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> Disorders > Mental or Behavioral Dysfunction > Neuroticism

Neuroticism

Neuroticism is a personality trait characterized by emotional instability, anxiety, and a tendency to experience negative emotions.
Individuals high in neuroticism may be more susceptible to stress, depression, and other mental health issues.
This MeSH term provides a comprehensive overview of the scientific understanding of neuroticism, including its biological basis, genetic factors, and relationship to various health outcomes.
Reserchers can utilize this information to better understand the role of neuroticism in human behavior and health, and to develop effective interventions and treatments.
Experince the power of neuroticism-mastering tools at PubCompare.ai to enhance the reproducibility and accuracy of your research.

Most cited protocols related to «Neuroticism»

1
Emotional Suppression Neuroimaging Measures
Cited 69 times
Individual difference measures were administered before fMRI scanning. The primary measure of interest was the suppression scale of the ERQ (Gross and John, 2003 (link)). This scale consists of four items designed to assess individual differences in suppression use (e.g., “I control my emotions by not expressing them”). This scale previously has been shown to have good internal consistency and test-retest reliability and to be independent of intelligence and socioeconomic status (Gross and John, 2003 (link)). Suppression was normally distributed according to the Shapiro–Wilk test (p > 0.05). Control measures were also administered including: (1) the Chinese-version of 48-item Neuroticism questionnaire of the NEO Five Factor Personality Inventory (Costa and MacCrae, 1992 ), which assesses an individual’s tendency to experience psychological distress; (2) the trait version of the State Trait Anxiety Inventory (STAI trait version; Spielberger, 1970 ), which assesses relatively stable individual differences in anxiety proneness; and (3) the reappraisal scale of the ERQ, which assesses use of cognitive reappraisal in everyday life.
Chen S., Deng Z., Xu Y., Long Q., Yang J, & Yuan J. (2017). Individual Differences in Spontaneous Expressive Suppression Predict Amygdala Responses to Fearful Stimuli: The Role of Suppression Priming. Frontiers in Psychology, 8, 1.
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Publication 2017
Anxiety Chinese Cognition Emotions Factor V fMRI Neuroses, Anxiety Neuroticism Psychological Distress Ribs
2
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.
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Publication 2013
Anhedonia Bipolar Disorder Depressive Symptoms Ethnicity Gender Generic Drugs Major Depressive Disorder Mood Mood Disorders Neuroticism Self-Assessment
3
Mendelian Randomization of Depression and Traits
Cited 43 times
We used Mendelian randomization (MR) to assess whether causal effects
exist between depression and a number of other traits and disorders. MR uses
genetic variants as a proxy for environmental exposures, assuming that: i) the
genetic variants are associated with the exposure; ii) the genetic variants are
independent of confounders in the exposure-outcome association; iii) the genetic
variants are associated with the outcome only via their effect on the exposure,
i.e. there is no horizontal pleiotropy whereby the variants affect both exposure
and outcome independently. Individual genetic variants may be weak instruments
for assessing causality, particularly if they have only small effect sizes.
Using multiple genetic variants can increase the strength of the instrument, but
also increases the risk of violating the MR assumptions.
We performed bidirectional, two-sample MR between our meta-analysis
results for depression and all available traits which had a significant genetic
correlation with depression (identified in the previous section). Traits
directly related to or including depression (major depressive disorder,
depressive symptoms and PGC cross disorder) were excluded due to potential bias.
The genetic instruments for depression consisted of the independent, genome-wide
significant variants, their effect sizes and standard errors, as estimated in
our genome-wide meta-analysis. Summary statistics from genome-wide association
studies for the other traits were sourced from either publicly available
datasets or from the MR-Base database 11 (link).
Overlapping datasets for the exposure and the outcome can lead to bias and
inflation of causal estimates 68 (link). To
mitigate this, when the source of the other trait included UK Biobank, 23andMe
or any of the studies that contributed to PGC_139k, these studies were removed
from our meta-analysis of depression; for example, the neuroticism trait from
van den Berg, et al. 69 (link) was assessed
against a meta-analysis of depression using UK Biobank and 23andMe_307k only.
Where UK Biobank, 23andMe and PGC_139k were all included in the genome-wide
association study of the other trait then an alternative study was sought for
that other trait.
All analyses were performed using the MR-Base
“TwoSampleMR” v0.4.9 package11 (link) in R. To avoid bias in the MR estimates due to linkage
disequilibrium (r2), clumping was applied using the
“clump_data” function with an r2 < 0.001.
Genetic variants were required to be available in both the exposure and outcome
traits and were harmonised using the default parameters within the TwoSampleMR
package. Following this harmonisation, we only examined causal relationships
where there were at least 30 instrumental genetic variables.
Directional horizontal pleiotropy, where a genetic instrument has an
effect on an outcome independent of its influence on the exposure, can be a
problem in MR analysis, particularly when multiple genetic variants of unknown
function are used. We therefore firstly tested for directional horizontal
pleiotropy using the MR Egger intercept test, as previously described by
Hagenaars, Gale, Deary and Harris 70 . If
the MR Egger intercept test had a significant P-value
(P < 0.05) then it was excluded from the analysis.
However, no tests were excluded due to directional horizontal pleiotropy.
The second analysis conducted was a variant heterogeneity test for
global horizontal pleiotropy. Variant heterogeneity is an important metric, but
high heterogeneity doesn’t necessarily mean bias or unreliable results;
for example, every instrumental variable could have horizontal pleiotropic
effects but if they have a mean effect of 0 then there will be no bias, just
larger standard errors due to more noise. For analyses that had evidence of high
variant heterogeneity (P < 0.05), additional sensitivity
MR tests were conducted. The sensitivity tests that were used were the MR Egger
test and the weighted median test to examine whether the effect estimate was
consistent.
The principal MR test of a causal effect was conducted using
inverse-variance weighted (IVW) regression. This method is based on a regression
of the exposure and the outcome which assumes the intercept is constrained to
zero, and produces a causal estimate of the exposure-outcome association. Where
there was no evidence of global horizontal pleiotropy (P≥ 0.05) from the second analysis (see previous paragraph), a FDR
adjusted67
P-value < 0.01 from the IVW test was required for
evidence of a causal effect. Where there was evidence of global horizontal
pleiotropy (P < 0.05) from the second analysis, additional evidence was
also sought from the sensitivity tests (MR Egger test and the weighted median
test). To ensure that a causal effect was not driven by a single variant a
‘leave one variant out’ IVW regression analysis was conducted with
the least significant observed P-value used to assess whether
significance was maintained. We tested the causal effect of depression on 24
other traits, and the causal effect of 9 other traits on depression.
Howard D.M., Adams M.J., Clarke T.K., Hafferty J.D., Gibson J., Shirali M., Coleman J.R., Hagenaars S.P., Ward J., Wigmore E.M., Alloza C., Shen X., Barbu M.C., Xu E.Y., Whalley H.C., Marioni R.E., Porteous D.J., Davies G., Deary I.J., Hemani G., Berger K., Teismann H., Rawal R., Arolt V., Baune B.T., Dannlowski U., Domschke K., Tian C., Hinds D.A., Trzaskowski M., Byrne E.M., Ripke S., Smith D.J., Sullivan P.F., Wray N.R., Breen G., Lewis C.M, & McIntosh A.M. (2019). Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nature neuroscience, 22(3), 343-352.
Publication 2019
Debility Depressive Symptoms Environmental Exposure Genetic Diversity Genetic Heterogeneity Genome Hypersensitivity Major Depressive Disorder Neuroticism Reproduction Scabies
4
Validation of Psychological Well-Being Scale
Cited 38 times
All studies employed the PMH-scale. In addition, the following instruments were used in study 4 to determine convergent and discriminant validity2:

Social Support Scale (SOZU-K; [16 ]). Higher scores indicate greater levels of social support.

Satisfaction With Life Scale (SWLS; [11 (link)]). High scores denote high levels of satisfaction.

EuroQol Health Questionnaire (EQ-5D; [42 (link)]). Low scores point to good subjective health.

Subjective Happiness Scale (SHS; [37 (link)]). Higher scores essentially reflect higher levels of subjective happiness.

Depression Anxiety Stress Scales-21 (DASS-21; [34 (link)]). Higher scores mark greater levels of distress (stress, anxiety, and depression).

Neuroticism (N-scale, a modified version of the 12-item emotionality scale of the revised Freiburg Personality Inventory, FPI-R; [12 ]). Participants scored the items on a scale from 1 (not true) to 4 (true) in the modified version instead of a 2-point rating scale. High scores on the N-scale indicate high neuroticism.

Sense of Coherence Scale (SOC; [35 ]). High scores on the SOC point to a high sense of coherence.

Center for Epidemiological Studies Depression Scale (CES-D; German version; [43 (link)]; German: Allgemeine Depressionsskala Kurzform, ADS-K; [18 ]). Higher scores indicate pronounced levels of depressive symptoms.

General Self-efficacy Scale (GKE; [27 ]). Higher scores denote more self-efficacy.

Life Satisfaction Questionnaire (LZH; Lutz et al. 1992b, unpublished manuscript). Lower scores indicate higher life satisfaction.

Beck Depression Inventory (BDI; [4 (link)]). High scores signify more severe depression.

In study 5 (sensitivity to therapeutic change) the following instrument was used in addition to the PMH-scale:

Global Assessment of Functioning (GAF; [2 ]). This numeric 0-100 scale is used by mental health clinicians to rate the social, occupational, and psychological functioning of adults. High scores represent a high level of functioning.

Lukat J., Margraf J., Lutz R., van der Veld W.M, & Becker E.S. (2016). Psychometric properties of the Positive Mental Health Scale (PMH-scale). BMC Psychology, 4, 8.
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Publication 2016
Adult Anxiety Depressive Symptoms diacetoxyscirpenol Diagnostic Self Evaluation Emotions Happiness Hypersensitivity Mental Health Neuroticism Satisfaction Sense of Coherence Therapeutics
5
Genome-Wide Association Meta-Analysis
Cited 34 times
Our auxiliary genome-wide association studies of DS and neuroticism were conducted in 1000G-imputed data, combining new genome-wide association analyses with publicly available summary statistics from previously published studies. We applied a similar QC protocol to that used in our primary subjective well-being analysis. In the DS meta-analysis (N = 180,866), we weighted the UKB analysis by sample size and the two case-control studies by effective sample size. In the neuroticism meta-analysis, we performed a sample-size-weighted fixed-effects meta-analysis of the UKB data and the publicly available summary statistics from a previous GWAS of neuroticism.
Detailed cohort descriptions, information about cohort-level genotyping and imputation procedures and quality-control filters are provided in Supplementary Tables 8–12. See Supplementary Figure 7 for quantile-quantile plots of the neuroticism and DS meta-analysis results. Association results for the set of approximately independent set of SNPs that attained a p-value smaller than 10−5 are supplied in Supplementary Table 25.
Okbay A., Baselmans B.M., De Neve J.E., Turley P., Nivard M.G., Fontana M.A., Meddens S.F., Linnér R.K., Rietveld C.A., Derringer J., Gratten J., Lee J.J., Liu J.Z., de Vlaming R., Ahluwalia T.S., Buchwald J., Cavadino A., Frazier-Wood A.C., Davies G., Furlotte N.A., Garfield V., Geisel M.H., Gonzalez J.R., Haitjema S., Karlsson R., van der Laan S.W., Ladwig K.H., Lahti J., van der Lee S.J., Miller M.B., Lind P.A., Liu T., Matteson L., Mihailov E., Minica C.C., Nolte I.M., Mook-Kanamori D.O., van der Most P.J., Oldmeadow C., Qian Y., Raitakari O., Rawal R., Realo A., Rueedi R., Schmidt B., Smith A.V., Stergiakouli E., Tanaka T., Taylor K., Thorleifsson G., Wedenoja J., Wellmann J., Westra H.J., Willems S.M., Zhao W., Amin N., Bakshi A., Bergmann S., Bjornsdottir G., Boyle P.A., Cherney S., Cox S.R., Davis O.S., Ding J., Direk N., Eibich P., Emeny R.T., Fatemifar G., Faul J.D., Ferrucci L., Forstner A.J., Gieger C., Gupta R., Harris T.B., Harris J.M., Holliday E.G., Hottenga J.J., De Jager P.L., Kaakinen M.A., Kajantie E., Karhunen V., Kolcic I., Kumari M., Launer L.J., Franke L., Li-Gao R., Liewald D.C., Koini M., Loukola A., Marques-Vidal P., Montgomery G.W., Mosing M.A., Paternoster L., Pattie A., Petrovic K.E., Pulkki-Råback L., Quaye L., Räikkönen K., Rudan I., Scott R.J., Smith J.A., Sutin A.R., Trzaskowski M., Vinkhuyzen A.E., Yu L., Zabaneh D., Attia J.R., Bennett D.A., Berger K., Bertram L., Boomsma D.I., Snieder H., Chang S.C., Cucca F., Deary I.J., van Duijn C.M., Eriksson J.G., Bültmann U., de Geus E.J., Groenen P.J., Gudnason V., Hansen T., Hartman C.A., Haworth C.M., Hayward C., Heath A.C., Hinds D.A., Hyppönen E., Iacono W.G., Järvelin M.R., Jöckel K.H., Kaprio J., Kardia S.L., Keltikangas-Järvinen L., Kraft P., Kubzansky L.D., Lehtimäki T., Magnusson P.K., Martin N.G., McGue M., Metspalu A., Mills M., de Mutsert R., Oldehinkel A.J., Pasterkamp G., Pedersen N.L., Plomin R., Polasek O., Power C., Rich S.S., Rosendaal F.R., den Ruijter H.M., Schlessinger D., Schmidt H., Svento R., Schmidt R., Alizadeh B.Z., Sørensen T.I., Spector T.D., Starr J.M., Stefansson K., Steptoe A., Terracciano A., Thorsteinsdottir U., Thurik A.R., Timpson N.J., Tiemeier H., Uitterlinden A.G., Vollenweider P., Wagner G.G., Weir D.R., Yang J., Conley D.C., Smith G.D., Hofman A., Johannesson M., Laibson D.I., Medland S.E., Meyer M.N., Pickrell J.K., Esko T., Krueger R.F., Beauchamp J.P., Koellinger P.D., Benjamin D.J., Bartels M, & Cesarini D. (2016). Genetic variants associated with subjective well-being, depressive symptoms and neuroticism identified through genome-wide analyses. Nature genetics, 48(6), 624-633.
Publication 2016
Genome-Wide Association Study Neuroticism Single Nucleotide Polymorphism

Most recents protocols related to «Neuroticism»

1
Eysenck Personality Questionnaire-Revised Short for Chinese

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2023
Aged Chinese Extraversion, Psychological Mental Disorders Neuroticism
2
Cluster Analysis of Multifaceted Risk Factors in Mental Disorders
Descriptive analyses were performed to describe the demographic characteristics of the sample. Independent sample t-test or F-test were used for continuous variables, and χ2 test was used for classification variables, with P < 0.05 as the statistical significance threshold.
K-means is a clustering algorithm that uses distance as the similarity evaluation index. The closer the distance between two objects, the greater the similarity. K-means clustering algorithm believes that clusters are composed of close objects, so it will get compact and independent clusters as the goal. It has many advantages, such as fast and simple calculation, and its time complexity is close to linear (27 (link)).
We conducted cluster on two aspects of the analysis. First, the standardized values (Z-score) of SAS score, SDS score, Neuroticism score, CIAS-R score, ASLEC score, CTQ score, FAD score, and SSRS score-reverse were used as clustering components for analysis, to find out the specific multiple risk factors in the population with mental disorders. And χ2 test was performed on the clustering center and NSSI frequency to identify the high-risk population of NSSI. The second cluster analysis was conducted with the standardized values (Z-scores) of four NSSI functions valued by OSI-F to explore different functional subtypes in NSSI patients. The best clustering number is determined by calculating the within groups sum of square errors (WSSE) and elbow method using R language. Then the K-means cluster analysis was conducted by SPSS 26.0.
Yan H., Zhang Y., Lu Z., Li M., Ge Y., Mei D., Kang Z., Sun Y., Li Q., Yan H., Yang L., Song P., Shi C., Shang S, & Yue W. (2023). High-risk group and functional subtypes of non-suicidal self-injury in young adults with mental disorders. Frontiers in Psychiatry, 14, 1098178.
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Publication 2023
Elbow Mental Disorders Neuroticism Patients Population at Risk
3
NEO-FFI Personality Assessment Protocol
The NEOFFI (24 ) contains 60 items with a range response option from 1 (strongly disagree) to 5 (strongly agree), and is used to assess the five broad personalities including Neuroticism, Extraversion, Openness to experience, Agreeableness, and Conscientiousness. The NEOFFI is valid and reliable with excellent internal consistency scores of 0.82 to 0.89 in Chinese sample.
Yan H., Zhang Y., Lu Z., Li M., Ge Y., Mei D., Kang Z., Sun Y., Li Q., Yan H., Yang L., Song P., Shi C., Shang S, & Yue W. (2023). High-risk group and functional subtypes of non-suicidal self-injury in young adults with mental disorders. Frontiers in Psychiatry, 14, 1098178.
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Publication 2023
Chinese Extraversion, Psychological Neuroticism
4
Personality Traits Influence on Life Attitudes
People's inherent psychological traits can have an impact on positive life attitudes, depression levels, and pandemic preventive behavior intentions. Personality, as the sum of an individual emotions, thoughts, and behavioral tendencies, plays an important role in this process. Previous studies have shown that people's mental health (33 (link)), positive life attitudes (34 (link)) and behavioral tendencies (35 (link)) are significantly influenced by personality. For example, individuals with higher neuroticism scores were more likely to be depressed and have a pessimistic outlook on life (36 (link), 37 (link)). Individuals with higher conscientiousness scores were more likely to adopt preventive behaviors during the pandemic (38 (link), 39 (link)). To exclude the interference of personality on the findings, we included personality as control variables in this study. The Big Five personality scale developed by (40 (link)) was used in this study to measure the personality of the subjects. The scale contains five dimensions: neuroticism, conscientiousness, agreeableness, extraversion, and openness, and each dimension contains eight questions. The Likert scale with 5 points was adopted for all questions (from 1 very disagreed to 5 very agreed). In this study, the Cronbach's α scores were 0.91, 0.85, 0.71, 0.94, and 0.79, respectively.
Liu Z., Liu Y., Li A, & Zhu T. (2023). Nonlinear effects of pandemic uncertainty on depression, pandemic preventive behavior intentions, and positive life attitudes: Moderating effects of high and low uncertainty grouping. Frontiers in Public Health, 11, 1136152.
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Publication 2023
Emotions Extraversion, Psychological Mental Health Neuroticism Pandemics Pessimism Thinking
5
Individual Differences in Need for Closure, Loneliness, and Personality
We assessed individual differences in need for closure with 15 items (e.g., “I don’t like situations that are uncertain”) measured on a 7-point scale (1 = Strongly disagree to 7 = Strongly
Agree; e.g. “I don’t like situations that are uncertain”; [61 (link)]; α = .86). Second, we measured loneliness with five items (e.g., “I feel left out”) on a 4-point scale (1 = Never to 4 = Often; [62 (link)]; α = .91). Third, we assessed individual differences in Big 5 personality traits of extraversion (e.g., “I see myself as extraverted, enthusiastic”; α = .68), agreeableness (e.g., “I see myself as sympathetic, warm”; α = .33), conscientiousness (e.g., “I see myself as dependable, self-disciplined; α = .65), neuroticism (e.g., “I see myself as anxious, easily upset”; a = .74), and openness (e.g., “I see myself open to new experience, complex”; α = .42) with the Ten Item Personality Inventory (TIPI), with items answered on a 7-point scale (1 = Strongly Disagree to 7 = Strongly Agree) [63 ].
Leonhardt N.D., Fahmi S., Stellar J.E, & Impett E.A. (2023). Turning toward or away from God: COVID-19 and changes in religious devotion. PLOS ONE, 18(3), e0280775.
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Publication 2023
Extraversion, Psychological Feelings Neuroticism Personality Inventories

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More about "Neuroticism"

Neuroticism is a fundamental personality trait characterized by emotional instability, anxiety, and a propensity to experience negative emotions.
Individuals high in neuroticism may be more susceptible to stress, depression, and other mental health issues.
This concept is closely related to emotional lability, negative affectivity, and emotional sensitivity.
Neuroticism has a strong biological basis, with genetic and neurological factors playing a significant role in its development.
Research utilizing statistical software like SPSS, SAS, and Stata has provided insights into the underlying mechanisms and correlates of neuroticism.
Individuals with higher levels of neuroticism tend to exhibit increased stress reactivity, as measured by physiological markers such as cortisol levels.
This heightened stress response can lead to a variety of negative health outcomes, including an increased risk of cardiovascular disease, gastrointestinal disorders, and mental health problems like anxiety and depression.
Neuroticism has also been linked to various cognitive and behavioral patterns, such as rumination, avoidance, and a tendency to interpret ambiguous situations in a negative light.
These cognitive-emotional biases can further exacerbate the negative effects of neuroticism on mental and physical well-being.
Understanding and managing neuroticism is crucial for improving overall health and well-being.
Effective interventions, such as cognitive-behavioral therapy and mindfulness-based techniques, have been shown to help individuals with high neuroticism better regulate their emotions and cope with stress.
Researchers and clinicians can utilize the insights provided by statistical software like SPSS, SAS, and Stata to develop and refine these interventions, ultimately enhancing the quality of life for those affected by this personality trait.