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Article

The Big Five as Predictors of Cognitive Function in Individuals with Bipolar Disorder

by
Eva Fleischmann
1,
Nina Dalkner
1,*,
Frederike T. Fellendorf
1,
Susanne A. Bengesser
1,
Melanie Lenger
1,
Armin Birner
1,
Robert Queissner
1,
Martina Platzer
1,
Adelina Tmava-Berisha
1,
Alexander Maget
1,
Jolana Wagner-Skacel
2,
Tatjana Stross
1,
Franziska Schmiedhofer
1,
Stefan Smolle
1,
Annamaria Painold
1 and
Eva Z. Reininghaus
1
1
Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, 8036 Graz, Styria, Austria
2
Department of Medical Psychology and Psychotherapy, Medical University of Graz, 8036 Graz, Styria, Austria
*
Author to whom correspondence should be addressed.
Brain Sci. 2023, 13(5), 773; https://doi.org/10.3390/brainsci13050773
Submission received: 17 March 2023 / Revised: 2 April 2023 / Accepted: 2 May 2023 / Published: 8 May 2023
(This article belongs to the Special Issue Etiology, Pathogenesis and Treatment of Bipolar Disorder)
Review Reports Versions Notes

Abstract

:
The connection between cognitive function and the “Big Five” personality traits (openness, conscientiousness, extraversion, agreeableness, and neuroticism) in the general population is well known; however, studies researching bipolar disorder (BD) are scarce. Therefore, this study aimed to investigate the Big Five as predictors of executive function, verbal memory, attention, and processing speed in euthymic individuals with BD (cross-sectional: n = 129, including time point t1; longitudinal: n = 35, including t1 and t2). Participants completed the NEO Five-Factor Inventory, the Color and Word Interference Test, the Trail Making Test, the d2 Test of Attention Revised, and the California Verbal Learning Test. The results showed a significant negative correlation between executive function and neuroticism at t1. Changes in cognitive function between t1 and t2 did not correlate with and could not be predicted by the Big Five at t1. Additionally, worse executive function at t2 was predicted by higher neuroticism and lower conscientiousness at t1, and high neuroticism was a predictor of worse verbal memory at t2. The Big Five might not strongly impact cognitive function over short periods; however, they are significant predictors of cognitive function. Future studies should include a higher number of participants and more time in between points of measurement.

1. Introduction

Bipolar disorder (BD) is a severe mental disorder affecting 1–2% of the population worldwide. Characterized by depressive and (hypo-)manic episodes, the disorder typically manifests in young adulthood during the emergence of a stable structure of personality [1]. Individuals with BD often suffer from social problems, such as lower social skills [2] and lower perceived social support, than healthy controls (HC) [3], and experience stigma [4]. In addition, cognitive impairment is frequently found in individuals with BD [5,6,7], even before illness onset [8] and during times of euthymia [9].
Cognitive dysfunction has been found to worsen in concert with illness duration [10] as well as neuroprogression [11], although results suggesting no apparent link with the latter show the questionability of this issue [12]. The primarily affected domains of cognitive impairment are verbal memory [13,14], executive function [14], attention [14,15], and psychomotor processing speed [16], which represent three of the six domains of cognition described in the Diagnostic and Statistical Manual of Mental Disorders (DSM)-V [17]. Moreover, the fourth domain of social cognition can be impaired as well [18], specifically emotion recognition [19] and emotion regulation in social situations [20].
Correlates and predictors of cognitive impairment include psychotic symptoms and mood symptoms [21], especially depressive symptomatology [22] and a high number of manic episodes [23]. Furthermore, it has been suggested that psychopharmaceuticals might impede cognitive function as well, among them being antipsychotics and sodium valproate [24]. In daily life, cognitive function is negatively associated with work performance [9], sleep disturbance [25], and quality of life [26]. In addition, associations with personality were found, which will be explained in the following paragraphs.
The five-factor model (FFM; [27]), also known as the “Big Five” [28], includes five personality dimensions that remain stable over time: openness (O), conscientiousness (C), extraversion (E), agreeableness (A), and neuroticism (N). In comparison with HC, individuals with BD score higher in N [29,30]. N is associated with the occurrence of depressive and hypomanic symptoms [31], subjective well-being, insomnia, and anxiety [32]. C was reported to be lower in BD [29,30]. E was found to be lower by some studies [29,33], while genotype analysis has shown associations with both lower [30] and higher values of E [34]. E can, if strongly pronounced, predict the future onset of BD after a period of 14 years [35], as well as symptoms of hypomania [31], and is positively associated with social impairment [36]. According to genotype analysis, A might be lower in BD [30]. Low A was found to indicate a higher risk of developing (hypo)mania in individuals with depression and anxiety [37]. O was found to be more highly pronounced in BD, and seems to be associated with creative achievement [38].
In recent years, several studies have explored the relationship between these facets of personality and cognitive function in HC. Research has emphasized high N as one of the most significant personality predictors of impaired cognition [39,40,41]. According to Stephan [42], this might be explained by correlates of N impacting long-term cognitive function, such as high stress sensitivity [43], sleep disturbances [44], and alcohol use [45]. Low O is an important factor as well [42,46], as high O is associated with more activities that are cognitively stimulating [47]. Studies analyzing neural correlates of the FFM further support the notion that high N and low O impact cognition, finding relationships between worse white matter integrity and high N as well as between better white matter integrity and high O [48]. Additionally, low C might contribute to cognitive decline [46], as affected individuals display less behavior contributing to the preservation of cognitive function [49]. Lastly, low E is associated with lower verbal fluency, and findings for A are not consistent throughout the literature [50]. Table 1 shows a summary of the relationships between each of the Big Five and cognitive function in HC.
Concerning individuals with BD, two studies found high O to be a predictor of better cognitive function [38,51]. In particular, O’s association with ideas and values was the most important facet, correlating with several cognitive factor scores, such as auditory memory, emotional processing, verbal fluency, and processing speed [51]. Furthermore, a higher number of single-nucleotide polymorphisms of the brain-expressed protocadherin 17 gene showed correlations with both impaired cognition and higher N. Increased gene expression was found in individuals with BD compared to HCs [52]. Another study suggested a negative correlation between N and reaction time in the Affective Go/No-Go paradigm with a bias towards affective stimuli, which was not significant in HC and might suggest a greater receptivity to emotional stimuli in individuals with BD [53].
As there is currently a lack of studies investigating personality and cognitive function in BD, the aims of this study were 1. to investigate cross-sectional correlations between the Big Five as well as executive function, verbal memory, attention, and processing speed, and 2. to predict cognitive decline according to the Big Five in a longitudinal sample. It was hypothesized that high O, C, and E, as well as low N, would correlate with and predict better cognitive function, while analyses concerning A would be explorative.

2. Materials and Methods

2.1. Participants and Procedure

All participants were participants in the ongoing BIPFAT/BIPLONG study, which has aimed to investigate BD in a longitudinal setting since 2012. Conducted at the outpatient center for BD at the Medical University of Graz, Austria, Department of Psychiatry and Psychotherapeutic Medicine, the study’s focal points are lifestyle, lipid metabolism, inflammation processes, cognition, and brain function. Inclusion criteria were a BD diagnosis by trained specialists using the Structured Clinical Interview for DSM-IV [54], age between 18 and 70 years, and IQ of ≥80 at the time of measurement. Euthymia was defined in this study by a score of ≤12 on the Young Mania Rating Scale (YMRS) [55] and a score of ≤10 on the Hamilton Depression Scale (HAMD) [56]. Individuals were excluded if they suffered from severe immunological disorders, organic brain diseases, or dementia.
As cross-sectional and longitudinal comparisons were made, the study included two samples, with some participants being included in both. The cross-sectional sample, consisting of 129 individuals with BD, included data from the participants’ first visit (t1). The longitudinal sample of 35 individuals with BD additionally included assessments from a second visit yielding complete datasets (t2), taking place 345 days to 2106 days after the first visit. This study was conducted in accordance with the Declaration of Helsinki as well as approved by the Ethics Committee of the Medical University of Graz (EK-number: 25-335 ex 12/13), and all patients signed informed consent forms before participation.

2.2. Measurements

Participants completed a cognitive assessment as well as a personality questionnaire, all of which were administered in German. In addition, clinical and sociodemographic data were assessed by interview.

2.2.1. Neuropsychological Assessment

Verbal memory was measured with the California Verbal Learning Test (CVLT) [57]. In particular, recall trials 1-5, short delay free recall, short delay cued recall, long delay free recall, and long delay cued recall were measured.
To assess attention and processing speed, three tests were used: the Trail Making Test part A (TMT-A) [58], the d2 Test of Attention Revised (d2-R) [59], and the word-reading and color-naming trials from the Color and Word Interference Test by J. R. Stroop [60].
Cognitive flexibility, one aspect of executive function, was assessed by the Trail Making Test part B (TMT-B) [58], as well as the interference trial from the Color and Word Interference Test by J. R. Stroop [60].

2.2.2. Personality Assessment

The NEO Five-Factor Inventory (NEO-FFI) [61] was used to assess the five personality dimensions of the FFM. Participants were asked to rate 60 questions on a five-point Likert-type scale, with 1 = strongly disagree and 5 = strongly agree. For each factor, the sum score of the 15 corresponding questions is calculated, ranging from 15 to 75, with a higher score indicating a stronger expression of the respective factor. The NEO-FFI shows good internal consistency, with Cronbach’s alpha ranging between .63 and .82 for each factor [62].

2.3. Statistical Methods

For the cross-sectional sample, cognitive test scores were converted into z-scores and then summed up into three scores measuring three cognitive domains: (1) attention and processing speed (d2-R, Stroop color naming, Stroop word reading, TMT-A); (2) verbal learning and memory (CVLT trial 1-5, CVLT short delay free recall, CVLT long delay free recall, CVLT short delay cued recall, CVLT long delay cued recall); and (3) executive function (Stoop interference and TMT-B). Measures expressing reaction times were reversed before calculating the domain scores, because in contrast to the other scores, lower scores indicated higher performance. A higher domain score indicated higher performance.
For the longitudinal sample, the same procedure was repeated; however, not all cognitive test scores could be used for the creation of the domain scores, as they were not assessed at t2. For memory, only the score for recall trials 1-5 was included in the sum score. The sum score of attention and processing speed did not include the d2-R test, which was accounted for when calculating the sum score and comparing both samples. There were no differences in the calculation of executive function.
Mean Big Five values of the cross-sectional sample were compared to the norm sample using summary t-tests. To compare the t1 and t2 data of the longitudinal sample, t-tests were used for age and the HAMD, and a Wilcoxon Test was used for YMRS due to outliers. A repeated-measures multivariate analysis of variance (MANCOVA) was employed to test for differences in cognition between both time points. Only the variables assessed at both time points were included: TMT-A, TMT-B, recall trials 1-5 of the CVLT, and the Stroop test. Covariates included age, sex, education, time difference between t1 and t2, BDI, and illness duration. Key assumptions of the repeated-measures MANCOVAs (linearity and normality) were tested graphically, as well as with the Kolmogorov–Smirnov test.
Cross-sectional partial correlation analyses between each of the Big Five and verbal memory, executive function, attention, and processing speed were performed at time t1. Age, sex, education, BDI, and illness duration were used as covariates. A false discovery rate (FDR) was used to correct for alpha error cumulation. Furthermore, the same analyses were performed for the purpose of correlating each of the Big Five assessed at t1 with cognition at t1 and t2, as well as the difference between the three cognitive functions measured at t2 as compared to t1. In addition to the previously mentioned covariates, the time difference between t1 and t2 was used for the latter analysis.
Three multiple hierarchical regression analyses to predict executive function, verbal memory, attention, and processing speed at t2 were conducted. The first step included the variables of age, sex, education, time difference between t1 and t2, BDI, and illness duration, while the second step included the Big Five at t1. Three more hierarchical regression analyses to predict changes in cognitive function were calculated with the same variables. Conditions for multiple regression analyses were tested by using correlations (linearity), scatterplots (homoscedasticity), histograms (normal distribution of error variance), Durbin–Watson tests (lack of autocorrelations), and both variance inflation factor and tolerance (lack of multicollinearity).
The current study included participants who completed all relevant questionnaires. In sum, 12 individuals had to be excluded in the cross-sectional sample and 5 in the longitudinal sample due to missing data.
A post-hoc sensitivity analysis performed with G*Power [63] showed that a correlation coefficient with 129 participants would be sensitive to effects of r = .24 with 80% power (Correlation ρ H0 = 0, power = 0.80, α = 0.05, two-tailed). For regression analyses with 35 participants, the threshold of sensitivity was .48 (H0 ρ2 = 0, power = 0.80, α = 0.05, number of predictors = 11, two-tailed).

3. Results

3.1. Sample Characteristics

Sociodemographic information of the cross-sectional sample (n = 129) is displayed in Table 2, and of the longitudinal sample (n = 35) in Table 3. The Big Five scores of the cross-sectional sample were compared to the norm sample (n = 871) [61] with t-tests, and it was found that N was higher (M = 20.99, SD = 7.89, t(998) = 8.45, p < 0.001), while E (M = 26.88, SD = 6.47, t(998) = −2.35, p = 0.019) as well as C (M = 32.61, SD = 6.11, t(153,965) = −2.16, p = 0.032) were lower in individuals with BD. There was no difference in O (M = 29.47, SD = 6.53, t(998) = −0.07, p = 0.948) or A (M = 30.45, SD = 5.38, t(156,218) = −0.12, p = 0.906).
T-tests showed that both groups of the longitudinal sample differed in age, but not HAMD (see Table 3). A Wilcoxon test further resulted in no differences between the groups regarding YMRS. A repeated-measures MANCOVA with the covariates of age, sex, education, BDI, time difference between t1 and t2, and illness duration regarding cognition was not significant (F(6,19) = 0.68, p = 0.666).

3.2. Partial Correlation Analyses

Partial correlation analyses including the three parameters of cognitive function; the Big Five; and the covariates of age, sex, education, BDI, illness duration, and time difference between t1 and t2 for the longitudinal sample are shown for both samples in Table 4 and Table 5, respectively. In the cross-sectional sample, only executive function correlated negatively with N after correction with FDR. In contrast, the positive correlation between attention, processing speed, and O did not remain significant. In the longitudinal sample, partial correlation analyses with the same variables were conducted for both time points t1 and t2, and for the cognitive function differences between t1 and t2. N at t1 correlated significantly with memory (r = −0.52, p = 0.007) and executive function at t2 (r = −0.43, p = 0.034); however, neither correlation remained significant after the usage of FDR. Correlations between cognitive function differences and the Big Five did not yield any significant results.

3.3. Multiple Hierarchical Regression Analyses

3.3.1. Executive Function

A multiple hierarchical regression analysis was performed to predict executive function at t2 (see Table 6). The variables of age, sex, education, time difference between t1 and t2, BDI, and illness duration entered in the first step did not yield a significant result (R2 = 0.38, R2 corr. = 0.23, F(6,24) = 2.46, p = 0.053). The second step, comprising the Big Five at t1, was significant (R2 = 0.65, R2 corr. = 0.45, F(11,19) = 3.20, p = 0.013). The results were significant for the predictors of education, time difference between t1 and t2, C, and N.
A second multiple hierarchical regression analysis predicting the change in executive function between t1 and t2, with the variables of age, sex, education, time difference between t1 and t2, BDI, and illness duration entered in the first step and the Big Five at t2 entered in the second step, was not significant (Model 1: R2 = 0.26, R2 corr. = 0.07, F(6,24) = 1.39, p = 0.260; Model 2: R2 = 0.37, R2 corr. = 0.01, F(11,19) = 1.03, p = 0.463).

3.3.2. Verbal Memory

A multiple hierarchical regression analysis predicting verbal memory at t2 with the predictors of age, sex, education, time difference between t1 and t2, BDI, and illness duration showed a significant first step (R2 = 0.45, R2 corr. = 0.31, F(6,24) = 3.27, p = 0.017). Age was a significant predictor (see Table 7). The second step, including the Big Five at t1, was significant as well (R2 = 0.68, R2 corr. = 0.49, F(11,19) = 3.61, p = 0.007), showing significant effects of age and N.
A second multiple hierarchical regression analysis with the variables of age, sex, education, BDI, illness duration, and time difference between t1 and t2 entered in the first step was conducted to predict the change in verbal memory between t1 and t2. The Big Five were entered in the second step, and neither model yielded significant results (Model 1: R2 = 0.13, R2 corr. = −0.09, F(6,24) = 0.60, p = 0.729; Model 2: R2 = 0.40, R2 corr. = 0.06, F(11,19) = 1.17, p = 0.368).

3.3.3. Attention and Processing Speed

A multiple hierarchical regression analysis predicting attention and processing speed at t2 with the variables of age, sex, education, time difference between t1 and t2, BDI, and illness duration as predictors in the first step was significant (R2 = 0.52, R2 corr. = 0.40, F(6,24) = 4.26, p = 0.005). The Big Five at t1 were included in the second step, which yielded significant results (R2 = 0.60, R2 corr. = 0.43, F(11,19) = 2.84, p = 0.022). Age was the sole significant predictor in the second step (see Table 8).
Another multiple hierarchical regression analysis, with the predictors of attention and processing speed difference between t1 and t2, was conducted. The first step included age, sex, education, time difference between t1 and t2, BDI, and illness duration, and the second step included the Big Five at t1, with neither model showing significant results (Model 1: R2 = 0.34, R2 corr. = 0.17, F(6,24) = 2.04, p = 0.099; Model 2: R2 = 0.41, R2 corr. = 0.07, F(11,19) = 1.21, p = 0.344).

4. Discussion

A cross-sectional (n = 129) and a longitudinal (n = 35) subsample of euthymic individuals with BD were investigated concerning the association between cognitive function and the Big Five. A significant negative correlation between executive function and neuroticism at t1 was found. Partial correlations and regression analyses predicting changes in cognitive function between t1 and t2 with the Big Five did not yield any significant results. High neuroticism at t1 was a significant predictor of worse executive function and verbal memory at t2, while high conscientiousness at t1 significantly predicted worse executive function at t2.
N was higher, and E, as well as C, were lower in the cross-sectional BD sample than in the norm sample [61], which is consistent with other studies [29,33]. Similarly, one study found this FFI triad with the opposite configuration to be a strong correlate of positive mental health [57], showing individuals with BD to be naturally vulnerable. Findings suggesting higher O [30,38,64] and lower A in BD [30] could not be replicated, and should be investigated in more detail.
Interestingly, variables of the Big Five at t1 predicted executive function and verbal memory at t2, but not the change in cognitive function between t1 and t2 in the longitudinal sample. It should be mentioned that mean cognitive test scores did not differ between t1 and t2. This lack of significant results might indicate that while certain aspects of personality are important for cognition, the influence exerted is only observable over a longer period of time, or that it is stronger during a different stage of development. Gale et al. [65] proposed that an association between personality and cognitive function found in mid-life might be a reflection of a correlation between these traits in childhood. It should be further considered that any meaningful associations between the Big Five and cognition might have been underestimated as a consequence of the small sample sizes.
Only the negative correlation between N and executive function remaining significant after FDR in the cross-sectional sample. N being a significant predictor of executive function and verbal memory in the longitudinal sample cemented N’s standing as the most influential Big Five variable on cognitive function. This connection between N and cognition was found in studies examining HC [39,40,41], and seems to be applicable to individuals with BD as well. A study by Chang et al. [52] yielded the same result. Similarly to HC, higher N might contribute to a higher frequency of unhealthy behaviors impacting long-term cognition among individuals with BD [44,45], which they are known to exhibit more than HC: they exercise less [66] and are more likely to smoke [67] and have a less healthy diet [68]. This is supported by a positive correlation between N and cardiovascular disease [32].
Executive function and verbal memory seem to be affected the most by the aforementioned correlates of N. Regarding the former, healthy older adults showed a negative association between N and executive function [69,70]. Fittingly, our previous research suggested a higher prevalence of metabolic syndrome in individuals with BD, which was found to be a risk factor for impairment of executive function [71]. In addition, damage to the left dorsolateral prefrontal cortex, which is important for executive function, was found to be associated with high N [72]. Verbal memory was predicted by N as well. Other studies found comparable results, suggesting a link between high N and worse memory recall in HC [65,73], as well as memory complaints [74] and greater decreases in verbal memory in older adults [75]. These results underline the importance of preventing cognitive decline in individuals with BD. For example, mindfulness training was found to reduce N [76], and a unified protocol designed specifically for decreasing N was successful [77]. Additionally, low C might contribute to cognitive decline [46], as affected individuals display less behavior contributing to the preservation of cognitive function [49].
C was an important predictor of executive function as well. Similarly, Sutin et al. [50] found better executive function to be predicted by higher C. High C has been linked to high achievement by students [78], better cognitive performance, greater pursuit of cognitive activities [79], and less cognitive decline [46]. As C was lower in the BD group compared to the norm sample, both C and executive function are important points to consider in therapy. In contrast to our result, other findings showed no association between C and cognitive function in HC [40] or individuals with BD [51]. Apart from the dissimilar study populations, these diverse results might be explained by different measures of executive function, which is a broad concept spanning several processes of cognition. For instance, Crow [40] used response to inhibition and sustained attention to represent executive function, while the current study used measures of cognitive flexibility, the TMT-B, and the interference trial of the Stroop test. In conclusion, the exact relationship between C and different categories of executive function in BD is still unclear.
Attention and processing speed were solely predicted by age at t1. As the tests assessing these cognitive domains were mainly dependent on speed, it is not surprising that none of the Big Five were significant predictors. The progression of time, not personality, was the most impactful factor affecting the decline in attention and processing speed, which are known to decrease with age [80]. A possible neuronal correlate is fractional anisotropy of white matter [81]. Zhao et al. [82] have recently suggested that the deterioration of the glial structure disturbs the ratio between neural activity and the availability of oxygen.
Except for a significant correlation between O, attention, and processing speed prior to FDR, O unexpectedly did not correlate with or predict any variables related to cognition. High O has been consistently linked to higher cognitive abilities in HC [42,46] and has been found in individuals with BD as well [38], although results are scarce, and this connection in individuals with BD remains to be elucidated.
Finally, education was a significant predictor of executive function, but not verbal memory nor attention and processing speed at t2. Education has been identified as an important factor for the development of executive function in early childhood [83]; in turn, executive function is integral for pre-school learning [84]. Moreover, the quality of education during adulthood impacts executive function as well [85]. Many studies have investigated this reciprocal relationship in children, while older adults have been in the spotlight of a few studies showing the same results: a higher level of education is associated with better executive function [86,87], which seems to be true for middle-aged individuals with BD as well. Considering that executive function includes working memory, inhibition, and attention shifting [88], it is evident that these skills facilitate educational achievement, while education might help to hone them. This is supported by the fact that high conscientiousness is also a significant predictor of executive function, a trait that is linked to high achievement [78]. This is particularly relevant for individuals with BD, as those with depression have a lower education level than the general population [89]. Considering verbal memory, other studies have found a significant association with education [90,91]. As for attention and processing speed, education does not seem to hold as much importance as age, which we discussed previously.
The first limitation of the current study was the small sample size, especially in the longitudinal subsample, which might have led to an underestimation of the relationship between cognition and the Big Five. In this context, possible attrition bias due to high dropout rate might have occured, although there were no significant differences between t1 and t2 except age. Secondly, there was no control group with which to compare individuals with BD. The influence pattern of personality and cognition might be different in HC, and it would have been essential to compare both groups. Thirdly, not all cognitive tests were administered at t2 for the longitudinal sample, leading to mean scores of the three cognitive domains that were not as differentiated as the scores at t1. Fourthly, information on medication intake was assessed, but could not be included in the statistical analyses due to missing data. As cognitive performance might have been inhibited by the effect of certain medication, it would have been important to include this variable as a covariate. Fifthly, the time span between t1 and t2 was heterogenous in the cross-sectional sample, although there were no outliers, and might have influenced the results. Finally, subscores of the Big Five were not administered and might provide a more differentiated picture of the relationship between cognition and personality. Related to this are different measures of executive function, which might lead to differing results and should be examined in more detail.

5. Conclusions

Individuals with BD have lower N, E, and C than HC. High N has a strong association with worse executive function and verbal memory, while C is positively correlated with executive function. While the Big Five might not strongly impact cognitive function over the span of several years, they are, nevertheless, significant predictors which might take effect over a longer period of time. Future longitudinal studies should include a higher number of participants as well as more time in between points of measurement.

Author Contributions

Conceptualization, E.Z.R. and N.D.; methodology, N.D., M.L., F.T.F. and E.F.; software, E.F.; validation, N.D. and E.Z.R.; formal analysis, E.F.; investigation, E.F., F.T.F., N.D., S.A.B., M.L., A.B., M.P., R.Q., A.T.-B., A.M., T.S., F.S., A.P. and S.S.; resources, N.D. and E.Z.R.; data curation, E.F.; writing—original draft preparation, E.F.; writing—review and editing, N.D., F.T.F., S.A.B., M.L., A.B., R.Q., M.P., A.T.-B., A.M., J.W.-S., F.S., S.S., T.S. and E.Z.R.; visualization, E.F.; supervision, E.Z.R.; project administration, T.S.; funding acquisition, E.Z.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the Medical University of Graz (protocol code EK-number: 25-335 ex 12/13).

Informed Consent Statement

Written informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Borghuis, J.; Denissen, J.J.A.; Oberski, D.; Sijtsma, K.; Meeus, W.H.J.; Branje, S.; Koot, H.M.; Bleidorn, W. Big Five Personality Stability, Change, and Codevelopment across Adolescence and Early Adulthood. J. Pers. Soc. Psychol. 2017, 113, 641–657. [Google Scholar] [CrossRef] [PubMed]
  2. Miller, M.L.; Strassnig, M.T.; Bromet, E.; Depp, C.A.; Jonas, K.; Lin, W.; Moore, R.C.; Patterson, T.L.; Penn, D.L.; Pinkham, A.E.; et al. Performance-Based Assessment of Social Skills in a Large Sample of Participants with Schizophrenia, Bipolar Disorder and Healthy Controls: Correlates of Social Competence and Social Appropriateness. Schizophr. Res. 2021, 236, 80–86. [Google Scholar] [CrossRef]
  3. Eidelman, P.; Gershon, A.; Kaplan, K.; McGlinchey, E.; Harvey, A.G. Social Support and Social Strain in Inter-Episode Bipolar Disorder. Bipolar Disord. 2012, 14, 628–640. [Google Scholar] [CrossRef] [PubMed]
  4. Perich, T.; Mitchell, P.B.; Vilus, B. Stigma in Bipolar Disorder: A Current Review of the Literature. Aust. N. Z. J. Psychiatry 2022, 56, 1060–1064. [Google Scholar] [CrossRef] [PubMed]
  5. Bora, E.; Hıdıroğlu, C.; Özerdem, A.; Kaçar, Ö.F.; Sarısoy, G.; Civil Arslan, F.; Aydemir, Ö.; Cubukcuoglu Tas, Z.; Vahip, S.; Atalay, A.; et al. Executive Dysfunction and Cognitive Subgroups in a Large Sample of Euthymic Patients with Bipolar Disorder. Eur. Neuropsychopharmacol. 2016, 26, 1338–1347. [Google Scholar] [CrossRef] [PubMed]
  6. Dickinson, T.; Becerra, R.; Coombes, J. Executive Functioning Deficits among Adults with Bipolar Disorder (Types I and II): A Systematic Review and Meta-Analysis. J. Affect. Disord. 2017, 218, 407–427. [Google Scholar] [CrossRef]
  7. Raucher-Chéné, D.; Achim, A.M.; Kaladjian, A.; Besche-Richard, C. Verbal Fluency in Bipolar Disord: A Systematic Review and Meta-Analysis. J. Affect. Disord. 2017, 207, 359–366. [Google Scholar] [CrossRef]
  8. Trotta, A.; Murray, R.M.; MacCabe, J.H. Do Premorbid and Post-Onset Cognitive Functioning Differ between Schizophrenia and Bipolar Disorder? A Systematic Review and Meta-Analysis. Psychol. Med. 2015, 45, 381–394. [Google Scholar] [CrossRef] [PubMed]
  9. Boland, E.M.; Stange, J.P.; Adams, A.M.; LaBelle, D.R.; Ong, M.-L.; Hamilton, J.L.; Connolly, S.L.; Black, C.L.; Cedeño, A.B.; Alloy, L.B. Associations Between Sleep Disturbance, Cognitive Functioning and Work Disability in Bipolar Disorder. Psychiatry Res. 2015, 230, 567–574. [Google Scholar] [CrossRef] [PubMed]
  10. Chumakov, E.M.; Petrova, N.N.; Limankin, O.V.; Ashenbrenner, Y.V. Cognitive impairment in remitted patients with bipolar disorder. Zh Nevrol. Psikhiatr Im. S. S. Korsakova 2021, 121, 12–18. [Google Scholar] [CrossRef]
  11. Librenza-Garcia, D.; Suh, J.S.; Watts, D.P.; Ballester, P.L.; Minuzzi, L.; Kapczinski, F.; Frey, B.N. Structural and Functional Brain Correlates of Neuroprogression in Bipolar Disorder. Curr. Top. Behav. Neurosci. 2021, 48, 197–213. [Google Scholar] [CrossRef]
  12. Cardoso, T.; Bauer, I.E.; Meyer, T.D.; Kapczinski, F.; Soares, J.C. Neuroprogression and Cognitive Functioning in Bipolar Disorder: A Systematic Review. Curr. Psychiatry Rep. 2015, 17, 75. [Google Scholar] [CrossRef] [PubMed]
  13. Cipriani, G.; Danti, S.; Carlesi, C.; Cammisuli, D.M.; Di Fiorino, M. Bipolar Disorder and Cognitive Dysfunction: A Complex Link. J. Nerv. Ment. Dis. 2017, 205, 743–756. [Google Scholar] [CrossRef] [PubMed]
  14. Cullen, B.; Ward, J.; Graham, N.A.; Deary, I.J.; Pell, J.P.; Smith, D.J.; Evans, J.J. Prevalence and Correlates of Cognitive Impairment in Euthymic Adults with Bipolar Disorder: A Systematic Review. J. Affect. Disord. 2016, 205, 165–181. [Google Scholar] [CrossRef] [PubMed]
  15. Arts, B.; Jabben, N.; Krabbendam, L.; van Os, J. Meta-Analyses of Cognitive Functioning in Euthymic Bipolar Patients and Their First-Degree Relatives. Psychol. Med. 2008, 38, 771–785. [Google Scholar] [CrossRef] [PubMed]
  16. Bora, E. Neurocognitive Features in Clinical Subgroups of Bipolar Disorder: A Meta-Analysis. J. Affect. Disord. 2018, 229, 125–134. [Google Scholar] [CrossRef]
  17. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; American Psychiatric Association: Washington, DC, USA, 2013. [Google Scholar]
  18. Gillissie, E.S.; Lui, L.M.W.; Ceban, F.; Miskowiak, K.; Gok, S.; Cao, B.; Teopiz, K.M.; Ho, R.; Lee, Y.; Rosenblat, J.D.; et al. Deficits of Social Cognition in Bipolar Disorder: Systematic Review and Meta-Analysis. Bipolar Disord. 2021, 24, 137–148. [Google Scholar] [CrossRef]
  19. Dehelean, L.; Romosan, A.M.; Bucatos, B.O.; Papava, I.; Balint, R.; Bortun, A.M.C.; Toma, M.M.; Bungau, S.; Romosan, R.S. Social and Neurocognitive Deficits in Remitted Patients with Schizophrenia, Schizoaffective and Bipolar Disorder. Healthcare 2021, 9, 365. [Google Scholar] [CrossRef]
  20. Kjærstad, H.L.; Eikeseth, F.F.; Vinberg, M.; Kessing, L.V.; Miskowiak, K. Neurocognitive Heterogeneity in Patients with Bipolar Disorder and Their Unaffected Relatives: Associations with Emotional Cognition. Psychol. Med. 2021, 51, 668–679. [Google Scholar] [CrossRef]
  21. Fountoulakis, K.N. Neurocognitive Impairment and Evidence-Based Treatment Options in Bipolar Disorder. Ann. Gen. Psychiatry 2020, 19, 54. [Google Scholar] [CrossRef]
  22. Esan, O.; Oladele, O.; Adediran, K.I.; Abiona, T.O. Neurocognitive Impairments (NCI) in Bipolar Disorder: Comparison with Schizophrenia and Healthy Controls. J. Affect. Disord. 2020, 277, 175–181. [Google Scholar] [CrossRef] [PubMed]
  23. Keramatian, K.; Torres, I.J.; Yatham, L.N. Neurocognitive Functioning in Bipolar Disorder: What We Know and What We Don’t. Dialogues Clin. Neurosci. 2021, 23, 29–38. [Google Scholar] [CrossRef] [PubMed]
  24. Xu, N.; Huggon, B.; Saunders, K.E.A. Cognitive Impairment in Patients with Bipolar Disorder: Impact of Pharmacological Treatment. CNS Drugs 2020, 34, 29–46. [Google Scholar] [CrossRef] [PubMed]
  25. Kanady, J.C.; Soehner, A.M.; Klein, A.B.; Harvey, A.G. The Association between Insomnia-Related Sleep Disruptions and Cognitive Dysfunction during the Inter-Episode Phase of Bipolar Disorder. Psychiatry Res. 2017, 88, 80–88. [Google Scholar] [CrossRef]
  26. Cotrena, C.; Branco, L.D.; Shansis, F.M.; Fonseca, R.P. Executive Function Impairments in Depression and Bipolar Disorder: Association with Functional Impairment and Quality of Life. J. Affect. Disord. 2016, 190, 744–753. [Google Scholar] [CrossRef]
  27. Digman, J.M.; Digman, J.M. Personality Structure Emergence of the Five-Factor Model. Annu. Rev. Psychol. 1990, 41, 114–440. [Google Scholar] [CrossRef]
  28. Goldberg, L.R. “The Structure of Phenotypic Personality Traits”: Author’s Reactions to the Six Comments. Am. Psychol. 1993, 48, 1303–1304. [Google Scholar] [CrossRef]
  29. Hanke, N.; Penzel, N.; Betz, L.T.; Rohde, M.; Kambeitz-Ilankovic, L.; Kambeitz, J. Personality Traits Differentiate Patients with Bipolar Disorder and Healthy Controls-A Meta-Analytic Approach. J. Affect. Disord. 2022, 302, 401–411. [Google Scholar] [CrossRef]
  30. Lee, B.D.; Gonzalez, S.; Villa, E.; Camarillo, C.; Rodriguez, M.; Yao, Y.; Guo, W.; Flores, D.; Jerez, A.; Raventos, H.; et al. A Genome-Wide Quantitative Trait Locus (QTL) Linkage Scan of NEO Personality Factors in Latino Families Segregating Bipolar Disorder. Am. J. Med. Genet. 2017, 174, 683–690. [Google Scholar] [CrossRef]
  31. Wilks, Z.; Perkins, A.M.; Cooper, A.; Pliszka, B.; Cleare, A.J.; Young, A.H. Relationship of a Big Five Personality Questionnaire to the Symptoms of Affective Disorders. J. Affect. Disord. 2020, 277, 14–20. [Google Scholar] [CrossRef]
  32. Zhang, F.; Baranova, A.; Zhou, C.; Cao, H.; Chen, J.; Zhang, X.; Xu, M. Causal Influences of Neuroticism on Mental Health and Cardiovascular Disease. Hum. Genet. 2021, 140, 1267–1281. [Google Scholar] [CrossRef] [PubMed]
  33. Canuto, A.; Giannakopoulos, P.; Moy, G.; Rubio, M.M.; Ebbing, K.; Meiler-Mititelu, C.; Herrmann, F.R.; Gold, G.; Delaloye, C.; Weber, K. Neurocognitive Deficits and Personality Traits among Euthymic Patients with Mood Disorders in Late Life. J. Neurol. Sci. 2010, 299, 24–29. [Google Scholar] [CrossRef] [PubMed]
  34. Middeldorp, C.M.; De Moor, M.H.M.; McGrath, L.M.; Gordon, S.D.; Blackwood, D.H.; Costa, P.T.; Terracciano, A.; Krueger, R.F.; De Geus, E.J.C.; Nyholt, D.R.; et al. The Genetic Association between Personality and Major Depressi0on or Bipolar Disorder. A Polygenic Score Analysis Using Genome-Wide Association Data. Transl. Psychiatry 2011, 1, 1–8. [Google Scholar] [CrossRef] [PubMed]
  35. Lönnqvist, J.E.; Verkasalo, M.; Haukka, J.; Nyman, K.; Tiihonen, J.; Laaksonen, I.; Leskinen, J.; Lönnqvist, J.; Henriksson, M. Premorbid Personality Factors in Schizophrenia and Bipolar Disorder: Results from a Large Cohort Study of Male Conscripts. J. Abnorm. Psychol. 2009, 118, 418–423. [Google Scholar] [CrossRef] [PubMed]
  36. Lima, I.M.M.; Peckham, A.D.; Johnson, S.L. Cognitive Deficits in Bipolar Disord: Implications for Emotion. Clin. Psychol. Rev. 2018, 59, 126–136. [Google Scholar] [CrossRef]
  37. Mesbah, R.; Koenders, M.A.; Spijker, A.T.; de Leeuw, M.; Boschloo, L.; Penninx, B.W.J.H.; van Hemert, A.M.; Giltay, E.J. Personality Traits and the Risk of Incident (Hypo)Mania among Subjects Initially Suffering from Depressive and Anxiety Disorders in a 9-Year Cohort Study. J. Affect. Disord. 2019, 259, 451–457. [Google Scholar] [CrossRef]
  38. Greenwood, T.A.; Chow, L.J.; Gur, R.C.; Kelsoe, J.R. Bipolar Spectrum Traits and the Space between Madness and Genius: The Muse Is in the Dose. J. Psychiatr. Res. 2022, 153, 149–158. [Google Scholar] [CrossRef]
  39. Best, R.D.; Cruitt, P.J.; Oltmanns, T.F.; Hill, P.L. Neuroticism Predicts Informant Reported Cognitive Problems through Health Behaviors. Aging Ment. Health 2021, 25, 2191–2199. [Google Scholar] [CrossRef]
  40. Crow, A.J.D. Associations Between Neuroticism and Executive Function Outcomes: Response Inhibition and Sustained Attention on a Continuous Performance Test. Percept. Mot. Skills 2019, 126, 623–638. [Google Scholar] [CrossRef]
  41. Olivo, G.; Gour, S.; Schiöth, H.B. Low Neuroticism and Cognitive Performance Are Differently Associated to Overweight and Obesity: A Cross-Sectional and Longitudinal UK Biobank Study. Psychoneuroendocrinology 2019, 101, 167–174. [Google Scholar] [CrossRef]
  42. Stephan, Y.; Sutin, A.; Luchetti, M.; Terracciano, A. Findings from Three Prospective Studies. J. Psychosom. Res. 2020, 128, 109885. [Google Scholar] [CrossRef] [PubMed]
  43. Leger, K.A.; Charles, S.T.; Turiano, N.A.; Almeida, D.M. Personality and Stressor-Related Affect. J. Pers. Soc. Psychol. 2016, 111, 917–928. [Google Scholar] [CrossRef] [PubMed]
  44. Stephan, Y.; Sutin, A.R.; Bayard, S.; Križan, Z.; Terracciano, A. Personality and Sleep Quality: Evidence from Four Prospective Studies. Health Psychol. 2018, 37, 271–281. [Google Scholar] [CrossRef] [PubMed]
  45. Hakulinen, C.; Elovainio, M.; Batty, G.D.; Virtanen, M.; Kivimäki, M.; Jokela, M. Personality and Alcohol Consumption: Pooled Analysis of 72,949 Adults from Eight Cohort Studies. Drug. Alcohol. Depend. 2015, 151, 110–114. [Google Scholar] [CrossRef]
  46. Sutin, A.; Stephan, Y.; Luchetti, M.; Aschwanden, D.; Sesker, A.; O’Súilleabháin, P.; Terracciano, A. Self-Reported and Mother-Rated Personality Traits at Age 16 Are Associated with Cognitive Function Measured Concurrently and 30 Years Later. Psychol. Med. 2021, 52, 3854–3864. [Google Scholar] [CrossRef]
  47. Stephan, Y.; Boiché, J.; Canada, B.; Terracciano, A. Association of Personality with Physical, Social, and Mental Activities across the Lifespan: Findings from US and French Samples. Br. J. Psychol. 2014, 105, 564–580. [Google Scholar] [CrossRef]
  48. Xu, J.; Potenza, M.N. White Matter Integrity and Five-Factor Personality Measures in Healthy Adults. Neuroimage 2012, 59, 800–807. [Google Scholar] [CrossRef]
  49. Sutin, A.; Stephan, Y.; Luchetti, M.; Terracciano, A. Five-Factor Model Personality Traits and Cognitive Function in Five Domains in Older Adulthood. BMC Geriatr. 2019, 19, 343. [Google Scholar] [CrossRef]
  50. Sutin, A.; Stephan, Y.; Damian, R.I.; Luchetti, M.; Strickhouser, J.; Terracciano, A. Five-Factor Model Personality Traits and Verbal Fluency in 10 Cohorts. Psychol. Aging 2019, 34, 362–373. [Google Scholar] [CrossRef]
  51. Stringer, D.; Marshall, D.; Pester, B.; Baker, A.; Langenecker, S.A.; Angers, K.; Frazier, N.; Archer, C.; Kamali, M.; McInnis, M.; et al. Openness Predicts Cognitive Functioning in Bipolar Disorder. J. Affect. Disord. 2014, 168, 51–57. [Google Scholar] [CrossRef]
  52. Chang, H.; Hoshina, N.; Zhang, C.; Ma, Y.; Cao, H.; Wang, Y.; Wu, D.D.; Bergen, S.E.; Landén, M.; Hultman, C.M.; et al. The Protocadherin 17 Gene Affects Cognition, Personality, Amygdala Structure and Function, Synapse Development and Risk of Major Mood Disorders. Mol. Psychiatry 2018, 23, 400–412. [Google Scholar] [CrossRef] [PubMed]
  53. Bauer, I.; Wu, M.-J.; Meyer, T.; Mwangi, B.; Ouyang, A.; Spiker, D.; Zunta-Soares, G.; Huang, H.; Soares, J. The Role of White Matter in Personality Traits and Affective Processing in Bipolar Disorder. J. Psychiatr. Res. 2016, 80, 64–72. [Google Scholar] [CrossRef] [PubMed]
  54. Wittchen, H.U.; Wunderlich, U.; Gruschwitz, S.; Zaudig, M. SCID: Clinical Interview for DSM-IV (German Version); Hogrefe: Göttingen, Germany, 1997. [Google Scholar]
  55. Young, R.C.; Biggs, J.T.; Ziegler, V.E.; Meyer, D.A. A Rating Scale for Mania: Reliability, Validity and Sensitivity. Br. J. Psychiatry 1978, 133, 429–435. [Google Scholar] [CrossRef] [PubMed]
  56. Hamilton, M. Development of a Rating Scale for Primary Depressive Illness. Br. J. Psychol. 1967, 6, 278–296. [Google Scholar] [CrossRef] [PubMed]
  57. Niemann, H.; Sturm, W.; Thöne-Otto, A.I.T.; Willmes, K. CVLT California Verbal Learning Test German Adaptation; Pearson Assessment: Frankfurt, Germany, 2008. [Google Scholar]
  58. Reitan, R.M. Trail Making Test: Manual for Administration and Scoring; Reitan Neuropsychology Laboratory: Tempe, AZ, USA, 1992. [Google Scholar]
  59. Brickenkamp, R.; Schmidt-Atzert, L.; Liepmann, D.; Schmidt-Atzert, L. D2-R: Test D2-Revision: Aufmerksamkeits-Und Konzentrationstest; Hogrefe: Göttingen, Germany, 2010. [Google Scholar]
  60. Bäumler, G. Farbe-Wort-Interferenztest (FWIT) nach J. R. Stroop: Handanweisung; Hogrefe: Göttingen, Germany, 1985. [Google Scholar]
  61. Borkenau, P.; Ostendorf, F. NEO-FFI: NEO-Fünf-Faktoren-Inventar Nach Costa und McCrae, Manual; Hogrefe: Göttingen, Germany, 2008. [Google Scholar]
  62. Körner, A.; Geyer, M.; Roth, M.; Drapeau, M.; Schmutzer, G.; Albani, C.; Schumann, S.; Brähler, E. Persönlichkeitsdiagnostik Mit Dem NEO−Fünf−Fakto− Ren−Inventar: Die 30−Item−Kurzversion (NEO−FFI−30). Psychother. Psychosom. Med. Psychol. 2008, 58, 238–245. [Google Scholar] [CrossRef] [PubMed]
  63. Faul, F.; Buchner, A.; Lang, A.-G. Statistical Power Analyses Using G*Power 3.1: Tests for Correlation and Regression Analyses. Behav. Res. Methods 2009, 41, 1149–1160. [Google Scholar] [CrossRef]
  64. Matsumoto, Y.; Suzuki, A.; Shirata, T.; Takahashi, N.; Noto, K.; Goto, K.; Otani, K. Implication of the DGKH Genotype in Openness to Experience, a Premorbid Personality Trait of Bipolar Disorder. J. Affect. Disord. 2018, 238, 539–541. [Google Scholar] [CrossRef]
  65. Gale, C.R.; Deary, I.J.; Kuh, D.; Huppert, F.; Richards, M. Neuroticism in Adolescence and Cognitive Function in Midlife in the British 1946 Birth Cohort: The HALCyon Program. J. Gerontol. B Psychol. Sci. Soc. Sci. 2010, 65, 50–56. [Google Scholar] [CrossRef]
  66. Vancampfort, D.; Sienaert, P.; Wyckaert, S.; Probst, M.; De Herdt, A.; De Hert, M.; Stubbs, B.; Buys, R. Cardiorespiratory Fitness in Outpatients with Bipolar Disorder versus Matched Controls: An Exploratory Study. J. Affect. Disord. 2016, 199, 1–5. [Google Scholar] [CrossRef]
  67. Jackson, J.G.; Diaz, F.J.; Lopez, L.; de Leon, J. A Combined Analysis of Worldwide Studies Demonstrates an Association between Bipolar Disorder and Tobacco Smoking Behaviors in Adults. Bipolar Disord. 2015, 17, 575–597. [Google Scholar] [CrossRef]
  68. Lopresti, A.L.; Jacka, F.N. Diet and Bipolar Disorder: A Review of Its Relationship and Potential Therapeutic Mechanisms of Action. J. Altern. Complement. Med. 2015, 21, 733–739. [Google Scholar] [CrossRef]
  69. Bell, T.; Hill, N.; Stavrinos, D. Personality Determinants of Subjective Executive Function in Older Adults. Aging Ment. Health 2020, 24, 1935–1944. [Google Scholar] [CrossRef]
  70. Kim, B.R.; Lee, R.; Kim, N.; Jeong, J.H.; Kim, G.H. The Moderating Role of Sleep Quality on the Association between Neuroticism and Frontal Executive Function in Older Adults. Behav. Sleep. Med. 2022, 20, 50–62. [Google Scholar] [CrossRef] [PubMed]
  71. Dalkner, N.; Bengesser, S.A.; Birner, A.; Fellendorf, F.T.; Fleischmann, E.; Großschädl, K.; Lenger, M.; Maget, A.; Platzer, M.; Queissner, R.; et al. Metabolic Syndrome Impairs Executive Function in Bipolar Disorder. Front. Neurosci. 2021, 15, 717824. [Google Scholar] [CrossRef] [PubMed]
  72. Forbes, C.E.; Poore, J.C.; Krueger, F.; Barbey, A.K.; Solomon, J.; Grafman, J. The Role of Executive Function and the Dorsolateral Prefrontal Cortex in the Expression of Neuroticism and Conscientiousness. Soc. Neurosci. 2014, 9, 139–151. [Google Scholar] [CrossRef] [PubMed]
  73. Mcdougall, S.; Pfeifer, G. Personality Differences in Mental Imagery and the Effects on Verbal Memory. Br. J. Psychol. 2012, 103, 556–573. [Google Scholar] [CrossRef]
  74. Merema, M.R.; Speelman, C.P.; Foster, J.K.; Kaczmarek, E.A. Neuroticism (Not Depressive Symptoms) Predicts Memory Complaints in Some Community-Dwelling Older Adults. Am. J. Geriatr. Psychiatry 2013, 21, 729–736. [Google Scholar] [CrossRef] [PubMed]
  75. Caselli, R.J.; Dueck, A.C.; Locke, D.E.C.; Henslin, B.R.; Johnson, T.A.; Woodruff, B.K.; Hoffman-Snyder, C.; Geda, Y.E. Impact of Personality on Cognitive Aging: A Prospective Cohort Study. J. Int. Neuropsychol. Soc. 2016, 22, 765–776. [Google Scholar] [CrossRef]
  76. Spinhoven, P.; Huijbers, M.J.; Ormel, J.; Speckens, A.E.M. Improvement of Mindfulness Skills during Mindfulness-Based Cognitive Therapy Predicts Long-Term Reductions of Neuroticism in Persons with Recurrent Depression in Remission. J. Affect. Disord. 2017, 213, 112–117. [Google Scholar] [CrossRef]
  77. Sauer-Zavala, S.; Fournier, J.C.; Jarvi Steele, S.; Woods, B.K.; Wang, M.; Farchione, T.J.; Barlow, D.H. Does the Unified Protocol Really Change Neuroticism? Results from a Randomized Trial. Psychol. Med. 2020, 51, 2378–2387. [Google Scholar] [CrossRef]
  78. Schneider, M.; Preckel, F. Variables Associated with Achievement in Higher Education: A Systematic Review of Meta-Analyses. Psychol. Bull. 2017, 143, 565–600. [Google Scholar] [CrossRef]
  79. Sutin, A.R.; Aschwanden, D.; Stephan, Y.; Terracciano, A. The Association between Facets of Conscientiousness and Performance-Based and Informant-Rated Cognition, Affect, and Activities in Older Adults. J. Pers. 2022, 90, 121–132. [Google Scholar] [CrossRef] [PubMed]
  80. Kestens, K.; Degeest, S.; Miatton, M.; Keppler, H. Visual and Verbal Working Memory and Processing Speed Across the Adult Lifespan: The Effect of Age, Sex, Educational Level, Awakeness, and Hearing Sensitivity. Front. Psychol. 2021, 12, 668828. [Google Scholar] [CrossRef] [PubMed]
  81. Kuznetsova, K.A.; Maniega, S.M.; Ritchie, S.J.; Cox, S.R.; Storkey, A.J.; Starr, J.M.; Wardlaw, J.M.; Deary, I.J.; Bastin, M.E. Brain White Matter Structure and Information Processing Speed in Healthy Older Age. Brain Struct. Funct. 2016, 221, 3223–3235. [Google Scholar] [CrossRef] [PubMed]
  82. Zhao, Y.; Liu, P.; Turner, M.P.; Abdelkarim, D.; Lu, H.; Rypma, B. The Neural–Vascular Basis of Age-Related Processing Speed Decline. Psychophysiology 2021, 58, e13845. [Google Scholar] [CrossRef]
  83. Blair, C. Executive Function and Early Childhood Education. Curr. Opin. Behav. Sci. 2016, 10, 102–107. [Google Scholar] [CrossRef]
  84. Clark, C.A.C.; Pritchard, V.E.; Woodward, L.J. Preschool Executive Functioning Abilities Predict Early Mathematics Achievement. Dev. Psychol. 2010, 46, 1176–1191. [Google Scholar] [CrossRef]
  85. Van der Elst, W.; Ouwehand, C.; van der Werf, G.; Kuyper, H.; Lee, N.; Jolles, J. The Amsterdam Executive Function Inventory (AEFI): Psychometric Properties and Demographically Corrected Normative Data for Adolescents Aged between 15 and 18 Years. J. Clin. Exp. Neuropsychol. 2012, 34, 160–171. [Google Scholar] [CrossRef]
  86. Ju, G.; Yoon, I.-Y.; Lee, S.D.; Kim, T.H.; Choe, J.Y.; Kim, K.W. Effects of Sleep Apnea Syndrome on Delayed Memory and Executive Function in Elderly Adults. J. Am. Geriatr. Soc. 2012, 60, 1099–1103. [Google Scholar] [CrossRef]
  87. Voos, M.C.; Custódio, E.B.; Malaquias, J.J. Relationship of Executive Function and Educational Status with Functional Balance in Older Adults. J. Geriatr. Phys. Ther. 2011, 34, 11–18. [Google Scholar] [CrossRef]
  88. Blair, C. Educating Executive Function. Wiley Interdiscip. Rev. Cogn. 2017, 8, e1403. [Google Scholar] [CrossRef]
  89. Lorant, V.; Deliège, D.; Eaton, W.; Robert, A.; Philippot, P.; Ansseau, M. Socioeconomic Inequalities in Depression: A Meta-Analysis. Am. J. Epidemiol. 2003, 157, 98–112. [Google Scholar] [CrossRef] [PubMed]
  90. Espenes, J.; Eliassen, I.V.; Öhman, F.; Hessen, E.; Waterloo, K.; Eckerström, M.; Lorentzen, I.M.; Bergland, C.; Halvari Niska, M.; Timón-Reina, S.; et al. Regression-Based Normative Data for the Rey Auditory Verbal Learning Test in Norwegian and Swedish Adults Aged 49–79 and Comparison with Published Norms. Clin. Neuropsychol. 2022, 36, 1–25. [Google Scholar] [CrossRef] [PubMed]
  91. De Wit, L.; Kirton, J.W.; O’Shea, D.M.; Szymkowicz, S.M.; McLaren, M.E.; Dotson, V.M. Effects of Body Mass Index and Education on Verbal and Nonverbal Memory. Neuropsychol. Dev. Cogn. B Aging Neuropsychol. Cogn. 2017, 24, 256–263. [Google Scholar] [CrossRef] [PubMed]
Table
Table 1. Correlations between the Big Five and cognitive function in healthy controls.
Table 1. Correlations between the Big Five and cognitive function in healthy controls.
Big Five FactorCorrelation with High Cognitive Function
Neuroticismnegative
Opennesspositive
Conscientiousnesspositive
Extraversionpositive
Agreeablenessunclear
Table
Table 2. Sociodemographic information, Big Five, and cognitive test scores of the cross-sectional sample of individuals with bipolar disorder.
Table 2. Sociodemographic information, Big Five, and cognitive test scores of the cross-sectional sample of individuals with bipolar disorder.
VariablesCross-Sectional Sample (n = 129)
M (±SD)
Age43.45 (13.25)
Sex (n)
Male64 (49.6%)
Female65 (50.4%)
Type of bipolar disorder
Type 187 (67.4%)
Type 242 (32.6%)
HAMD5.02 (4.03)
YMRS1.13 (2.62)
Big Five
Openness29.43 (6.74)
Conscientiousness31.11 (7.53)
Extraversion25.43 (6.94)
Agreeableness30.38 (6.37)
Neuroticism27.36 (8.68)
Cognition
TMT-A34.71 (15.04)
TMT-B80.64 (41.57)
d2-R146.76 (43.26)
Stroop color word reading31.53 (5.50)
Stroop color naming48.92 (8.31)
Stroop interference81.11 (20.45)
CVLT trial 1-553.09 (12.84)
CVLT short delay free recall10.64 (3.55)
CVLT short delay cued recall11.67 (3.22)
CVLT long delay free recall11.56 (3.54)
CVLT long delay cued recall11.97 (3.28)
Note. HAMD = Hamilton Depression Rating Scale; YMRS = Young Mania Rating Scale; TMT = Trail Making Test, d2-R = d2-Test Revised; CVLT = California Verbal Learning Test.
Table
Table 3. Sociodemographic information, Big Five, and cognitive test scores of the cross-sectional sample of individuals with bipolar disorder.
Table 3. Sociodemographic information, Big Five, and cognitive test scores of the cross-sectional sample of individuals with bipolar disorder.
VariablesLongitudinal Sample (n = 35)
t1 (M ± SD)t2 (M ± SD)Statisticspη2
Age43.99 (14.65)46.14 (14.67)t = −9.22<0.001
Sex (n)
Male16 (45.7%)
Female19 (54.3%)
Type of bipolar disorder
Type 123 (65.7%)
Type 212 (34.3%)
HAMD3.63 (3.24)3.17 (3.09)t = 0.200.846
YMRS1.88 (3.98)1.46 (3.15)Z = −0.410.682
Time difference between t1 and t2 (days) 959.03 (462.92)
Big Five
Openness29.49 (6.46)
Conscientiousness31.43 (7.44)
Extraversion25.51 (6.46)
Agreeableness30.11 (6.12)
Neuroticism26.97 (8.06)
Cognition
TMT-A34.82 (10.78)28.18 (12.89)F = 1.530.2280.03
TMT-B83.30 (46.30)63.19 (31.31)F = 1.780.1950.05
Stroop color word reading32.42 (5.29)32.64 (6.66)F = 1.210.2820.00
Stroop color naming50.52 (9.10)50.26 (10.66)F = 1.200.2830.01
Stroop interference84.21 (20.03)81.57 (23.71)F = 1.150.2940.03
CVLT trial 1-552.51 (13.92)51.05 (11.28)F = 0.100.7570.01
Note. Cognitive differences were calculated using repeated-measures multivariate analyses of covariance (controlled for age, sex, education, BDI, illness duration, and time difference) testing differences between t1 and t2. HAMD = Hamilton Depression Rating Scale; YMRS = Young Mania Rating Scale; TMT = Trail Making Test, d2-R = d2-Test Revised; CVLT = California Verbal Learning Test.
Table
Table 4. Correlations between the Big Five and cognitive function at t1 in the cross-sectional sample of individuals with bipolar disorder (n = 129).
Table 4. Correlations between the Big Five and cognitive function at t1 in the cross-sectional sample of individuals with bipolar disorder (n = 129).
Big FiveCognitive Function
Executive Function aVerbal Memory bAttention and Processing Speed c
rprprp
Openness0.150.1120.040.6870.220.020
Conscientiousness0.020.827−0.080.396−0.010.904
Extraversion0.040.6910.110.2690.130.168
Agreeableness−0.010.942−0.030.7920.070.448
Neuroticism−0.280.0030.090.927−0.180.058
Note. BD = bipolar disorder; a sum score of the Trail Making Test (TMT) part B and the interference trial of J. Stroop’s Color and Word Interference Test; b sum score of California Verbal Learning Test (CVLT) trial 1-5, CVLT short delay free recall, CVLT long delay free recall, CVLT short delay cued recall, and CVLT long delay cued recall; c sum score of d2 Test of Attention Revised, Stroop’s word-reading and color-naming trials, and TMT part A. Results remaining significant after the employment of False Discovery Rate (FDR) are marked in bold letters. Covariates included age, sex, education, and BDI.
Table
Table 5. Correlations between the Big Five at t1 and the cognitive function differences between t1 and t2 in the longitudinal sample of individuals with bipolar disorder (n = 35).
Table 5. Correlations between the Big Five at t1 and the cognitive function differences between t1 and t2 in the longitudinal sample of individuals with bipolar disorder (n = 35).
Big FiveCognitive Function Differences between t1 and t2
Executive Function aVerbal Memory bAttention and Processing Speed c
rprprp
Openness0.010.962−0.070.7510.100.966
Conscientiousness−0.300.145−0.140.491−0.090.683
Extraversion0.150.482−0.020.9160.200.338
Agreeableness−0.190.368−0.380.059−0.190.376
Neuroticism−0.020.9290.300.141−0.050.823
Note. BD = bipolar disorder; a difference between sum scores at t1 and t2 according to the Trail Making Test (TMT) part B and the interference trial of J. Stroop’s Color and Word Interference Test; b difference between California Verbal Learning Test (CVLT) trial 1-5 scores at t1 and t2; c difference between sum scores at t1 and t2 according to Stroop’s word-reading and color-naming trials as well as TMT part A. A false discovery rate (FDR) correction for multiple comparisons was used. Covariates included age, sex, education, BDI, illness duration, and time difference between t1 and t2.
Table
Table 6. Multiple hierarchical regression analysis predicting executive function at t2.
Table 6. Multiple hierarchical regression analysis predicting executive function at t2.
ModelVariablesExecutive Function a
βtp
Model 1Age−0.24−0.960.347
Sex0.160.920.366
Education0.432.170.041
Time difference t1–t20.130.760.458
BDI−0.14−0.820.418
Illness duration0.080.380.710
Model 2Age−0.120.580.576
Sex0.351.890.074
Education0.412.260.036
Time difference t1–t20.562.600.018
BDI0.381.660.113
Illness duration0.090.470.644
Openness−0.05−0.320.753
Conscientiousness0.482.550.019
Extraversion−0.30−1.440.166
Agreeableness−0.09−0.510.616
Neuroticism−0.82−3.300.004
Note. Model 1: R2 = 0.38, R2 corr. = 0.23, SE = 1.48. Model 2: R2 = 0.65, R2 corr. = 0.45, SE = 1.25. Significant p-values (p < 0.05) are written in bold. a Sum score of the Trail Making Test part B and the interference trial of J. Stroop’s Color and Word Interference Test.
Table
Table 7. Multiple hierarchical regression analysis predicting verbal memory at t2.
Table 7. Multiple hierarchical regression analysis predicting verbal memory at t2.
ModelVariablesVerbal Memory a
βtp
Model 1Age−0.62−2.680.013
Sex0.171.050.303
Education0.110.560.581
Time difference t1–t20.090.540.594
BDI0.110.670.512
Illness duration0.130.620.538
Model 2Age−0.55−2.660.016
Sex0.331.870.078
Education−0.02−0.140.890
Time difference t1–t20.231.110.280
BDI0.452.030.057
Illness duration0.221.130.273
Openness0.161.080.293
Conscientiousness0.130.730.477
Extraversion−0.27−1.380.184
Agreeableness0.171.080.295
Neuroticism−0.68−2.840.011
Note. Model 1: R2 = 0.45, R2 corr. = 0.31, SE = 0.81. Model 2: R2 = 0.68, R2 corr. = 0.49, SE = 0.70. Significant p-values (p < 0.05) are written in bold. a Score on the California Verbal Learning Test (CVLT) trial 1-5.
Table
Table 8. Multiple hierarchical regression analysis predicting attention and processing speed at t2.
Table 8. Multiple hierarchical regression analysis predicting attention and processing speed at t2.
ModelVariablesAttention and Processing Speed a
βtp
Model 1Age−0.45−2.050.051
Sex0.060.420.680
Education0.231.330.197
Time difference t1–t20.150.100.349
BDI−0.27−1.710.099
Illness duration0.010.040.970
Model 2Age−0.39−2.100.049
Sex0.261.240.229
Education0.201.070.300
Time difference t1–t20.361.600.127
BDI0.010.030.977
Illness duration0.020.100.922
Openness0.000.001.00
Conscientiousness0.241.230.235
Extraversion−0.29−1.340.195
Agreeableness−0.05−0.270.791
Neuroticism−0.52−1.020.057
Note. Model 1: R2 = 0.52, R2 corr. = 0.40, SE = 1.85; Model 2: R2 = 0.60, R2 corr. = 0.43, SE = 1.84. Significant p-values (p < 0.05) are written in bold. a Sum score of Stroop word-reading and color-naming trials of the Color and Word Interference Test by J. Stroop and the Trail Making Test (TMT), part A.
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Fleischmann, E.; Dalkner, N.; Fellendorf, F.T.; Bengesser, S.A.; Lenger, M.; Birner, A.; Queissner, R.; Platzer, M.; Tmava-Berisha, A.; Maget, A.; et al. The Big Five as Predictors of Cognitive Function in Individuals with Bipolar Disorder. Brain Sci. 2023, 13, 773. https://doi.org/10.3390/brainsci13050773

AMA Style

Fleischmann E, Dalkner N, Fellendorf FT, Bengesser SA, Lenger M, Birner A, Queissner R, Platzer M, Tmava-Berisha A, Maget A, et al. The Big Five as Predictors of Cognitive Function in Individuals with Bipolar Disorder. Brain Sciences. 2023; 13(5):773. https://doi.org/10.3390/brainsci13050773

Chicago/Turabian Style

Fleischmann, Eva, Nina Dalkner, Frederike T. Fellendorf, Susanne A. Bengesser, Melanie Lenger, Armin Birner, Robert Queissner, Martina Platzer, Adelina Tmava-Berisha, Alexander Maget, and et al. 2023. "The Big Five as Predictors of Cognitive Function in Individuals with Bipolar Disorder" Brain Sciences 13, no. 5: 773. https://doi.org/10.3390/brainsci13050773

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