questionnaire, was associated with an increased number of self-reported physical diseases in individuals with MDD. Among individual diseases, only asthma had a modest but significant association with MDD. However, we did not find a familial correlation between neuroticism and asthma, suggesting that the phenotypic association between neuroticism and asthma is unlikely to have a genetic component. Third, a new statistical tool was used to estimate the proportion of phenotypic variance of complex diseases/ traits that can be explained by common tag single nucleotide polymorphisms (SNPs), termed “SNP heritability”. Analysis indicated that a substantial proportion of phenotype variance of both BMI and MDD was explained by common genetic variants. There was also suggestive evidence for a substantial genetic correlation between BMI and MDD, although the estimates had large standard errors. This imprecision almost certainly reflects the fact that, although the samples used here were large, even greater sample sizes are required for analyses of SNP heritability. Fourth, I used 32 SNPs identified from a published meta-analysis of genome-wide association studies (GWAS) on BMI to construct both weighted and un-weighted genetic risk scores (GRS) for BMI. Perhaps surprisingly, only 1.27% of the variance of BMI score was explained by the GRSs derived from these SNPs. Subsequent analyses showed that neither GRS alone, nor GRS combined with ‘traditional’ risk factors, can provide, in our present state of knowledge, a useful tool to discriminate the presence or absence of obesity in depressed people. Fifth, Mendelian randomization (MR) was used to attempt to disentangle the causal relationship between increased BMI and MDD. Although conventional regression analysis suggested a strong association between increased BMI and MDD, MR analysis failed to support the hypothesis that increased BMI is a causal factor in the development of MDD. Finally, I further explored the association between MDD and physical diseases by reviewing published genome wide association study (GWAS) data on MDD to examine whether identified risk loci for MDD overlapped with loci implicated in physical diseases. I then analysed our own GWAS data on depression to examine the presence of case/control differences at loci where there had been physical disease ‘hits’ in published studies. Analyses indicated that the SNP rs1342326, near the IL33 gene, which was genome-wide significant in a GWAS on asthma, was over-represented in individuals with MDD. However, MR analysis did not support a causal relationship between suffering from asthma and having MDD. These results could suggest a single point of genetic overlap between asthma and MDD that might contribute to the observed phenotypic overlap between the two disorders, and highlight the need for further studies in larger samples. In summary, the analyses presented in this thesis show that the relationship between MDD and physical diseases/ obesity is complicated, but suggests that genetic factors play a role in the overlap between depression and BMI and, by implication, diseases associated with high BMI. Only one non-BMI associated disease, asthma, has an identifiable polymorphism that is also associated with depression and withstands correction for multiple testing. Further larger scale studies searching for disease associated variants are vital in order to understand the pathogenesis of the co-occurrence between MDD and physical diseases/ obesity. These will include genome-wide and, eventually, deep sequencing studies of very large cohorts such as the UK Biobank.
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Withall, Adrienne Lee. "Cognitive function and recovery in major depressive disorder." Thesis, The University of Sydney, 2006. https://hdl.handle.net/2123/28184.

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The term ‘depression’ is used to refer to conditions ranging from non-clinical low mood to severe clinical conditions often resulting in hospitalization (American Psychiatric Association, 1994). Most people will experience low mood at some time during their lives however Major Depressive Disorder (MDD) is much more severe and very common, being experienced by approximately one in five persons. MDD is also associated with significantly impaired daily function (Andrews, Henderson, & Hall, 2001; Bland, 1997). Depression is also a chronic illness that is predicted to become the second leading cause of world-wide disability by the year 2020, second only to ischaemic heart disease (Lewis & Araya, 2001).
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Smolkina, Milana. "Epidemiological and genetic associations between Cannabis Use Disorder and Major Depressive Disorder." Thesis, King's College London (University of London), 2019. https://kclpure.kcl.ac.uk/portal/en/theses/epidemiological-and-genetic-associations-between-cannabis-use-disorder-and-major-depressive-disorder(aae240ea-e4b3-4c30-8fc0-fba14831b3a1).html.

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Background: Cannabis is the most commonly used illicit drug in the United Kingdom and worldwide. It is associated with a number of negative outcomes, which includes developing Cannabis Use Disorder (CUD). Individuals who meet criteria for CUD are at heightened risk for experiencing Major Depressive Disorder (MDD), the leading cause of disability worldwide. While this association has frequently been reported, the underlying mechanisms remain controversial. Aims of thesis: This thesis aims to investigate the degree of co-morbidity between lifetime rates of CUD and MDD, test whether this co-morbidity is accounted for by shared covariates, and test different twin models to investigate the sources (environmental or genetic) of and mechanisms underlying this co-morbidity. Methods: Data analysis was conducted on a sample of 3824 Australian twins and their non-twin siblings. Epidemiological analyses, using multivariable logistic regressions, tested whether CUD and MDD were significantly co-morbid in this sample, and to what extent covariates influenced this relationship. Twin models – bivariate correlated liabilities, discordant twin and co-morbidity models – examined whether the co-morbidity between the disorders could be explained by a) shared genetic and environmental factors, b) causal processes, and c) 13 different models of co-morbidity. Results: The epidemiological analyses found that MDD and CUD were significantly co-morbid in this sample: meeting diagnostic criteria for one disorder more than doubled the odds of meeting criteria for the other (odds ratio = 2.23, 95% confidence interval = 1.84–2.70). This co-morbidity could not be fully attributed to various psychiatric, trauma-related, parental, peer and demographic covariates. Bivariate twin analyses found that – when separated into genetic and environmental correlations – the only significant correlation between MDD and CUD was genetic (r =.41, 95% confidence interval = .24–.60). A possible causal relationship could not be excluded, because MDD and CUD were significantly associated (odds ratio = 2.83, 95% confidence interval = 1.12–7.19) in monozygotic twins discordant for both disorders. Co-morbidity model analyses indicated that the direction of influence was from CUD to MDD, and that CUD risk factors may cause MDD symptoms, particularly in individuals at high risk of CUD.
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Farmer, Caroline. "Understanding poor help-seeking rates for major depressive disorder." Thesis, University of Exeter, 2013. http://hdl.handle.net/10871/14620.

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The principal aim of this thesis was to further understanding of the factors influencing decisions to seek treatment for Major Depressive Disorder. A review of the literature (Chapter 2) revealed a number of factors associated with help-seeking for mental health disorders. However, there was no existing theoretical model of decisions to seek treatment for MDD, and there was limited understanding of the psychological and emotional processes involved in seeking treatment for depressive symptoms. In Study 1 (Chapter 3), a qualitative study explored participants’ accounts of seeking treatment for MDD, which resulted in the development of a theoretical model of help-seeking. This model highlighted the role of identity and goals in decisions to seek treatment for MDD. Subsequent studies in this thesis sought to test predictions made by this model. Study 2 (Chapter 4) explored the relationship between identity and goal conflict and depressed individuals’ acknowledgement of depressive symptoms and perceived need for treatment. Findings of this study suggested that greater identity conflict, but not goal, conflict was associated with reduced acknowledgement of depressive symptoms and less perceived need for treatment. Study 3 (Chapter 5) sought to replicate this relationship, and also tested the extent to which identity as a depressed person and socio-cognitive models of health behaviour could predict (i) intentions to seek treatment for depression and (ii) current treatment seeking. This study found that identity conflict was associated with reduced acknowledgement of depressive symptoms, but failed to replicate the relationship between identity conflict and perceived need for treatment for depression. However, greater identity as a depressed person was associated with both current treatment seeking and greater intentions to seek help. Analyses demonstrated that the Theory of Planned Behaviour and the Health Belief Model predicted current help-seeking and help-seeking intentions, but identity as a depressed person explained an additional significant proportion of the variance. A unified model, drawing on these two theories and incorporating identity was found to be the best fit in accounting for intentions to seek help for depression. The findings of Study 4 (Chapter 6) demonstrated that identity as a depressed person was also a significant predictor of prospective help-seeking behaviour for MDD. In this study, the majority of factors from the Theory of Planned Behaviour and Health Belief Model, extended to include identity, predicted help-seeking behaviour indirectly via intentions to seek help. However, intentions to seek help only predicted a small proportion of the variance in help-seeking behaviour, and the findings revealed that a subsample of factors, including identity, directly predicted help-seeking behaviour. The final study sought to use an online focus group to develop a measure of symptom avoidance in depressed individuals. This study faced methodological difficulties, and Chapter 7 reflects on the use of online focus groups to explore patient experiences of illness. The findings of this study highlighted participant experiences of using an online focus group method to discuss personal experiences of MDD, and this chapter provides specific guidance for other researchers planning to use this method in the context of health research. The implications of the findings of this thesis are discussed in Chapter 8, alongside recommendations for future help-seeking research.
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Zeng, Yanni. "The identification of risk factors for major depressive disorder." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28702.

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For complex traits, population genetic studies ask: to what extent do genetic variation and environmental variation influence, determine and predict phenotypic variation? More specifically, researchers ask two questions. First, how much of the phenotypic variation is genetic in origin? Second, if the genetic component of a trait has been ascertained, then by what mechanisms do the causal variants contribute to the genetic variation that impacts on the phenotype? Previous studies have indicated a polygenic structure for many complex traits, which means that the genetic variation in those traits is the result of the cumulative effect from hundreds or even thousands of genetic variants. To further decipher the polygenic genetic architecture of a complex trait, genetic studies aim to identify the number, the location in the genome, and the distribution of the effect sizes of causal variants, as well as their individual and interacting effects. Linkage analysis and genome-wide association studies (GWAS), either based on single variants or sets of variants categorized by functional annotations, can be applied to map the potentially causal variants in the genome. The identification of disease-associated loci, however, is only the starting point in identifying causal variants. Causal variants are usually difficult to distinguish from the large number of variants in linkage disequilibrium (LD) within the associated loci, and may be in incomplete LD with genotyped variants. Computational prediction integrated with multi-level ‘Omic’ data will help the prioritization of candidate causal variants, which then become important targets for experimental validation (Chapter 1). Major depressive disorder (MDD) is a complex trait, contributes the second most important burden to global disease. Both genetic and environmental components have been suggested for this disorder in previous studies, although a clear partitioning of the contribution of each component and the identification of major contributing components is yet to be achieved. In efforts to map causal genetic variants, genome-wide association studies of MDD have identified few significant associations so far. The polygenic architecture combined with the widespread clinical and genetic heterogeneity of MDD between populations may impede the identification of causal variants (Chapter 2). In this thesis, I will present three studies; the first study estimated the proportions of the phenotypic variation that are genetic or familial environmental in origin in two depression definitions(chapter 3), followed by two studies where distinct (non- GWAS) methods were used to identify candidate causal genetic variants for MDD (chapter 4,5). In detail, in chapter 3, a variance component analysis was applied to GS:SFHS (Generation Scotland: Scottish Family Health Study) to investigate the relative genetic and environmental contributions to diagnosed major depressive disorder (MDD) and self-declared depression (SDD). Models for MDD and SDD that simultaneously included genetic and environmental effects suggested that narrow-sense heritability could be inflated by the environments shared by nuclear family members. The most parsimonious models selected for both MDD and SDD included SNP and pedigree-associated genetic effects and the effect of the common environment of couples. In chapter 4, I integrated pathway analysis and multi-level regional heritability analyses in a pipeline designed to identify MDD-associated pathways. The pipeline was applied to two independent GWAS studies (GS:SFHS and PGC1-MDD). The NETRIN1 signalling pathway showed the most consistent association with MDD across the two samples. Polygenic risk scores (PRSs) from this pathway showed predictive accuracy better than whole-genome PRSs when using AUC statistics, logistic regression and the linear mixed model. In chapter 5, genome-wide Haplotype-block-based regional heritability mapping (HRHM) was applied to identify haplotype blocks significantly contributing to MDD. A haplotype block across a 24kb region within the TOX2 gene reached genotype-wide significance in GS:SFHS. Single-SNP and haplotype based association tests were used to localize the association signal within the region identified by HRHM, and demonstrated that five out of nine genotyped SNPs and two haplotypes were significantly associated with MDD. The results were replicated in the UK-Ireland group in PGC2-MDD. The brain expression of TOX2 and brain-specific LncRNA RP1-269M15.3 were also significantly regulated by MDD-associated SNPs within the identified haplotype block. The three studies highlight the value of the application of multiple population genetics and bioinformatics methods to multiple family-based and population-based cohorts in identification of risk factors for MDD.
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Lavanty, Brittany. "Describing Emotions: Major Depressive Disorder and Conceptual Metaphor Theory." Case Western Reserve University School of Graduate Studies / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1428942943.

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Books on the topic "Major depressive disorder"

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Kim, Yong-Ku, ed. Major Depressive Disorder. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6044-0.

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McIntyre, Roger S., and Danielle S. Cha, eds. Cognitive Impairment in Major Depressive Disorder. Cambridge: Cambridge University Press, 2016. http://dx.doi.org/10.1017/cbo9781139860567.

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Association, American Psychiatric. Practice guideline for major depressive disorder in adults. Washington: American Psychiatric Association, 1993.

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American Psychiatric Association. Practice guideline for major depressive disorder in adults. Washington, DC: The Association, 1993.

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Kim, Yong-Ku, and Meysam Amidfar, eds. Translational Research Methods for Major Depressive Disorder. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2083-0.

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Association, American Psychiatric, ed. Treatment works: Major depressive disorder : a patient and family guide. Washington, DC: American Psychiatric Association, 2000.

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Alexopoulos, George, Siegfried Kasper, Hans-Jürgen Möller, and Carmen Moreno. Guide to Assessment Scales in Major Depressive Disorder. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04627-3.

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Tolman, Anton O. Major depressive disorder: The latest assessment and treatment strategies. Kansas City, MO: Compact Clinicals, 1995.

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Understanding and managing the pieces of major depressive disorder. Carlsbard, Calif: NEI Press, 2009.

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American Psychiatric Association. Practice guideline for the treatment of patients with major depressive disorder. Washington, D.C: The Association, 2000.

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Book chapters on the topic "Major depressive disorder"

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MacKinnon, Dean F. "Depressive Disorders: Major Depressive Disorder and Persistent Depressive Disorder." In Psychiatry, 902–65. Chichester, UK: John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118753378.ch50.

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Khanfer, Riyad, John Ryan, Howard Aizenstein, Seema Mutti, David Busse, Ilona S. Yim, J. Rick Turner, et al. "Major Depressive Disorder." In Encyclopedia of Behavioral Medicine, 1187. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_101008.

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Hutchins, Tiffany, Giacomo Vivanti, Natasa Mateljevic, Roger J. Jou, Frederick Shic, Lauren Cornew, Timothy P. L. Roberts, et al. "Major Depressive Disorder." In Encyclopedia of Autism Spectrum Disorders, 1796. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_100844.

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Marazziti, Donatella, Grazia Rutigliano, Stefano Baroni, and Liliana Dell’Osso. "Major Depressive Disorder." In Metabolism of Human Diseases, 11–16. Vienna: Springer Vienna, 2014. http://dx.doi.org/10.1007/978-3-7091-0715-7_3.

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Hudak, Robert, and Jessica M. Gannon. "Major Depressive Disorder." In Handbook of Psychiatric Disorders in Adults in the Primary Care Setting, 1–25. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98709-1_1.

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Oette, Mark, Marvin J. Stone, Hendrik P. N. Scholl, Peter Charbel Issa, Monika Fleckenstein, Steffen Schmitz-Valckenberg, Frank G. Holz, et al. "Major Depressive Disorder." In Encyclopedia of Molecular Mechanisms of Disease, 1250. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-29676-8_9265.

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Rittberg, Barry R. "Major Depressive Disorder." In The Medical Basis of Psychiatry, 79–90. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2528-5_5.

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Evrensel, Alper, Barış Önen Ünsalver, Mehmet Emin Ceylan, and Nevzat Tarhan. "Vaccination and Immunotherapy for Major Depression." In Major Depressive Disorder, 503–13. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6044-0_25.

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Lee, Sang-Hyuk, and Sung Joon Cho. "Cognitive Behavioral Therapy and Mindfulness-Based Cognitive Therapy for Depressive Disorders." In Major Depressive Disorder, 295–310. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6044-0_16.

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Islam, Farhana, Ilona Gorbovskaya, and Daniel J. Müller. "Pharmacogenetic/Pharmacogenomic Tests for Treatment Prediction in Depression." In Major Depressive Disorder, 231–55. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6044-0_13.

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Conference papers on the topic "Major depressive disorder"

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Fu, Kexin. "Binge Eating Disorder and Major Depressive Disorder." In 2021 2nd International Conference on Mental Health and Humanities Education（ICMHHE 2021）. Paris, France: Atlantis Press, 2021. http://dx.doi.org/10.2991/assehr.k.210617.060.

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Al-Naggar, Ahmed N., Saeed H. Bamashmos, Mohamad Wadaane, Mohamad Abou Ali, and Lara Hamawy. "Major depressive disorder early detection." In 2019 First International Conference of Intelligent Computing and Engineering (ICOICE). IEEE, 2019. http://dx.doi.org/10.1109/icoice48418.2019.9035159.

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Bandopadhyay, Saikat, Srijan Nag, Sujay Saha, and Anupam Ghosh. "Identification of Major Depressive Disorder." In ISMSI '20: 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3396474.3396480.

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Mumtaz, Wajid, Aamir Saeed Malik, Syed Saad Azhar Ali, Mohd Azhar Mohd Yasin, and Hafeezullah Amin. "Detrended fluctuation analysis for major depressive disorder." In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2015. http://dx.doi.org/10.1109/embc.2015.7319311.

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Bhagawati, Mrinalini, and Sudip Paul. "Machine Learning for Major Depressive Disorder: An Overview." In 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2021. http://dx.doi.org/10.1109/icrito51393.2021.9596323.

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Livne, Ofir, Deborah Hasin, and Silvia Martins. "Probability and Predictors of Cannabis Use Disorder Among Cannabis Users with Depressive Disorders." In 2021 Virtual Scientific Meeting of the Research Society on Marijuana. Research Society on Marijuana, 2022. http://dx.doi.org/10.26828/cannabis.2022.01.000.44.

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Introduction and Aims: Cannabis use and cannabis use disorders (CUD) have been increasing in the US. Recent studies have aimed to assess the rates of transition from cannabis use to CUD over time across several sociodemographic corelates (e.g., age and sex). Depressive disorders are a strong clinical correlate of cannabis use, and carry a substantial burden of disease. The underlying mechanisms involved in the relationship between depression and cannabis use are still not fully understood. While certain studies have examined changes in rates of cannabis among depressed and non-depressed individuals over time, no studies have quantified the effect of depressive disorders on cannabis users’ transition rates to CUD. Methods: Participants were individuals ≥18 years interviewed in the National Epidemiologic Survey on Alcohol and Related Conditions-III in 2012–2013. Survival plots assessed the probability of transition from cannabis use to CUD over time. Differences in probability of transition to CUD was assessed among cannabis users with and without predisposing depressive disorders ( major depressive disorder or dysthymia with an initial diagnosis prior to onset of cannabis use). Results: Among lifetime cannabis users (N = 11,272), the 5-year probability of transition to CUD was approximately 3.9% for cannabis users without depressive disorders and 7.3% for those with a depressive disorder. A higher probability of transition from cannabis use to CUD among those with a predisposing depressive disorder was observed over all time points that were examined in the study. Cannabis users with depressive disorders who were male and belonging to an early-onset of cannabis use age group (<16) transitioned significantly more rapidly to CUD than females and those with a later- onset of cannabis use Conclusions: This is the first study to explore the effect of depressive disorders on rates of transition from cannabis use to the DSM-5 CUD diagnosis. The current study identified specific predictors of this transition. Findings inform clinicians who treat individuals with depressive disorders that initiate cannabis use as to the risk of developing CUD and the need for harm prevention targeted at this specific population.
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Sen, Bhaskar, Bryon Mueller, Bonnie Klimes-Dougan, Kathryn Cullen, and Keshab K. Parhi. "Classification of Major Depressive Disorder from Resting-State fMRI." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8856453.

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Mumtaz, Wajid, Aamir Saeed Malik, Syed Saad Azhar Ali, and Mohd Azhar Mohd Yasin. "P300 intensities and latencies for major depressive disorder detection." In 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). IEEE, 2015. http://dx.doi.org/10.1109/icsipa.2015.7412250.

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Campbell, Edward L., Laura Docío Fernández, Nicholas Cummins, and Carmen García Mateo. "Speech and Text Processing for Major Depressive Disorder Detection." In IberSPEECH 2022. ISCA: ISCA, 2022. http://dx.doi.org/10.21437/iberspeech.2022-18.

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Yaqi, Huang. "Precision Medicine: Neuropsychodynamic Psychiatry Model of Major Depressive Disorder." In 2021 4th International Conference on Humanities Education and Social Sciences (ICHESS 2021). Paris, France: Atlantis Press, 2022. http://dx.doi.org/10.2991/assehr.k.211220.268.

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Reports on the topic "Major depressive disorder"

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Chen, Hongguang, Chuchu Fang, Xin Zhang, Qin Zou, Xing Wang, and Mengqian Li. Hormone about the major depressive disorder: a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2022. http://dx.doi.org/10.37766/inplasy2022.3.0156.

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Xu, Guixing, Qiwei Xiao, Biqing Huang, Hanzhou Lei, Zihan Yin, Liuyang Huang, Zhuo Zhou, et al. Acupuncture For Major Depressive Disorder: A systematic review and meta-analysis with trials sequential analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2021. http://dx.doi.org/10.37766/inplasy2021.12.0025.

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Zhan, Yuji, Mengxin Rui, Wenfeng Zeng, and Yunxia Wang. Efficacy and safety of escitalopram and agomelatine in the treatment of major depressive disorder-A meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2021. http://dx.doi.org/10.37766/inplasy2021.12.0074.

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Review question / Objective: The aim of this meta-analysis of randomized controlled trials is to evaluate the efficacy and safety of escitalopram and agomelatine in the major depressive disorder. Condition being studied: Major depressive disorder (MDD), is one of the most common, costly, and disabling mental health conditions worldwide, with an estimated 246 million sufferers globally in 2020.At present, there is a great demand for effective antidepressant treatment in medicine. Information sources: We will search, with no time restrictions, the following databases for relevant English language literature: PubMed, the Cochrane Central Register of Controlled Trials and Web of Science. The search string will be built as follows: (escitalopram) AND (agomelatine) AND (major depressed disorder).
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Irwin, Courtney L., Patrícia S. Coelho, Bruno Kluwe-Schiavon, Anabela Silva-Fernandes, Óscar F. Gonçalves, Jorge Leite, and Sandra Carvalho. Treatment-related changes of molecular biomarkers in major depressive disorder: a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, October 2021. http://dx.doi.org/10.37766/inplasy2021.10.0105.

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Review question / Objective: The aim of this review is two-fold: first, we sought to identify candidate biomarkers that could provide information on whether an individual with MDD would respond positively to common non-pharmacological treatments, and secondly, to conduct a meta-analysis to determine whether one form of common non-pharmacological treatment (namely CBT, tDCS and TMS) would produce better results over another in regards to its influence on biomarker levels. Information sources: The information sources used were: three online databases (PubMed, Scopus, and PsycINFO) to identify English-language human randomised controlled trials unrestricted by year of publication.
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Si, Tianmei, Yunai Su, Qin Xin, Chong Ye, Bin Wang, and Miaomiao Jia. Major depressive disorder with suicidal ideation or behaviour in Chinese population: protocol for a scoping review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2022. http://dx.doi.org/10.37766/inplasy2022.3.0173.

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Xu, Guixing, Qiwei Xiao, Biqing Huang, Hanzhou Lei, Zihan Yin, Liuyang Huang, Zhuo Zhou, et al. Evidence-based Acupuncture for Major Depressive Disorder: a Systematic Review and Meta-Analysis of high-quality RCTs. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, October 2021. http://dx.doi.org/10.37766/inplasy2021.10.0073.

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Cai, Hong, Pan Chen, and Yu-tao Xiang. Global prevalence of major depressive disorder in LGBT: a meta-analysis and systematic review of epidemiological surveys. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2022. http://dx.doi.org/10.37766/inplasy2022.3.0061.

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Zhou, Zhuo, Guixing Xu, Liuyang Huang, Hao Tian, Fengyuan Huang, Yilin Liu, Mingsheng Sun, and Fanrong Liang. Effectiveness and Safety of Electroacupuncture for Depression: a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, January 2022. http://dx.doi.org/10.37766/inplasy2022.1.0068.

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Review question / Objective: Is electroacupuncture a safe therapy for the treatment of depression? Is electroacupuncture effective for the treatment of depression, as compared with sham control, or conventional drugs？ Condition being studied: Depression is a mood disorder that causes sufferers to feel sadness, decreased interest, guilt, self-blame, loss of energy, and experience sleep disorders such as insomnia. People suffering from depression even feel they have no way out and have suicidal thoughts. In the United States, the prevalence of a major depressive disorder is 16.2%1-3. The 2010 Global Burden of Disease Study identified major depression as the second leading cause of disability worldwide and a leading cause of the burden of suicide and ischaemic heart disease. At present, depression patients are mainly treated with antidepressants, but the efficacy is extremely unstable. Studies have shown that acupuncture can help improve symptoms in patients with depression, but these clinical studies have not been systematically evaluated, and further confirmation is needed to confirm the efficacy of electroacupuncture in treating depression.
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Zhang, Jian, Xi Yang, Rongyi Sun, Yang Cai, and Keming Gao. Efficacy and Safety of Antidiabetic Agents for Major Depressive Disorder and Bipolar Depression: A meta-analysis of randomized, double-blind, placebo-controlled trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2020. http://dx.doi.org/10.37766/inplasy2020.9.0058.

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Cai, Hong, Xiao-meng Xie, Ling Zhang, Qinge Zhang, and Yutao Xiang. Prevalence of suicidal behaviors in patients with major depressive disorder: a meta-analysis and systematic review of comparative studies. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2021. http://dx.doi.org/10.37766/inplasy2021.2.0078.

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