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Identifying Disease-Specific Neural Reactivity to Psychosocial Stress in Borderline Personality Disorder

Open AccessPublished:December 17, 2021DOI:https://doi.org/10.1016/j.bpsc.2021.11.015

      Abstract

      Background

      Patients with borderline personality disorder (BPD) typically present emotion dysregulation (ED) when faced with adversity. However, it is argued that altered stress response may be more influenced by ED than BPD-specific traits. Here, we investigated this issue with functional magnetic resonance imaging using another ED condition as clinical control, i.e., bipolar disorder (BD), and controlling for ED traits.

      Methods

      We recruited 17 patients with BD, 24 patients with BPD, and 32 healthy control (HC) subjects. We adapted a functional magnetic resonance imaging–compatible psychosocial stressor task (Montreal Imaging Stress Task) in which participants are placed under time pressure when performing mental calculations and then receive immediate performance feedback (positive, negative, and neutral). ED traits were measured via self-report questionnaires targeting cognitive emotion dysregulation, affective lability, and trait anger and anxiety.

      Results

      Relative to patients with BD and HC subjects, patients with BPD exhibited overactive corticolimbic reactivity across all conditions, particularly in self-monitoring and emotion regulation regions such as the orbitofrontal cortex and anterior insula, even when controlling for ED. Conversely, patients with BD exhibited hypoactive corticolimbic reactivity to all feedback conditions compared with patients with BPD and HC subjects, even after controlling for ED. HC subjects exhibited significantly lower amygdala/hippocampus activity compared with both clinical groups, although this did not survive when controlling for ED.

      Conclusions

      This study provides new insight into BPD-specific neural stress responding, suggesting hyperactive self- and emotion-regulatory neural psychosocial stress responding, independent of ED traits. The findings also highlight the importance of considering BPD as a diagnostic profile distinguishable from other ED disorder clinical groups.

      Keywords

      Patients diagnosed with borderline personality disorder (BPD) are characterized by pervasive instability of affect, interpersonal relationships, and self-image (
      American Psychiatric Association
      Diagnostic and Statistical Manual of Mental Disorders: DSM-5.
      ). Emotion dysregulation (ED), a core trait of BPD (
      • Carpenter R.W.
      • Trull T.J.
      Components of emotion dysregulation in borderline personality disorder: A review.
      ,
      • Glenn C.R.
      • Klonsky E.D.
      Emotion dysregulation as a core feature of borderline personality disorder.
      ), is linked to affective lability and high rejection sensitivity. These traits likely underlie bouts of rage and intense feelings of anger (
      • Newhill C.E.
      • Eack S.M.
      • Mulvey E.P.
      A growth curve analysis of emotion dysregulation as a mediator for violence in individuals with and without borderline personality disorder.
      ), often provoked by interpersonal threat signaling rejection and exclusion (
      • Lobbestael J.
      • McNally R.J.
      An empirical test of rejection- and anger-related interpretation bias in borderline personality disorder.
      ).
      Clinical neuroimaging and physiological studies point to impaired neural psychosocial stress reactivity in BPD. Social stress, e.g., rejection, elicits enhanced responses in the dorsal anterior cingulate cortex (dACC), precuneus, medial prefrontal cortex (PFC), and insula in patients with BPD relative to healthy control (HC) subjects (
      • Domsalla M.
      • Koppe G.
      • Niedtfeld I.
      • Vollstädt-Klein S.
      • Schmahl C.
      • Bohus M.
      • Lis S.
      Cerebral processing of social rejection in patients with borderline personality disorder.
      ). When imagining anger-eliciting scenarios, patients with BPD show increased activation in the insula and striatum compared with HC subjects (
      • Krauch M.
      • Ueltzhöffer K.
      • Brunner R.
      • Kaess M.
      • Hensel S.
      • Herpertz S.C.
      • Bertsch K.
      Heightened salience of anger and aggression in female adolescents with borderline personality disorder-A script-based fMRI study.
      ). Finally, negative stimuli elicit hyperactive BPD amygdala reactivity (
      • Schulze L.
      • Schmahl C.
      • Niedtfeld I.
      Neural correlates of disturbed emotion processing in borderline personality disorder: A multimodal meta-analysis [published correction appears in Biol Psychiatry 2016; 79:621–623].
      ). In physiological literature, meta-analytic evidence shows dampened cortisol during psychosocial stress reactivity in patients with BPD in comparison to HC subjects and those with other personality disorders, suggesting syndrome-specific psychosocial stress reactivity (
      • Drews E.
      • Fertuck E.A.
      • Koenig J.
      • Kaess M.
      • Arntz A.
      Hypothalamic-pituitary-adrenal axis functioning in borderline personality disorder: A meta-analysis.
      ). Dampened cortisol reactivity to psychosocial stress correlates with increased activity in corticolimbic structures, e.g., medial PFC (
      • Ming Q.
      • Zhong X.
      • Zhang X.
      • Pu W.
      • Dong D.
      • Jiang Y.
      • et al.
      State-independent and dependent neural responses to psychosocial stress in current and remitted depression.
      ), pointing to an exaggerated BPD corticolimbic responding to social stress.
      Still, an ongoing debate exists over whether BPD stress reactivity results from BPD-specific features or generalized traits related to ED (
      • Fitzpatrick S.
      • Varma S.
      • Kuo J.R.
      Is borderline personality disorder really an emotion dysregulation disorder and, if so, how? A comprehensive experimental paradigm.
      ). In a study showing nonsignificant differences in stress responding with respect to general anxiety disorder, evidence suggests that BPD physiological stress reactivity is more specific to ED traits than unique BPD features (
      • Fitzpatrick S.
      • Varma S.
      • Kuo J.R.
      Is borderline personality disorder really an emotion dysregulation disorder and, if so, how? A comprehensive experimental paradigm.
      ). Still, conclusions are premature without assessing brain functioning during stress response and comparing other ED disorder groups. In addition, controlling for ED-related affective domains concurrently would be crucial for isolating BPD-specific traits underlying neural stress responding.
      This study, therefore, aimed to investigate whole-brain neural reactivity to psychosocial stress in patients with BPD relative to HC subjects and patients with bipolar disorder (BD), an ED disorder group. BD constitutes severe ED characterized by pronounced emotional lability (
      • Henry C.
      • Phillips M.
      • Leibenluft E.
      • M’Bailara K.
      • Houenou J.
      • Leboyer M.
      Emotional dysfunction as a marker of bipolar disorders.
      ) and voluntary emotional control deficits (
      • Phillips M.L.
      • Ladouceur C.D.
      • Drevets W.C.
      A neural model of voluntary and automatic emotion regulation: Implications for understanding the pathophysiology and neurodevelopment of bipolar disorder.
      ). ED in BD is attributed to altered functional corticolimbic connectivity (
      • Almeida J.R.C.
      • Mechelli A.
      • Hassel S.
      • Versace A.
      • Kupfer D.J.
      • Phillips M.L.
      Abnormally increased effective connectivity between parahippocampal gyrus and ventromedial prefrontal regions during emotion labeling in bipolar disorder.
      ), reflecting impaired cognitive control during emotion events. Indeed, increased limbic activity, particularly within the amygdala, may reflect heightened emotional reactivity to emotional stimuli (
      • Chase H.W.
      • Phillips M.L.
      Elucidating neural network functional connectivity abnormalities in bipolar disorder: Toward a harmonized methodological approach.
      ) and may result from decreased cognitive control over limbic structures (
      • Zhang L.
      • Opmeer E.M.
      • van der Meer L.
      • Aleman A.
      • Ćurčić-Blake B.
      • Ruhé H.G.
      Altered frontal-amygdala effective connectivity during effortful emotion regulation in bipolar disorder.
      ). This emphasizes the importance of both dorsal frontal and limbic areas in emotion regulation in BD (
      • Green M.J.
      • Cahill C.M.
      • Malhi G.S.
      The cognitive and neurophysiological basis of emotion dysregulation in bipolar disorder.
      ). Thus, were BPD stress reactivity to be disorder specific, BD should stand as an important clinical group to demonstrate these effects on neural responding in patients with BPD.
      Here, we used a magnetic resonance imaging (MRI)–compatible computerized paradigm inducing psychosocial stress via feedback on personal performance of mental calculations in patients with BD, patients with BPD, and HC subjects (
      • Dedovic K.
      • Renwick R.
      • Mahani N.K.
      • Engert V.
      • Lupien S.J.
      • Pruessner J.C.
      The Montreal Imaging Stress Task: Using functional imaging to investigate the effects of perceiving and processing psychosocial stress in the human brain.
      ). Psychosocial stress tasks are instrumental in assessing neurobiological reactivity to interpersonal stress (
      • Drews E.
      • Fertuck E.A.
      • Koenig J.
      • Kaess M.
      • Arntz A.
      Hypothalamic-pituitary-adrenal axis functioning in borderline personality disorder: A meta-analysis.
      ). To further control for the influence of ED traits in BPD stress responding, we assessed various self-reported traits related to ED, i.e., affective lability, anger, anxiety, and cognitive ED (cED). We predict that compared with patients with BD and HC subjects, patients with BPD exhibit global corticolimbic hyperactivity to social performance feedback, even when controlling for ED. Given substantial and consistent evidence on hyperactive limbic activity in both BD and BPD, we expect significant differences between both clinical groups and HC subjects, with the former exhibiting elevated activity relative to the latter.

      Methods and Materials

      Participants

      A total of 79 participants were originally recruited (54 females). Both patients with BD (n = 18, 8 females) and BPD (n = 24, 23 females) were recruited through a specialized outpatients program of the Geneva University Hospital. BPD psychiatric diagnosis was established using the Structured Clinical Interview for DSM-IV (
      • First M.B.
      • Gibbon M.
      • Spitzer R.L.
      • Williams J.B.W.
      • Benjamin L.S.
      Structured Clinical Interview for DSM-IV Axis II Personality Disorders (SCID-II).
      ). BD diagnosis was established with the Mini-International Neuropsychiatric Interview (
      • Sheehan D.V.
      • Lecrubier Y.
      • Sheehan K.H.
      • Amorim P.
      • Janavs J.
      • Weiller E.
      • et al.
      The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10.
      ) as part of the standard clinical evaluation. In addition, patients were also interviewed by trained psychologists using the Diagnostic Interview for Genetic Studies (
      • Preisig M.
      • Fenton B.T.
      • Matthey M.L.
      • Berney A.
      • Ferrero F.
      Diagnostic interview for genetic studies (DIGS): Inter-rater and test-retest reliability of the French version.
      ,
      • Nurnberger Jr., J.I.
      • Blehar M.C.
      • Kaufmann C.A.
      • York-Cooler C.
      • Simpson S.G.
      • Harkavy-Friedman J.
      • et al.
      Diagnostic interview for genetic studies. Rationale, unique features, and training. NIMH Genetics Initiative.
      ) for the study. Patients with BD (8 BD I, 8 BD II, 2 BD not otherwise specified) were euthymic (<6 on Young Mania Rating Scale and <12 on Montgomery–Åsberg Depression Rating Scale) and had been on stable medication for at least 4 weeks. HC subjects (n = 37, 23 females) were recruited from the general population in Geneva via web announcements on classified websites, local databases, and flyers distributed in the University of Geneva Medical School. HC subjects participated if reporting no history of psychiatric, neurologic, or psychotherapeutic treatment and no parental history of psychiatric disorders. HC subjects participated if reporting no history of psychiatric, neurologic, or psychotherapeutic treatment, no parental history of psychiatric disorders, and no more than one lifetime mood disorder episode.
      Exclusion criteria for all participants were antecedent head trauma and any contraindication for MRI safety prerequisites (e.g., metal objects in body). All participants had normal or corrected-to-normal vision and provided informed consent via a signed consent form. This study was approved by the University of Geneva research ethics committee (CER 13-081).

      Experimental Task

      The experimental task used our adapted version of the Montreal Imaging Stress Task (
      • Murray R.J.
      • Apazoglou K.
      • Celen Z.
      • Dayer A.
      • Aubry J.M.
      • Van De Ville D.
      • et al.
      Maladaptive emotion regulation traits predict altered corticolimbic recovery from psychosocial stress.
      ), in which task performance is followed by rest periods. In the Montreal Imaging Stress Task, the participants perform challenging arithmetic calculations in a given time frame while being compared with a fictive control group. Combining such social evaluative threat with uncontrollability reliably produces elevated psychosocial stress that allows for assessing direct reactivity during the task (
      • Dickerson S.S.
      • Kemeny M.E.
      Acute stressors and cortisol responses: A theoretical integration and synthesis of laboratory research.
      ). The participants were first asked to perform mental arithmetic calculations in blocks of five trials presented on a computer screen (Figure 1). At the beginning of every trial, a response cursor appeared at a default position of 5 on a horizontal response scale (0–10), and the participants used a button box to move the cursor right or left to select the correct number. Time pressure was induced via a white bar indicating the passage of time with a black line moving from left to right. In each trial, the participants had a maximum of 9 s to select their response. At the end of the fifth trial in the respective block, the participants viewed their performance feedback on the screen (8 s), which could be positive, negative, or neutral (control condition), as well as their ranking with respect to 34 fictitious same-age participants. This ranking could be high (positive condition), low (negative condition), or absent (control condition). The control condition consisted of a single-digit number to be found on the response scale. Positive and negative feedback conditions thus represented the psychosocial stress period.
      Figure thumbnail gr1
      Figure 1Adapted Montreal Imaging Stress Task [paralleling our previous study (
      • Murray R.J.
      • Apazoglou K.
      • Celen Z.
      • Dayer A.
      • Aubry J.M.
      • Van De Ville D.
      • et al.
      Maladaptive emotion regulation traits predict altered corticolimbic recovery from psychosocial stress.
      )]. This example shows the negative condition, where feedback was negative and ranking was low.
      After feedback, participants were instructed to close their eyes and rest for a recovery period of 90 s, following previously validated designs (
      • Eryilmaz H.
      • Van De Ville D.
      • Schwartz S.
      • Vuilleumier P.
      Lasting impact of regret and gratification on resting brain activity and its relation to depressive traits.
      ,
      • Eryilmaz H.
      • Van De Ville D.
      • Schwartz S.
      • Vuilleumier P.
      Impact of transient emotions on functional connectivity during subsequent resting state: A wavelet correlation approach.
      ). To control for visual movement artifacts stemming from reading the instructions on the screen, however, we discarded the first 5 s of the recovery period. After 90 s, the participants were alerted to reopen their eyes by an acoustic signal. Each performance feedback condition appeared four times for a total of 12 events in two separate sessions, each session lasting 10 to 12 minutes.

      Behavioral Measures

      During the Montreal Imaging Stress Task, we measured accuracy (percentage of correct answers averaged over the five-trial block for each condition) and reaction time (milliseconds to answer, averaged over the five-trial block in each condition). We ran a 3 × 3 repeated-measures analysis of variance (ANOVA), using condition (positive, negative, neutral feedback) as a within-subjects factor and diagnosis (BD, BPD, HC) as a between-subjects factor. Statistical analyses were conducted using IBM SPSS Statistics 25 for Windows.

      Psychological Measures

      We administered the following self-report questionnaires prior to the laboratory visit to measure different trait variables associated with ED, i.e., cED, affective lability, and trait anger and anxiety. We assessed cED via the 36-item Cognitive Emotion Regulation Questionnaire, wherein we included only the nonadaptive cognitive emotion regulation subscale (
      • Garnefski N.
      • Kraaij V.
      • Spinhoven P.
      Negative life events, cognitive emotion regulation and emotional problems.
      ). Global affective lability was assessed via the Affective Lability Scale, a 54-item scale measuring tendencies in mood shifts between what the individual considers normal to affective domains of anger, anxiety, depression, and elation (
      • Harvey P.D.
      • Greenberg B.R.
      • Serper M.R.
      The affective lability scales: Development, reliability, and validity.
      ). Trait anger was evaluated via the 22-item trait subscale of the State-Trait Anger Expression Inventory (
      • Spielberger C.D.
      Manual for the State-Trait Anger Expression Inventory.
      ). Trait anxiety was measured via the 20-item trait subscale of the State-Trait Anxiety Inventory (
      • Spielberger C.D.
      • Gorsuch R.L.
      • Lushene R.
      • Vagg P.R.
      • Jacobs G.A.
      Manual for the State Trait Anxiety Inventory.
      ). To control for ED in BPD, we included these four scores as covariates in a subsequent functional MRI (fMRI) analysis.

      Psychophysiological Stress Induction Measure

      Similar to our recent study (
      • Murray R.J.
      • Apazoglou K.
      • Celen Z.
      • Dayer A.
      • Aubry J.M.
      • Van De Ville D.
      • et al.
      Maladaptive emotion regulation traits predict altered corticolimbic recovery from psychosocial stress.
      ), we measured heart rate during the recovery period as a psychophysiological marker of sympathetic arousal because increased heart rate indicates successful stress induction (
      • Fechir M.
      • Gamer M.
      • Blasius I.
      • Bauermann T.
      • Breimhorst M.
      • Schlindwein P.
      • et al.
      Functional imaging of sympathetic activation during mental stress.
      ). Methods and results are provided in the Supplement.

      Functional MRI

      Functional brain images were acquired with a 3T Magnetom TIM Trio scanner (Siemens) and a 32-channel head coil using a standard echo-planar imaging sequence (36 transverse slices with 20% gap, 64 × 64 base resolution, voxel size: 3.2 × 3.2 × 3.2 mm, repetition time: 2100 ms, echo time: 30 ms, flip angle: 80°, field of view: 192 mm). The MRI data were collected at the Brain and Behaviour Laboratory at the University of Geneva Medical School and computations were performed using high-performance computing at the University of Geneva on the “Baobab” cluster, one of the scientific computing clusters provided by the university.
      Preprocessing of the fMRI data was effectuated using the standard procedures implemented in SPM12 (http://www.fil.ion.ucl.ac.uk/spm). Head movement was calculated by computing maximum framewise displacement (
      • Power J.D.
      • Barnes K.A.
      • Snyder A.Z.
      • Schlaggar B.L.
      • Petersen S.E.
      Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion [published correction appears in Neuroimage 2012; 63:999].
      ). At the first level, a general linear model of individual fMRI data was designed using eight events with varying durations: five sequential screens of 1) calculations (∼45 s maximum) or 2) control calculations (∼45 s maximum); three different 8-s feedback periods of 3) positive, 4) negative, or 5) neutral feedback; and three 85-s recovery periods following 6) positive, 7) negative, or 8) neutral feedback.
      At the second (group) level, we created a 3 × 3 flexible factorial design for the feedback period testing for condition (positive, negative, neutral feedback) and diagnosis (BD, BPD, HC) with subject as a random-effects factor (
      • Gläscher J.
      • Gitelman D.
      Contrast weights in flexible factorial design with multiple groups of subjects.
      ). This was used to assess the main effects of condition (i.e., [positive + negative] > neutral, positive < > negative), diagnosis (BD < > BPD + HC, BPD < > BD + HC, HC < > BD + BPD), and a possible two-way condition × diagnosis interaction.
      Second-level analyses and multiple comparison corrections were implemented using SPM12. All results are presented using p < .05 cluster-level familywise error corrected (FWEc), with the cluster-forming threshold at voxel-level p < .001 (i.e., pFWEc < .05). When possible, the correction threshold levels were increased to improve anatomical precision, thus using a stricter cluster-forming threshold at voxel-level pFWE < .05 (i.e., pFWE < .05). Peak cluster locations of all analyses are reported using the Montreal Neurological Institute coordinates. Areas of neural regions were defined with the aid of the Harvard-Oxford Cortical-Subcortical Structural Atlas (
      • Goldstein J.M.
      • Seidman L.J.
      • Makris N.
      • Ahern T.
      • O’Brien L.M.
      • Caviness Jr., V.S.
      • et al.
      Hypothalamic abnormalities in schizophrenia: Sex effects and genetic vulnerability.
      ) and the probabilistic cerebellar atlas (
      • Diedrichsen J.
      • Balsters J.H.
      • Flavell J.
      • Cussans E.
      • Ramnani N.
      A probabilistic MR atlas of the human cerebellum.
      ).

      Manipulation Check

      Before conducting the fMRI second-level analyses, we controlled for feedback incongruence during the reactivity period because feedback is manipulated to ensure balanced positive, negative, and neutral conditions, and some participants may have noticed incongruence between their performance and the subsequent feedback. We thus used a 3 × 3 flexible factorial design with congruence (incongruent, congruent, neutral feedback) as a within-subjects factor, diagnosis (BD, BPD, HC) as a between-subjects factor, and subject as a random-effects factor. Congruent feedback corresponded with the participant’s performance (e.g., positive feedback after positive performance), whereas incongruent feedback did not correspond with the participant’s performance (e.g., positive feedback after negative performance). Positive performance reflected a success rate of 60% or higher on the five calculation trials of the respective block, whereas negative performance reflected 40% or lower on the five calculation trials of the respective block, paralleling our previous study (
      • Murray R.J.
      • Apazoglou K.
      • Celen Z.
      • Dayer A.
      • Aubry J.M.
      • Van De Ville D.
      • et al.
      Maladaptive emotion regulation traits predict altered corticolimbic recovery from psychosocial stress.
      ). In our analysis, we investigated the contrasts of incongruence and congruence during feedback reactivity.

      Results

      Six participants were removed for invalid/missing data (e.g., missing MRI data), resulting in a final sample of 73 participants, with 17 patients with BD (8 female), 24 patients with BPD (23 female), and 32 HC subjects (19 females) (Table 1). The age differences between groups were not statistically significant (F2,70 = 0.785, p = .460). A χ2 analysis of group differences in handedness yielded no significant results (p = .464).
      Table 1Demographic and Medical History
      Clinical VariablesBD, n = 17BPD, n = 24HC, n = 32Total
      Female, n8231950
      Male, n911323
      Age, Years, Mean (SD)28.29 (6.17)26.08 (4.94)26.38 (6.5)26.73 (5.94)
      Age Range, Years20–3918–3918–3918–39
      Left Handed, n1023
      Education, Years14.5914.7115.0014.81
      Hospitalizations, Mean, n3.692.600.041.71
      Medicated,
      Psychotropic medication only.
      n
      134017
      Substance Abuse, n145322
      Anxiety/Phobia,
      Includes agoraphobia, social anxiety, general anxiety, obsessive-compulsive disorder, panic disorder, and posttraumatic stress disorder.
      n
      618125
      ADHD, n66315
      Age of Onset
      Concerning mood disorder episodes.
      , Years, Mean
      18.3617.4120.0018.06
      ADHD, attention-deficit/hyperactivity disorder; BD, bipolar disorder; BPD, borderline personality disorder; HC, healthy control.
      a Psychotropic medication only.
      b Includes agoraphobia, social anxiety, general anxiety, obsessive-compulsive disorder, panic disorder, and posttraumatic stress disorder.
      c Concerning mood disorder episodes.

      Behavioral Measures

      To investigate the participants’ commitment to the experimental task, performance accuracy was analyzed: 52.50% (±19.76%) of responses were correct for the calculation and 96.44% (±6.26%) were correct for control conditions. The average reaction time was 5835.89 ms (±1249.60 ms) for the calculation and 1888.21 ms (±512.02 ms) for control conditions. We observed a significant main effect of condition for both accuracy (F1,70 = 378.93, p < .001, partial η2 = 0.844) and reaction time (F1,70 = 610.17, p < .001, partial η2 = 0.897). The accuracy was significantly lower and reaction time was significantly longer in the calculation versus control condition, as expected. There was a significant main effect of diagnosis in both accuracy (F1,70 = 7.208, p = .001, partial η2 = 0.171) and reaction time (F1,70 = 4.680, p = .012, partial η2 = 0.118). Patients with BPD responded more poorly and slowly than HC subjects across both conditions. There was a significant diagnosis × condition interaction effect for accuracy only (F1,70 = 3.912, p = .025, partial η2 = 0.101), where the difference between patients with BPD and HC subjects was greater during calculation than during control conditions (Table 2).
      Table 2Task-Based Performance Variables for BD (n = 17), BPD (n = 24), and HC (n = 32) Groups
      PerformanceConditionBDBPDHCDiagnosis Effects
      Effect Sizep Value
      Accuracy, %Calculation54.2642.0859.380.148.004
      Control96.4794.5897.810.051.162
      Reaction Time, ms, MeanCalculation5883.446283.55474.920.080.054
      Control1939.682021.491760.910.052.152
      Accuracy was significantly lower, and reaction time was significantly longer in the calculation condition (total average) than in the control condition (total average). Effect size reflects partial η2 for between-subjects effects of diagnosis from a one-way analysis of variance with condition (calculation, control) as the dependent factor and diagnosis (BD, BPD, and HC) as the fixed group factor.
      BD, bipolar disorder; BPD, borderline personality disorder; HC, healthy control.
      Given gender and performance differences across groups, we complemented our group analysis by controlling for both variables by including them as covariates in a subsequent fMRI analysis.

      Psychological Measures

      Concerning psychological measures, we conducted a 3 × 4 repeated-measures ANOVA with diagnosis (BD, BPD, HC) as a between-subjects factor and measure (Affective Lability Scale, Cognitive Emotion Regulation Questionnaire, State-Trait Anxiety Inventory, State-Trait Anger Expression Inventory) as a within-subjects factor. We examined the effects of diagnosis and diagnosis × measure interaction using the standardized values of each measure. We observed a main effect of diagnosis (F1,55 = 53.428, p < .001, partial η2 = 0.660). Bonferroni-corrected t tests showed BPD to be significantly higher overall than both BD and HC (p values < .001). We also observed a significant diagnosis × measure interaction (F5,146 = 2.707, p = .020, partial η2 = 0.090), where patients with BD showed no statistical differences from HC subjects for the Cognitive Emotion Regulation Questionnaire and State-Trait Anger Expression Inventory (Table 3).
      Table 3Psychological Measures for Each Diagnosis Group
      MeasureBDBPDHCDiagnosis Effect
      Effect Sizep Value
      ALS Total1.021.860.450.628<.001
      CERQ Nonadapt37.9148.9233.450.290<.001
      STAI Trait43.2160.0933.070.684<.001
      STAXI Trait16.3825.0417.030.378<.001
      Effect size reflects partial η2 for between-subjects effects of diagnosis from separate one-way analyses of variance with measure (ALS total, CERQ nonadapt, STAI trait, STAXI trait) as the dependent variable and diagnosis (BD, BPD, HC) as the fixed group factor. ALS: BD, n = 14; BPD, n = 23; HC, n = 22. CERQ: BD, n = 14; BPD, n = 24; HC, n = 28. STAI: BD, n = 14; BPD, n = 23; HC, n = 28. STAXI: BD, n = 13; BPD, n = 24; HC, n = 25.
      ALS, Affective Lability Scale; BD, bipolar disorder; BPD, borderline personality disorder; CERQ, Cognitive Emotion Regulation Questionnaire; HC, healthy control; STAI, State-Trait Anxiety Inventory; STAXI, State-Trait Anger Expression Inventory.

      Psychophysiological Stress Induction Measure

      Detailed results for the psychophysiological stress induction measure are provided in the Supplement and provide evidence that stress was successfully induced during the reactivity period (Figure S1). We did not observe an effect of diagnosis, even when controlling for gender, performance, and ED.

      Functional MRI

      In our principal analyses, we examined whole-brain blood oxygen level–dependent reactivity to social feedback. We tested the main effects of condition (positive, negative, neutral), diagnosis (BD, BPD, HC), and their interaction. Before this, however, we conducted a manipulation check, using a flexible factorial design for testing the main effects of congruence (incongruent, congruent, neutral feedback) and an effect of a congruence × diagnosis interaction.

      Neural Reactivity to Feedback: Manipulation Check

      In our manipulation check, we observed a main effect of congruence: incongruent (vs. congruent) feedback elicited significantly greater activity within the dACC, anterior insula, striatum, and occipital lobe (pFWE < .05) (Figure S2). We observed no main effect of diagnosis or condition × diagnosis interaction, suggesting no group differences linked to incongruence processing. To ensure that the blood oxygen level–dependent signal retrieved from our analyses could not be explained by incongruence processing, we used these thresholded clusters from the congruence analyses as explicit masks when viewing the effects of condition, thus eliminating the effect of incongruence when comparing social (i.e., positive and negative) to neutral feedback.

      Neural Reactivity to Feedback: Main Effects

      Using an exclusive incongruent > congruent feedback reactivity mask (Figure S2), we observed a robust main effect of condition. Both positive and negative feedback, relative to neutral, elicited significantly greater reactivity in the paracingulate gyrus, orbitofrontal cortex (OFC), anterior insula, superior frontal gyrus, mid cingulate gyrus, temporoparietal junction, precuneus, and lateral occipital cortex (pFWE < .05) (Figure S3 and Table S1). These effects thus likely occur independent of incongruence processing and support the findings of our previous study (
      • Murray R.J.
      • Apazoglou K.
      • Celen Z.
      • Dayer A.
      • Aubry J.M.
      • Van De Ville D.
      • et al.
      Maladaptive emotion regulation traits predict altered corticolimbic recovery from psychosocial stress.
      ).
      We also found significant main effects of diagnosis for feedback reactivity irrespective of condition. Compared with patients with BPD and HC subjects, patients with BD showed increased activity within the amygdala, extending to the anterior hippocampus (pFWEc < .05) (Figure S4). When controlling for gender and performance (accuracy/reaction time), however, this effect did not survive. Given the important link between limbic hyperactivity and BD (
      • Chase H.W.
      • Phillips M.L.
      Elucidating neural network functional connectivity abnormalities in bipolar disorder: Toward a harmonized methodological approach.
      ,
      • Strakowski S.M.
      • Adler C.M.
      • Almeida J.
      • Altshuler L.L.
      • Blumberg H.P.
      • Chang K.D.
      • et al.
      The functional neuroanatomy of bipolar disorder: A consensus model.
      ), we subsequently conducted a small-volume correction analysis using this limbic area cluster as a mask when controlling for gender and performance. This, however, revealed no significant difference. Compared with patients with BD and HC subjects, patients with BPD demonstrated hyperactivity in the pregenual ACC, paracingulate gyrus, OFC, anterior insula, striatum (putamen), superior frontal gyrus, dACC, precentral gyrus, supramarginal gyrus, and lateral occipital cortex, activations that survived when controlling for gender and performance (Figure 2A and Table 4). Conversely, when examining hypoactivations, patients with BD showed significantly lower reactivity relative to patients with BPD and HC subjects in several regions, including the dorsolateral PFC, dorsomedial PFC (dmPFC), pregenual ACC, paracingulate gyrus, dACC, anterior insula, OFC, striatum (putamen), and the temporoparietal junction, accounting for gender and performance differences (pFWE < .05) (Figure 2B and Table 4). Compared with patients with BPD and BD, HC subjects demonstrated significantly decreased reactivity within the amygdala/hippocampus and thalamus (pFWEc < .05) (Figure S5). Although controlling for gender and performance removed these effects, we used these clusters as a mask for a more detailed, subsequent small-volume correction analysis, given the limbic region’s relevance to our hypotheses. This revealed significant effects within the amygdala and thalamus (pFWEc < .05) (Figure 2C and Table 4). We observed no condition × diagnosis interaction effects.
      Figure thumbnail gr2
      Figure 2Main effects of diagnosis during feedback reactivity, controlling for gender and performance. Figure illustrates whole-brain blood oxygen level–dependent activations while viewing feedback (positive, negative, neutral), with increased activations in (A) patients with borderline personality disorder (BPD) vs. patients with bipolar disorder (BD) and healthy control (HC) subjects (pFWEc < .05, k = 118 voxels) with a significant effect of diagnosis whereby BPD yielded significantly greater activity in clusters relative to both patients with BD and HC subjects (F2,216 = 9.487, p < .001, partial η2 = 0.081), (B) patients with BPD and HC subjects vs. patients with BD (pFWE < .05, k = 05) with a significant effect of diagnosis where BD yielded significantly decreased activity in clusters relative to both BPD and HC groups (F2,216 = 13.808, p < .001, partial η2 = 0.113), and (C) BD and BPD groups vs. HC group (pFWEc < .05, k = 02) with a significant effect of diagnosis where HC group demonstrated significantly decreased activity in clusters relative to both BD and BPD groups (F2,216 = 9.818, p < .001, partial η2 = 0.083). Small-volume correction conducted from limbic area gray matter mask retrieved from same contrast but without controlling for gender and performance. All results have been corrected for gender and performance differences between groups. Amg, amygdala; Ant Ins, anterior insula; dACC, dorsal anterior cingulate cortex; dmPFC, dorsomedial prefrontal cortex; FWE, p < .05 voxel-level familywise error–corrected; FWEc, p < .05 cluster-level FWE-corrected, cluster-forming threshold at voxel-level p < .001; PCG, paracingulate gyrus; pgACC, pregenual ACC.
      Table 4Main Effects of Diagnosis During Feedback Reactivity, Controlling for Gender and Performance
      Activation ConditionClusterSubclusterHemkTxyz
      BPD > (BD + HC)Orbitofrontal cortexL4656.62−2427−9
      Anterior insulaL5.23−3912−6
      Striatum (putamen)L5.17−183−12
      Superior frontal gyrusR4545.89122463
      Dorsal ACCR4.6662424
      Superior frontal gyrusL4.53−211266
      Lateral occipital cortexL1185.19−27−6936
      Lateral occipital cortexL4.64−18−6636
      Lateral occipital cortexL3.55−15−6651
      Precentral gyrusL3035.05−39342
      Precentral gyrusL4.53−42324
      Precentral gyrusL4.46−27−648
      Pregenual ACCL1844.79−6429
      Pregenual ACCR4.429399
      Paracingulate gyrusR4.2712450
      Supramarginal gyrusR5434.7663−4221
      Precentral gyrusR4.6845033
      Precentral gyrusR4.5142−2163
      BD < (BPD + HC)Frontal operculum/anterior insulaR236.6136246
      Lateral occipital cortexR426.3727−6336
      Supramarginal gyrusL1386.21−51−4233
      Supramarginal gyrusL5.3−54−4545
      Postcentral gyrusL5.07−36−3039
      Supramarginal gyrusR67654−4245
      Precentral gyrusR895.8236−1842
      Precentral gyrusR5.5539−2163
      Superior frontal gyrus/dmPFCR1545.74122763
      Superior frontal gyrus/dmPFCL5.64−63054
      Superior frontal gyrus/dmPFCR5.57121866
      Orbitofrontal cortexL255.7−2421−9
      Orbitofrontal cortexL5.4−2730−12
      Lateral occipital cortexL75.64−51−7212
      Mid frontal gyrus/dlPFCR715.4851930
      Mid frontal gyrus/dlPFCR5.439345
      Mid frontal gyrus/dlPFCL325.48−39645
      Frontal pole/dlPFCR125.3645489
      Superior frontal gyrusL105.21−121266
      Lateral occipital cortexL145.21−27−6639
      Precentral gyrusR115.1815−2772
      Paracingulate gyrusL125.13−63336
      Temporoparietal junctionR405.1357−3021
      Supramarginal gyrusR5.0363−4221
      Temporoparietal junctionR4.9551−2712
      Dorsal ACCR115.0732727
      Frontal pole/dmPFCR55.0464845
      Lateral occipital cortexL105.02−42−72−15
      Lateral occipital cortexL4.72−48−66−18
      Lateral occipital cortexL55.02−42−846
      Striatum (putamen)L55−183−12
      Mid frontal gyrus/dlPFCR54.98512436
      CuneusR54.946−8724
      Paracingulate gyrusR74.7693636
      Pregenual ACCL64.62−6399
      HC < (BPD + BD)
      Small volume correction conducted from limbic area gray matter mask retrieved from same contrast when not controlling for gender and performance.
      AmygdalaL303.95−21−12−12
      ThalamusR23.366−3−3
      Table illustrates statistics of whole-brain BOLD activations while viewing feedback (positive, negative, neutral), with increased activations in BPD vs. BD and HC (cluster-level FWE-corrected p < .05, k = 118 voxels), BPD and HC vs. BD (cluster-level FWE-corrected p < .05, k = 05), and BD and BPD vs. HC (cluster-level FWE-corrected p < .05, k = 02). All results have been corrected for gender and performance differences between groups.
      ACC, anterior cingulate cortex; BD, bipolar disorder; BOLD, blood oxygen level–dependent; BPD, borderline personality disorder; dlPFC, dorsolateral prefrontal cortex; dmPFC, dorsomedial prefrontal cortex; FWE, familywise error; HC, healthy control; hem, hemisphere; k, voxel extent threshold; L, left; R, right; T, peak-level t statistic.
      a Small volume correction conducted from limbic area gray matter mask retrieved from same contrast when not controlling for gender and performance.
      To examine lower-level group differences, we conducted three 3 × 3 repeated-measures ANOVAs with condition (positive, negative, neutral) as a within-subjects factor and diagnosis (BD, BPD, HC) as a between-subjects factor on beta values of all ensuing clusters for the three contrasts BPD > (BD + HC), BD < (BPD + HC), and HC < (BD + BPD) as the dependent variable. For the purpose of these analyses, we examined diagnosis only. For BPD > (BD + HC), there was a significant effect of diagnosis (F2,70 = 4.669, p = .012, partial η2 = 0.118), where cluster activations were significantly higher in patients with BPD than BD (p = .010, Games-Howell corrected for nonhomogeneous variances). For BD < (BPD + HC), there was a significant effect of diagnosis (F2,70 = 6.626, p = .002, partial η2 = 0.159), where cluster activations were significantly lower (Bonferroni-corrected) in patients with BD than BPD (p = .003) and HC subjects (p = .009). Finally, for (BD + BPD) > HC, there was a significant effect of diagnosis (F2,70 = 5.650, p = .005, partial η2 = 0.139), where amygdala and thalamus activations were significantly lower (Bonferroni-corrected) in HC subjects than in BD (p = .016) and BPD (p = .025). This suggests lower limbic feedback reactivity in HC subjects compared with both clinical groups when controlling for gender and performance.

      Neural Reactivity to Feedback: Controlling for ED

      To exclude potential confounders from ED, we additionally controlled for affective lability, anger, anxiety, and cED, including them as covariates with gender and performance. Despite this, we continued to observe significant main effects of diagnosis across all three feedback conditions, where BPD exhibited significant hyperactivity relative to both patients with BD and HC subjects in the mid frontal gyrus/dmPFC, precentral gyrus/dorsolateral PFC, OFC, striatum, and anterior insula (pFWEc < .05) (Figure 3A and Table 5). Imputing average beta values of all hyperactive clusters in 3 × 3 repeated-measures ANOVA, with condition as within-subject and diagnosis as between-subject factors, yielded a significant effect of diagnosis (F2,55 = 9.074, p < .001, partial η2 = 0.248) where activations in patients with BPD were different significantly (Bonferroni-corrected) from those with BD (p = .003) and marginally from HC subjects (p = .052) (Figure 3A and Table 5).
      Figure thumbnail gr3
      Figure 3Main effect of diagnosis during feedback reactivity period, controlling for gender, performance, and emotion dysregulation. Figure illustrates whole-brain blood oxygen level–dependent (BOLD) activations while viewing feedback (positive, negative, neutral). (A) Overlay shows borderline personality disorder (BPD) BOLD activations relative to both bipolar disorder (BD) and healthy control (HC) groups (pFWEc < .05, k = 133 voxels). Boxplot figure shows a significant effect of diagnosis (F2,55 = 9.074, p < .001, partial η2 = 0.248) in global beta values of activated clusters, with increased activations in BPD group being different significantly from the BD group (p = .003) and marginally from the HC group (p = .052). (B) Overlay of BOLD deactivations of the BD group relative to BPD and HC groups (pFWE < .05, k = 05). Boxplot figure shows a significant effect of diagnosis (F2,55 = 10.101, p < .001, partial η2 = 0.269) in global beta values, where BD demonstrated significantly decreased activity in clusters relative to the BPD group (p < .001) and the HC group (p = .005). (C) Overlay of HC BOLD activations relative to both BD and BPD (pFWEc < .05, k = 247). Boxplot figure shows a significant effect of diagnosis (F2,55 = 3.358, p < .042, partial η2 = 0.109) in global beta values, where the HC group demonstrated significantly increased activity in clusters relative to the BD group (p = .038). All results have been corrected for gender, performance, and emotion dysregulation differences between groups. Ant Ins, anterior insula; dmPFC, dorsomedial prefrontal cortex; FWE, p < .05 voxel-level familywise error–corrected; FWEc, p < .05 cluster-level FWE-corrected, cluster-forming threshold at voxel-level p < .001; MFG, mid frontal gyrus; OFC, orbitofrontal cortex; pgACC, pregenual anterior cingulate cortex.
      Table 5Main Effect of Diagnosis During Feedback Reactivity Period, Controlling for Gender, Performance, and Emotion Dysregulation
      Activation ConditionClusterSubclusterHemkTxyz
      BPD > (BD + HC)Orbitofrontal cortexR1336.672724−12
      Frontal operculumR3.2642216
      Orbitofrontal cortexL3505.84−2730−9
      Striatum (putamen)L5.52−2121−6
      Striatum (caudate)R4.7690
      Mid frontal gyrusL4805.34−36348
      Superior frontal gyrus/dmPFCL5.16−151866
      Mid frontal gyrusL4.63−331248
      Precentral gyrusR1504.642624
      Mid frontal gyrusR4.1842633
      Inferior frontal gyrus/dlPFCR3.8511212
      BD < (BPD + HC)Superior frontal gyrusL1126.88−122154
      Superior frontal gyrusL4.82−63654
      Frontal pole/vlPFCR116.863039−12
      Superior frontal gyrus/dmPFCR1216.24152754
      Superior frontal gyrus/dmPFCR5.58181857
      Superior frontal gyrus/dmPFCR5.4592448
      Mid frontal gyrus/dlPFCL676.11−39645
      Precentral gyrusL5.43−33339
      Mid frontal gyrusL4.82−331248
      Superior parietal lobeL336.08−33−4863
      Anterior insulaL655.77−3618−6
      Temporoparietal junctionR605.7551−3939
      Lateral occipital cortexL145.53−51−7215
      Frontal pole/dlPFCR95.445489
      Frontal pole/dlPFCR5.02513912
      Orbitofrontal cortexL125.35−2730−9
      Pregenual anterior cingulateL705.3−3399
      Pregenual anterior cingulateL/R5.2703918
      Pregenual anterior cingulateR5.269399
      Frontal operculum/anterior insulaR205.236246
      Anterior insulaR4.933921−3
      Inferior frontal gyrusR275.15511530
      Postcentral gyrusR175.1139−2466
      Heschl’s gyrusL75.03−45−276
      Mid frontal gyrusR174.9733348
      Superior parietal lobe/angular gyrusR124.9336−5451
      Superior parietal lobe/angular gyrusR4.6927−6048
      Lateral occipital cortexL54.86−42−72−15
      Lateral occipital cortexR64.7530−6336
      HC > (BD + BPD)Fusiform gyrusL2475.59−33−75−15
      Fusiform gyrusL4.67−24−75−3
      Fusiform gyrusL4.57−21−75−12
      Table illustrates whole-brain BOLD responding while viewing feedback (positive, negative, neutral). BPD vs. BD and HC (FWEc-corrected p < .05, k = 133 voxels); BPD and HC vs. BD (FWE-corrected p < .05, k = 05); HC vs. BD and BPD (FWEc-corrected p < .05, k = 247). All results have been corrected for gender, performance, and emotion dysregulation differences between groups.
      BD, bipolar disorder; BOLD, blood oxygen level–dependent; BPD, borderline personality disorder; dlPFC, dorsolateral prefrontal cortex; dMPFC, dorsomedial prefrontal cortex; FWE, familywise error; FWEc, cluster-level FWE; HC, healthy control; hem, hemisphere; k, voxel threshold; L, left; R, right; T, peak-level t statistic; vlPFC, ventrolateral prefrontal cortex.
      Conversely, BD exhibited significant and extensive hypoactivations relative to patients with BPD and HC subjects in the frontal pole, dorsolateral PFC, ventrolateral PFC, pregenual ACC, OFC, superior frontal gyrus/dmPFC, anterior insula, inferior frontal gyrus, mid frontal gyrus, postcentral gyrus, the Heschl’s gyrus, temporoparietal junction, superior parietal lobule, angular gyrus, and lateral occipital cortex (pFWE < .05) (Figure 3B and Table 5). Repeated-measures ANOVA yielded a significant effect of diagnosis (F2,55 = 10.101, p < .001, partial η2 = 0.269) where BD activity within these clusters was significantly lower (Games-Howell corrected) than that in patients with BPD (p < .001) and HC subjects (p = .005) (Figure 3B and Table 5). Finally, HC subjects exhibited significantly greater fusiform activity extending into the lingual gyrus relative to both patients with BD and BPD (pFWEc < .05) (Figure 3C and Table 5).

      Sensitivity Analysis

      Given the imbalance of medicated patients with BD relative to patients with BPD, we conducted post hoc sensitivity analyses (
      • Cinelli C.
      • Hazlett C.
      Making sense of sensitivity: Extending omitted variable bias.
      ) to verify the robustness of the main effect of diagnosis on beta values resulting from the contrasts BPD > (BD + HC) and BD < (BPD + HC) when accounting for gender, performance, and psychological measures (see the Supplement). The results revealed low sensitivity of both models to an unobserved confounder, suggesting that our estimated effect of diagnosis on neural reactivity was robust to a single confounder (e.g., medication) in both contrasts.

      Discussion

      This study attempted to provide a clearer assessment of BPD-specific neural stress reactivity independent of ED by using psychosocial stress induction in fMRI, including an ED disorder clinical group (BD), and providing a thorough measurement of ED-relevant domains (affective lability, anger, anxiety, cED). Here, we show that patients with BPD exhibit hyperactive corticolimbic stress response during feedback reactivity periods, regardless of the condition and relative to patients with BD and HC subjects, when controlling for gender, behavioral performance differences, and ED. Conversely, patients with BD exhibited extensive hypoactive neural stress responding across all feedback conditions, relative to patients with BPD and HC subjects. Finally, HC subjects showed significantly less limbic stress responding, relative to patients with BPD and BD, although this difference may be explained by ED because we saw no significant differences when controlling for ED. Together, these findings suggest consistent patterns of neural stress responding in self- and emotion-regulatory neural regions in patients with BPD and BD that cannot be explained by ED trait differences. This may signify neural traits associated with stress response specific to BPD and BD and not generalizable to ED characteristics per se.

      Neural Hyperactivity in BPD

      Our results illustrate elevated neural sensitivity to feedback in BPD, regardless of valence, in corticolimbic areas, extending the literature showing BPD corticolimbic hyperarousal to social stimuli (
      • Krause-Utz A.
      • Winter D.
      • Niedtfeld I.
      • Schmahl C.
      The latest neuroimaging findings in borderline personality disorder.
      ), particularly when threatened with social rejection (
      • Ruocco A.C.
      • Medaglia J.D.
      • Tinker J.R.
      • Ayaz H.
      • Forman E.M.
      • Newman C.F.
      • et al.
      Medial prefrontal cortex hyperactivation during social exclusion in borderline personality disorder.
      ). Paralleling our findings, patients with BPD show elevated dACC and dmPFC response when presented with social exclusion (
      • Domsalla M.
      • Koppe G.
      • Niedtfeld I.
      • Vollstädt-Klein S.
      • Schmahl C.
      • Bohus M.
      • Lis S.
      Cerebral processing of social rejection in patients with borderline personality disorder.
      ). Thus, irrespective of its valence, performance feedback may invoke rejection-related fears in BPD, eliciting corticolimbic hyperactivity. These neurobiological findings support physiological meta-analytic evidence pointing to a BPD-specific reduction of cortisol suppression of psychosocial stress reactivity (
      • Drews E.
      • Fertuck E.A.
      • Koenig J.
      • Kaess M.
      • Arntz A.
      Hypothalamic-pituitary-adrenal axis functioning in borderline personality disorder: A meta-analysis.
      ), which likely associates with dysregulated medial frontal stress response (
      • Ming Q.
      • Zhong X.
      • Zhang X.
      • Pu W.
      • Dong D.
      • Jiang Y.
      • et al.
      State-independent and dependent neural responses to psychosocial stress in current and remitted depression.
      ).
      However, our finding showing BPD stress hyperactivity extending to positive and neutral feedback contradicts earlier stress research in BPD, which showed context-dependent subjective emotional responding in BPD specific to negative feedback relative to clinical controls (
      • Gratz K.L.
      • Rosenthal M.Z.
      • Tull M.T.
      • Lejuez C.W.
      • Gunderson J.G.
      An experimental investigation of emotional reactivity and delayed emotional recovery in borderline personality disorder: The role of shame.
      ). Still, it extends empirical evidence that suggests rejection-related fears may generalize to nonthreatening social information. For example, individuals with BPD show difficulty distinguishing rewarding from nonrewarding social information (
      • Enzi B.
      • Doering S.
      • Faber C.
      • Hinrichs J.
      • Bahmer J.
      • Northoff G.
      Reduced deactivation in reward circuitry and midline structures during emotion processing in borderline personality disorder.
      ), often misinterpreting neutral social information as hostile (
      • Krause-Utz A.
      • Winter D.
      • Niedtfeld I.
      • Schmahl C.
      The latest neuroimaging findings in borderline personality disorder.
      ,
      • van Zutphen L.
      • Siep N.
      • Jacob G.A.
      • Goebel R.
      • Arntz A.
      Emotional sensitivity, emotion regulation and impulsivity in borderline personality disorder: A critical review of fMRI studies.
      ). The ambivalence between negative and neutral stimuli relates to ACC, amygdala, and striatal dysfunction (
      • Niedtfeld I.
      • Schulze L.
      • Kirsch P.
      • Herpertz S.C.
      • Bohus M.
      • Schmahl C.
      Affect regulation and pain in borderline personality disorder: A possible link to the understanding of self-injury.
      ), supporting our findings. Thus, BPD neural hyperactivity to performance feedback may generalize to positive and neutral feedback.
      Contrary to our neuroimaging findings, we observed no differences in heart rate between patients with BPD and HC subjects. Although this could support meta-analytic findings showing null-to-small effects in overall emotional physiological reactivity in patients with BPD versus HC subjects (
      • Bortolla R.
      • Cavicchioli M.
      • Fossati A.
      • Maffei C.
      Emotional reactivity in borderline personality disorder: Theoretical considerations based on a meta-analytic review of laboratory studies.
      ), it may nonetheless relate to important heterogeneity within BPD samples (
      • Bortolla R.
      • Cavicchioli M.
      • Fossati A.
      • Maffei C.
      Emotional reactivity in borderline personality disorder: Theoretical considerations based on a meta-analytic review of laboratory studies.
      ). Indeed, the literature demonstrates variations in BPD personality profiles (
      • Kopala-Sibley D.C.
      • Zuroff D.C.
      • Russell J.J.
      • Moskowitz D.S.
      • Paris J.
      Understanding heterogeneity in borderline personality disorder: Differences in affective reactivity explained by the traits of dependency and self-criticism.
      ), suggesting important interindividual differences in BPD stress responding (
      • Gunderson J.G.
      Disturbed relationships as a phenotype for borderline personality disorder.
      ). Although outside the scope of this study, future research would benefit from assessing interindividual differences in ED and their interaction with feedback physiological reactivity in patients with BPD and BD and HC subjects.

      Neural Hypoactivity in BD

      Patients with BD showed varying activation patterns, with hypoactivity in dorsal cognitive control regions and hyperactivity in limbic affective regions, although the latter may be more sensitive to gender and performance differences. Nonetheless, these findings parallel the current neural model of BD, explaining affective disturbances as a result of damped inhibitory control over elevated affective reactivity (
      • Chase H.W.
      • Phillips M.L.
      Elucidating neural network functional connectivity abnormalities in bipolar disorder: Toward a harmonized methodological approach.
      ,
      • Strakowski S.M.
      • Adler C.M.
      • Almeida J.
      • Altshuler L.L.
      • Blumberg H.P.
      • Chang K.D.
      • et al.
      The functional neuroanatomy of bipolar disorder: A consensus model.
      ). Moreover, the literature shows that patients with euthymic BD exhibit neuropsychological impairments related to inhibitory control and selective attention (
      • Green M.J.
      • Cahill C.M.
      • Malhi G.S.
      The cognitive and neurophysiological basis of emotion dysregulation in bipolar disorder.
      ), suggesting an inability to inhibit intrusive cognitions. Together, our findings reveal extensive dampening of dorsal regulatory regions relative to patients with BPD and HC subjects that may be specific to euthymic BD and independent of ED traits.

      Limitations

      Although the ED measures conducted in this study have been validated empirically, they are nonetheless limited to subjective self-reporting. Objective assessment of ED is thus limited here and would require further behavioral testing in ED clinical groups. In addition, because we did not observe the effects of an interaction in our fMRI data, low-level group differences (e.g., BPD > HC) should be considered with caution and more as descriptive than conclusive (
      • Nieuwenhuis S.
      • Forstmann B.U.
      • Wagenmakers E.J.
      Erroneous analyses of interactions in neuroscience: A problem of significance.
      ). These warrant future investigation, nonetheless. Moreover, as the sample size was limited, larger and more gender-balanced samples might allow for increased variance in interindividual traits and increased likelihood of interaction effects.

      Conclusions

      These results reveal increased corticolimbic reactivity during psychosocial stress in patients with BPD compared with an ED disorder clinical control group and HC subjects, even when controlling for ED. We believe that these results provide a clearer assessment of BPD neural stress responding when considering ED traits and thus show hyperactive corticolimbic psychosocial stress reactivity to likely occur in BPD as a function of disease-specific traits rather than shared ED features. These results thus highlight the importance of considering BPD as a clinical diagnostic profile distinguishable from other ED disorder groups.

      Acknowledgments and Disclosures

      The study is supported by the Swiss National Center of Competence in Research (“Synapsy: the Synaptic Basis of Mental Diseases,” Grant No. 51NF40-185897 [to CP] ) and the Swiss National Foundation (Grant No. 32003B_156914 [to CP] ) and by a Boninchi Foundation grant (to KG).
      The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.
      We thank Mr. Bruno Bonet and Dr. Frédéric Grouiller of the Brain and Behaviour Laboratory for their operational support in facilitating the acquisition of the MRI and physiological data, Dr. Marie-Pierre Deiber and Dr. Ben Meuleman for their methodological inputs, Dr. Kalliopi Apazoglou for her assistance with the bipolar disorder literature review, and Dr. Sylvain Delplanque for his assistance in preparing the physiological analysis.
      The authors report no biomedical financial interests or potential conflicts of interest.

      Supplementary Material

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      Linked Article

      • The Search for Disorder-Specific Neural Characteristics in Borderline Personality Disorder—Beyond Generalized Emotion Dysregulation
        Biological Psychiatry: Cognitive Neuroscience and NeuroimagingVol. 7Issue 11
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          Linehan’s original conceptualization of the pathogenesis of borderline personality disorder (BPD) proposes a biosocial diathesis-stress model, positing that BPD can be characterized largely as a disorder of emotion dysregulation (ED) resulting from maladaptive transactions between biological vulnerabilities and an invalidating environment (1). According to Linehan’s model, ED in BPD includes high baseline negative emotional intensity as well as high emotional reactivity—proposed to be sequelae of stress vulnerability—and ED occurs as the individual is further unable to effectively modulate the intensity of the emotional response to stressors.
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