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Mood Disorders

Mood Disorders represent a group of psychiatric conditions characterized by disturbances in emotional state.
These include major depressive disorder, persistent depressive disorder, bipolar disorder, and related conditions.
Mood Disorders can significantly impact an individual's quality of life, work, and relationships.
Symptoms may include persistent sadness, loss of interest, changes in sleep and appetite, and difficulty concentrating.
Effective treatments, such as psychotherapy and medications, are available to manage Mood Disorders and improve patient outcomes.
Reserch into the underlying causes, risk factors, and optimal treatment approaches continues to evolve, offering hope for those affected by these challenging mental health conditions.

Most cited protocols related to «Mood Disorders»

1
Genetic Architecture of Major Depression
Cited 325 times
Genotyping procedures can be found in the primary reports for each cohort (summarized in Supplementary Table 3). Individual genotype data for all PGC29 samples, GERA, and iPSYCH were processed using the PGC “ricopili” pipeline (URLs) for standardized quality control, imputation, and analysis19 (link). The cohorts from deCODE, Generation Scotland, UK Biobank, and 23andMeD were processed by the collaborating research teams using comparable procedures. SNPs and insertion-deletion polymorphisms were imputed using the 1000 Genomes Project multi-ancestry reference panel (URLs)86 (link). More detailed information on sample QC is provided in the Supplementary Note.
Linkage disequilibrium (LD) score regression (LDSC)22 (link),24 (link) was used to estimate
hSNP2 from GWA summary statistics. Estimates of
hSNP2 on the liability scale depend on the assumed lifetime prevalence of MDD in the population (K), and we assumed K=0.15 but also evaluated a range of estimates of K to explore sensitivity including 95% confidence intervals (Supplementary Fig. 1). LDSC bivariate genetic correlations attributable to genome-wide SNPs (rg) were estimated across all MDD and major depression cohorts and between the full meta-analyzed cohort and other traits and disorders.
LDSC was also used to partition
hSNP2 by genomic features24 (link),46 (link). We tested for enrichment of
hSNP2 based on genomic annotations partitioning
hSNP2 proportional to bp length represented by each annotation. We used the “baseline model” which consists of 53 functional categories. The categories are fully described elsewhere46 (link), and included conserved regions47 (link), USCC gene models (exons, introns, promoters, UTRs), and functional genomic annotations constructed using data from ENCODE 87 (link) and the Roadmap Epigenomics Consortium88 (link). We complemented these annotations by adding introgressed regions from the Neanderthal genome in European populations89 (link) and open chromatin regions from the brain dorsolateral prefrontal cortex. The open chromatin regions were obtained from an ATAC-seq experiment performed in 288 samples (N=135 controls, N=137 schizophrenia, N=10 bipolar, and N=6 affective disorder)90 . Peaks called with MACS91 (link) (1% FDR) were retained if their coordinates overlapped in at least two samples. The peaks were re-centered and set to a fixed width of 300bp using the diffbind R package92 (link). To prevent upward bias in heritability enrichment estimation, we added two categories created by expanding both the Neanderthal introgressed regions and open chromatin regions by 250bp on each side.
We used LDSC to estimate rg between major depression and a range of other disorders, diseases, and human traits22 (link). The intent of these comparisons was to evaluate the extent of shared common variant genetic architectures in order to suggest hypotheses about the fundamental genetic basis of major depression (given its extensive comorbidity with psychiatric and medical conditions and its association with anthropometric and other risk factors). Subject overlap of itself does not bias rg. These rg are mostly based on studies of independent subjects and the estimates should be unbiased by confounding of genetic and non-genetic effects (except if there is genotype by environment correlation). When GWA studies include overlapping samples, rg remains unbiased but the intercept of the LDSC regression is an estimate of the correlation between association statistics attributable to sample overlap. These calculations were done using the internal PGC GWA library and with LD-Hub (URLs)60 (link).
Wray N.R., Ripke S., Mattheisen M., Trzaskowski M., Byrne E.M., Abdellaoui A., Adams M.J., Agerbo E., Air T.M., Andlauer T.F., Bacanu S.A., Bækvad-Hansen M., Beekman A.T., Bigdeli T.B., Binder E.B., Blackwood D.H., Bryois J., Buttenschøn H.N., Bybjerg-Grauholm J., Cai N., Castelao E., Christensen J.H., Clarke T.K., Coleman J.R., Colodro-Conde L., Couvy-Duchesne B., Craddock N., Crawford G.E., Crowley C.A., Dashti H.S., Davies G., Deary I.J., Degenhardt F., Derks E.M., Direk N., Dolan C.V., Dunn E.C., Eley T.C., Eriksson N., Escott-Price V., Kiadeh F.F., Finucane H.K., Forstner A.J., Frank J., Gaspar H.A., Gill M., Giusti-Rodríguez P., Goes F.S., Gordon S.D., Grove J., Hall L.S., Hannon E., Hansen C.S., Hansen T.F., Herms S., Hickie I.B., Hoffmann P., Homuth G., Horn C., Hottenga J.J., Hougaard D.M., Hu M., Hyde C.L., Ising M., Jansen R., Jin F., Jorgenson E., Knowles J.A., Kohane I.S., Kraft J., Kretzschmar W.W., Krogh J., Kutalik Z., Lane J.M., Li Y., Li Y., Lind P.A., Liu X., Lu L., MacIntyre D.J., MacKinnon D.F., Maier R.M., Maier W., Marchini J., Mbarek H., McGrath P., McGuffin P., Medland S.E., Mehta D., Middeldorp C.M., Mihailov E., Milaneschi Y., Milani L., Mill J., Mondimore F.M., Montgomery G.W., Mostafavi S., Mullins N., Nauck M., Ng B., Nivard M.G., Nyholt D.R., O’Reilly P.F., Oskarsson H., Owen M.J., Painter J.N., Pedersen C.B., Pedersen M.G., Peterson R.E., Pettersson E., Peyrot W.J., Pistis G., Posthuma D., Purcell S.M., Quiroz J.A., Qvist P., Rice J.P., Riley B.P., Rivera M., Mirza S.S., Saxena R., Schoevers R., Schulte E.C., Shen L., Shi J., Shyn S.I., Sigurdsson E., Sinnamon G.C., Smit J.H., Smith D.J., Stefansson H., Steinberg S., Stockmeier C.A., Streit F., Strohmaier J., Tansey K.E., Teismann H., Teumer A., Thompson W., Thomson P.A., Thorgeirsson T.E., Tian C., Traylor M., Treutlein J., Trubetskoy V., Uitterlinden A.G., Umbricht D., Van der Auwera S., van Hemert A.M., Viktorin A., Visscher P.M., Wang Y., Webb B.T., Weinsheimer S.M., Wellmann J., Willemsen G., Witt S.H., Wu Y., Xi H.S., Yang J., Zhang F., Arolt V., Baune B.T., Berger K., Boomsma D.I., Cichon S., Dannlowski U., de Geus E., DePaulo J.R., Domenici E., Domschke K., Esko T., Grabe H.J., Hamilton S.P., Hayward C., Heath A.C., Hinds D.A., Kendler K.S., Kloiber S., Lewis G., Li Q.S., Lucae S., Madden P.A., Magnusson P.K., Martin N.G., McIntosh A.M., Metspalu A., Mors O., Mortensen P.B., Müller-Myhsok B., Nordentoft M., Nöthen M.M., O’Donovan M.C., Paciga S.A., Pedersen N.L., Penninx B.W., Perlis R.H., Porteous D.J., Potash J.B., Preisig M., Rietschel M., Schaefer C., Schulze T.G., Smoller J.W., Stefansson K., Tiemeier H., Uher R., Völzke H., Weissman M.M., Werge T., Winslow A.R., Lewis C.M., Levinson D.F., Breen G., Børglum A.D, & Sullivan P.F. (2018). Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nature genetics, 50(5), 668-681.
Publication 2018
ATAC-Seq Brain Chromatin DNA Library Dorsolateral Prefrontal Cortex Europeans Exons Genetic Diversity Genetic Polymorphism Genome Genome-Wide Association Study Genotype Genotyping Techniques Homo sapiens Hypersensitivity INDEL Mutation Introns Mood Disorders Neanderthals Reproduction Schizophrenia Single Nucleotide Polymorphism Unipolar Depression Untranslated Regions
2
Genome-wide association study of major depressive disorder
Cited 99 times
Full sample details are given in the Supplementary Methods. For the
discovery phase, we included all identified primary MDD samples21 (link)–25 (link),27 (link),28 (link),41 (link) that conducted genome-wide genotyping
(> 200K single-nucleotide polymorphisms (SNPs)) on individual subjects of
European ancestry. Cases were required to have diagnoses of DSM-IV lifetime MDD
established using structured diagnostic instruments from direct interviews by
trained interviewers (two studies required recurrent MDD and one recurrent,
early-onset MDD) or clinician-administered DSM-IV checklists. Most studies
ascertained cases from clinical sources, and most controls were randomly
selected from the population and screened for lifetime history of MDD. The
sample sizes reported here differ from the primary reports due to different
quality control procedures and apportioning of overlapping controls. We
determined the relatedness of all pairs of individuals using genotypes of SNPs
present on all platforms, and excluded one of each duplicate or closely related
pair. The discovery mega-analysis consists of 18 759 independent and unrelated
subjects of recent European ancestry (9240 MDD cases and 9519 controls).
There were two sets of analyses conducted on additional samples. For MDD
replication, we used meta-analysis to combine the autosomal discovery results
(554 SNPs with P< 0.001) with summary association results
from independent samples42 (link)–48 (link) (6783
MDD cases and 50 695 controls). The discovery SNP results were grouped into
regions defined by linkage disequilibrium using an iterative process after
ranking all SNPs by association P-value: for SNPs with
r2 > 0.2 in a 1Mb window (based on HapMap3
CEU+TSI), the most strongly associated SNP was retained. In addition,
given the close genetic and phenotypic relationships between MDD and BIP, we
combined the MDD discovery sample and the PGC BIP mega-analysis36 (link) to evaluate 819 autosomal SNPs
with P < 0.0001 in either of the separate analyses. (See
Sklar et al.36 (link) for complete description). In effect, we tested for
associations with a more broadly defined mood disorder phenotype. After
resolving overlapping control samples, there were 32 050 independent subjects
(9238 MDD cases/8039 controls and 6998 BIP cases/7775 controls).
A mega-analysis of genome-wide association studies for major depressive disorder. (2012). Molecular psychiatry, 18(4), 10.1038/mp.2012.21.
Publication 2012
Diagnosis Europeans Genome Genotype Interviewers Mood Disorders Phenotype
3
Diagnostic Assessment of DSM-5 Disorders
Cited 77 times

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Publication 2014
Agoraphobia Alcohol Use Disorder Anxiety Disorders Cannabis Central Nervous System Stimulants Club Drugs Cocaine Conduct Disorder Diagnosis Disorder, Depressive Drug Use Disorders Dysthymic Disorder Hallucinogens Heroin Inhalation Drug Administration Manic Episode Mood Disorders Opioids Panic Disorder Pharmaceutical Preparations Phobia, Social Phobia, Specific Post-Traumatic Stress Disorder Sedatives Solvents Tobacco Products Tobacco Use Disorder Tranquilizing Agents
4
Longitudinal Study of Childhood Bipolar Disorder
Cited 60 times
The methods for COBY have been described in detail elsewhere.4 (link), 7 (link) Briefly, youth ages 7 to 17 years 11 months with Diagnostic and Statistical Manual-IV (DSM-IV) bipolar-I, II, and operationally 4 (link), 7 (link) defined bipolar-NOS were included. Youth with COBY-defined bipolar-NOS were previously shown to convert to bipolar-I/II and have a comparable, but less severe clinical picture, and similar family history, rates of comorbid disorders, and longitudinal outcome as compared to bipolar-I subjects.4 (link), 7 (link)
Youth with schizophrenia, mental retardation, autism, and mood disorders secondary to substances, medications or medical conditions were excluded.
Subjects were recruited from outpatient clinics (67.6%), inpatient units (14.3%), advertisement (13.3%), and referrals from other physicians (4.8%), and were enrolled independent of current mood state or treatment status.
The analyses presented in this report are based on the prospective evaluation of 413 subjects, including 244 (59.1%) with bipolar-I, 28 (6.8%) with bipolar-II and 141 (34.1%) with bipolar-NOS who had at least one follow-up assessment. At the time this article was written, subjects had been prospectively interviewed every 37.5±20.8 weeks for an average of 191.5 ± 75.7 weeks. Subjects with bipolar-II were followed significantly longer (227.4±76.6 weeks) than the other two bipolar subtypes (bipolar-I: 183.2±71.8 weeks, bipolar-NOS: 198.7±79.8 weeks) (F=5.4, p=.005).
As described in more detail in a prior publication, 7 (link) at intake subjects with bipolar-NOS were the youngest, followed by subjects with bipolar-I and then those with bipolar-II (Table 1). More youth with bipolar-NOS were in Tanner stage-I of sexual development than those with bipolar-II and more subjects with bipolar-II were Tanner IV/V than those with bipolar-I and -II. The mean age-of-onset for mood symptoms and DSM-IV mood episodes was 8.4 and 9.3 years, respectively (for definition of age-of-onset see below). Subjects with bipolar-II had the onset of their mood symptoms and episodes significantly later than the other two bipolar subtypes. As expected, by definition, the polarity of the index episode reflected the bipolar subtype with mania or hypomania being more common in youth with bipolar-I than those with bipolar-II or NOS. However, youth with bipolar-II had significantly more depressive index episodes that the other two bipolar subtypes. Subjects with bipolar-I had more lifetime psychosis than those with bipolar-NOS (for all above comparisons p-values <.05, Cohen’s d: 0.3–0.9). There were no other significant between group differences.
The subject retention rate at the time this manuscript was written was 86%, with 93% of subjects completing at least one follow-up interview. Except for lower rates of anxiety disorders in subjects who dropped from the study (54.5%, vs. 38.7%, p=0.02) there were no other demographic or clinical differences between the subjects who continued or withdrew from COBY.
Birmaher B., Axelson D., Goldstein B., Strober M., Gill M.K., Hunt J., Houck P., Ha W., Iyengar S., Kim E., Yen S., Hower H., Esposito C., Goldstein T., Ryan N, & Keller M. (2009). Four-Year Longitudinal Course of Children and Adolescents with Bipolar Spectrum Disorder: The Course and Outcome of Bipolar Youth (COBY) Study. The American journal of psychiatry, 166(7), 795-804.
Publication 2009
Anxiety Disorders Autistic Disorder Diagnosis Inpatient Intellectual Disability Mania Mood Mood Disorders Pharmaceutical Preparations Physicians Psychotic Disorders Retention (Psychology) Schizophrenia Sexual Development Youth
5
Mood Disorder Spectrum Comparison in UK Biobank
Cited 53 times
To investigate the validity of our proposed definitions of probable major depression and probable bipolar disorder, we used available data on social, demographic, lifestyle, personality and clinical variables from the UK Biobank baseline assessment and compared across the mood disorder spectrum, from a ‘no mood disorder’ comparison group, through probable single episode major depression, probable recurrent major depression (moderate), probable recurrent major depression (severe) and probable bipolar disorder.
The variables included in these analyses were: gender; ethnicity; socioeconomic status (neighbourhood-level, assessed using the Townsend deprivation score where a negative score represents greater affluence [11] ); educational level (age left education and whether achieved an undergraduate degree); self-reported inability to work due to sickness; self-assessment of having a long-term illness; self-assessment of overall health rating; current depressive symptoms (defined as ‘depressed mood’ and/or ‘unenthusiasm/uninterest’ (anhedonia) for at least ‘nearly every day in past 2 weeks’); neuroticism score; smoking status and alcohol use.
All descriptive analyses were carried out using the statistical software Stata [12] and SPSS [13] . In order to examine the association between probable mood disorder category and demographic and clinical variables, tests of association were used which accounted for trend across the categories. An extension of the Kruskal-Wallis test allowed for the comparison of both continuous and categorical variables across an ordered categorical variable (nptrend command in Stata) [14] (link).
This study was conducted under generic approval from the NHS National Research Ethics Service (approval letter dated 17th June 2011, Ref 11/NW/0382).
Smith D.J., Nicholl B.I., Cullen B., Martin D., Ul-Haq Z., Evans J., Gill J.M., Roberts B., Gallacher J., Mackay D., Hotopf M., Deary I., Craddock N, & Pell J.P. (2013). Prevalence and Characteristics of Probable Major Depression and Bipolar Disorder within UK Biobank: Cross-Sectional Study of 172,751 Participants. PLoS ONE, 8(11), e75362.
Publication 2013
Anhedonia Bipolar Disorder Depressive Symptoms Ethnicity Gender Generic Drugs Major Depressive Disorder Mood Mood Disorders Neuroticism Self-Assessment

Most recents protocols related to «Mood Disorders»

1
Antidepressant Effects of Saccharomyces boulardii
Not available on PMC !

Example 2

The antidepressant effects of the yeast Saccharomyces boulardii are evaluated by chronic administration to adult male CD1 mice in the forced swimming test.

The forced-swimming test, well known to the skilled person, is used to measure the antidepressant effects of a pharmacological compound. This test is based on the work of Porsolt et al. (1977) Act. Int. Pharmacodyn. Ther. 229:327-336 and has since been classically used to predict the clinical efficacy of antidepressant compounds.

Briefly, this test takes place in a cylindrical container filled with water (water height 10 cm) at 23° C. The mouse is placed in this container for 6 minutes, and the duration of immobility of the animal is measured for the last 4 minutes.

The antidepressant compounds administered prior to this test significantly reduce the immobility time of the animals.

US11872257B2. Saccharomyces boulardii for the treatment of mood disorders (2024-01-16). BIOCODEX [FR]. Inventors: Emmanuelle Simon O'Brien [FR], Marc Verleye [FR], Marie-Emmanuelle Le Guern [FR].
Patent 2024
Adult Animals Antidepressive Agents Males Mice, House Mood Disorders Saccharomyces boulardii Yeast, Dried
2
Comorbidity of Substance Use Disorders and Psychiatric Diagnoses
The SUD diagnoses and psychiatric diagnoses according to ICD-10 criteria, were made by a medical specialist or clinical psychologist using standardized clinical interviews and tools.
For the purpose of the current study, information on the dependence-level SUD diagnosis and any co-occurring psychiatric diagnosis was obtained from the medical record. The following binary SUD diagnosis (1 = presence, 0 = absence) were included in analyses: Alcohol use disorder (F10); Opioid use disorder (F11); Cannabis use disorder (F12); Sedatives use disorder (F13); Stimulant use disorder (F15). The psychiatric diagnoses were grouped into the following binary variables (1 = presence, 0 = absence): Mood disorders (F30-F39); Anxiety disorders (F40-F49); Personality disorders (F60-F69); ADHD (F90-F90.0), and other psychiatric diagnoses.
Andersson H.W., Mosti M.P, & Nordfjaern T. (2023). Inpatients in substance use treatment with co-occurring psychiatric disorders: a prospective cohort study of characteristics and relapse predictors. BMC Psychiatry, 23, 152.
Publication 2023
Alcohol Use Disorder Anxiety Disorders Cannabis Diagnosis Diagnosis, Psychiatric Disorder, Attention Deficit-Hyperactivity Mood Disorders Opioid Use Disorder Personality Disorders Psychologist Sedatives
3
Comorbid Psychiatric Disorders in Patients
The proportion of patients with COD was not significantly different for male (45.4%) and female (52.0%) patients (p = 0.139). The prevalence rates for the types of co-occurring psychiatric diagnoses are shown in Table 2. Among patients with COD, anxiety (22.9%) and mood disorders (17.3%) were the two most common psychiatric disorders. About one in five had more than one COD (21.4%), with a higher prevalence rate of multiple CODs among females (30.3%) than males (18.0%). Having anxiety disorders were significantly more prevalent among females (30.3%) than males (19.8%).

Prevalence of co-occurring psychiatric disorders

Total(N = 611)Females(N = 175)Males(N = 434)Females versus malesref
N%N%N%ORp-value
Without COD32252.78448.023754.6
With COD28947.39152.019745.41.300.139
Psychiatric diagnoses1
- Anxiety disorders F40-4914022.95330.38619.81.760.005
- Mood disorders F30-3910617.33620.67016.11.350.191
- ADHD F90.0-F90.97912.92112.05813.40.880.650
- Personality disorders F60-697011.52715.4439.91.660.053
- Multiple CODs13121.453*30.37818.01.98< 0.001

1 Other psychiatric diagnoses (n = 17) included Schizophrenia, F20-F29 (n = 8); Behavioral syndromes associated with physiological disorders and physical factors (n = 16); Mental retardation, F70-F79 (n = 6); Pervasive and specific developmental disorders, F80-89 (n = 16); Behavioral disorders, F91-F98 (n = 8)

Andersson H.W., Mosti M.P, & Nordfjaern T. (2023). Inpatients in substance use treatment with co-occurring psychiatric disorders: a prospective cohort study of characteristics and relapse predictors. BMC Psychiatry, 23, 152.
Publication 2023
Anxiety Disorders Behavior Disorders Cods Developmental Disabilities Diagnosis, Psychiatric Disorder, Attention Deficit-Hyperactivity Females Intellectual Disability Males Mental Disorders Mood Mood Disorders Patients Personality Disorders Physical Examination physiology Schizophrenia Syndrome Woman
4
Comparative Study of Schizophrenia Spectrum
The study was approved by the Medical Science Research Ethics Committee of the First Affiliated Hospital of China Medical University (approval reference number [2012]25–1). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. All participants provided written informed consent by themselves or by their parents/guardians if they were under 18 years old after a complete description of the study. SZ and GHR participants were recruited from the inpatient and outpatient services at Shenyang Mental Health Center and the Department of Psychiatry at First Affiliated Hospital of China Medical University. Healthy controls (HC) participants were recruited from the local community by advertisement.
All components of the study were conducted at a single site and included both longitudinal and cross-sectional study cohorts, aged 13–45 years. All participants were evaluated by 2 trained psychiatrists to determine the presence or absence of Axis I psychiatric diagnoses using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders-IV-Text Revision (DSM-IV) Axis I Disorders (SCID) in those 18 years old and older and the Schedule for Affective Disorders and Schizophrenia for School-Age Children-present and Lifetime Version (K-SADS-PL) in those younger than 18 years. SZ participants met DSM-IV diagnostic criteria for SZ and not any other Axis I disorder. GHR participants were first-degree relatives of individuals with SZ and did not meet criteria for any DSM-IV Axis I disorder. HC participants did not have current or lifetime Axis I disorder or history of psychotic, mood, or other Axis I disorders in first-degree relatives as determined by detailed family history. Participants were excluded if any of the following were present: (1) the existence of substance/alcohol abuse or dependence or concomitant major medical disorder, (2) any magnetic resonance imaging (MRI) contraindications, and (3) history of head trauma with loss of consciousness for ≥ 5 min or any neurological disorder. Symptom severity was measured using the Brief Psychiatric Rating Scale (BPRS).
Duan J., Gong X., Womer F.Y., Sun K., Tang L., Liu J., Zheng J., Zhu Y., Tang Y., Zhang X, & Wang F. (2023). Neurodevelopmental trajectories, polygenic risk, and lipometabolism in vulnerability and resilience to schizophrenia. BMC Psychiatry, 23, 153.
Publication 2023
Abuse, Alcohol Child concomitant disease Craniocerebral Trauma Diagnosis Diagnosis, Psychiatric Epistropheus Ethics Committees, Research Healthy Volunteers Homo sapiens Inpatient Legal Guardians Mental Disorders Mental Health Services Mood Mood Disorders Nervous System Disorder Outpatients Parent Psychiatrist Sadness Schizophrenia SCID Mice Youth
5
Adolescent Depression, Suicide Attempts, and Brain Function
A total of 71 first-episode drug-naïve adolescent depression patients were recruited from the Department of Psychiatry of Wuhan Mental Health Center. Patients were diagnosed with depression by two experienced psychiatrists based on DSM-IV criteria. To be eligible for study inclusion, patients had to be 7–17 years of age, right-handed, meet the diagnostic criteria for an acute episode of depression, have a history of SA within the past 14 days, be free of serious physical illnesses, be free of the alcohol and/or substance abuse or dependence, and be free of other Axis I disorders including schizophrenia, bipolar disorder, and substance-induced mood disorders. In addition, 54 age- and sex-matched healthy control individuals were recruited from the Wuhan Mental Health Center medical examination center. These controls were right-handed, had no history or family history of psychiatric disorders, and were free of any severe physical illness. All participants provided written informed consent for study participation. The Ethics Committee of Wuhan Mental Health Center approved this research, which was conducted in accordance with the guidelines of the Declaration of Helsinki.
SA was defined as any self-destructive behavior intended to terminate one’s own life that did not result in death (O’Carroll et al., 1996 (link); Li et al., 2021 (link)). The patients included in this study were confirmed to have a history of SA through interviews with experienced psychiatrists, who also collected relevant details including the numbers of SAs and the dates on which they had occurred. When ambiguous results were obtained, the psychiatrists also made inquiries with the parents or clinicians of that patient to confirm these results. The Suicidal Ideation Questionnaire Junior (SIQ-JR; Keane et al., 1996 (link)) scale was conducted on the same day as the rs-fMRI to evaluate the severity of suicidal ideation, while the child depression inventory (CDI; Akimana et al., 2019 (link)) was used to assess depression severity.
Zhou Y., Song Y., Chen C., Yan S., Chen M, & Liu T. (2023). Abnormal amplitude of low-frequency fluctuation values as a neuroimaging biomarker for major depressive disorder with suicidal attempts in adolescents: A resting-state fMRI and support vector machine analysis. Frontiers in Psychology, 14, 1146944.
Publication 2023
Adolescent Bipolar Disorder Child Diagnosis Epistropheus Ethanol Ethics Committees fMRI Mental Disorders Mental Health Mood Disorders Parent Patients Pharmaceutical Preparations Physical Examination Psychiatrist Schizophrenia Sexual Health Substance Abuse

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More about "Mood Disorders"

Mood Disorders are a group of psychiatric conditions characterized by disturbances in emotional state, including major depressive disorder (MDD), persistent depressive disorder (PDD), bipolar disorder (BD), and related mental health conditions.
These affective disorders can significantly impact an individual's quality of life, work, and interpersonal relationships.
Symptoms may involve persistent sadness, loss of interest, changes in sleep and appetite, and difficulty concentrating.
Effective treatments, such as psychotherapy (e.g., cognitive-behavioral therapy, CBT) and pharmacological interventions (e.g., antidepressants, mood stabilizers), are available to manage Mood Disorders and improve patient outcomes.
SAS 9.4, SPSS version 21, Stata 12.0, and other statistical software packages can be utilized to analyze data and support research into the underlying causes, risk factors, and optimal treatment approaches for these challenging mental health conditions.
Ongoing research, including studies conducted using SPSS v24 and STATA software version 11.0, continues to evolve, offering hope and insights for those affected by Mood Disorders.