Personality and Primary Emotional Traits: Disentangling Multiple Sclerosis Related Fatigue and Depression

Personality and Primary Emotional Traits: Disentangling Multiple Sclerosis Related Fatigue and... Abstract Objective It remains an unresolved research objective to clarify the overlap/association between fatigue (especially its cognitive facet) and depression in People with MS (PwMS). Therefore, in this study the patterns of personality and primary emotional traits (PETs) associated with each (motoric/cognitive fatigue and depression in PwMS) were investigated and compared in order to disentangle the three constructs in PwMS. Additionally, differences in personality and PETs between PwMS and healthy controls (HC) were examined. Method Associations between motoric/cognitive fatigue, depression, personality and PETs were investigated in 52 PwMS. Personality and PETs were also assessed in a gender matched HC sample (N = 52) and results regarding these were compared between PwMS and HC. Results Low extraversion was the only significant predictor of MS related motoric fatigue (β = −.341, p = .013). High neuroticism was a predictor of both MS related cognitive fatigue (β = .426, p = .002) and depression (β = .443, p < .001). Whereas neuroticism was the only significant predictor for MS related cognitive fatigue, the cluster of (high) neuroticism, (high) SADNESS (β = .273, p = .023), and (low) extraversion (β = −.237, p = .025) predicted MS related depression. PwMS showed significantly higher scores in neuroticism and FEAR compared to HC. Conclusions MS related motoric/cognitive fatigue and depression in PwMS share variance. But the substantial amount of non-shared variance (motoric fatigue, depression: 72%; cognitive fatigue, depression: 66%) together with additional predictors for depression (SADNESS being a predictor of depression only), indicate that MS related motoric/cognitive fatigue and depression are distinguishable. Consequently, we recommend assessing especially SADNESS scores to distinguish between MS related fatigue and depression. Multiple sclerosis, Depression, Fatigue, Personality, Big Five, Primary emotional traits Introduction Multiple Sclerosis, Fatigue, and Depression Multiple Sclerosis (MS) is a neurological disorder affecting about 2.3 million persons worldwide (Multiple Sclerosis International Federation, 2016). One of the most commonly reported symptoms of MS is fatigue (Krupp, Alvarez, LaRocca, & Scheinberg, 1988; Stuke et al., 2009; Wood et al., 2013), which is often split into a motoric and a cognitive fatigue facet (Penner et al., 2009). In addition to fatigue, many PwMS suffer from depression (Arnett, Barwick, & Beeney, 2008; Siegert & Abernathy, 2005; Please note that for means of simplification and because an exact definition of depression is not in the scope of the present research endeavor, depressive symptoms as well as Major Depressive Disorder will be subsumed under the term “depression” in the following sections). An overlap between facets of fatigue in PwMS and depression in PwMS has often been posited, as fatigue is a central symptom of both MS and depression (American Psychiatric Association, 2013). In line with this, many studies report a strong association between fatigue—especially its cognitive facet—and depression in PwMS (e.g. Bakshi et al., 2000; Ford, Trigwell, & Johnson, 1998; Kroencke, Lynch, & Denney, 2000; Penner et al., 2007; Schreurs, de Ridder & Benzing, 2002). Although this overlap has often been observed, contradicting results also exist (as outlined with the following studies: e. g., Krupp et al., 1988; Janardhan & Bakshi, 2002). Personality and Primary Emotional Traits (PETs) Following the classic Big Five personality theory, personality can be divided into five traits commonly known as neuroticism, extraversion, openness, agreeableness, and conscientiousness (for further information see Costa & McCrae, 1992; The Big Five of Personality refer to the original used lexical approach to come up with the well-replicated personality structure introduced in the following. The works by Costa & McCrae built upon this model their Five Factor Model of Personality resulting in the famous inventories NEO-FFI and NEO-PI-R. Given the large overlap between both theories, we use the term Big Five and Five Factor Model somewhat interchangeably [as many other researchers also do]). Neuroticism, in particular, has been detected as a vulnerability factor for many disorders, including affective disorders such as depression (Hengartner et al., 2016; Jylhä & Isometsä, 2006; Kendler, Kuhn, & Prescott, 2004). Moreover, neuroticism has been positively associated with MS related fatigue (both motoric and cognitive; Lahey, 2009; Penner et al., 2007). Additionally, extraversion has been found to have a negative relationship with both facets of fatigue (Penner et al., 2007; Johnson, DeLuca & Natelson, 1996; Merkelbach, König, & Sittinger, 2003). Based on another perspective on personality, Jaak Panksepp provided evidence for seven neural circuitries across the mammalian brains for positive (SEEKING, CARE, LUST, PLAY) and negative affect (SADNESS, FEAR, ANGER) (Panksepp, 1998, 2011; Please note that the primary emotional systems/traits are written in capital letters to not confound them with other terms used in (personality) psychology). These primary emotional systems are assumed to influence human personality bottom-up. Hence, individual differences in these primary emotional systems can be assessed via a trait approach (primary emotional traits (PETs)) by the Affective Neuroscience Personality Scales (ANPS) (Davis, Panksepp, & Normansell, 2003; see Montag & Panksepp, 2017 for a more detailed explanation of the PETs; see Panksepp, 2011; Montag, Sindermann, Becker, & Panksepp, 2016 for an overview of underlying brain structures and molecules). Noticeably, individual differences in PETs are somewhat linked to the Big Five model. As such, high scores in FEAR, ANGER and SADNESS have been found to positively correlate with neuroticism (Davis & Panksepp, 2011; Montag & Panksepp, 2017; Reuter et al., 2017; Sindermann et al., 2016). To our knowledge, PETs have not been studied in the context of MS related fatigue or MS related depression before. But recent work by Montag et al. shows robust associations between individual differences in PETs and depressive tendencies (Montag, Widernhorn-Müller, Panksepp, & Kiefer, 2016). Additionally, one can denote that the associations between the PETs and the Big Five has allowed for a credible assumption of a further relationship with symptoms of fatigue and depression in PwMS. Research Objectives This field of research is still struggling to answer whether or not fatigue—specifically cognitive fatigue—in PwMS is independent from depression in PwMS. In the present study we aimed to revisit this important research question by utilizing a personality approach. In light of this, we (exploratively) investigated and compared the patterns of associations between MS related fatigue and personality and PETs, with the patterns of associations between MS related depression and personality and PETs. Going beyond this, it must be noted that differences in personality traits between PwMS and HC have already been discovered in earlier studies. They reported increased neuroticism, in detail with its subfacets anxiety, hostility and vulnerability, as well as decreased extraversion, agreeableness and conscientiousness in PwMS compared to HC (Benedict, Priore, Miller, Munschauer, & Jacobs, 2001; Johnson et al., 1996; Penner et al., 2007). Based on these outcomes, the second aim of the study was to replicate the findings on differences in personality between PwMS and HC. Additionally, we compared PETs between PwMS and a control sample, which is novel within this research. We expected neuroticism, FEAR, ANGER, and SADNESS to be higher in PwMS whereas we expected extraversion, agreeableness and conscientiousness to be higher in the HC sample. Methods Sample The data of N = 52 PwMS were available for the study (males: n = 9, females: n = 43; age: M = 45.13, SD = 9.56). PwMS who did not fill in the questionnaires properly (see Supplementary material online) were not included in the study. The age of MS diagnosis lay between 15 and 58 (M = 37.21, SD = 11.12). Of note, as the exact date of birth but only the year in which the MS diagnosis was made was available, these results are based on the difference between year of birth and year of diagnosis. The range of disease duration was from less than 1 year to more than 29 years (M = 8.67, SD = 7.36). As the exact date of data assessment but only the year in which the MS diagnosis was made was available, these results are based on the difference between year of data assessment and year of diagnosis. The majority of the PwMS (N = 29) stated they had “Mittlere Reife” (a German form of graduation from school, which does not permit studying at a university) as their highest educational degree. More detailed information about the PwMS sample is presented in Table 1. All PwMS filled in all questionnaires in paper-pencil format, and they were used as an initial assessment on arrival at the Neurological Rehabilitation Centre Godeshöhe. Table 1. Detailed description of the PwMS sample N % Type of MS  Relapsing-remitting 28 54  Secondary progressive 7 13  Primary progressive 9 17  Clinically isolated syndromea 1 2  Uncertain course of MS 7 13 EDSS scoreb  1 3 6  1.5 6 12  2 2 4  2.5 7 14  3 6 12  3.5 8 15  4 8 15  4.5 4 8  5.5 1 2  6 3 6  6.5 1 2  7.5 2 4  Missing 1 2 Medication  MS medication 26 50  Antidepressive medicationc 10 19 N % Type of MS  Relapsing-remitting 28 54  Secondary progressive 7 13  Primary progressive 9 17  Clinically isolated syndromea 1 2  Uncertain course of MS 7 13 EDSS scoreb  1 3 6  1.5 6 12  2 2 4  2.5 7 14  3 6 12  3.5 8 15  4 8 15  4.5 4 8  5.5 1 2  6 3 6  6.5 1 2  7.5 2 4  Missing 1 2 Medication  MS medication 26 50  Antidepressive medicationc 10 19 aEpisode caused by inflammatory/demyelination in one or more sites in the CNS. bExpanded Disability Status Scale. cPrimarily serotonin-specific reuptake inhibitors (SSRI), or selective serotonin-norepinephrine reuptake inhibitors (SNRI). Most of the PwMS were already receiving this before admission into the rehabilitation centre. If the precentage data of Type of MS and EDSS score does not sum up to exactly 100% this is due to rounding inaccuracies. Table 1. Detailed description of the PwMS sample N % Type of MS  Relapsing-remitting 28 54  Secondary progressive 7 13  Primary progressive 9 17  Clinically isolated syndromea 1 2  Uncertain course of MS 7 13 EDSS scoreb  1 3 6  1.5 6 12  2 2 4  2.5 7 14  3 6 12  3.5 8 15  4 8 15  4.5 4 8  5.5 1 2  6 3 6  6.5 1 2  7.5 2 4  Missing 1 2 Medication  MS medication 26 50  Antidepressive medicationc 10 19 N % Type of MS  Relapsing-remitting 28 54  Secondary progressive 7 13  Primary progressive 9 17  Clinically isolated syndromea 1 2  Uncertain course of MS 7 13 EDSS scoreb  1 3 6  1.5 6 12  2 2 4  2.5 7 14  3 6 12  3.5 8 15  4 8 15  4.5 4 8  5.5 1 2  6 3 6  6.5 1 2  7.5 2 4  Missing 1 2 Medication  MS medication 26 50  Antidepressive medicationc 10 19 aEpisode caused by inflammatory/demyelination in one or more sites in the CNS. bExpanded Disability Status Scale. cPrimarily serotonin-specific reuptake inhibitors (SSRI), or selective serotonin-norepinephrine reuptake inhibitors (SNRI). Most of the PwMS were already receiving this before admission into the rehabilitation centre. If the precentage data of Type of MS and EDSS score does not sum up to exactly 100% this is due to rounding inaccuracies. To find a gender matched control sample, all participants of the Ulm Gene Brain Behavior Project (UGBBP) were screened for depression (Becks Depression Inventory - II (BDI-II) score lower than 13 (Hautzinger, Keller & Kühner, 2006; Cut-Offs from DGPPN et al., 2015)). Every participant who reported a BDI-II score of 13 or higher and/or a history of traumatic brain injury or other neurological or psychological disorders was excluded. A sample of N = 490 participants remained. From these, N = 52 healthy and gender matched controls (males: n = 9, females: n = 43; Age: M = 33.13, SD = 10.04) were randomly selected (at first, the subjects from the UGBBP, which were closest in age to each PwMS were chosen. If more than one participant from the UGBBP had the same age, the HC matches were selected randomly from these). As the UGBBP mostly consists of students, a perfect age match with the PwMS sample was not possible (hence, age was controlled for in the analyses when needed). The majority of the HC stated “Abitur” (N = 18; graduation from school, which does permit to study at a university) or university graduation (N = 21) as their highest educational degree. The study was approved by the local ethics committee at the University of Bonn, Bonn, Germany, and all participants gave informed written/electronic consent prior to participation. Material and Questionnaires Specified information about how the scores of the different questionnaires/scales were calculated is presented in the Supplementary material online. The internal consistencies of all questionnaires/scales are presented in Supplementary material online, Table 1. To assess fatigue in PwMS the Fatigue Scale for Motor and Cognitive Functions (FSMC) was administered (Penner et al., 2009). This scale consists of 20 items. In addition to the total sum score, sum scores for the subscales motor and cognitive fatigue are calculated—each consisting of 10 items. The Allgemeine Depressionsskala (ADS; translated from German as the General Depression Scale) was used to assess depressive symptoms within the last week in PwMS (Hautzinger, Bailer, Hofmeister, & Keller, 2012). It consists of 20 items. In order to assess the Big Five of Personality the German version of the NEO Five Factor Inventory (NEO-FFI) was administered (Costa & McCrae, 1992; Ostendorf, & Angleitner, 2003). This inventory assesses individual differences in neuroticism, extraversion, openness, agreeableness and conscientiousness, with each scale consisting of 12 items. Besides the NEO-FFI, the Affective Neuroscience Personality Scales (ANPS) were also administered to the PwMS as well as the HC sample (Davis, Panksepp, & Normansell, 2003). The German version has been used prior to this study (e. g. Sindermann et al., 2016) and a manual has been published by Reuter, Panksepp, Davis, and Montag (2017). The questionnaire assesses individual differences in the PETs SEEKING, FEAR, CARE, ANGER, PLAY, and SADNESS. Each scale consists of 14 items. Additionally, it includes the scale Spirituality, which consists of 12 items. The PET LUST is not assessed due to potential negative carry over effects in answering the other scales, when being asked about one’s own sexual activity. In total the German ANPS include 110 items. Statistical Analyses Control variables First, the effects of disease duration, age and gender on the FSMC, ADS, NEO-FFI, and ANPS were tested. Therefore, correlations between disease duration, age and the FSMC, ADS, NEO-FFI, and ANPS were calculated in each sample separately (note that FSMC and ADS scores were not available in the HC sample). Also, MANOVAs for testing the effects of gender were implemented in each sample. For a detailed description of why effects of these control variables were tested, see Supplementary material online. Given (i) the age correlations with personality and PETs presented in the Results section and (ii) the fact that the PwMS sample was significantly older than the HC sample (T(102) = 6.24, p < .001), all further analyses were controlled for age. Gender is represented as a variable when dealing with the regression models. But due to the small number of males in both samples and the one-on-one gender matching procedure, we did not take into account gender as a further variable in the analyses when contrasting the PwMS sample with the HC sample. It was not controlled for disease duration in further analyses as this variable correlated significantly with the CARE scale of the ANPS only; the CARE scale was not expected, and indeed not found, to be correlated with the FSMC scales or the ADS. Research objectives To investigate the first research objective, namely the associations between MS related fatigue facets/depression and personality and PETs, the partial correlations between the FSMC, the ADS, the NEO-FFI, and the ANPS were calculated in the PwMS sample. Age was included as control variable (see paragraph about control variables and significant correlations with age presented in the Results section). Hence, important predictors, which should be included in the following regression analyses (correlations with (motoric/cognitive) fatigue, ADS: p < .05), were examined by these correlational analyses first. Afterwards, these were included as predictors in the hierarchical stepwise regression analyses. This method examined which dimensions of the NEO-FFI and ANPS would be the best predictors of the FSMC, its subscales and the ADS. Detailed information about the regression models and possible issues of multi-collinearity of the predictors are described in the Supplementary material online. This procedure was followed in the PwMS sample only, because the FSMC and the ADS were assessed in this sample only. Finally, the second research question, namely the differences in personality and PETs between PwMS and HC, were investigated. Therefore, the mean scores of the NEO-FFI and the sum scores of the ANPS were compared between PwMS and the HC sample using a MANCOVA. Again, age was included as covariate. As we implemented five comparisons between PwMS and HC for the NEO-FFI and seven for the ANPS, Bonferroni correction for multiple testing was later carried out by dividing the significance level by five (NEO-FFI: p = .05/5 = .01) and seven (ANPS: p = .05/7 = .007). Results Effects of Disease Duration, Age, and Gender The only significant correlation with disease duration was found with the CARE scale of the ANPS (r = −.34, p = .013). No significant (p < .05) associations with age or gender effects were found on the FSMC and its subcales or the ADS. In the PwMS sample age was significantly related to conscientiousness (r = −.34, p = .013) of the NEO-FFI, SEEKING (r = −.29, p = .037), FEAR (r = −.35, p = .011), and PLAY (r = −.31, p = .027) of the ANPS. Gender had significant influence on neuroticism (F(1,50) = 13.08, p = .001) and Spirituality (F(1,50) = 5.31, p = .025) in the PwMS sample. Males scored lower than females in both scales. In the HC sample age did correlate significantly with FEAR (r = −.29, p = .035), CARE (r = −.46, p = .001), PLAY (r = −.30, p = .029), and SADNESS (r = −.38, p = .006) of the ANPS. Significant gender differences were found in agreeableness (F(1,50) = 4.65, p = .036), FEAR (F(1,50) = 5.64, p = .021), CARE (F(1,50) = 14.95, p < .001), and SADNESS (F(1,50) = 7.68, p = .008) in this sample; males again scored lower. Correlations between Fatigue, Depression, Personality, and PETs in the PwMS Sample As seen in Fig. 1, both facets of fatigue and depression are robustly linked in the PwMS sample. A somewhat stronger association to the ADS score is found for the subscale measuring cognitive fatigue. Fig. 1. View largeDownload slide Partial correlations between the FSMC scales and the ADS (controlled for age) Fig. 1. View largeDownload slide Partial correlations between the FSMC scales and the ADS (controlled for age) As seen in Table 2, neuroticism, extraversion, and ANGER were significantly (p < .05) linked to the total FSMC score. Motoric fatigue was significantly (p < .05) positively related to neuroticism and negatively to extraversion. Cognitive fatigue showed significant positive correlations with neuroticism and ANGER (p < .05). The ADS was significantly positively related to neuroticism, FEAR, ANGER and SADNESS (p < .05). Furthermore significant negative associations were found between the ADS and extraversion and PLAY (p < .05). Table 2. Partial correlations of the FSMC and the ADS with the NEO-FFI scales and the ANPS in the PwMS sample FSMC total FSMC motor FSMC cognitive ADS NEO-FFI  Neuroticism r = .40 r = .31 r = .44 r = .63 p = .003 p = .025 p = .001 p< .001  Extraversion r = −.29 r = −.31 r = −.25 r = −.44 p = .039 p = .026 p = .080 p = .001  Openness r = .18 r = .21 r = .13 r = .12 p = .216 p = .132 p = .354 p = .400  Agreeableness r = −.12 r = −.09 r = −.14 r = −.26 p = .398 p = .543 p = .339 p = .062  Conscientiousness r = −.24 r = −.17 r = −.27 r = −.22 p = .087 p = .229 p = .052 p = .125 ANPS  SEEKING r = −.10 r = −.11 r = −.08 r = −.14 p = .481 p = .432 p = .575 p = .328  FEAR r = .05 r = .05 r = .04 r= .42 p = .748 p = .730 p = .782 p= .002  CARE r = .00 r = .00 r = −.01 r = −.04 p = .989 p = .990 p = .971 p = .760  ANGER r= .35 r = .27 r= .39 r= .43 p= .011 p = .054 p= .005 p= .002  PLAY r = −.11 r = −.16 r = −.06 r= −.36 p = .441 p = .259 p = .670 p= .010  SADNESS r = .09 r = .13 r = .05 r= .55 p = .527 p = .375 p = .708 p < .001  Spirituality r = .10 r = .09 r = .10 r = −.09 p = .502 p = .554 p = .509 p = .549 FSMC total FSMC motor FSMC cognitive ADS NEO-FFI  Neuroticism r = .40 r = .31 r = .44 r = .63 p = .003 p = .025 p = .001 p< .001  Extraversion r = −.29 r = −.31 r = −.25 r = −.44 p = .039 p = .026 p = .080 p = .001  Openness r = .18 r = .21 r = .13 r = .12 p = .216 p = .132 p = .354 p = .400  Agreeableness r = −.12 r = −.09 r = −.14 r = −.26 p = .398 p = .543 p = .339 p = .062  Conscientiousness r = −.24 r = −.17 r = −.27 r = −.22 p = .087 p = .229 p = .052 p = .125 ANPS  SEEKING r = −.10 r = −.11 r = −.08 r = −.14 p = .481 p = .432 p = .575 p = .328  FEAR r = .05 r = .05 r = .04 r= .42 p = .748 p = .730 p = .782 p= .002  CARE r = .00 r = .00 r = −.01 r = −.04 p = .989 p = .990 p = .971 p = .760  ANGER r= .35 r = .27 r= .39 r= .43 p= .011 p = .054 p= .005 p= .002  PLAY r = −.11 r = −.16 r = −.06 r= −.36 p = .441 p = .259 p = .670 p= .010  SADNESS r = .09 r = .13 r = .05 r= .55 p = .527 p = .375 p = .708 p < .001  Spirituality r = .10 r = .09 r = .10 r = −.09 p = .502 p = .554 p = .509 p = .549 Note: All correlations/p-values are controlled for age. Significant results are marked for better clarity with bold letters in this table. Table 2. Partial correlations of the FSMC and the ADS with the NEO-FFI scales and the ANPS in the PwMS sample FSMC total FSMC motor FSMC cognitive ADS NEO-FFI  Neuroticism r = .40 r = .31 r = .44 r = .63 p = .003 p = .025 p = .001 p< .001  Extraversion r = −.29 r = −.31 r = −.25 r = −.44 p = .039 p = .026 p = .080 p = .001  Openness r = .18 r = .21 r = .13 r = .12 p = .216 p = .132 p = .354 p = .400  Agreeableness r = −.12 r = −.09 r = −.14 r = −.26 p = .398 p = .543 p = .339 p = .062  Conscientiousness r = −.24 r = −.17 r = −.27 r = −.22 p = .087 p = .229 p = .052 p = .125 ANPS  SEEKING r = −.10 r = −.11 r = −.08 r = −.14 p = .481 p = .432 p = .575 p = .328  FEAR r = .05 r = .05 r = .04 r= .42 p = .748 p = .730 p = .782 p= .002  CARE r = .00 r = .00 r = −.01 r = −.04 p = .989 p = .990 p = .971 p = .760  ANGER r= .35 r = .27 r= .39 r= .43 p= .011 p = .054 p= .005 p= .002  PLAY r = −.11 r = −.16 r = −.06 r= −.36 p = .441 p = .259 p = .670 p= .010  SADNESS r = .09 r = .13 r = .05 r= .55 p = .527 p = .375 p = .708 p < .001  Spirituality r = .10 r = .09 r = .10 r = −.09 p = .502 p = .554 p = .509 p = .549 FSMC total FSMC motor FSMC cognitive ADS NEO-FFI  Neuroticism r = .40 r = .31 r = .44 r = .63 p = .003 p = .025 p = .001 p< .001  Extraversion r = −.29 r = −.31 r = −.25 r = −.44 p = .039 p = .026 p = .080 p = .001  Openness r = .18 r = .21 r = .13 r = .12 p = .216 p = .132 p = .354 p = .400  Agreeableness r = −.12 r = −.09 r = −.14 r = −.26 p = .398 p = .543 p = .339 p = .062  Conscientiousness r = −.24 r = −.17 r = −.27 r = −.22 p = .087 p = .229 p = .052 p = .125 ANPS  SEEKING r = −.10 r = −.11 r = −.08 r = −.14 p = .481 p = .432 p = .575 p = .328  FEAR r = .05 r = .05 r = .04 r= .42 p = .748 p = .730 p = .782 p= .002  CARE r = .00 r = .00 r = −.01 r = −.04 p = .989 p = .990 p = .971 p = .760  ANGER r= .35 r = .27 r= .39 r= .43 p= .011 p = .054 p= .005 p= .002  PLAY r = −.11 r = −.16 r = −.06 r= −.36 p = .441 p = .259 p = .670 p= .010  SADNESS r = .09 r = .13 r = .05 r= .55 p = .527 p = .375 p = .708 p < .001  Spirituality r = .10 r = .09 r = .10 r = −.09 p = .502 p = .554 p = .509 p = .549 Note: All correlations/p-values are controlled for age. Significant results are marked for better clarity with bold letters in this table. Regression Analyses in the PwMS Sample In Table 3 the hierarchical stepwise regression models predicting the FSMC total score, its motor and cognitive subscales, as well as the ADS are presented. High neuroticism appeared to be the most important predictor for all investigated variables with the exception of the FSMC motor subscale. Motoric fatigue was significantly predicted by (low) extraversion. Over neuroticism, high SADNESS followed by low extraversion explained a significant part of the variance in the ADS. Table 3. Significant predictors in the final regression models for the FSMC scales and the ADS in the PwMS sample β T p ∆R2 R2 FSMC total score  Neuroticism .370 2.82 .007 .137 .137 FSMC motor  Extraversion −.341 −2.57 .013 .116 .116 FSMC cognitive  Neuroticism .426 3.33 .002 .181 .181 ADS  Neuroticism .443 3.78 < .001 .412 .412  SADNESS .273 2.35 .023 .067 .480  Extraversion −.237 −2.32 .025 .052 .532 β T p ∆R2 R2 FSMC total score  Neuroticism .370 2.82 .007 .137 .137 FSMC motor  Extraversion −.341 −2.57 .013 .116 .116 FSMC cognitive  Neuroticism .426 3.33 .002 .181 .181 ADS  Neuroticism .443 3.78 < .001 .412 .412  SADNESS .273 2.35 .023 .067 .480  Extraversion −.237 −2.32 .025 .052 .532 Note: In the first step of the hierarchical stepwise regression models, age and gender were included. In the regression model for the FSMC total score only the variables neuroticism, extraversion, and ANGER were included in the second step and the corresponding gender by trait interaction terms in a third step. For the FSMC motor subscale only the variables neuroticism and extraversion were included in the second step of the regression model and the corresponding gender by trait interaction terms in a third step. Neuroticism and ANGER were included in the regression model for the FSMC cognitive subscale in the second step; the corresponding gender by trait interaction terms in a third step. In the regression model for the ADS neuroticism, extraversion, FEAR, ANGER, PLAY, and SADNESS were included in the second step and the corresponding gender by trait interaction terms in a third step. Only variables are presented, which explained a significant (p < .05) proportion of variance in the dependent variable within the step they were included into the model. Table 3. Significant predictors in the final regression models for the FSMC scales and the ADS in the PwMS sample β T p ∆R2 R2 FSMC total score  Neuroticism .370 2.82 .007 .137 .137 FSMC motor  Extraversion −.341 −2.57 .013 .116 .116 FSMC cognitive  Neuroticism .426 3.33 .002 .181 .181 ADS  Neuroticism .443 3.78 < .001 .412 .412  SADNESS .273 2.35 .023 .067 .480  Extraversion −.237 −2.32 .025 .052 .532 β T p ∆R2 R2 FSMC total score  Neuroticism .370 2.82 .007 .137 .137 FSMC motor  Extraversion −.341 −2.57 .013 .116 .116 FSMC cognitive  Neuroticism .426 3.33 .002 .181 .181 ADS  Neuroticism .443 3.78 < .001 .412 .412  SADNESS .273 2.35 .023 .067 .480  Extraversion −.237 −2.32 .025 .052 .532 Note: In the first step of the hierarchical stepwise regression models, age and gender were included. In the regression model for the FSMC total score only the variables neuroticism, extraversion, and ANGER were included in the second step and the corresponding gender by trait interaction terms in a third step. For the FSMC motor subscale only the variables neuroticism and extraversion were included in the second step of the regression model and the corresponding gender by trait interaction terms in a third step. Neuroticism and ANGER were included in the regression model for the FSMC cognitive subscale in the second step; the corresponding gender by trait interaction terms in a third step. In the regression model for the ADS neuroticism, extraversion, FEAR, ANGER, PLAY, and SADNESS were included in the second step and the corresponding gender by trait interaction terms in a third step. Only variables are presented, which explained a significant (p < .05) proportion of variance in the dependent variable within the step they were included into the model. Mean Values of the Questionnaires in the Different Samples As presented in Figs. 2 and 3 as well as in the Supplementary material online, Table 4 (for exact mean values and standard deviations), the PwMS sample showed significantly higher scores in neuroticism, FEAR, CARE (only when controlling for age) and SADNESS and lower scores in openness compared to the matched HC sample. For the NEO-FFI, only the difference in neuroticism would hold for multiple testing (p = .05/5 = .01). For the ANPS, the difference in FEAR would hold for multiple testing (p = .05/7 = .007). Fig. 2. View largeDownload slide Mean values and standard deviations (±1 SD) of the NEO-FFI scores in the PwMS and the HC sample. Asterisks indicate significant differences between PwMS and HC sample when controlling for age (***p ≤ .001, **p ≤ .010, *p ≤ .050). Fig. 2. View largeDownload slide Mean values and standard deviations (±1 SD) of the NEO-FFI scores in the PwMS and the HC sample. Asterisks indicate significant differences between PwMS and HC sample when controlling for age (***p ≤ .001, **p ≤ .010, *p ≤ .050). Fig. 3. View largeDownload slide Mean values and standard deviations (±1 SD) of the ANPS scores in the PwMS and the HC sample. Asterisks indicate significant differences between PwMS and HC sample when controlling for age (***p ≤ .001, **p ≤ .010, *p ≤ .050). Please note that the difference in the CARE scale between the PwMS and the HC sample is only significant when controlling for age (mean values and standard errors for the CARE scale corrected for age: PwMS: 43.40 (0.72); HC: 41.06 (0.72)). Fig. 3. View largeDownload slide Mean values and standard deviations (±1 SD) of the ANPS scores in the PwMS and the HC sample. Asterisks indicate significant differences between PwMS and HC sample when controlling for age (***p ≤ .001, **p ≤ .010, *p ≤ .050). Please note that the difference in the CARE scale between the PwMS and the HC sample is only significant when controlling for age (mean values and standard errors for the CARE scale corrected for age: PwMS: 43.40 (0.72); HC: 41.06 (0.72)). Discussion The first aim of the present study was to disentangle fatigue and depression in PwMS by investigating the underlying personality traits and PETs. Additionally, the differences between PwMS and the HC sample in the Big Five of Personality and the PETs were examined. In line with most of the literature, fatigue—especially cognitive fatigue—and depression in PwMS were significantly associated (e.g. Bakshi et al., 2000; Ford et al., 1998; Kroencke et al., 2000; Penner et al., 2007; Schreurs, de Ridder, & Benzing, 2002). More specifically, the correlation of r = .53 / r = .58 between the FSMC motor/cognitive subscales and the ADS speaks for a shared variance of about 28%–34%, hence at least 66% of the variance are due to non-shared factors in the present sample under investigation. A closer look at the underlying personality traits and PETs helps to understand which traits target the shared and non-shared variance. First, the significant predictor for motoric fatigue was low extraversion. This indicates that persons who are less socially outgoing, less assertive and vivid show stronger bodily exhaustion when fatigue sets in (e. g., when walking or doing other bodily movement). Of note, the associations between extraversion and the FSMC scales and the ADS did only change slightly when building the extraversion score without the NEO-FFI items 32 (“I often feel as if I’m bursting with energy.”) and 52 (“I am a very active person.”). Which facet of extraversion ultimately will be of highest importance needs to be studied in future research endeavors using inventories such as the NEO-PI-R giving also insights into more narrow sub-facets of the Big Five. Additionally, clearly, we are not able to predict causality with respect to these variables, and both causal directions would be meaningful. Either persons with a premorbid lower extraversion demonstrate higher motoric fatigue when suffering from MS, or the higher motoric fatigue stemming from MS could result into less extraverted behavior. High neuroticism, predominantly used to describe traits of anxiety, emotional instability, and mood swings, was the most important predictor for cognitive fatigue as well as depression. Thus, this personality trait seems to explain the shared variance in MS related cognitive fatigue and depression. In detail, the MS related association among others means that neurotic PwMS tend to report more problems with keeping their concentration, more difficulties learning new information, forgetting things more often, thus, suffering from MS related cognitive fatigue. In contrast to cognitive fatigue, depression in PwMS was also predicted by high SADNESS of the ANPS and low extraversion of the NEO-FFI. High SADNESS can be described by high feelings of loneliness, thinking often about loved ones and past relationships and pronounced distress when being separated from loved one’s. In line with a recent study by Montag, Widernhorn-Müller and colleagues (2016), SADNESS represents the core primary emotional system underlying depression out of Panksepp’s Affective Neuroscience theory. In general, the observed patterns between high FEAR/SADNESS (to a lesser degree also with lower PLAY/high ANGER) and higher depression mirror what has been observed by Montag, Widernhorn-Müller and colleagues (2016). In sum, our findings indicate that depression in PwMS shares some variance with cognitive fatigue (explained by high neuroticism) and also motoric fatigue (explained by low extraversion). Beyond these results, the non-shared variance of depression in PwMS is associated with the PET SADNESS not playing a role for fatigue. Regarding the second aim of the study, PwMS showed significant differences in neuroticism (higher), and also openness (lower), FEAR (higher), CARE (higher) and SADNESS (higher) compared to the HC sample. This is partly in line with Penner and colleagues (2007) who also found increased neuroticism scores in PwMS compared to healthy subjects, but additionally significantly lower scores in extraversion. The latter association could not be observed in our sample, but the importance of the neuroticism finding is underlined by our findings from the regression models outlined already above. Note that the difference in CARE is only significant when controlling for age. And the differences in FEAR and SADNESS could be explained by the fact that the HC sample was healthy, thus—in contrast to the PwMS sample—no participant suffered from depression. Some limitations need to be mentioned: The present study has a cross-sectional design disallowing a causal relationship to be assumed. Thus, it remains unclear whether the differences in the personality traits and PETs between PwMS and HC are predisposing factors for the emergence of MS or if these are caused by the attack of one’s own immune system on the neuron myelin. To gain insight into causal mechanisms longitudinal studies are needed. In conclusion, the present study is the first to disentangle MS related fatigue and depression by the inclusion of both personality trait and PET measures. Evidence is presented that both fatigue and depression are robustly associated with each other in PwMS, most likely due to the shared variance explained by neuroticism and extraversion. Still, there is enough room for non-shared variance (in our study depression in PwMS was further predicted by SADNESS, which had no influence on fatigue). This knowledge should be taken into account when distinguishing and treating MS related fatigue and MS related depression. As might be expected, in clinical settings using the rather long original ANPS (110 items) might be too time consuming. A solution could be to use the short version by Pingeault, Falissard, Côté and Berthoz (2012) consisting of only 36 items. But this measure clearly needs to be validated in the context of MS related fatigue and depression, first. Therefore, in order to save time, another option would be to only assess the SADNESS items of the original ANPS (14 items) together with MS related motoric/cognitive fatigue and MS related depression. However, researchers and clinical staff should do this with caution, as important aspects (e. g., associations between other MS related symptoms and PETs other than SADNESS) could remain unobserved. Finally, as the ANPS has been constructed on abundant neuroscientific data, the present findings provide researchers with novel ideas as to what brain areas and neurotransmitters/neuropeptides underlie the different facets of fatigue and especially depression in MS (e.g., Panksepp, 2011). Funding This work was supported by the German Research Foundation [Heisenberg grant: DFG MO MO2363/3-2 to C.M.]; and the German Academic Scholarship Foundation (Studienstiftung des deutschen Volkes) [to C.S.]. Conflict of interest The authors declare that there is no conflict of interest. Authors' Contributions C.M., S.M., J.N., J.S., and H.K. designed the study. C.S. and C.M. drafted the manuscript. C.S. conducted the statistical analyses. M.S. worked over the manuscript to improve it and corrected the English language and grammar. All authors commented on the manuscript and critically revised it. All authors approved the final version of the manuscript. References American Psychiatric Association . ( 2013 ). Diagnostic and statistical manual of mental disorders ( 5th ed. ). Washington, DC : American Psychiatric Association Publishing . Arnett , P. A. , Barwick , F. H. , & Beeney , J. E. ( 2008 ). Depression in multiple sclerosis: Review and theoretical proposal . Journal of the International Neuropsychological Society , 14 , 691 – 724 . Google Scholar PubMed Bakshi , R. , Shaikh , Z. A. , Miletich , R. S. , Czarnecki , D. , Dmochowski , J. , Henschel , K. et al. . ( 2000 ). Fatigue in multiple sclerosis and its relationship to depression and neurologic disability . 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Prevalence and concurrence of anxiety, depression and fatigue over time in multiple sclerosis . Multiple Sclerosis Journal 19 , 217 – 224 . Google Scholar CrossRef Search ADS PubMed © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Clinical Neuropsychology Oxford University Press

Personality and Primary Emotional Traits: Disentangling Multiple Sclerosis Related Fatigue and Depression

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Abstract

Abstract Objective It remains an unresolved research objective to clarify the overlap/association between fatigue (especially its cognitive facet) and depression in People with MS (PwMS). Therefore, in this study the patterns of personality and primary emotional traits (PETs) associated with each (motoric/cognitive fatigue and depression in PwMS) were investigated and compared in order to disentangle the three constructs in PwMS. Additionally, differences in personality and PETs between PwMS and healthy controls (HC) were examined. Method Associations between motoric/cognitive fatigue, depression, personality and PETs were investigated in 52 PwMS. Personality and PETs were also assessed in a gender matched HC sample (N = 52) and results regarding these were compared between PwMS and HC. Results Low extraversion was the only significant predictor of MS related motoric fatigue (β = −.341, p = .013). High neuroticism was a predictor of both MS related cognitive fatigue (β = .426, p = .002) and depression (β = .443, p < .001). Whereas neuroticism was the only significant predictor for MS related cognitive fatigue, the cluster of (high) neuroticism, (high) SADNESS (β = .273, p = .023), and (low) extraversion (β = −.237, p = .025) predicted MS related depression. PwMS showed significantly higher scores in neuroticism and FEAR compared to HC. Conclusions MS related motoric/cognitive fatigue and depression in PwMS share variance. But the substantial amount of non-shared variance (motoric fatigue, depression: 72%; cognitive fatigue, depression: 66%) together with additional predictors for depression (SADNESS being a predictor of depression only), indicate that MS related motoric/cognitive fatigue and depression are distinguishable. Consequently, we recommend assessing especially SADNESS scores to distinguish between MS related fatigue and depression. Multiple sclerosis, Depression, Fatigue, Personality, Big Five, Primary emotional traits Introduction Multiple Sclerosis, Fatigue, and Depression Multiple Sclerosis (MS) is a neurological disorder affecting about 2.3 million persons worldwide (Multiple Sclerosis International Federation, 2016). One of the most commonly reported symptoms of MS is fatigue (Krupp, Alvarez, LaRocca, & Scheinberg, 1988; Stuke et al., 2009; Wood et al., 2013), which is often split into a motoric and a cognitive fatigue facet (Penner et al., 2009). In addition to fatigue, many PwMS suffer from depression (Arnett, Barwick, & Beeney, 2008; Siegert & Abernathy, 2005; Please note that for means of simplification and because an exact definition of depression is not in the scope of the present research endeavor, depressive symptoms as well as Major Depressive Disorder will be subsumed under the term “depression” in the following sections). An overlap between facets of fatigue in PwMS and depression in PwMS has often been posited, as fatigue is a central symptom of both MS and depression (American Psychiatric Association, 2013). In line with this, many studies report a strong association between fatigue—especially its cognitive facet—and depression in PwMS (e.g. Bakshi et al., 2000; Ford, Trigwell, & Johnson, 1998; Kroencke, Lynch, & Denney, 2000; Penner et al., 2007; Schreurs, de Ridder & Benzing, 2002). Although this overlap has often been observed, contradicting results also exist (as outlined with the following studies: e. g., Krupp et al., 1988; Janardhan & Bakshi, 2002). Personality and Primary Emotional Traits (PETs) Following the classic Big Five personality theory, personality can be divided into five traits commonly known as neuroticism, extraversion, openness, agreeableness, and conscientiousness (for further information see Costa & McCrae, 1992; The Big Five of Personality refer to the original used lexical approach to come up with the well-replicated personality structure introduced in the following. The works by Costa & McCrae built upon this model their Five Factor Model of Personality resulting in the famous inventories NEO-FFI and NEO-PI-R. Given the large overlap between both theories, we use the term Big Five and Five Factor Model somewhat interchangeably [as many other researchers also do]). Neuroticism, in particular, has been detected as a vulnerability factor for many disorders, including affective disorders such as depression (Hengartner et al., 2016; Jylhä & Isometsä, 2006; Kendler, Kuhn, & Prescott, 2004). Moreover, neuroticism has been positively associated with MS related fatigue (both motoric and cognitive; Lahey, 2009; Penner et al., 2007). Additionally, extraversion has been found to have a negative relationship with both facets of fatigue (Penner et al., 2007; Johnson, DeLuca & Natelson, 1996; Merkelbach, König, & Sittinger, 2003). Based on another perspective on personality, Jaak Panksepp provided evidence for seven neural circuitries across the mammalian brains for positive (SEEKING, CARE, LUST, PLAY) and negative affect (SADNESS, FEAR, ANGER) (Panksepp, 1998, 2011; Please note that the primary emotional systems/traits are written in capital letters to not confound them with other terms used in (personality) psychology). These primary emotional systems are assumed to influence human personality bottom-up. Hence, individual differences in these primary emotional systems can be assessed via a trait approach (primary emotional traits (PETs)) by the Affective Neuroscience Personality Scales (ANPS) (Davis, Panksepp, & Normansell, 2003; see Montag & Panksepp, 2017 for a more detailed explanation of the PETs; see Panksepp, 2011; Montag, Sindermann, Becker, & Panksepp, 2016 for an overview of underlying brain structures and molecules). Noticeably, individual differences in PETs are somewhat linked to the Big Five model. As such, high scores in FEAR, ANGER and SADNESS have been found to positively correlate with neuroticism (Davis & Panksepp, 2011; Montag & Panksepp, 2017; Reuter et al., 2017; Sindermann et al., 2016). To our knowledge, PETs have not been studied in the context of MS related fatigue or MS related depression before. But recent work by Montag et al. shows robust associations between individual differences in PETs and depressive tendencies (Montag, Widernhorn-Müller, Panksepp, & Kiefer, 2016). Additionally, one can denote that the associations between the PETs and the Big Five has allowed for a credible assumption of a further relationship with symptoms of fatigue and depression in PwMS. Research Objectives This field of research is still struggling to answer whether or not fatigue—specifically cognitive fatigue—in PwMS is independent from depression in PwMS. In the present study we aimed to revisit this important research question by utilizing a personality approach. In light of this, we (exploratively) investigated and compared the patterns of associations between MS related fatigue and personality and PETs, with the patterns of associations between MS related depression and personality and PETs. Going beyond this, it must be noted that differences in personality traits between PwMS and HC have already been discovered in earlier studies. They reported increased neuroticism, in detail with its subfacets anxiety, hostility and vulnerability, as well as decreased extraversion, agreeableness and conscientiousness in PwMS compared to HC (Benedict, Priore, Miller, Munschauer, & Jacobs, 2001; Johnson et al., 1996; Penner et al., 2007). Based on these outcomes, the second aim of the study was to replicate the findings on differences in personality between PwMS and HC. Additionally, we compared PETs between PwMS and a control sample, which is novel within this research. We expected neuroticism, FEAR, ANGER, and SADNESS to be higher in PwMS whereas we expected extraversion, agreeableness and conscientiousness to be higher in the HC sample. Methods Sample The data of N = 52 PwMS were available for the study (males: n = 9, females: n = 43; age: M = 45.13, SD = 9.56). PwMS who did not fill in the questionnaires properly (see Supplementary material online) were not included in the study. The age of MS diagnosis lay between 15 and 58 (M = 37.21, SD = 11.12). Of note, as the exact date of birth but only the year in which the MS diagnosis was made was available, these results are based on the difference between year of birth and year of diagnosis. The range of disease duration was from less than 1 year to more than 29 years (M = 8.67, SD = 7.36). As the exact date of data assessment but only the year in which the MS diagnosis was made was available, these results are based on the difference between year of data assessment and year of diagnosis. The majority of the PwMS (N = 29) stated they had “Mittlere Reife” (a German form of graduation from school, which does not permit studying at a university) as their highest educational degree. More detailed information about the PwMS sample is presented in Table 1. All PwMS filled in all questionnaires in paper-pencil format, and they were used as an initial assessment on arrival at the Neurological Rehabilitation Centre Godeshöhe. Table 1. Detailed description of the PwMS sample N % Type of MS  Relapsing-remitting 28 54  Secondary progressive 7 13  Primary progressive 9 17  Clinically isolated syndromea 1 2  Uncertain course of MS 7 13 EDSS scoreb  1 3 6  1.5 6 12  2 2 4  2.5 7 14  3 6 12  3.5 8 15  4 8 15  4.5 4 8  5.5 1 2  6 3 6  6.5 1 2  7.5 2 4  Missing 1 2 Medication  MS medication 26 50  Antidepressive medicationc 10 19 N % Type of MS  Relapsing-remitting 28 54  Secondary progressive 7 13  Primary progressive 9 17  Clinically isolated syndromea 1 2  Uncertain course of MS 7 13 EDSS scoreb  1 3 6  1.5 6 12  2 2 4  2.5 7 14  3 6 12  3.5 8 15  4 8 15  4.5 4 8  5.5 1 2  6 3 6  6.5 1 2  7.5 2 4  Missing 1 2 Medication  MS medication 26 50  Antidepressive medicationc 10 19 aEpisode caused by inflammatory/demyelination in one or more sites in the CNS. bExpanded Disability Status Scale. cPrimarily serotonin-specific reuptake inhibitors (SSRI), or selective serotonin-norepinephrine reuptake inhibitors (SNRI). Most of the PwMS were already receiving this before admission into the rehabilitation centre. If the precentage data of Type of MS and EDSS score does not sum up to exactly 100% this is due to rounding inaccuracies. Table 1. Detailed description of the PwMS sample N % Type of MS  Relapsing-remitting 28 54  Secondary progressive 7 13  Primary progressive 9 17  Clinically isolated syndromea 1 2  Uncertain course of MS 7 13 EDSS scoreb  1 3 6  1.5 6 12  2 2 4  2.5 7 14  3 6 12  3.5 8 15  4 8 15  4.5 4 8  5.5 1 2  6 3 6  6.5 1 2  7.5 2 4  Missing 1 2 Medication  MS medication 26 50  Antidepressive medicationc 10 19 N % Type of MS  Relapsing-remitting 28 54  Secondary progressive 7 13  Primary progressive 9 17  Clinically isolated syndromea 1 2  Uncertain course of MS 7 13 EDSS scoreb  1 3 6  1.5 6 12  2 2 4  2.5 7 14  3 6 12  3.5 8 15  4 8 15  4.5 4 8  5.5 1 2  6 3 6  6.5 1 2  7.5 2 4  Missing 1 2 Medication  MS medication 26 50  Antidepressive medicationc 10 19 aEpisode caused by inflammatory/demyelination in one or more sites in the CNS. bExpanded Disability Status Scale. cPrimarily serotonin-specific reuptake inhibitors (SSRI), or selective serotonin-norepinephrine reuptake inhibitors (SNRI). Most of the PwMS were already receiving this before admission into the rehabilitation centre. If the precentage data of Type of MS and EDSS score does not sum up to exactly 100% this is due to rounding inaccuracies. To find a gender matched control sample, all participants of the Ulm Gene Brain Behavior Project (UGBBP) were screened for depression (Becks Depression Inventory - II (BDI-II) score lower than 13 (Hautzinger, Keller & Kühner, 2006; Cut-Offs from DGPPN et al., 2015)). Every participant who reported a BDI-II score of 13 or higher and/or a history of traumatic brain injury or other neurological or psychological disorders was excluded. A sample of N = 490 participants remained. From these, N = 52 healthy and gender matched controls (males: n = 9, females: n = 43; Age: M = 33.13, SD = 10.04) were randomly selected (at first, the subjects from the UGBBP, which were closest in age to each PwMS were chosen. If more than one participant from the UGBBP had the same age, the HC matches were selected randomly from these). As the UGBBP mostly consists of students, a perfect age match with the PwMS sample was not possible (hence, age was controlled for in the analyses when needed). The majority of the HC stated “Abitur” (N = 18; graduation from school, which does permit to study at a university) or university graduation (N = 21) as their highest educational degree. The study was approved by the local ethics committee at the University of Bonn, Bonn, Germany, and all participants gave informed written/electronic consent prior to participation. Material and Questionnaires Specified information about how the scores of the different questionnaires/scales were calculated is presented in the Supplementary material online. The internal consistencies of all questionnaires/scales are presented in Supplementary material online, Table 1. To assess fatigue in PwMS the Fatigue Scale for Motor and Cognitive Functions (FSMC) was administered (Penner et al., 2009). This scale consists of 20 items. In addition to the total sum score, sum scores for the subscales motor and cognitive fatigue are calculated—each consisting of 10 items. The Allgemeine Depressionsskala (ADS; translated from German as the General Depression Scale) was used to assess depressive symptoms within the last week in PwMS (Hautzinger, Bailer, Hofmeister, & Keller, 2012). It consists of 20 items. In order to assess the Big Five of Personality the German version of the NEO Five Factor Inventory (NEO-FFI) was administered (Costa & McCrae, 1992; Ostendorf, & Angleitner, 2003). This inventory assesses individual differences in neuroticism, extraversion, openness, agreeableness and conscientiousness, with each scale consisting of 12 items. Besides the NEO-FFI, the Affective Neuroscience Personality Scales (ANPS) were also administered to the PwMS as well as the HC sample (Davis, Panksepp, & Normansell, 2003). The German version has been used prior to this study (e. g. Sindermann et al., 2016) and a manual has been published by Reuter, Panksepp, Davis, and Montag (2017). The questionnaire assesses individual differences in the PETs SEEKING, FEAR, CARE, ANGER, PLAY, and SADNESS. Each scale consists of 14 items. Additionally, it includes the scale Spirituality, which consists of 12 items. The PET LUST is not assessed due to potential negative carry over effects in answering the other scales, when being asked about one’s own sexual activity. In total the German ANPS include 110 items. Statistical Analyses Control variables First, the effects of disease duration, age and gender on the FSMC, ADS, NEO-FFI, and ANPS were tested. Therefore, correlations between disease duration, age and the FSMC, ADS, NEO-FFI, and ANPS were calculated in each sample separately (note that FSMC and ADS scores were not available in the HC sample). Also, MANOVAs for testing the effects of gender were implemented in each sample. For a detailed description of why effects of these control variables were tested, see Supplementary material online. Given (i) the age correlations with personality and PETs presented in the Results section and (ii) the fact that the PwMS sample was significantly older than the HC sample (T(102) = 6.24, p < .001), all further analyses were controlled for age. Gender is represented as a variable when dealing with the regression models. But due to the small number of males in both samples and the one-on-one gender matching procedure, we did not take into account gender as a further variable in the analyses when contrasting the PwMS sample with the HC sample. It was not controlled for disease duration in further analyses as this variable correlated significantly with the CARE scale of the ANPS only; the CARE scale was not expected, and indeed not found, to be correlated with the FSMC scales or the ADS. Research objectives To investigate the first research objective, namely the associations between MS related fatigue facets/depression and personality and PETs, the partial correlations between the FSMC, the ADS, the NEO-FFI, and the ANPS were calculated in the PwMS sample. Age was included as control variable (see paragraph about control variables and significant correlations with age presented in the Results section). Hence, important predictors, which should be included in the following regression analyses (correlations with (motoric/cognitive) fatigue, ADS: p < .05), were examined by these correlational analyses first. Afterwards, these were included as predictors in the hierarchical stepwise regression analyses. This method examined which dimensions of the NEO-FFI and ANPS would be the best predictors of the FSMC, its subscales and the ADS. Detailed information about the regression models and possible issues of multi-collinearity of the predictors are described in the Supplementary material online. This procedure was followed in the PwMS sample only, because the FSMC and the ADS were assessed in this sample only. Finally, the second research question, namely the differences in personality and PETs between PwMS and HC, were investigated. Therefore, the mean scores of the NEO-FFI and the sum scores of the ANPS were compared between PwMS and the HC sample using a MANCOVA. Again, age was included as covariate. As we implemented five comparisons between PwMS and HC for the NEO-FFI and seven for the ANPS, Bonferroni correction for multiple testing was later carried out by dividing the significance level by five (NEO-FFI: p = .05/5 = .01) and seven (ANPS: p = .05/7 = .007). Results Effects of Disease Duration, Age, and Gender The only significant correlation with disease duration was found with the CARE scale of the ANPS (r = −.34, p = .013). No significant (p < .05) associations with age or gender effects were found on the FSMC and its subcales or the ADS. In the PwMS sample age was significantly related to conscientiousness (r = −.34, p = .013) of the NEO-FFI, SEEKING (r = −.29, p = .037), FEAR (r = −.35, p = .011), and PLAY (r = −.31, p = .027) of the ANPS. Gender had significant influence on neuroticism (F(1,50) = 13.08, p = .001) and Spirituality (F(1,50) = 5.31, p = .025) in the PwMS sample. Males scored lower than females in both scales. In the HC sample age did correlate significantly with FEAR (r = −.29, p = .035), CARE (r = −.46, p = .001), PLAY (r = −.30, p = .029), and SADNESS (r = −.38, p = .006) of the ANPS. Significant gender differences were found in agreeableness (F(1,50) = 4.65, p = .036), FEAR (F(1,50) = 5.64, p = .021), CARE (F(1,50) = 14.95, p < .001), and SADNESS (F(1,50) = 7.68, p = .008) in this sample; males again scored lower. Correlations between Fatigue, Depression, Personality, and PETs in the PwMS Sample As seen in Fig. 1, both facets of fatigue and depression are robustly linked in the PwMS sample. A somewhat stronger association to the ADS score is found for the subscale measuring cognitive fatigue. Fig. 1. View largeDownload slide Partial correlations between the FSMC scales and the ADS (controlled for age) Fig. 1. View largeDownload slide Partial correlations between the FSMC scales and the ADS (controlled for age) As seen in Table 2, neuroticism, extraversion, and ANGER were significantly (p < .05) linked to the total FSMC score. Motoric fatigue was significantly (p < .05) positively related to neuroticism and negatively to extraversion. Cognitive fatigue showed significant positive correlations with neuroticism and ANGER (p < .05). The ADS was significantly positively related to neuroticism, FEAR, ANGER and SADNESS (p < .05). Furthermore significant negative associations were found between the ADS and extraversion and PLAY (p < .05). Table 2. Partial correlations of the FSMC and the ADS with the NEO-FFI scales and the ANPS in the PwMS sample FSMC total FSMC motor FSMC cognitive ADS NEO-FFI  Neuroticism r = .40 r = .31 r = .44 r = .63 p = .003 p = .025 p = .001 p< .001  Extraversion r = −.29 r = −.31 r = −.25 r = −.44 p = .039 p = .026 p = .080 p = .001  Openness r = .18 r = .21 r = .13 r = .12 p = .216 p = .132 p = .354 p = .400  Agreeableness r = −.12 r = −.09 r = −.14 r = −.26 p = .398 p = .543 p = .339 p = .062  Conscientiousness r = −.24 r = −.17 r = −.27 r = −.22 p = .087 p = .229 p = .052 p = .125 ANPS  SEEKING r = −.10 r = −.11 r = −.08 r = −.14 p = .481 p = .432 p = .575 p = .328  FEAR r = .05 r = .05 r = .04 r= .42 p = .748 p = .730 p = .782 p= .002  CARE r = .00 r = .00 r = −.01 r = −.04 p = .989 p = .990 p = .971 p = .760  ANGER r= .35 r = .27 r= .39 r= .43 p= .011 p = .054 p= .005 p= .002  PLAY r = −.11 r = −.16 r = −.06 r= −.36 p = .441 p = .259 p = .670 p= .010  SADNESS r = .09 r = .13 r = .05 r= .55 p = .527 p = .375 p = .708 p < .001  Spirituality r = .10 r = .09 r = .10 r = −.09 p = .502 p = .554 p = .509 p = .549 FSMC total FSMC motor FSMC cognitive ADS NEO-FFI  Neuroticism r = .40 r = .31 r = .44 r = .63 p = .003 p = .025 p = .001 p< .001  Extraversion r = −.29 r = −.31 r = −.25 r = −.44 p = .039 p = .026 p = .080 p = .001  Openness r = .18 r = .21 r = .13 r = .12 p = .216 p = .132 p = .354 p = .400  Agreeableness r = −.12 r = −.09 r = −.14 r = −.26 p = .398 p = .543 p = .339 p = .062  Conscientiousness r = −.24 r = −.17 r = −.27 r = −.22 p = .087 p = .229 p = .052 p = .125 ANPS  SEEKING r = −.10 r = −.11 r = −.08 r = −.14 p = .481 p = .432 p = .575 p = .328  FEAR r = .05 r = .05 r = .04 r= .42 p = .748 p = .730 p = .782 p= .002  CARE r = .00 r = .00 r = −.01 r = −.04 p = .989 p = .990 p = .971 p = .760  ANGER r= .35 r = .27 r= .39 r= .43 p= .011 p = .054 p= .005 p= .002  PLAY r = −.11 r = −.16 r = −.06 r= −.36 p = .441 p = .259 p = .670 p= .010  SADNESS r = .09 r = .13 r = .05 r= .55 p = .527 p = .375 p = .708 p < .001  Spirituality r = .10 r = .09 r = .10 r = −.09 p = .502 p = .554 p = .509 p = .549 Note: All correlations/p-values are controlled for age. Significant results are marked for better clarity with bold letters in this table. Table 2. Partial correlations of the FSMC and the ADS with the NEO-FFI scales and the ANPS in the PwMS sample FSMC total FSMC motor FSMC cognitive ADS NEO-FFI  Neuroticism r = .40 r = .31 r = .44 r = .63 p = .003 p = .025 p = .001 p< .001  Extraversion r = −.29 r = −.31 r = −.25 r = −.44 p = .039 p = .026 p = .080 p = .001  Openness r = .18 r = .21 r = .13 r = .12 p = .216 p = .132 p = .354 p = .400  Agreeableness r = −.12 r = −.09 r = −.14 r = −.26 p = .398 p = .543 p = .339 p = .062  Conscientiousness r = −.24 r = −.17 r = −.27 r = −.22 p = .087 p = .229 p = .052 p = .125 ANPS  SEEKING r = −.10 r = −.11 r = −.08 r = −.14 p = .481 p = .432 p = .575 p = .328  FEAR r = .05 r = .05 r = .04 r= .42 p = .748 p = .730 p = .782 p= .002  CARE r = .00 r = .00 r = −.01 r = −.04 p = .989 p = .990 p = .971 p = .760  ANGER r= .35 r = .27 r= .39 r= .43 p= .011 p = .054 p= .005 p= .002  PLAY r = −.11 r = −.16 r = −.06 r= −.36 p = .441 p = .259 p = .670 p= .010  SADNESS r = .09 r = .13 r = .05 r= .55 p = .527 p = .375 p = .708 p < .001  Spirituality r = .10 r = .09 r = .10 r = −.09 p = .502 p = .554 p = .509 p = .549 FSMC total FSMC motor FSMC cognitive ADS NEO-FFI  Neuroticism r = .40 r = .31 r = .44 r = .63 p = .003 p = .025 p = .001 p< .001  Extraversion r = −.29 r = −.31 r = −.25 r = −.44 p = .039 p = .026 p = .080 p = .001  Openness r = .18 r = .21 r = .13 r = .12 p = .216 p = .132 p = .354 p = .400  Agreeableness r = −.12 r = −.09 r = −.14 r = −.26 p = .398 p = .543 p = .339 p = .062  Conscientiousness r = −.24 r = −.17 r = −.27 r = −.22 p = .087 p = .229 p = .052 p = .125 ANPS  SEEKING r = −.10 r = −.11 r = −.08 r = −.14 p = .481 p = .432 p = .575 p = .328  FEAR r = .05 r = .05 r = .04 r= .42 p = .748 p = .730 p = .782 p= .002  CARE r = .00 r = .00 r = −.01 r = −.04 p = .989 p = .990 p = .971 p = .760  ANGER r= .35 r = .27 r= .39 r= .43 p= .011 p = .054 p= .005 p= .002  PLAY r = −.11 r = −.16 r = −.06 r= −.36 p = .441 p = .259 p = .670 p= .010  SADNESS r = .09 r = .13 r = .05 r= .55 p = .527 p = .375 p = .708 p < .001  Spirituality r = .10 r = .09 r = .10 r = −.09 p = .502 p = .554 p = .509 p = .549 Note: All correlations/p-values are controlled for age. Significant results are marked for better clarity with bold letters in this table. Regression Analyses in the PwMS Sample In Table 3 the hierarchical stepwise regression models predicting the FSMC total score, its motor and cognitive subscales, as well as the ADS are presented. High neuroticism appeared to be the most important predictor for all investigated variables with the exception of the FSMC motor subscale. Motoric fatigue was significantly predicted by (low) extraversion. Over neuroticism, high SADNESS followed by low extraversion explained a significant part of the variance in the ADS. Table 3. Significant predictors in the final regression models for the FSMC scales and the ADS in the PwMS sample β T p ∆R2 R2 FSMC total score  Neuroticism .370 2.82 .007 .137 .137 FSMC motor  Extraversion −.341 −2.57 .013 .116 .116 FSMC cognitive  Neuroticism .426 3.33 .002 .181 .181 ADS  Neuroticism .443 3.78 < .001 .412 .412  SADNESS .273 2.35 .023 .067 .480  Extraversion −.237 −2.32 .025 .052 .532 β T p ∆R2 R2 FSMC total score  Neuroticism .370 2.82 .007 .137 .137 FSMC motor  Extraversion −.341 −2.57 .013 .116 .116 FSMC cognitive  Neuroticism .426 3.33 .002 .181 .181 ADS  Neuroticism .443 3.78 < .001 .412 .412  SADNESS .273 2.35 .023 .067 .480  Extraversion −.237 −2.32 .025 .052 .532 Note: In the first step of the hierarchical stepwise regression models, age and gender were included. In the regression model for the FSMC total score only the variables neuroticism, extraversion, and ANGER were included in the second step and the corresponding gender by trait interaction terms in a third step. For the FSMC motor subscale only the variables neuroticism and extraversion were included in the second step of the regression model and the corresponding gender by trait interaction terms in a third step. Neuroticism and ANGER were included in the regression model for the FSMC cognitive subscale in the second step; the corresponding gender by trait interaction terms in a third step. In the regression model for the ADS neuroticism, extraversion, FEAR, ANGER, PLAY, and SADNESS were included in the second step and the corresponding gender by trait interaction terms in a third step. Only variables are presented, which explained a significant (p < .05) proportion of variance in the dependent variable within the step they were included into the model. Table 3. Significant predictors in the final regression models for the FSMC scales and the ADS in the PwMS sample β T p ∆R2 R2 FSMC total score  Neuroticism .370 2.82 .007 .137 .137 FSMC motor  Extraversion −.341 −2.57 .013 .116 .116 FSMC cognitive  Neuroticism .426 3.33 .002 .181 .181 ADS  Neuroticism .443 3.78 < .001 .412 .412  SADNESS .273 2.35 .023 .067 .480  Extraversion −.237 −2.32 .025 .052 .532 β T p ∆R2 R2 FSMC total score  Neuroticism .370 2.82 .007 .137 .137 FSMC motor  Extraversion −.341 −2.57 .013 .116 .116 FSMC cognitive  Neuroticism .426 3.33 .002 .181 .181 ADS  Neuroticism .443 3.78 < .001 .412 .412  SADNESS .273 2.35 .023 .067 .480  Extraversion −.237 −2.32 .025 .052 .532 Note: In the first step of the hierarchical stepwise regression models, age and gender were included. In the regression model for the FSMC total score only the variables neuroticism, extraversion, and ANGER were included in the second step and the corresponding gender by trait interaction terms in a third step. For the FSMC motor subscale only the variables neuroticism and extraversion were included in the second step of the regression model and the corresponding gender by trait interaction terms in a third step. Neuroticism and ANGER were included in the regression model for the FSMC cognitive subscale in the second step; the corresponding gender by trait interaction terms in a third step. In the regression model for the ADS neuroticism, extraversion, FEAR, ANGER, PLAY, and SADNESS were included in the second step and the corresponding gender by trait interaction terms in a third step. Only variables are presented, which explained a significant (p < .05) proportion of variance in the dependent variable within the step they were included into the model. Mean Values of the Questionnaires in the Different Samples As presented in Figs. 2 and 3 as well as in the Supplementary material online, Table 4 (for exact mean values and standard deviations), the PwMS sample showed significantly higher scores in neuroticism, FEAR, CARE (only when controlling for age) and SADNESS and lower scores in openness compared to the matched HC sample. For the NEO-FFI, only the difference in neuroticism would hold for multiple testing (p = .05/5 = .01). For the ANPS, the difference in FEAR would hold for multiple testing (p = .05/7 = .007). Fig. 2. View largeDownload slide Mean values and standard deviations (±1 SD) of the NEO-FFI scores in the PwMS and the HC sample. Asterisks indicate significant differences between PwMS and HC sample when controlling for age (***p ≤ .001, **p ≤ .010, *p ≤ .050). Fig. 2. View largeDownload slide Mean values and standard deviations (±1 SD) of the NEO-FFI scores in the PwMS and the HC sample. Asterisks indicate significant differences between PwMS and HC sample when controlling for age (***p ≤ .001, **p ≤ .010, *p ≤ .050). Fig. 3. View largeDownload slide Mean values and standard deviations (±1 SD) of the ANPS scores in the PwMS and the HC sample. Asterisks indicate significant differences between PwMS and HC sample when controlling for age (***p ≤ .001, **p ≤ .010, *p ≤ .050). Please note that the difference in the CARE scale between the PwMS and the HC sample is only significant when controlling for age (mean values and standard errors for the CARE scale corrected for age: PwMS: 43.40 (0.72); HC: 41.06 (0.72)). Fig. 3. View largeDownload slide Mean values and standard deviations (±1 SD) of the ANPS scores in the PwMS and the HC sample. Asterisks indicate significant differences between PwMS and HC sample when controlling for age (***p ≤ .001, **p ≤ .010, *p ≤ .050). Please note that the difference in the CARE scale between the PwMS and the HC sample is only significant when controlling for age (mean values and standard errors for the CARE scale corrected for age: PwMS: 43.40 (0.72); HC: 41.06 (0.72)). Discussion The first aim of the present study was to disentangle fatigue and depression in PwMS by investigating the underlying personality traits and PETs. Additionally, the differences between PwMS and the HC sample in the Big Five of Personality and the PETs were examined. In line with most of the literature, fatigue—especially cognitive fatigue—and depression in PwMS were significantly associated (e.g. Bakshi et al., 2000; Ford et al., 1998; Kroencke et al., 2000; Penner et al., 2007; Schreurs, de Ridder, & Benzing, 2002). More specifically, the correlation of r = .53 / r = .58 between the FSMC motor/cognitive subscales and the ADS speaks for a shared variance of about 28%–34%, hence at least 66% of the variance are due to non-shared factors in the present sample under investigation. A closer look at the underlying personality traits and PETs helps to understand which traits target the shared and non-shared variance. First, the significant predictor for motoric fatigue was low extraversion. This indicates that persons who are less socially outgoing, less assertive and vivid show stronger bodily exhaustion when fatigue sets in (e. g., when walking or doing other bodily movement). Of note, the associations between extraversion and the FSMC scales and the ADS did only change slightly when building the extraversion score without the NEO-FFI items 32 (“I often feel as if I’m bursting with energy.”) and 52 (“I am a very active person.”). Which facet of extraversion ultimately will be of highest importance needs to be studied in future research endeavors using inventories such as the NEO-PI-R giving also insights into more narrow sub-facets of the Big Five. Additionally, clearly, we are not able to predict causality with respect to these variables, and both causal directions would be meaningful. Either persons with a premorbid lower extraversion demonstrate higher motoric fatigue when suffering from MS, or the higher motoric fatigue stemming from MS could result into less extraverted behavior. High neuroticism, predominantly used to describe traits of anxiety, emotional instability, and mood swings, was the most important predictor for cognitive fatigue as well as depression. Thus, this personality trait seems to explain the shared variance in MS related cognitive fatigue and depression. In detail, the MS related association among others means that neurotic PwMS tend to report more problems with keeping their concentration, more difficulties learning new information, forgetting things more often, thus, suffering from MS related cognitive fatigue. In contrast to cognitive fatigue, depression in PwMS was also predicted by high SADNESS of the ANPS and low extraversion of the NEO-FFI. High SADNESS can be described by high feelings of loneliness, thinking often about loved ones and past relationships and pronounced distress when being separated from loved one’s. In line with a recent study by Montag, Widernhorn-Müller and colleagues (2016), SADNESS represents the core primary emotional system underlying depression out of Panksepp’s Affective Neuroscience theory. In general, the observed patterns between high FEAR/SADNESS (to a lesser degree also with lower PLAY/high ANGER) and higher depression mirror what has been observed by Montag, Widernhorn-Müller and colleagues (2016). In sum, our findings indicate that depression in PwMS shares some variance with cognitive fatigue (explained by high neuroticism) and also motoric fatigue (explained by low extraversion). Beyond these results, the non-shared variance of depression in PwMS is associated with the PET SADNESS not playing a role for fatigue. Regarding the second aim of the study, PwMS showed significant differences in neuroticism (higher), and also openness (lower), FEAR (higher), CARE (higher) and SADNESS (higher) compared to the HC sample. This is partly in line with Penner and colleagues (2007) who also found increased neuroticism scores in PwMS compared to healthy subjects, but additionally significantly lower scores in extraversion. The latter association could not be observed in our sample, but the importance of the neuroticism finding is underlined by our findings from the regression models outlined already above. Note that the difference in CARE is only significant when controlling for age. And the differences in FEAR and SADNESS could be explained by the fact that the HC sample was healthy, thus—in contrast to the PwMS sample—no participant suffered from depression. Some limitations need to be mentioned: The present study has a cross-sectional design disallowing a causal relationship to be assumed. Thus, it remains unclear whether the differences in the personality traits and PETs between PwMS and HC are predisposing factors for the emergence of MS or if these are caused by the attack of one’s own immune system on the neuron myelin. To gain insight into causal mechanisms longitudinal studies are needed. In conclusion, the present study is the first to disentangle MS related fatigue and depression by the inclusion of both personality trait and PET measures. Evidence is presented that both fatigue and depression are robustly associated with each other in PwMS, most likely due to the shared variance explained by neuroticism and extraversion. Still, there is enough room for non-shared variance (in our study depression in PwMS was further predicted by SADNESS, which had no influence on fatigue). This knowledge should be taken into account when distinguishing and treating MS related fatigue and MS related depression. As might be expected, in clinical settings using the rather long original ANPS (110 items) might be too time consuming. A solution could be to use the short version by Pingeault, Falissard, Côté and Berthoz (2012) consisting of only 36 items. But this measure clearly needs to be validated in the context of MS related fatigue and depression, first. Therefore, in order to save time, another option would be to only assess the SADNESS items of the original ANPS (14 items) together with MS related motoric/cognitive fatigue and MS related depression. However, researchers and clinical staff should do this with caution, as important aspects (e. g., associations between other MS related symptoms and PETs other than SADNESS) could remain unobserved. Finally, as the ANPS has been constructed on abundant neuroscientific data, the present findings provide researchers with novel ideas as to what brain areas and neurotransmitters/neuropeptides underlie the different facets of fatigue and especially depression in MS (e.g., Panksepp, 2011). Funding This work was supported by the German Research Foundation [Heisenberg grant: DFG MO MO2363/3-2 to C.M.]; and the German Academic Scholarship Foundation (Studienstiftung des deutschen Volkes) [to C.S.]. Conflict of interest The authors declare that there is no conflict of interest. Authors' Contributions C.M., S.M., J.N., J.S., and H.K. designed the study. C.S. and C.M. drafted the manuscript. C.S. conducted the statistical analyses. M.S. worked over the manuscript to improve it and corrected the English language and grammar. All authors commented on the manuscript and critically revised it. All authors approved the final version of the manuscript. References American Psychiatric Association . ( 2013 ). Diagnostic and statistical manual of mental disorders ( 5th ed. ). Washington, DC : American Psychiatric Association Publishing . Arnett , P. A. , Barwick , F. H. , & Beeney , J. E. ( 2008 ). Depression in multiple sclerosis: Review and theoretical proposal . Journal of the International Neuropsychological Society , 14 , 691 – 724 . Google Scholar PubMed Bakshi , R. , Shaikh , Z. A. , Miletich , R. S. , Czarnecki , D. , Dmochowski , J. , Henschel , K. et al. . ( 2000 ). Fatigue in multiple sclerosis and its relationship to depression and neurologic disability . 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Archives of Clinical NeuropsychologyOxford University Press

Published: Aug 1, 2018

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