Skip to main content
NCBI home page
Cambridge Open Access logoLink to Cambridge Open Access
. 2019 Mar 14;50(4):625–635. doi: 10.1017/S0033291719000448

Stuck in a negative me: fMRI study on the role of disturbed self-views in social feedback processing in borderline personality disorder

Charlotte C van Schie 1,2,✉, Chui-De Chiu 3, Serge A R B Rombouts 1,2,4, Willem J Heiser 1,5, Bernet M Elzinga 1,2
PMCID: PMC7093320  PMID: 30867073

Abstract

Background

Interpersonal difficulties in borderline personality disorder (BPD) could be related to the disturbed self-views of BPD patients. This study investigates affective and neural responses to positive and negative social feedback (SF) of BPD patients compared with healthy (HC) and low self-esteem (LSE) controls and how this relates to individual self-views.

Methods

BPD (N = 26), HC (N = 32), and LSE (N = 22) performed a SF task in a magnetic resonance imaging scanner. Participants received 15 negative, intermediate and positive evaluative feedback words putatively given by another participant and rated their mood and applicability of the words to the self.

Results

BPD had more negative self-views than HC and felt worse after negative feedback. Applicability of feedback was a less strong determinant of mood in BPD than HC. Increased precuneus activation was observed in HC to negative compared with positive feedback, whereas in BPD, this was similarly low for both valences. HC showed increased temporoparietal junction (TPJ) activation to positive v. negative feedback, while BPD showed more TPJ activation to negative feedback. The LSE group showed a different pattern of results suggesting that LSE cannot explain these findings in BPD.

Conclusions

The negative self-views that BPD have, may obstruct critically examining negative feedback, resulting in lower mood. Moreover, where HC focus on the positive feedback (based on TPJ activation), BPD seem to focus more on negative feedback, potentially maintaining negative self-views. Better balanced self-views may make BPD better equipped to deal with potential negative feedback and more open to positive interactions.

Key words: Borderline personality disorder, precuneus, self-esteem, self-views, social feedback, TPJ

Introduction

Humans have an innate need to form relationships (Over, 2016). However, for patients with borderline personality disorder (BPD), this is a struggle as they encounter interpersonal problems on a daily basis (Lieb et al., 2004; Lazarus et al., 2014). Interpersonal difficulties are strongly interlinked with identity disturbance as the way we view ourselves shapes our interaction with others, just as our self-views are shaped by interaction with others (Jorgensen, 2010; Kerr et al., 2015; van Schie et al., 2018). Since BPD patients tend to have fragmented and conflicting self-views that are predominantly negative (Wilkinson-Ryan and Westen, 2000; Zeigler-Hill, 2006), this may make them more vulnerable for interpersonal difficulties. As of yet, the role of identity disturbances of BPD in the context of interpersonal difficulties has not been addressed (Kerr et al., 2015). Therefore, the current study aims to investigate the role of negative self-views of BPD on the affective and neural responses to negative as well as positive social feedback (SF).

Core features of interpersonal difficulties in BPD are a heightened sensitivity to rejection and altered social cognition (Gunderson, 2007; Fertuck et al., 2009; Ha et al., 2013; Lis and Bohus, 2013; Sharp et al., 2013). Studies have shown that patients with BPD perceive others’ faces as more untrustworthy and tend to anticipate threat rather than acceptance in social interactions (Daros et al., 2013; Fertuck et al., 2013; Miano et al., 2013; Nicol et al., 2013; Deckers et al., 2015). This lack of trust and expectation of rejection may relate to feeling less included during pleasant interactions (Staebler et al., 2011; Renneberg et al., 2012) and showing less cooperative behavior (King-Casas et al., 2008; Saunders et al., 2015). Altered responses in brain activation to negative emotional stimuli, i.e. increases in insula, posterior cingulate cortex (PCC) activation and decreases in dorsolateral prefrontal cortex, indicate increased sensitivity to and difficulty in dealing with negative stimuli (Ruocco et al., 2013; Schulze et al., 2016). The tendency to mistrust others has been conceptualized as not only obstructing the building of relationships but also social learning from others about the self (Fonagy and Allison, 2014; Fonagy et al., 2015; Bo et al., 2017).

Accordingly, interpersonal difficulties are tightly linked to a disturbed view of the self, or say, identity disturbance (Bender and Skodol, 2007). Self-views are organized knowledge about various aspects of the self, which is related to but separable from the evaluative attitude toward the self (i.e. self-esteem) (Higgins, 1987; Campbell et al., 2003; Swann and Brooks, 2012; Chiu et al., 2017). In interaction with others, individuals generally prefer feedback that is consistent with their self-views (Stinson et al., 2010; van Schie et al., 2018). For example, we previously found that negative feedback that is inconsistent with someone's self-view has more detrimental impact on mood than consistent negative feedback (van Schie et al., 2018). Moreover, there are individual differences with regard to the impact of (in)consistent feedback: individuals with lower self-esteem seem to have more difficulty in dealing with (inconsistent) negative feedback (vanDellen et al., 2011) as indicated by lower mood and precuneus activity (van Schie et al., 2018). Therefore, the way we respond to feedback during social interactions is impacted by both valence and consistency of feedback with self-views as well as a global self-evaluation (Markus and Wurf, 1987; Chen et al., 2006).

Disturbed self-views in BPD could be highly relevant in SF processing (Wilkinson-Ryan and Westen, 2000; Zeigler-Hill, 2006). On the one hand, literature on the preference for consistent information explains the ease with which BPD patients process negative information compared with positive information (Stinson et al., 2010; Winter et al., 2015; Auerbach et al., 2016; Korn et al., 2016). On the other hand, one might expect that negative feedback may yield relatively mild emotional responses in BPD, since it is consistent with their self-views, whereas positive information may not elicit much positive responses because of its inconsistency with their self-views (Higgins, 1987; Winter et al., 2015; Auerbach et al., 2016; Korn et al., 2016). However, literature on rejection sensitivity suggests that BPD may be particularly prone to negative feedback regardless of its consistency with the self-view (Berenson et al., 2011; Miano et al., 2013).

The current study investigates how self-views of BPD influence the affective and neural responses to negative as well as positive SF. First, we aim to ascertain that self-views of BPD patients are more negative than healthy control (HC) participants. Next, we investigate how valence of the feedback and consistency with the self-views moderate the responses to SF. Importantly, as self-esteem influences responses to SF and BPD patients are characterized by low self-esteem (LSE) (Zeigler-Hill, 2006; Brown, 2010), we compare BPD patients not only with HC but also with a group of participants matched by the level of self-esteem. This LSE group allows for specifying the effect of disturbed self-views in BPD that goes beyond BPDs LSE.

Methods and materials

Participants

Participants (N = 80, all female, age M = 29.9, s.d. = 9.4, range = 18–54 years) consisted of patients with the BPD group (N = 26), HC group (N = 32), and LSE control group (N = 22). Participants included in the LSE group had a score lower than 18 on the Rosenberg Self-Esteem Scale [RSES, clinical cut-off for LSE (Schmitt and Allik, 2005; Korrelboom, 2011)]. As expected, BPD patients (M = 11.28, s.d. = 6.1) had lower levels of self-esteem compared with HC (M = 23.75, s.d. = 3.2). The LSE (M = 12.73, s.d. = 2.9) were matched with BPD patients on the level of trait self-esteem. HC and LSE were included to resemble BPD in gender, age, education, and handedness to BPD, see Table 1. We distinguished three levels of education: high school level only, continued education on vocational level, and continued education on higher level. HC (but not LSE) reported higher education levels compared with BPD, see Table 1.

Table 1.

Demographic data (N = 80)

HC (N = 32) LSE (N = 22) BPD (N = 26) Comparison
Variable Mean (s.d.)/count (%) Mean (s.d.)/count (%) Mean (s.d.)/count (%)
Age (years) 28.12 (9.8) 31.91 (8.9) 30.46 (9.2) F(2,77) = 1.13, p = 0.330
Education levela χ2(2) = 9.77, p = 0.006
HC > BPD, LSE = HC and BPD
High School 1 (3.1%) 4 (18.2%) 8 (30.8%)
Vocational training (MBO) 19 (59.4%) 7 (31.8%) 14 (53.8%)
Higher education (HBO and University) 12 (37.5%) 11 (50.0%) 4 (15.4%)
Handedness 8.09 (5.0) 8.33 (4.7) 7.38 (6.2) F(2,76) = 0.21, p = 0.811
Right handed (8+) 28 (87.5%) 19 (90.4%) 22 (84.6%)
Left handed (−8+) 1 (3.1%) 1 (4.8%) 3 (11.5%)
Ambidextrous (−7−7) 3 (9.4%) 1 (4.8%) 1 (3.9%)
Trait self-esteem (RSES) 23.75 (3.2) 12.73 (2.9) 11.28 (6.1) F(2,76) = 74.47, p < 0.001
HC > LSE = BPD
Axis I disorder (DSM-IV) [Current/lifetime (incl. current)]
Mood disorders 0/3 5/11 7/18
 MDD 0/3 4/10 6/15
 Dysthymia 0/0 1/1 1/3
Anxiety disorders 0/2 5/6 6/9
 Panic disorder 0/1 0/0 2/6
 Agoraphobia 0/1 0/0 1/1
 Social phobia 0/0 3/3 1/1
 Specific phobia 0/0 0/0 1/1
 OCD 0/0 0/1 0/0
 GAD 0/0 2/2 2/2
PTSD 0/1 0/0 2/2
ADHD 0 /0 2/2 5/5
Substance abuse and addiction 0/0 0/0 6/9
 Alcohol 0/0 0/0 2/4
 Drugs 0/0 0/0 4/5
Other disorders 0/0 4/7 1/1
Borderline symptoms
VKP-BPD 1.34 (1.6) 7.48 (3.9) –
BPD-SI – – 31.05 (10.2)
Axis II disorders (DSM-IV)
Borderline – – 26
Antisocial – – 2
Paranoid – – 1
Open in a new tab

MBO: middelbaar beroepsonderwijs (in Dutch); MDD: major depressive disorder.

a

Group differences in the education level were assessed using a Kruskal–Wallis test followed by three Wilcoxon rank sum tests (Bonferroni corrected).

Exclusion criteria for all participants were incompatibility with a magnetic resonance imaging (MRI) scanner, and usage of benzodiazepines (equivalent of >20 mg of oxazepam) or antipsychotics. Exclusion criteria for HC: any current Axis I or Axis II disorder. Exclusion criteria for LSE: current Axis I and II disorders were allowed, except for a diagnosis of BPD. HC and LSE participants were recruited from the general population where LSE were specifically targeted with adverts seeking insecure individuals. BPD patients were recruited from a mental health institution (GGZ Rivierduinen). Medication use, both for physical ailments and psychotropic medication, was taken into account with HC (N = 3, 9%), LSE (N = 4, 18%) and BPD (N = 11, 42%) reporting medication use (for specifications see online Supplementary Table S1). Two HC and one BPD participant were excluded from analyses because of scanner artifacts. One BPD participant was excluded because of neural abnormalities resulting in a final set of 80 participants.

Participants signed informed consent. The study was approved by the medical ethics committee of the Leiden University Medical Center (P12.249) and performed in accordance with the Helsinki Declaration of 1975, as revised in 2008 and the Dutch Medical Research Involving Human Subjects Act (WMO). Part of the HC and LSE data has been reported in van Schie et al. (2018).

Social feedback task

Before performing the SF task in an MRI scanner, participants were introduced to another female participant who in reality was a confederate to the study. Together they were instructed that the participant would receive feedback of the confederate based on a personal interview. The interview, consisting of nine personal questions and three moral dilemmas, was held without the confederate, outside the scanner. The voice recorded interview was supposedly handed over to the confederate to provide feedback choosing from the provided list of trait words. The chosen trait words were presented on screen to the participant while in an MRI scanner. In reality, the SF was the same for each participant and consisted of 15 negative (e.g. arrogant), 15 intermediate (e.g. reserved) and 15 positive (e.g. happy) evaluative words, presented one by one (2500 ms) in random order (with no consecutive trials of the same valence). After each word, the participant was asked how she was feeling at that moment on a scale from really bad (1) to really good (4) using button boxes attached to the participants’ legs. We used an event-related design with a jittered interstimulus interval (M = 2004 ms, s.d. = 370) and self-paced responses to each trial. Outside the scanner, before debriefing, participants were asked to rate the feedback words on valence from very negative (−4) to very positive (4) with 0 indicating intermediate and on applicability to self from not at all applicable (1) to very much applicable (4). Finally, a manipulation check interview was held with indirect and direct questions to assess whether participants believed the cover story, see online Supplementary material. Most participants believed that the feedback was given by the confederate (HC: 91%, LSE: 95%, BPD: 96%) and groups did not differ in this respect, χ2(4) = 1.20, p = 0.879.

The SF task has been validated before in healthy participants and shown to affect mood, with a better mood after positive feedback and a worse mood after negative feedback especially when feedback is inapplicable. Moreover, a broad neural network is involved in the processing of SF including anterior cingulate cortex (ACC), insula, precuneus, PCC, and temporoparietal junction (TPJ) areas, with differential activation for negative, positive and more or less applicable feedback. For more details, see van Schie et al. (2018).

Clinical measures

Axis I disorders

The MINI, a semi structured interview, was used to assess lifetime and current Axis-I disorders based on DSM-IV (First et al., 1997). BPD patients were assessed by a trained psychologist as part of their intake and diagnosis at the mental health institute. HC and LSE participants were assessed by a trained psychologist (C.v.S).

Axis II disorders

Axis II disorders in BPD patients were assessed using IPDE-IV (Loranger, 1999). Borderline symptom severity was assessed using the Borderline Personality Disorder-Severity Interview (BPD-SI) (Giesen-Bloo et al., 2010). HC and LSE were screened for personality disorders using the self-report standardized assessment of personality – abbreviated scale (SAPAS-SR) (Germans et al., 2008). A score of four or greater indicates the likelihood of a personality disorder and led to exclusion in the case of HC. In the case of LSE, it led to a follow-up to exclude BPD using the SCID-II, a semi structured interview used to diagnose personality disorders (First et al., 2000). Furthermore, the presence of borderline symptoms was assessed in both HC and LSE using the BPD items of the ‘Questionnaire for Personality traits’ [Vragenlijst voor Kenmerken van de Persoonlijkheid (VKP) (Duijsens et al., 1996)].

Trait self-esteem

The RSES measures the level of trait self-esteem using 10 items with a four point scale (Rosenberg, 1965). The Dutch translation has been well validated (Schmitt and Allik, 2005; Frank et al., 2008). The reliability of the RSES was good (α = 0.92).

Procedure

Participants were screened over phone and with online questionnaires to assess compatibility with the MRI scanner (e.g. no metal objects in their body), Axis I disorders (First et al., 1997), Axis II disorders (First et al., 2000; Germans et al., 2008), BPD symptoms (Duijsens et al., 1996), handedness (van Strien, 1992) and medication use. After screening and inclusion two appointments were made. During the first appointment participants signed informed consent, filled in a demographic form and the RSES and were prepared for the MRI scan session. During the second appointment, they performed the SF task in the MRI scanner. After the experiment participants were debriefed of the set-up of the experiment and received a monetary reward of €30.

Data acquisition, preprocessing and analysis

The SF task was programmed in E-prime 2.0. Responses to the SF task were prepared for analysis using Excel 2010 and IBM SPSS statistics version 23. Preprocessing of fMRI data was performed in Feat v6.00 in FSL 5.0.7. Details on data acquisition and preprocessing can be found in the online Supplementary materials. For both affective and neural responses, the moderation of valence and applicability on group effect was tested by trial level modeling using multilevel analyses and parametric modulation. The reference category (i.e. intercept) of valence was set to ‘intermediate feedback’ enabling the contrasting of negative and positive feedback with intermediate feedback. Applicability ratings were recoded from values 1, 2, 3, 4 to values −3, −1, 1, 3, contrasting inapplicable to applicable feedback. Our main interest was to compare BPD with HC. To investigate the specificity of the results, BPD were also compared with LSE, making BPD the reference group.

Analysis of affect

Behavioral and self-report data were analyzed using R version 3.3.3 (R Core Team, 2013), packages: lme4 (linear mixed-effect modeling) and ggplot2 (for creating figures) (Wickham, 2009; Bates et al., 2015). To test the significance of main and interaction effects, models increasing in complexity were compared using the χ2 test.

To ascertain whether self-views of BPD patients are more negative, applicability ratings for each feedback word for each participant were used as outcome, predefined valence categories were used on the first level and group membership on the second level. To investigate the impact of the SF on affect, mood ratings for each feedback word for each participant were used as outcome, while the predefined valence categories and individual applicability ratings were specified on the first level. The second level consisted of group membership. The two-way interaction effects of group by valence and group by applicability and the three-way interaction of group by valence by applicability were tested.

Analysis of fMRI

Analysis of fMRI data was performed in Feat v6.00 in FSL 5.0.7. On the individual level, an event related design was used for modeling both valence and applicability. For valence, the onset and duration of each feedback word per valence was specified, resulting in three regressors (Neg, Int and Pos valence). The parametric modulation analysis for applicability contained three regressors (one per valence) in which trials were weighted by the applicability ratings. The onset and duration of the mood question was modeled as a regressor but not of interest to the hypotheses. Six motion parameters indicating rotation and translation, and mean time series of white matter and cerebrospinal fluid were added as confound regressors (Birn et al., 2006; Mier et al., 2013; Cheng and Puce, 2014). The bold response was convolved with the double-gamma HRF function. Six t-contrasts were formulated to contrast negative feedback to intermediate (Neg v. Int) and positive feedback (Neg v. Pos) and positive to intermediate feedback (Pos v. Int) in both directions. The general relation between applicability and neural activation was tested (Applicability), as well as the specific relation for applicability of negative, intermediate and positive feedback (interaction effects: Applicability × Neg, Applicability × Int and Applicability × Pos).

For group level inference, a mixed effects model with the FLAME1 method was used. The three groups with separate variance estimation were specified in the model. T-contrasts were formulated to compare BPD with HC and BPD with LSE in both directions on all lower level contrasts. This resulted in the test of two two-way interaction effects, i.e. group by valence and group by applicability and one three way-interaction effect, i.e. group by applicability by valence. Data were cluster corrected supported by the findings of Eklund et al. (2016) indicating that the FLAME1 method and cluster correction of Z > 2.3 and cluster p < 0.05 keep the amount of false positives within allowable limits.

For labeling peak voxels the following atlases were used: Harvard-Oxford structural atlas for cortical and subcortical regions (Frazier et al., 2005; Desikan et al., 2006; Makris et al., 2006; Goldstein et al., 2007), Mars connectivity-based parcellation for TPJ and inferior parietal lobe (IPL) areas (Mars et al., 2011, 2012), the cerebellar atlas for cerebellum coordinates (Diedrichsen et al., 2009), and the Talairach Deamon labels for Brodmann areas (Lancaster et al., 2000).

Results

Applicability of feedback to self-views

Applicability ratings were affected by a valence by group interaction [χ2(4) = 106.19, p < 0.001], see online Supplementary Tables S4 and S5 for model comparisons and parameters. Consistent with our hypothesis, BPD patients rated the intermediate (b = −0.40, s.e. = 0.16, t = −2.50) and especially negative feedback (b = −0.53, s.e. = 0.16, t = −3.36) as more applicable compared with HC, see Fig. 1a. Positive feedback was rated as less applicable by BPD compared with HC (b = 1.07, s.e. = 0.16, t = 6.74). Compared with LSE, BPD also rated negative feedback as more applicable (b = −0.43, s.e. = 0.17, t = −2.43) and positive feedback as less applicable (b = 0.63, s.e. = 0.18, t = 3.61) but did not differ in applicability of intermediate feedback (b = −0.15, s.e. = 0.18, t = −0.83). Moreover, using the valence ratings (i.e. degree of negativity or positivity), we found that all three groups rated the valence of the words in a similar way [χ2(2) = 2.4, p = 0.307], with negative and positive words being more emotional than intermediate words, see online Supplementary Tables S2 and S3. However, there was a trend for an interaction effect between valence and group [χ2(4) = 8.42, p = 0.077], which could indicate that negative feedback was rated slightly less negative by BPD than HC (b = −0.43, s.e. = 0.16, t = −2.69), see also online Supplementary Table S3 for model parameters.

Fig. 1.

Fig. 1.

Open in a new tab

(a) Mean applicability ratings by group after negative, intermediate and positive feedback (error bars indicate 95% confidence intervals). (b) Illustration of mood ratings by group after negative, intermediate and positive feedback at the mean level of applicability of feedback. (c) Illustration of mean mood ratings by group after negative, intermediate and positive feedback for not to very applicable feedback. Applicability has a greater impact on mood during negative and intermediate feedback than positive feedback. Applicability has a greater impact on the mood of HC compared with BPD. Mood rating is rescaled to scores 1–4 for display purposes.

Affective responses

Mood was affected by group [χ2(2) = 11.4, p = 0.003] with BPD reporting a worse mood than HC overall (b = 0.81, s.e. = 0.19, t = 4.28), see Table 2 and online Supplementary Table S6. Valence moderated the group effect [χ2(4) = 39.89, p < 0.001]. BPD reported a worse mood after negative (b = −0.14, s.e. = 0.15, t = −0.95) and intermediate feedback (b = −0.81, s.e. = 0.19, t = 4.28) and similar mood after positive feedback (b = −0.49, s.e. = 0.13, t = −3.70) compared with HC, see Fig. 1b. Compared with LSE, BPD reported equal mood after intermediate (b = 0.19, s.e. = 0.21, t = 0.91) and positive feedback (b = 0.11, s.e. = 0.15, t = 0.75) but a better mood after negative feedback (b = −0.50, s.e. = 0.16, t = −3.10).

Table 2.

Effect parameters of model predicting mood ratings by valence category (intermediate = reference), group (BPD = reference), and applicability of feedback and two-way interactions

Effect Estimate Std. error t-value
Intermediate valence (Intercept) −0.21 0.14 −1.45
Negative valence −0.83 0.11 −7.64***
Positive valence 1.57 0.10 15.03***
HC 0.81 0.19 4.28**
LSE 0.19 0.21 0.91
Applicability 0.21 0.03 7.19***
Negative valence × HC −0.14 0.15 −0.95
Positive valence × HC −0.49 0.13 −3.70***
Negative valence × LSE −0.50 0.16 −3.10***
Positive valence × LSE 0.11 0.15 0.75
Negative valence × Applicability −0.02 0.03 −0.78
Positive valence × Applicability −0.10 0.04 −2.78**
Applicability × HC 0.07 0.03 2.27**
Applicability × LSE 0.01 0.03 0.23
Open in a new tab

Significance level (***<0.001, **<0.01, *<0.05, ^<0.10) based on χ2 test of model comparisons, see online Supplementary Table S6.

Applicability moderated the group effect as well [χ2(4) = 14.8, p = 0.005]. BPD mood ratings were less affected by applicability compared with HC (b = 0.07, s.e. = 0.03, t = 2.27), but did not differ in this respect from LSE (b = 0.01, s.e. = 0.03, t = 0.23), see Fig. 1c. There was no three-way interaction of valence by applicability by group [χ2(4) = 8.0, p = 0.090].

Neural responses

Groups differed in neural correlates of feedback valence, see Table 3 for clusters and peak voxels†,1. In response to negative feedback compared with positive feedback, HC showed stronger left precuneus activation, whereas BPD showed relatively low and equal precuneus activation for negative and positive feedback, see Fig. 2. In this precuneus cluster, LSE showed relatively high and equal activation for negative and positive feedback, albeit not significantly different from BPD, see Fig. 2. In response to positive compared with negative feedback, HC showed stronger right anterior TPJ activation, whereas BPD showed the reverse pattern, with stronger TPJ activation for negative feedback compared with positive feedback. Compared with LSE, BPD showed stronger left precuneus activation during negative compared with positive feedback, see Table 3 and Fig. 2. However, this cluster in the left precuneus did not overlap with the cluster found in comparison to HC. Groups did not differ in neural correlates of applicability. The three-way interaction of applicability by negative valence of BPD compared with HC in the motor cortex, superior parietal lobule and inferior parietal lobule is probably attributable to button press movements (Mars et al., 2011).

Table 3.

Selected neural correlates for group comparisons on contrasts of valence and applicability of feedbacka, cluster corrected Z = 2.3, cluster p < 0.05

Voxel test value MNI coordinates
Group contrast Valence contrast Cluster size Cluster p value Label peak voxels Z X Y Z
HC > BPD Negative > positive 365 0.028 L Lateral occipital cortex 4.00 −10 −68 62
L Superior parietal lobule, BA7 3.20 −26 −54 56
L Superior parietal lobule, BA7 3.08 −32 −56 52
L Lateral occipital cortex, BA7 3.01 −32 −66 56
L Superior parietal lobule 2.98 −30 −56 48
L Lateral occipital cortex 2.91 −18 −74 58
Positive > negative 378 0.023 R Postcentral gyrus, IPLA, BA3 3.33 62 −12 30
R Parietal operculum cortex, IPLA, BA40 3.28 56 −24 16
R Supramarginal gyrus, IPLA 3.24 54 −20 30
R Supramarginal gyrus, TPJa 3.17 56 −42 16
R Parietal operculum cortex, IPLA, BA40 2.98 60 −26 20
R Superior temporal gyrus 2.93 70 −36 4
BPD > HC Negative × Applicability 383 0.022 R Postcentral gyrus, SPLA, IPLB 3.33 36 −30 56
R Postcentral gyrus, SPLA, BA5 3.13 36 −36 64
R Postcentral gyrus, IPLB 3.12 40 −28 52
R Postcentral gyrus, SPLA, BA40 3.10 36 −34 60
R Precentral gyrus, BA4 2.88 42 −18 58
R Postcentral gyrus, IPLB, BA40 2.75 48 −32 60
BPD > LSE Negative > positive 331 0.046 L Precuneus 3.33 −16 −62 24
L Cuneus 3.30 −12 −82 26
L Lateral occipital cortex 3.18 −24 −78 32
L Precuneus, BA31 3.06 −4 −74 34
L Cuneus 2.96 −20 −68 28
L Precuneus, BA7 2.94 −8 −72 36
Intermediate × Applicability 793 <0.001 L Paracingulate gyrus 3.54 −10 42 26
L Frontal pole, BA9 3.33 −2 58 22
L Superior frontal gyrus 3.32 −8 54 30
L Superior frontal gyrus, BA9 3.26 −4 52 28
R Frontal pole 3.19 4 60 26
R Paracingulate gyrus 3.17 10 54 14
LSE > BPD Positive > intermediate 458 0.007 L Lateral occipital cortex 3.24 −38 −66 34
L Lateral occipital cortex 3.22 −46 −72 18
L Lateral occipital cortex 3.22 −38 −78 24
L Lateral occipital cortex 3.20 −32 −74 24
L Lateral occipital cortex, BA39 3.09 −46 −70 14
L Lateral occipital cortex 3.00 −42 −64 14
Open in a new tab
a

Contrasts without any above threshold clusters are not reported in this table.

Fig. 2.

Fig. 2.

Open in a new tab

Left: Clusters of neural activation indicating HC > BPD (blue) and BPD > LSE (orange). Right: Mean contrast values for the HC > BPD clusters (blue clusters) by group and contrast.

Exploratory findings

For exploratory purposes, we checked whether LSE differed in self-views from HC by rerunning the model with applicability ratings as an outcome but with HC set as a reference group instead of BPD. We found that despite lower self-esteem, LSE did not report that negative feedback was more applicable to them (b = 0.11, s.e. = 0.17, t = 0.65), neither was intermediate feedback (b = 0.26, s.e. = 0.17, t = 1.52). However, they did report that positive feedback is less applicable to them (b = −0.44, s.e. = 0.17, t = −2.64).

Confounds

To control for potential effects of whether the participant believed the SF paradigm (yes/no), medication status (on/off) and current depression comorbidity, we took this into account in additional affective and neural analyses. These confounds had no effects on the affective results.

Handedness was also taken into account in neural analyses. The stronger precuneus activation in HC compared with BPD found after negative feedback compared with positive feedback did not survive significance threshold after current depression or handedness was taken into account.

Discussion

The current study investigated how disturbed self-views related to interpersonal difficulties in patients with BPD by examining affective and neural responses to negative and positive SF which varied in applicability to the self-view. BPD patients reported more negative self-views compared with both the HC and LSE groups, confirming previous findings of a negative self-referential bias (Winter et al., 2015; Korn et al., 2016) and extending them by showing that distorted self-views in BPD cannot be attributed to LSE alone.

The consistency of self-views with SF was an important determinant of affective and neural responses. In general, more applicable feedback is related to a better mood especially during negative and intermediate feedback (van Schie et al., 2018). In BPD, however, the applicability of negative feedback did not reduce its negative impact resulting in a lower mood after negative feedback than HC. Thus, BPD responded similarly to all negative feedback regardless of consistency to the self. Responses in HC, on the other hand, vary with the consistency of the feedback with the self-view. Another study also found that BPD have trouble ignoring negative stimuli even when these are irrelevant (Domes et al., 2006). BPD can perhaps not rely on their self-knowledge to judge the accuracy of the feedback and respond to negative feedback in a more rigid way (Fonagy et al., 2015).

Analyses of the neural responses seemed to corroborate these affective findings. In general, applicability of feedback relates to increased precuneus activation, especially during negative feedback (van Schie et al., 2018). The precuneus is implicated in putting self-relevant stimuli in autobiographical context (Northoff et al., 2006). Relating feedback to existing self-knowledge could be a way of critically evaluating negative feedback, reducing its impact on state affect when a specific trait is already incorporated in the self-view (vanDellen et al., 2011). Interestingly, BPD differed from HC in precuneus activation during negative v. positive feedback: BPD showed low recruitment and lack of differentiation in precuneus activity. HC in contrast, showed greater precuneus recruitment in response to negative compared with positive feedback. Where healthy individuals may benefit from using their (positive) self-views to evaluate the relevance of the negative feedback, BPD patients may have more difficulties in relying on their self-knowledge to critically evaluate the negative feedback (Fonagy and Allison, 2014).

Individuals with low levels of self-esteem were more adversely affected by negative feedback than BPD. However, their self-views were less negative and more positive than BPD and only differed from HC in having less positive self-views (not more negative). It is therefore surprising that when negative feedback is equally applicable to HC and LSE, LSE responded with worse mood. Other research has suggested that when faced with threats to the self, e.g. through negative feedback, individuals with LSE have difficulties in recovering their state affect and give in to the threat, resulting in lower mood (vanDellen et al., 2011). When looking at the precuneus activation for LSE in this study, it seemed heightened for both negative and positive feedback, though this was not significantly different from BPD or HC. One possible explanation could be that LSE considered the relevance of negative (and positive) feedback to the self, given the precuneus activity, but were less able to disregard the negative feedback, resulting in even worse mood (vanDellen et al., 2011). This intriguing hypothesis should be addressed in future studies.

Against our expectation, positive feedback related to similar positive moods in BPD and HC. Therefore despite less positive self-views, BPD patients were able to benefit in mood from positive feedback. One other study found that BPD patients can positively update their self-view after positive feedback (Korn et al., 2016).

However, it is interesting that in terms of neural responses, BPD showed more right TPJ activation during negative compared with positive feedback, whereas the reverse pattern was observed in HC. The TPJ has been implicated in processes of reorienting attention as well as social cognition and self-other processing (Donaldson et al., 2015; Schurz et al., 2017). A stronger TPJ activation after negative compared with positive feedback could indicate that BPD have a stronger reorientation of attention toward negative compared with positive feedback than HC (Schurz et al., 2017). This would be in line with a more negative interpersonal evaluation bias previously found in BPD (Barnow et al., 2009). In addition, two interesting integrative perspectives on TPJ functionality relevant to this study are that the TPJ subserves the creation of a social context for understanding another person and that the TPJ plays a role in the differentiation between or blending of self- and other-representation. A possible explanation of the current TPJ finding could be that HC use the positive feedback to create a shared representation of self and other knowledge. Given the heightened TPJ activation in response to negative feedback patients with BPD may create this self-other representation after negative rather than positive feedback. This is in line with previous research indicating that BPD showed less emotional contagion for positive social signals (Matzke et al., 2014).

Though clinically self-other differentiation is very relevant to BPD pathology, future research is needed to clarify whether and how exactly TPJ activation relates to social interactions and maladaptive self-other differentiation in BPD. Of interest here, is that the characteristic interpersonal difficulties observed in BPD may be rooted in maladaptive self-other knowledge (Fertuck et al., 2013; Miano et al., 2013; Nicol et al., 2013; Fonagy and Allison, 2014; Deckers et al., 2015; Bo et al., 2017). Speculatively, SF processing observed in BPD not only supports negative interpersonal bias but also interferes with learning (positive aspects) about the self, maintaining disturbed self-views.

We like to mention a few strengths and limitations of the study that should be taken into account. In terms of strengths, first, the inclusion of the LSE group allowed us to decouple BPD pathology from LSE. Second, the SF paradigm designed for this study has high ecological validity, as it is credible and personally relevant. Third, using multilevel and parametric modulation analyses enabled us to model the idiosyncratic applicability ratings and investigate the effect of each feedback word on an individual. Herewith, we could take into account specific (disturbed) self-knowledge when receiving SF. Finally, by using both self-report ratings and fMRI activity, we integrated affective and neural responses (Eisenberger, 2015).

With respect to limitations, like many studies in this field, we only investigated female BPD patients and results may therefore not be generalizable to men. Second, the applicability ratings of the feedback words were measured after the SF task. The applicability ratings therefore may be influenced by having been evaluated by these words, though participants were instructed explicitly to rate the applicability regardless of the feedback of the confederate. Arousal of the feedback words was not directly assessed. Though responses seem specific to valence, as groups differed in their responses to positive and negative feedback, we cannot rule out the possibility that e.g. the BPD group perceived positive/negative feedback as more arousing than HC. Third, by comparing the BPD to a LSE control group we have taken an important confound into consideration. However, these groups may differ in other respects. Finally, it must be noted that the precuneus finding is affected by current depression and handedness and hence this finding should be interpreted with caution. Comorbidities are common in BPD, especially depression (Lieb et al., 2004) which in this sample was also quite prevalent (26%). Depression has been associated with a negative interpersonal style related to seeking verification of self-views which are often negative and at the same time relying on others for reassurance (Hames et al., 2013). Noteworthily, the LSE group showed a similar rate of depression comorbidity (23%). Nevertheless, the LSE group showed different responses to the SF than BPD. The pattern of results that we found in BPD is therefore unlikely explained by MDD only. Still, a replication of these results with a depression group and larger sample size is recommended.

In conclusion, consistency of SF with self-views is a crucial determinant of the emotional and neural reactions to social interactions. Where healthy individuals may benefit from using their self-views to critically examine negative feedback, BPD patients seem to be adversely impacted by negative feedback regardless of the consistency with the self-view. Moreover, the increased TPJ activation after negative but not positive feedback may point to altered self-other differentiating processes in BPD, potentially obstructing learning positive information from others about themselves. Importantly, disturbed self-views in BPD patients go beyond low self-evaluation. The current findings suggest that clinically, one should pay special attention to how positive feedback (e.g. compliments) is received and how information based on interactions with others is processed. A lack of other representation during positive interactions may obstruct learning about the self from others, maintaining disturbed self-views and relations.

Acknowledgements

We acknowledge the mental health institution Rivierduinen for their cooperation and their contribution in collecting data, in particular Bert van Klaveren and Doeska Birkhoff. Moreover, we thank all participants, especially the patients with BPD, for their effort and interest in participating in this study.

Author ORCIDs

Charlotte C. van Schie, 0000-0003-0631-3289

Financial support

This work was supported by the Netherlands Organisation for Scientific Research (NWO) with a VICI Grant (S.R., no. 016.130.677), (B.E., no. 453.15.006), and VIDI Grant (B.E., no. 016.085.353), by the Research Grant Council (C.C., no. 24618115), and the Research Committee Funding from The Chinese University of Hong Kong (C.C., no. 4052174).

Conflict of interest

None.

†

The notes appear after the main text.

Notes

1

Complete overview of clusters and peak voxels of all contrasts can be found in online Supplementary Table S7.

Supplementary material

For supplementary material accompanying this paper visit http://dx.doi.org/10.1017/S0033291719000448.

S0033291719000448sup001.pdf (255.1KB, pdf)

click here to view supplementary material

References

  1. Auerbach RP, Tarlow N, Bondy E, Stewart JG, Aguirre B, Kaplan C, Yang W and Pizzagalli DA (2016) Electrocortical reactivity during self-referential processing in female youth with borderline personality disorder. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 1, 335–344. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Barnow S, Stopsack M, Grabe HJ, Meinke C, Spitzer C, Kronmuller K and Sieswerda S (2009) Interpersonal evaluation bias in borderline personality disorder. Behaviour Research and Therapy 47, 359–365. [DOI] [PubMed] [Google Scholar]
  3. Bates D, Maechler M, Bolker BM and Walker SC (2015) Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67, 1–48. [Google Scholar]
  4. Bender DS and Skodol AE (2007) Borderline personality as a self-other representational disturbance. Journal of Personality Disorders 21, 500–517. [DOI] [PubMed] [Google Scholar]
  5. Berenson KR, Downey G, Rafaeli E, Coifman KG and Paquin NL (2011) The rejection-rage contingency in borderline personality disorder. Journal of Abnormal Psychology 120, 681–690. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Birn RM, Diamond JB, Smith MA and Bandettini PA (2006) Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. Neuroimage 31, 1536–1548. [DOI] [PubMed] [Google Scholar]
  7. Bo S, Sharp C, Fonagy P and Kongerslev M (2017) Hypermentalizing, attachment, and epistemic trust in adolescent BPD: clinical illustrations. Personality Disorders: Theory, Research, and Treatment 8, 172–182. [DOI] [PubMed] [Google Scholar]
  8. Brown JD (2010) High self-esteem buffers negative feedback: once more with feeling. Cognition & Emotion 24, 1389–1404. [Google Scholar]
  9. Campbell JD, Assanand S and Di Paula A (2003) The structure of self-concept and its relation to psychological adjustment. Journal of Personality 71, 115–140. [DOI] [PubMed] [Google Scholar]
  10. Chen S, English T and Peng K (2006) Self-verification and contextualized self-views. Personality and Social Psychology Bulletin 32, 930–942. [DOI] [PubMed] [Google Scholar]
  11. Cheng H and Puce A (2014) Reducing respiratory effect in motion correction for EPI images with sequential slice acquisition order. Journal of Neuroscience Methods 227, 83–89. [DOI] [PubMed] [Google Scholar]
  12. Chiu C-D, Chang J-H and Hui CM (2017) Self-concept integration and differentiation in subclinical individuals with dissociation proneness. Self and Identity 16, 664–683. [Google Scholar]
  13. Daros AR, Zakzanis KK and Ruocco AC (2013) Facial emotion recognition in borderline personality disorder. Psychological Medicine 43, 1953–1963. [DOI] [PubMed] [Google Scholar]
  14. Deckers JW, Lobbestael J, van Wingen GA, Kessels RP, Arntz A and Egger JI (2015) The influence of stress on social cognition in patients with borderline personality disorder. Psychoneuroendocrinology 52, 119–129. [DOI] [PubMed] [Google Scholar]
  15. Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS and Killiany RJ (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980. [DOI] [PubMed] [Google Scholar]
  16. Diedrichsen J, Balsters JH, Flavell J, Cussans E and Ramnani N (2009) A probabilistic MR atlas of the human cerebellum. Neuroimage 46, 39–46. [DOI] [PubMed] [Google Scholar]
  17. Domes G, Winter B, Schnell K, Vohs K, Fast K and Herpertz SC (2006) The influence of emotions on inhibitory functioning in borderline personality disorder. Psychological Medicine 36, 1163–1172. [DOI] [PubMed] [Google Scholar]
  18. Donaldson PH, Rinehart NJ and Enticott PG (2015) Noninvasive stimulation of the temporoparietal junction: a systematic review. Neuroscience and Biobehavioral Reviews 55, 547–572. [DOI] [PubMed] [Google Scholar]
  19. Duijsens IJ, Eurelings-Bontekoe EHM and Diekstra RFW (1996) The VKP, a self-report instrument for DSM-III-R and ICD-10 personality disorders: construction and psychometric properties. Personality and Individual Differences 20, 171–182. [Google Scholar]
  20. Eisenberger NI (2015) Meta-analytic evidence for the role of the anterior cingulate cortex in social pain. Social Cognitive and Affective Neuroscience 10, 1–2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Eklund A, Nichols TE and Knutsson H (2016) Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proceedings of the National Academy of Sciences of the United States of America 113, 7900–7905. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Fertuck EA, Grinband J and Stanley B (2013) Facial trust appraisal negatively biased in borderline personality disorder. Psychiatry Research 207, 195–202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Fertuck EA, Jekal A, Song I, Wyman B, Morris MC, Wilson ST, Brodsky BS and Stanley B (2009) Enhanced ‘reading the mind in the eyes’ in borderline personality disorder compared to healthy controls. Psychological Medicine 39, 1979–1988. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. First MB, Spitzer RL, Gibbon M and Williams JBW (1997) Structured Clinical Interview for DSM-IV-TR Axis I Disorders (SCID-I/P). New York: Biometrics Research, New York State Psychiatric Institute. [Google Scholar]
  25. First MB, Gibbon M, Spitzer RL, Williams JBM and Benjamin LS (2000) Structured Clinical Interview for DSM-IV Axis II Personality Disorders (SCID-II). Washington, DC: American Psychiatric Press, Inc. [Google Scholar]
  26. Fonagy P and Allison E (2014) The role of mentalizing and epistemic trust in the therapeutic relationship. Psychotherapy 51, 372–380. [DOI] [PubMed] [Google Scholar]
  27. Fonagy P, Luyten P and Allison E (2015) Epistemic petrification and the restoration of epistemic trust: a new conceptualization of borderline personality disorder and its psychosocial treatment. Journal of Personality Disorders 29, 575–609. [DOI] [PubMed] [Google Scholar]
  28. Frank E, De Raedt R, Barbez C and Rosseel Y (2008) Psychometric properties of the Dutch Rosenberg self-esteem scale. Psychologica Belgica 48, 25–35. [Google Scholar]
  29. Frazier JA, Chiu S, Breeze JL, Makris N, Lange N, Kennedy DN, Herbert MR, Bent EK, Koneru VK, Dieterich ME, Hodge SM, Rauch SL, Grant PE, Cohen BM, Seidman LJ, Caviness VS and Biederman J (2005) Structural brain magnetic resonance imaging of limbic and thalamic volumes in pediatric bipolar disorder. American Journal of Psychiatry 162, 1256–1265. [DOI] [PubMed] [Google Scholar]
  30. Germans S, van Heck GL, Moran P and Hodiamont PPG (2008) The self-report standardized assessment of personality-abbreviated scale: preliminary results of a brief screening test for personality disorders. Personality and Mental Health 2, 70–76. [Google Scholar]
  31. Giesen-Bloo JH, Wachters LM, Schouten E and Arntz A (2010) The borderline personality disorder severity index-IV: psychometric evaluation and dimensional structure. Personality and Individual Differences 49, 136–141. [Google Scholar]
  32. Goldstein JM, Seidman LJ, Makris N, Ahern T, O'Brien LM, Caviness VS Jr, Kennedy DN, Faraone SV and Tsuang MT (2007) Hypothalamic abnormalities in schizophrenia: sex effects and genetic vulnerability. Biological Psychiatry 61, 935–945. [DOI] [PubMed] [Google Scholar]
  33. Gunderson JG (2007) Disturbed relationships as a phenotype for borderline personality disorder. The American Journal of Psychiatry 164, 1637–1640. [DOI] [PubMed] [Google Scholar]
  34. Ha C, Sharp C, Ensink K, Fonagy P and Cirino P (2013) The measurement of reflective function in adolescents with and without borderline traits. Journal of Adolescence 36, 1215–1223. [DOI] [PubMed] [Google Scholar]
  35. Hames JL, Hagan CR and Joiner TE (2013) Interpersonal processes in depression. Annual Review of Clinical Psychology 9, 355–377. [DOI] [PubMed] [Google Scholar]
  36. Higgins ET (1987) Self-Discrepancy: a theory relating self and affect. Psychological Review 94, 319–340. [PubMed] [Google Scholar]
  37. Jorgensen CR (2010) Invited essay: identity and borderline personality disorder. Journal of Personality Disorders 24, 344–364. [DOI] [PubMed] [Google Scholar]
  38. Kerr IB, Finlayson-Short L, McCutcheon LK, Beard H and Chanen AM (2015) The ‘Self’ and borderline personality disorder: conceptual and clinical considerations. Psychopathology 48, 339–348. [DOI] [PubMed] [Google Scholar]
  39. King-Casas B, Sharp C, Lomax-Bream L, Lohrenz T, Fonagy P and Montague PR (2008) The rupture and repair of cooperation in borderline personality disorder. Science 321, 806–810. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Korn CW, La Rosee L, Heekeren HR and Roepke S (2016) Social feedback processing in borderline personality disorder. Psychological Medicine 46, 575–587. [DOI] [PubMed] [Google Scholar]
  41. Korrelboom K (2011) COMET Voor Negatief Zelfbeeld. Houten: Bohn Stafleu van Loghum. [Google Scholar]
  42. Lancaster JL, Woldorff MG, Parsons LM, Liotti M, Freitas CS, Rainey L, Kochunov PV, Nickerson D, Mikiten SA and Fox PT (2000) Automated Talairach atlas labels for functional brain mapping. Human Brain Mapping 10, 120–131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Lazarus SA, Cheavens JS, Festa F and Zachary Rosenthal M (2014) Interpersonal functioning in borderline personality disorder: a systematic review of behavioral and laboratory-based assessments. Clinical Psychology Review 34, 193–205. [DOI] [PubMed] [Google Scholar]
  44. Lieb K, Zanarini MC, Schmahl C, Linehan MM and Bohus M (2004) Borderline personality disorder. The Lancet 364, 453–461. [DOI] [PubMed] [Google Scholar]
  45. Lis S and Bohus M (2013) Social interaction in borderline personality disorder. Current Psychiatric Reports 15, 338. [DOI] [PubMed] [Google Scholar]
  46. Loranger AW (1999) IPDE: International Personality Disorder Examination: DSM-IV and ICD-10 Interviews. Odessa, FL: PARS Psychological Assessment Resources. [Google Scholar]
  47. Makris N, Goldstein JM, Kennedy D, Hodge SM, Caviness VS, Faraone SV, Tsuang MT and Seidman LJ (2006) Decreased volume of left and total anterior insular lobule in schizophrenia. Schizophrenia Research 83, 155–171. [DOI] [PubMed] [Google Scholar]
  48. Markus H and Wurf E (1987) The dynamic self-concept: a social psychological perspective. Annual Review of Psychology 38, 299–337. [Google Scholar]
  49. Mars RB, Jbabdi S, Sallet J, O'Reilly JX, Croxson PL, Olivier E, Noonan MP, Bergmann C, Mitchell AS, Baxter MG, Behrens TE, Johansen-Berg H, Tomassini V, Miller KL and Rushworth MF (2011) Diffusion-weighted imaging tractography-based parcellation of the human parietal cortex and comparison with human and macaque resting-state functional connectivity. The Journal of Neuroscience 31, 4087–4100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Mars RB, Sallet J, Schuffelgen U, Jbabdi S, Toni I and Rushworth MF (2012) Connectivity-based subdivisions of the human right ‘temporoparietal junction area’: evidence for different areas participating in different cortical networks. Cerebral Cortex 22, 1894–1903. [DOI] [PubMed] [Google Scholar]
  51. Matzke B, Herpertz SC, Berger C, Fleischer M and Domes G (2014) Facial reactions during emotion recognition in borderline personality disorder: a facial electromyography study. Psychopathology 47, 101–110. [DOI] [PubMed] [Google Scholar]
  52. Miano A, Fertuck EA, Arntz A and Stanley B (2013) Rejection sensitivity is a mediator between borderline personality disorder features and facial trust appraisal. Journal of Personality Disorders 27, 442–456. [DOI] [PubMed] [Google Scholar]
  53. Mier D, Lis S, Esslinger C, Sauer C, Hagenhoff M, Ulferts J, Gallhofer B and Kirsch P (2013) Neuronal correlates of social cognition in borderline personality disorder. Social Cognitive and Affective Neuroscience 8, 531–537. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Nicol K, Pope M, Sprengelmeyer R, Young AW and Hall J (2013) Social judgement in borderline personality disorder. PLoS One 8, e73440. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Northoff G, Heinzel A, de Greck M, Bermpohl F, Dobrowolny H and Panksepp J (2006) Self-referential processing in our brain – a meta-analysis of imaging studies on the self. Neuroimage 31, 440–457. [DOI] [PubMed] [Google Scholar]
  56. Over H (2016) The origins of belonging: social motivation in infants and young children. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 371, 1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. R Core Team (2013) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. [Google Scholar]
  58. Renneberg B, Herm K, Hahn A, Staebler K, Lammers CH and Roepke S (2012) Perception of social participation in borderline personality disorder. Clinical Psychology and Psychotherapy 19, 473–480. [DOI] [PubMed] [Google Scholar]
  59. Rosenberg M (1965) Society and the Adolescent Self-Image. Princeton, NJ: Princeton University Press. [Google Scholar]
  60. Ruocco AC, Amirthavasagam S, Choi-Kain LW and McMain SF (2013) Neural correlates of negative emotionality in borderline personality disorder: an activation-likelihood-estimation meta-analysis. Biological Psychiatry 73, 153–160. [DOI] [PubMed] [Google Scholar]
  61. Saunders KE, Goodwin GM and Rogers RD (2015) Borderline personality disorder, but not euthymic bipolar disorder, is associated with a failure to sustain reciprocal cooperative behaviour: implications for spectrum models of mood disorders. Psychological Medicine 45, 1591–1600. [DOI] [PubMed] [Google Scholar]
  62. Schmitt DP and Allik J (2005) Simultaneous administration of the Rosenberg self-esteem scale in 53 nations: exploring the universal and culture-specific features of global self-esteem. Journal of Personality and Social Psychology 89, 623–642. [DOI] [PubMed] [Google Scholar]
  63. Schulze L, Schmahl C and Niedtfeld I (2016) Neural correlates of disturbed emotion processing in borderline personality disorder: a multimodal meta-analysis. Biological Psychiatry 79, 97–106. [DOI] [PubMed] [Google Scholar]
  64. Schurz M, Tholen MG, Perner J, Mars RB and Sallet J (2017) Specifying the brain anatomy underlying temporo-parietal junction activations for theory of mind: a review using probabilistic atlases from different imaging modalities. Human Brain Mapping 38, 4788–4805. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Sharp C, Ha C, Carbone C, Kim S, Perry K, Williams L and Fonagy P (2013) Hypermentalizing in adolescent inpatients: treatment effects and association with borderline traits. Journal of Personality Disorders 27, 3–18. [DOI] [PubMed] [Google Scholar]
  66. Staebler K, Renneberg B, Stopsack M, Fiedler P, Weiler M and Roepke S (2011) Facial emotional expression in reaction to social exclusion in borderline personality disorder. Psychological Medicine 41, 1929–1938. [DOI] [PubMed] [Google Scholar]
  67. Stinson DA, Logel C, Holmes JG, Wood JV, Forest AL, Gaucher D, Fitzsimons GM and Kath J (2010) The regulatory function of self-esteem: testing the epistemic and acceptance signaling systems. Journal of Personality and Social Psychology 99, 993–1013. [DOI] [PubMed] [Google Scholar]
  68. Swann WB Jr and Brooks M (2012) Why threats trigger compensatory reactions: The need for coherence and quest for self-verification. Social Cognition 30, 758–777. [Google Scholar]
  69. van Schie CC, Chiu CD, Rombouts SARB, Heiser WJ and Elzinga BM (2018) When compliments don't hit but critiques do: an fMRI study into self-esteem and self-knowledge in processing social feedback. Social Cognitive and Affective Neuroscience 13, 404–417. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. van Strien JW (1992) Classificatie van links- en rechtshandige proefpersonen. Nederlands Tijdschrift voor de Psychologie en Haar Grensgebieden 47, 88–92. [Google Scholar]
  71. vanDellen MR, Campbell WK, Hoyle RH and Bradfield EK (2011) Compensating, resisting, and breaking: a meta-analytic examination of reactions to self-esteem threat. Personality and Social Psychology Review 15, 51–74. [DOI] [PubMed] [Google Scholar]
  72. Wickham H (2009) ggplot2: Elegant Graphics for Data Analysis. New York: Springer-Verlag. [Google Scholar]
  73. Wilkinson-Ryan T and Westen D (2000). Disturbance in borderline personality disorder: an empirical investigation. American Journal of Psychiatry 157, 528–541. [DOI] [PubMed] [Google Scholar]
  74. Winter D, Herbert C, Koplin K, Schmahl C, Bohus M and Lis S (2015) Negative evaluation bias for positive self-referential information in borderline personality disorder. PLoS One 10, e0117083. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Zeigler-Hill V (2006) Borderline personality features: instability of self-esteem and affect. Journal of Social and Clinical Psychology 26, 668–687. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

For supplementary material accompanying this paper visit http://dx.doi.org/10.1017/S0033291719000448.

S0033291719000448sup001.pdf (255.1KB, pdf)

click here to view supplementary material

Articles from Psychological Medicine are provided here courtesy of Cambridge University Press

RESOURCES