Attachment, affective temperament, and personality disorders: A study of their relationships in psychiatric outpatients

Journal of Affective Disorders 151 (2013) 932–941

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Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad

Research Report

Attachment, affective temperament, and personality disorders: A study of their relationships in psychiatric outpatients Kai MacDonald a,n,2, Rustin Berlow b,1,2, Michael L. Thomas a a b

University of California Medical Center, Department of Psychiatry, USA San Diego, California, USA

art ic l e i nf o

a b s t r a c t

Article history: Received 18 August 2012 Received in revised form 25 February 2013 Accepted 31 July 2013 Available online 20 August 2013

Background: As the result of extensive translational and cross-disciplinary research, attachment theory is now a construct with significant neuropsychiatric traction. The correlation of attachment with other influential conceptual models (i.e. temperament and personality) is therefore of interest. Consequently, we explored how two attachment dimensions (attachment anxiety and attachment avoidance) correlated with measures of temperament and personality in 357 psychiatric outpatients. Methods: We performed a retrospective review of four questionnaires (the Experiences in Close Relationship scale (ECR-R), Temperament and Character inventory (TCI), Temperament Evaluation of the Memphis, Pisa, Paris and San Diego questionnaire (TEMPS-A), and Personality Self-Portrait Questionnaire (PSQ)). Frequency measures and correlations were examined, as was the predictive value of attachment security for a personality disorder (PD). Results: Significant, robust correlations were found between attachment anxiety and (1) several negative affective temperaments (dysthymic and cyclothymic); (2) several indices of personality pathology (low self-directedness (TCI), DSM-IV paranoid, borderline, histrionic, avoidant and dependent personality traits). Attachment avoidance had fewer large correlations. In an exploratory model, the negative predictive value of attachment security for a PD was 86%. Limitations: Subjects were a relatively homogeneous subset of ambulatory psychiatric outpatients. PD diagnoses were via self-report. Conclusions: Clinically, these findings highlight the significant overlap between attachment, affective temperament, and personality and support the value of attachment as a screen for PDs. More broadly, given our growing understanding of the neurobiology of attachment (i.e. links with the oxytocin system), these results raise interesting questions about underlying biological systems and psychiatric treatment. & 2013 Elsevier B.V. All rights reserved.

Keywords: Attachment Personality Temperament Mood disorders Questionnaires Psychometrics

1. Introduction In neuropsychiatry, several different explanatory models are invoked to describe long-term patterns of mood, affect, and behavior (Kendler et al., 2010). These include temperament (including affective temperament), personality (dimensional personality traits as well as the categorical descriptors in the DSM system), and a model perhaps least well known to psychiatric clinicians: attachment theory. Like personality classifications, an attachment model contains both categorical descriptors (so-called attachment styles), as well as a dimensional model, defined by two orthogonal parameters: attachment anxiety and attachment avoidance (Brennan et al., 1998). A significant body of research has demonstrated that these two attachment n

Corresponding author. Tel.: þ 1 619 203 7393. E-mail address: [email protected] (K. MacDonald). 1 Private practice. 2 Contributed equally to this work.

0165-0327/$ - see front matter & 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jad.2013.07.040

dimensions have import in terms of both therapeutic and intimate relationships (Ciechanowski et al., 2002; Maunder et al., 2006b; Mikulincer and Shaver, 2003), neurobiology (Vrticka and Vuilleumier, 2012), and even response to certain putative psychopharmacological agents (i.e. oxytocin) (Bartz et al., 2010b, 2011; De Dreu, 2012; Kiss et al., 2011; Love et al., 2012). For psychiatric clinicians to benefit from the growing body of attachment-anchored research, it is important to delineate the relationships between better-known conceptual models (temperament and personality) and attachment. From a wider vantage, it is also important to understand how these models relate back to clinical symptoms, syndromes, and ultimately, underlying neural systems. This report examines both of these issues in a relatively large group of psychiatric outpatients. The best-known of the descriptive models above are the categorical DSM personality disorders (PDs), which can be assayed by self-reports like the Personality Self-Portrait Questionnaire (PSQ) (Oldham and Morris, 1995). Patients with PDs have prototypical, often dysfunctional patterns of relating to others: almost

K. MacDonald et al. / Journal of Affective Disorders 151 (2013) 932–941

all PDs are characterized by difficulties in interpersonal function (Widiger and Frances, 1985), and a majority of the DSM-IV PD criteria are interpersonal (American Psychiatric Association, 2000). Since they relate so closely to interpersonal function over time, most DSM-IV PDs can easily be fit into an attachment-oriented model describing mental models of self and other, patterns of affect regulation, and behavior in close relationships (Brennan et al., 1998; Meyer and Pilkonis, 2005; Shorey and Snyder, 2006). Using a variety of different diagnostic instruments to measure personality and attachment, multiple studies have demonstrated considerable correlation between DSM-diagnosed PDs and attachment dimensions (Choi-Kain et al., 2009; Fossati et al., 2003; Shorey and Snyder, 2006 for review). Aside from the categorical DSM-IV PDs, two other influential perspectives on long-standing individual variations are Cloninger's Temperament and Character Inventory (TCI) (Cloninger, 2008; Cloninger et al., 1994) and Akiskal's Temperament Evaluation of Memphis, Pisa, Paris and San Diego-auto questionnaire version (TEMPS-A) (Akiskal et al., 2005b). According to Cloninger, temperament and character are separable dimensions of personality, temperament referring to innate or reflexive responses to certain classes of stimuli, and character describing learned tendencies and values (Cloninger et al., 1993). Arising from a different research tradition, Akiskal's TEMPS-A – initially inspired by Kraepelin's conception of the interepisode “fundamental states” (“Grundszustande”) of manic-depressive patients – expands and slightly modifies Kraeplin's original categories from a set of four (depressive, manic, irritable and cyclothymic) to five (adding anxious) (Akiskal et al., 2005b; Kraepelin, 1913). As in Cloninger's model, affective temperament as captured by the TEMPS-A reflects clinically observable, biologically-based predispositions toward certain patterns of emotion, cognition and behavior, similar to the concept of endophenotypes (Panksepp, 2006). In terms of their relationship to attachment theory, both the TCI and the TEMPS-A have relational implications. In the TCI, both the temperament category of reward dependence (related to warm social affiliations, separation distress, sympathy, and social sensitivity), and the character component cooperativeness (ability to accept of others, listen and cooperate), describe social and relational proclivities. Concerning the TEMPS-A, although its questions are devoid of specific relational language or context, Akiskal's list of the survival-related events that elicit and reflect affective proclivities (i.e., being enamored and jilted, dominance-submission hierarchies, tendency toward fealty, territoriality), are inherently relational (Akiskal et al., 2005a). Furthermore, from a neurobiological vantage, both of these scales ultimately reflect the function of underlying socioemotional brain systems, and components of each scale have been associated with central systems related to social reward (Bachner-Melman et al., 2005; Gerretsen et al., 2010; Lebreton et al., 2009), including the oxytocin system (Bell et al., 2006; Cloninger, 1994; Groppe et al., 2013; Kawamura et al., 2011; Tost et al., 2010a). As such, both in their theoretical structure as well as their putative underlying neurobiology, both the TCI and the TEMPS-A have conceptual overlap with attachment. Though both the TCI and the TEMPS-A have implications regarding close relationships, a more explicit approach to describe individual variations in socioemotional and relational tendencies comes from attachment theory, one of the most generative psychobiological theories of the last 40 years (Roisman et al., 2007). Since its initial formulation by Bowlby (1969/1982), attachment theory has become a key framework for understanding individual differences in relational style and personality (Meyer and Pilkonis, 2005; Mikulincer and Shaver, 2007). Moreover, attachment theory has generated what is now a diverse crossdisciplinary and translational research enterprise encompassing ethology, evolutionary biology, genetics/epigenetics, functional

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brain imaging, psychiatric diagnosis, psychotherapy and psychopharmacology (Bartz et al., 2011; Buchheim et al., 2009; Levitan et al., 2009; Quirin et al., 2010; Ravitz et al., 2010; Shorey and Snyder, 2006; van Ijzendoorn et al., 2010). Within the attachment field, perhaps the most well studied self-report instrument is the Experiences in Close Relationships scale (ECR-R). As mentioned above, the ECR-R assays two dimensions of attachment: attachment anxiety (the tendency for distress related to separation and rejection), and attachment avoidance (the tendency to be wary of close attachments) (Brennan et al., 1998). Each of these dimensions demonstrate sizeable genetic influences (Crawford et al., 2007a; Donnellan et al., 2008; Picardi et al., 2011), and are related to a host of variables of import to neuropsychiatry, including stress-related physiology (HPA activation, HRV, inflammatory responses) (Gouin et al., 2009; Kidd et al., 2011; Maunder et al., 2006a; Quirin et al., 2008), neurophysiological response to socioemotional cues (DeWall et al., 2012; Gillath et al., 2005; Rognoni et al., 2008; Zayas et al., 2009), dopaminergic and serotonergic receptor variants (Gillath et al., 2008), hippocampal size and cell concentration (Quirin et al., 2010), as well as both subjective and behavioral responses to stressful and emotional stimuli (Dewitte et al., 2007, 2008; Maunder et al., 2006a; Vrticka et al., 2012) (for review see Ravitz et al. (2010) and Vrticka and Vuilleumier (2012)). More explicitly related to psychiatric diagnosis and treatment is the fact that attachment correlates with and informs mood and anxiety disorders (Conradi and de Jonge, 2009; Levitan et al., 2009; Marazziti et al., 2007; Meyer et al., 2001; Monk et al., 2008; Morriss et al., 2009), behavior in healthcare relationships (Bennett et al., 2011), hospitalization and health care utilization (Ciechanowski et al., 2003; Hoermann et al., 2004), chronic pain and pain-related cognitive style (Martinez et al., 2012; McWilliams and Asmundson, 2007; Meredith et al., 2008), therapeutic alliance (Diener et al., 2009) and psychotherapeutic outcomes (Dales and Jerry, 2008; Daniel, 2006; McBride et al., 2006). Lastly, attachment often distinguishes between responses to the attachment-related hormone oxytocin (Bartz et al., 2010a, 2011; De Dreu, 2012; Kiss et al., 2011; MacDonald, 2012), an agent which has putative therapeutic potential in a number of brain-based illnesses (MacDonald and Feifel, 2013). Overall, this surfeit of data supports the contention that for psychiatric clinicians, attachment theory should not be considered simply a psychological or social construct, but also a neurobiologically-informed theoretical framework with broad relevance to axis I disorders and axis II, mood regulation, and interpersonal events both in and out of the clinical sphere. Though relationships between temperament, personality, and attachment have been studied in a variety of normal (Chotai et al., 2005; Crawford et al., 2007a; Gjerde et al., 2004; Martinotti et al., 2008; Picardi et al., 2005) and psychiatric samples (Fossati et al., 2003; Levy et al., 2005; Marazziti et al., 2007; Meyer et al., 2001; Riggs et al., 2007), there are few such studies in more heterogeneous, ambulatory outpatient psychiatric practices (see Kooiman et al., 2013). Furthermore, only a few small studies have examined correlations between the TEMPS-A and the ECR-R (Harnic et al., 2010; Iliceto et al., 2012). Therewith, our primary aim was to describe the correlations between adult attachment, temperament, and DSM-IV PDs in a relatively large group of psychiatric outpatients. Secondarily, we wished to explore the value of self-reported attachment as a screening tool for PDs.

2. Methods 2.1. Sample Patients are referred to the two generalist outpatient private psychiatric practices in the study from a variety of sources: selfreferral, therapists, medical specialists, and present for a variety of

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difficulties, including mood, attention, substance abuse, anxiety, and relationships problems. No diagnoses were excluded. As part of a standard intake, patients complete a variety of self-report scales, and report demographic information (age, gender, relationship status, number of children). Patient charts were examined by the investigators or a research assistant, except in the case of incomplete data (i.e., did not fill out one of the scales). As this was a retrospective chart review, waiver of need for consent was obtained from the IRB of the University of California San Diego Medical Center, San Diego. 2.2. Scales TEMPS-A: The Temperament Evaluation of Memphis, Pisa and San Diego (TEMPS-A) is a reliable and valid set of 44 true/false questions that yields scores in five affective domains: Dysthymic, Anxious, Cyclothymic, Irritable and Hyperthymic. Each scale has a threshold score that is predictive of clinical difficulties in that area (Akiskal et al., 2005b). TCI-R: The Temperament and Character Inventory (TCI-R) is a set of 125 true/false questions that yields seven subscales: Novelty Seeking, Harm Avoidance, Reward Dependence and Persistence, Self-Directedness, Cooperativeness and Self Transcendence. These subscales have norms and clinical correlates which have been reviewed elsewhere (Cloninger et al., 1994). PSQ: The New Personality Self-Portrait Questionnaire is a 107 item DSM-based self-report measure of personality (Oldham and Morris, 1995), which yields scores for all of the current DSM-IV PDs. The PSQ correlates well with clinician ratings of axis II disorders using either the SCID-P (First et al., 2007), or the PDQ (Hyler et al., 1992). ECR-R: The ECR-R is a well-validated 36-item self-report scale which has 18 questions assessing attachment anxiety (sensitivity to or worry about partner rejection or relationship loss), and 18 questions assessing attachment avoidance (discomfort with/avoidance of close relationships) (Brennan et al., 1998). Participants indicate on a 7-point scale the extent to which they agree or disagree with each item in terms of how they “generally experience close relationships”. From these responses, one can calculate average mean scores for attachment anxiety and attachment avoidance, as well as one of four attachment categories. 2.3. Statistical and data analysis Data was examined initially for missing values. Descriptive statistics were obtained for all variables and tests of normality of continuous measures were made. Data was also examined for homogeneity of variance. No significant variation from the normal distribution was found. For data analysis, we dichotomized marital status into ever married vs. never married: (52%, 48% respectively). Regarding the ECR-R, subjects were classified into four attachment style groups based on their ECR-R values for attachment anxiety (AX) and attachment avoidance (AV). The midpoint score of 4 for AX and AV were used for this classification. As a result the following groups were created: Secure¼ (AX and AV o4); Preoccupied (AX and AV 4 ¼4); Fearful (AX4 ¼4 and AV o4); and Dismissive (AXo4 and AV 4 ¼4) (Brennan et al., 1998). Given the potential for self-reports to overestimate PDs (PDs) (Perry, 1992; Zimmerman, 1994) and in order to optimize specificity, the presence of a PD was determined using a combination of two criteria. First, as a low TCI Self-Directedness (SD) score is predictive of and highly associated with axis II disorders (Bulik et al., 1995; Cloninger, 2000; Gutierrez et al., 2008; Svrakic et al., 1993), we used an SD score of less than 13 on SD as one standard. Second, specific PDs were defined based on scores on the PSQ,

with cut-offs as determined by the number of official criteria in the DSM-IV that were endorsed (e.g. paranoid PD requires endorsement of at least four traits). Partial traits (answer of “Maybe” on the PSQ) were summed as half-traits. In order to be classified as having a PD, patients would need to meet both the first and second criteria. Continuous data were analyzed using Pearson Correlation and categorical data using chi-square, t-test or ANOVA as appropriate. All analyses were performed using SPSS version 21, all tests were two-tailed. Statistical significance was determined using the Bonferroni approach to control the familywise error rate of the study. Specifically, we fixed the planned Type-I error rate for each domain of analysis by dividing α¼.05 by the total number of scales on each instrument: .01 for TEMPS-A scales; .007 for TCI scales; .005 for PSQ scales; and .025 for ECR-R scales. In our exploratory predictive model, negative predictive value of attachment security for a PD was calculated by dividing “true negatives” (No PD, secure attachment) by the sum of “true negatives” plus “false negative” (PD with secure attachment).

3. Results 3.1. Demographics, attachment, personality disorders The demographics of our sample of 357 psychiatric outpatients are seen in Table 1. Using the categories ever-married vs. nevermarried and number of children as proxy markers for involvement in important relationships, we found no significant difference in attachment dimensions and number of children or marital status. Notably, attachment anxiety (AX) scores in our sample ranged from 1 to 6.7 with a mean of 3.28 (SD ¼1.57); attachment avoidance (AV) scores ranged from 1 to 6.0 with a mean of (SD ¼3.24) (Table 1). Males did not differ significantly from females on AX (Male ¼3.17, SD ¼1.47 vs. Female ¼3.25, SD ¼1.59 respectively; F(1,355)¼1.52, p¼ .64) or AV (M ¼3.20, SD ¼1.30 vs. M¼3.22, SD ¼1.28 respectively; F(1,355)¼.24, p¼ .86). About half of our sample fell into the category of secure attachment style (51%, n ¼182), while the other half was almost evenly divided into fearful (17%, n ¼61), dismissive (18%, n ¼ 63), and preoccupied (14%, n¼ 51). Based on a combination of DSM criteria (PSQ) and SD o13 (TCI), 27.5% (n ¼ 98) of the patients in our sample had one of the 10 DSM PDs and 20.2% (n ¼72) had more than one.

Table 1 Demographics, attachment parameters and personality disorders (n¼ 357). N (%) Female Male Mean age (SD) Never married Married, separated, divorced Attachment anxiety Attachment avoidance Attachment styles Secure Dismissive Preoccupied Fearful Any personality disorder (PD) More than one PD

197 160 37.4 171 186 3.28 3.24 182 63 51 61 98 72

(55.2%) (44.8%) (12.9) (48%) (52%) (SD ¼1.57) (SD ¼1.36) (51%) (17.6%) (14.3%) (17.1%) (27.5%) (20.2%)

Attachment style calculated from the ECR-R. Personality disorder categorization based on combined criteria of self-reported DSM-IV criteriaþ SD o 13 (see Section 2).

K. MacDonald et al. / Journal of Affective Disorders 151 (2013) 932–941

3.2. Correlations between adult attachment, temperament, and personality scales 3.2.1. Affective temperament – the TEMPS-A Correlations between TEMPS-A, AX, and AV scales are reported in Table 2. All correlations are significant, but the magnitudes of association range from small to medium. All are positive except for the Hyperthymic scale. The strongest correlations are between AX and Dysthymic and Cyclothymic temperaments (r ¼.375 and r ¼.382 respectively). Indeed, AX is more strongly correlated with all but one of the TEMPS-A scales (Hyperthymic). None of the correlation between AV and TEMPS-A were greater than .35, our effect size cutoff (Cohen, 1992) (rmin ¼.149, rmax ¼ .239, rmean ¼ .202.). 3.2.2. Correlations between ECR-R, and TCI Correlations between the TEMPS-A, TCI, and ECR-R are reported in Table 2. All correlations are significant, but the magnitudes of association range from small to medium. In terms of the TCI, we note the strongest correlation are between ECR-R Attachment Anxiety (AX) and Self-Directedness (r ¼  .390, p ¼.000). We also note the correlation of Harm Avoidance and AX (r ¼.245, p ¼.000)). Correlations between reward dependence and attachment dimensions had effect sizes that were surprisingly small (.102 for AX and  .142 for AV); indeed, none of the correlations between AV and the TCI were greater than .35 (our effect size cut off; Cohen, 1992). 3.2.3. Correlations between ECR-R and personality disorders (PSQ) Correlations between ECR-R and PSQ are also noted in Table 2. Several correlations are significant, but the magnitudes of association range from small to medium. The strongest correlations are between AX and paranoid (r ¼.418), borderline (r ¼.428), histrionic (r ¼ .372 ) and dependant personality traits (r¼ .395). The largest Table 2 Correlations between adult attachment, temperament, and personality (n¼ 357). Attachment anxiety TEMPS-A Dysthymic Anxious Cyclothymic Irritable Hyperthymic TCI-R Novelty seeking Harm avoidance Reward dependence Persistence Self-directedness Cooperativeness Self transcendence PSQ Paranoid Schizoid Schizotypal Antisocial Borderline Histrionic Narcissistic Avoidant Dependent Obsessive compulsive

Attachment avoidance

.375nn .288nn .382nn .273nn  .149nn

.239nn .149nn .206nn .214nn .153nn

.213nn .245nn 0.102  .154nn  .390nn  .205nn 0.065

0.073 .174nn  .142nn  0.054  .214nn  .202nn 0.021

.418nn .179nn .242nn .193nn .428nn .372nn .239nn .362nn .395nn .150nn

.279nn .295nn .168nn .135n .198nn 0.085 0.104 .278nn .149nn 0.086

TEMPS-A: The Temperament Evaluation of Memphis, Pisa and San Diego, TCI-R: Temperament and Character Inventory, PSQ: The New Personality Self-Portrait Questionnaire. nn Correlation is significant at the 0.01 level (2-tailed). Pearson Correlation, 2-tailed. Bold: r ¼.35 or greater.

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effect sizes for AV were schizoid (r ¼.295), paranoid (r ¼.279) and avoidant personality traits (r ¼.278). None of the correlations between AV and axis II were greater than .35 (Cohen, 1992). 3.2.4. Classification accuracy of PD from attachment dimensions To assess the utility of the ECR-R as a screening tool for the presence of a PD, we calculated the percentages of patients in each of the four attachment categories who met criteria for PD (Table 3). Of note, patients with a secure attachment style had the lowest probability of having a PD (n ¼25; 14%); of patients with an insecure attachment style, preoccupied attachment style had the highest probability (n ¼29; 57%). Using these percentages, we found that the negative predictive value of secure attachment for any PD was 86%; only 14% of patients with secure attachment had a PD, and given that secure attachment describes patients who had both AX below 4 and AV below 4, both of these dimensions contributed to this “predictive” calculation. In other words, though we did not directly test the differential contribution of each of these attachment dimensions to this predictive function, and though AX had the most robust correlations with different PDs, both AX and AV contributed important information in our PD screening model. Finally, those patients in each of the four attachment categories who met criteria for specific PDs are seen in Table 4. We note the very low percentage of patients with most PDs that endorsed secure attachment, as well as the specific PDs (paranoid, borderline, histrionic and avoidant) in which there was a large percentage differences between secure and one of the insecure attachment styles (i.e. dismissive, fearful and preoccupied).

4. Discussion Our primary research question was the relationship between adult attachment, temperament and personality in a relatively large (n ¼357) group of psychiatric outpatients. In brief, our findings support the hypothesis that adult attachment has significant overlap with both temperament and personality variables. Specifically, we found that attachment anxiety showed moderate, significant correlations with (1) negative affective temperaments on the TEMPS-A (especially dysthymic and anxious temperament, but also cyclothymic and irritable); and (2) several measures of personality disorders (PD) (i.e. low self-directedness on the TCI and DSM-IV paranoid, borderline, histrionic, and dependent traits). Attachment avoidance demonstrated less robust correlations. Moreover, in an exploratory analysis, we found that secure attachment had an acceptably specific negative predictive value for a PD (86%), and therefore, self-report attachment measures like Table 3 Percentage of sample with a personality disorder (PD) by attachment style. Attachment category

N (%) in this category

Secure 182 (51%) Insecurea 175 (49%) Fearful 61 (17.1%) Dismissive 63 (17.6%) Preoccupied 51 (14.3%)

Number (%) in this category with one or more PD

Number (%) in this category with more than one PD

25 73 28 16 29

18 54 17 15 23

(14%) (42%) (46%) (25%) (57%)

(10%) (31%) (28%) (24%) (45%)

The percentage of patients in each attachment style meeting criteria for a personality disorder (DSM-IV symptomsþ SD o 13 (see Section 2). a Three individual attachment styles (Fearful-avoidant, Dismissive and Preoccupied) combine to make the insecure category. Attachment categories calculated from ECR-R: Secure¼ (Attachment anxiety (AX) and Attachment avoidance (AV) o4); Preoccupied (AX and AV 4 ¼4); Fearful (AX 4 ¼ 4 and AV o4); Dismissive (AXo 4 and AV 4 ¼4) (see Section 2).

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K. MacDonald et al. / Journal of Affective Disorders 151 (2013) 932–941

Table 4 Percentage in attachment categories with specific personality disorders. Category

PAR (%)

SCZ (%)

STY (%)

ASP (%)

BOR (%)

HIS (%)

NAR (%)

AVO (%)

DPN (%)

OCP (%)

Secure Insecure Fearful Dismissive Preoccupied

5 19 16 11 31

2 6 3 3 12

3 8 7 6 12

1 7 10 10 2

7 16 15 11 24

4 10 11 3 18

3 5 7 2 8

5 22 21 17 29

4 17 18 13 22

4 12 13 3 22

Patients were classified into attachment categories (secure, insecure (i.e. fearful, dismissive, preoccupied)) based on ECR-R score, and into personality disorder category based on combined DSM and TCI criteria (see Section 2). PAR: Paranoid personality disorder (PD); SCZ: schizoid PD; STY: schizotypal PD; ASP: antisocial PD; BOR: borderline PD; HIS: histrionic PD; NAR: narcisstic PD; AVO: avoidant PD; DPN: dependant PD; OCP: obsessive-compulsive PD.

the ECR-R may serve as a useful screening tool for PDs. Below, we discuss several clinical and theoretical implications of these findings and comment on their implications in terms of putative underlying neurobiology of attachment, temperament, and personality. 4.1. Attachment and temperament In outpatient psychiatric practice, where patients have complex diagnostic presentations (Kendler et al., 2010) and present with syndromes which sometimes change as the result of treatment, assessment of affective temperament is vital (Akiskal et al., 2005a; Mendlowicz et al., 2005). Our finding of moderate-sized, statistically significant correlations between attachment anxiety and several of negative affective temperaments (especially dysthymic and cyclothymic) resonates with other studies of patients with mood and anxiety disorders which consistently find that of the two dimensions of adult attachment – attachment anxiety (AX) and attachment avoidance (AV) – AX has a greater correlation with axis I mood and anxiety disorders than AV (Levitan et al., 2009; Marazziti et al., 2007; Morriss et al., 2009). Our findings also are in line with smaller studies using the ECR-R and TEMPS-A (Iliceto et al., 2012), as well as a broad array of prior research using a variety of different measures of negative affect (Cooper et al., 1998; Crawford et al., 2007b; Donnellan et al., 2008; Fraley et al., 2011; Noftle and Shaver, 2006; Picardi et al., 2005), all of which indicate a consistent, strong (0.4–0.5), and partially genetically-determined (Crawford et al., 2007a; Donnellan et al., 2008) relationship between AX and trait proclivities toward negative affect. Altogether, these findings suggest that though the term “attachment anxiety” has an anxious evocation, the hyperactive emotion regulation strategy associated with it (vs. the hypoactivation strategy associated with AV) overlaps considerably with a broad tendency to experience a variety of negative and unstable affects as much as simply anxiety (Meredith et al., 2006; Meyer et al., 2005; Rognoni et al., 2008; Shaver and Mikulincer, 2007). Neurobiologically, these findings resonate with studies indicating that AX is related to amygdala responses to a variety of stimuli (Lemche et al., 2006; Vrticka et al., 2008), in light of the role of amygdala as a central hub of networks associated with both mood and anxiety disorders (Hariri, 2012; Rauch et al., 2003; Sibille et al., 2009). From an attachment perspective, the correlation between negative affectivity and AX foregrounds the fact that human attachment serves a regulatory function for many homeostatic systems, including those involved with emotion modulation (Hofer, 1994; Shaver and Mikulincer, 2007). That said, attachment dimensions are not simply proxy markers for temperamental affective proclivities (Donnellan et al., 2008). In actuality, the relationship between these two dimensions is complex,

demonstrating a variety of nonlinear, often recursive interactions (Crawford et al., 2007b; Donnellan et al., 2008, 2007; Noftle and Shaver, 2006; Robins et al., 2002). Consistent with a dynamic, developmental model of attachment, evidence supports a multicomponent, interactive model wherein heritable temperamental variations interact with actual relationships to create other-related memory networks, so-called internal working models of attachment. Through adult development, these memory networks bias perception-action tendencies in relationships and both shape and are shaped by ongoing relational experience (see discussion in Donnellan et al. (2008)). Also useful in informing the correlation between attachment and affective temperament are a growing number of neuroimaging studies of attachment-related processes which demonstrate the engagement of brain areas associated with reward, mood regulation, anxiety and anxiety-related temperamental traits (i.e. amygdala, ventral striatum, dACC, anterior insula) (DeWall et al., 2012; Eisenberger et al., 2011; Vrticka et al., 2008; Vrticka and Vuilleumier, 2012). Surprisingly – and in contrast to both our initial hypothesis and other reports (Chotai et al., 2005; Martinotti et al., 2008) – we did not find robust correlations between attachment and the temperament dimensions of the TCI, though in light of recent findings regarding harm avoidance (HA) and oxytocin (Stankova et al., 2012; Wang et al., 2013), we highlight the significant, mediumsized correlation between AX and HA. This lack of large correlations was especially surprising for reward dependence (RD), which has been used as a proxy marker for attachment in studies of the neurobiology of sociality (Lebreton et al., 2009) and oxytocin (Bell et al., 2006; Tost et al., 2010b). Certainly, sociality has several facets, with RD describing a more general proclivity toward relationships than intimate relatedness, per se. In the context of the abovementioned broad correlations between AX and the TEMPS-A, this lower level of correlation between attachment and the TCI tentatively suggests that in a psychiatric sample, the TEMPS-A better captures attachment-related affective predispositions than the TCI.

4.2. Attachment and personality Aside from affective temperament, a second organizing construct informing clinical psychiatric care are personality traits and disorders (PDs), especially given that PDs have significant prognostic implications apart from axis I (Chen et al., 2006; Crawford et al., 2008; Johnson et al., 2005), and often – inadvertently or not – become a focus of outpatient treatment. Our finding of significant correlations between AX and multiple indices of personality pathology (including self-directedness (SD) on the TCI, and a number of DSM-IV PDs) is broadly consistent with other adult samples (Brennan et al., 1998; Choi-Kain et al., 2009; Crawford et al., 2006, 2007a; Fossati et al., 2003; Martinotti et al., 2008; Picardi et al., 2005; Riggs et al., 2007; Scott et al., 2009; Ulu and Tezer, 2010; Westen et al., 2006), as well as conceptual models of PD seen through the perspective of attachment (Meyer and Pilkonis, 2005). In terms of personality theory, these associations highlight the overlapping domains of adult attachment, self-representation, and personality structure. From the perspective of attachment, the significant correlation between low SD and AX dovetails with attachment theories' assertion that secure attachment is a necessary precondition for self-directed exploration and the development of a stable internal locus of control (Bowlby, 1969/ 1982), a concept acknowledged by Cloninger in his explanation of the SD construct (Cloninger et al., 1993). We note that though the majority of significant correlations in personality traits were found with AX, both AX and AV correlate differently with different PDs and both contribute to the ECR-R's tentative PD screening function (see also Crawford et al., 2007a; Scott et al., 2009).

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Our findings also inform the clinical care of patients with PDs. In treating PD patients, an attachment-centered approach (i.e. framing difficulties in terms of different mental models of self and other (Bartholomew and Horowitz, 1991) has at least three clinically helpful consequences. First, an attachment orientation toward PDs gives clinicians an organizing framework to understand and inquire about a particular individuals' style of emotion regulation, perception of other's intentions, as well as their orientation to and interest in both clinical and personal relationships (Collins and Feeney, 2004; Fraley et al., 2006; Simpson et al., 2007). Metacognitive therapeutic strategies which examine patients' perception of relationships and the minds of others apply at this level (Bateman and Fonagy, 2004). Secondly, an attachment-oriented model provides both clinician and patient with an adaptionist, experience-near, relationally-focused model and vocabulary with which to discuss the patient's experience. Importantly, this shared perspective may help the development of a therapeutic alliance (Shorey and Snyder, 2006; Svrakic et al., 2009). Lastly, as our understanding of the neural underpinnings of individual differences in attachment-related processes (Gillath et al., 2005; Vrticka et al., 2008), emotional responses to relational events (Eisenberger, 2012; Kross et al., 2007; Way et al., 2009), and theory-of-mind (Brune and Brune-Cohrs, 2006; Waytz et al., 2010) grows, an attachment-oriented model of personality anchors itself in the deepening soil of modern attachment theory and its multidisciplinary, biologically-anchored conceptual framework. In the future, this evolving intellectual enterprise will continue to provide a more nuanced, neurobiologically-informed scaffolding for our understanding of individual differences in personality and relational function. The benefits of an attachment-oriented perspective toward personality, we believe, strongly support the clinical value of assessing attachment in psychiatric outpatients. On this latter point, we also note our preliminary but clinically relevant finding of the potential value of the ECR-R as a screening tool for PDs. As highlighted in a recent review (Germans et al., 2012), a highly sensitive screening tool for PDs should both identify cases that need further evaluation and minimize false negatives (i.e. should have a high negative predictive value). In our sample, the adult attachment dimensions of the ECR-R (and the category of secure attachment as derived from them) demonstrated a high negative predictive value for a PD (86%). That is, patients with secure attachment in our sample had an acceptably low risk of meeting our relatively stringent PD criteria. Reinforcing the overlap between attachment and personality, this level of screening accuracy is in keeping with other, more specific personality screening instruments (Germans et al., 2012). Given the lack of specificity of the ECR-R for a particular PD, however, further clarification would of course be necessary for patients who screen positively. That said, in addition to the abovementioned clinical value of understanding a patient's attachment style, the ability of the ECR-R to serve as a screen for PDs – if confirmed – would add further benefit to its use. In the interest of brevity, future studies could also examine the predictive value of the valid and reliable 12-item short form ECR-R (Wei et al., 2007). Finally, when viewed from the dual perspectives of psychiatric and social neuroscience, our findings of correlations between adult attachment, temperament, and personality invite speculation regarding shared neurobiological substrates. Though space constrains anything more than a brief discussion (for reviews see Gillath et al., 2005, 2008; Quirin et al., 2010; Vrticka and Vuilleumier, 2012), we highlight three lines of informative, crossdisciplinary research on the topic. First, twin studies of adult attachment suggest that like neuroticism (Eaves et al., 1999), and personality traits (Plomin et al., 2008), adult attachment demonstrates significant (23–45%) heritability (Brussoni et al., 2000; Crawford et al., 2007a; Donnellan et al., 2008; Picardi et al.,

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2011), with stronger genetic effects on AX than AV (Crawford et al., 2007a; Donnellan et al., 2008; Picardi et al., 2011). Second, from a neural systems level of analysis, functional neuroimaging studies indicate that individual differences in the responses of appraisal, reward and regulatory systems correlate with selfreported adult attachment (Coan et al., 2006; Gillath et al., 2005; Love et al., 2012; Suslow et al., 2009; Vrticka et al., 2008). These and other similar studies indicate that intercalated neural systems mediate different components of attachment, social cognition, social reward and emotion regulation (Baskerville and Douglas, 2010; Gray, 1990; Lang and Davis, 2006; Nelson and Panksepp, 1998; Ochsner, 2008). Third, at the level of neurotransmitters, adult attachment has been correlated with many of the same central systems implicated in affect, mood and temperament, including the HPA axis (Quirin et al., 2008), serotonin (Caspers et al., 2009; Gillath et al., 2008), dopamine (Gillath et al., 2008; Reiner and Spangler, 2010), opiate (Barr et al., 2008; Way et al., 2009); and social neuropeptide (i.e. arginine vasopressin, oxytocin) (Strathearn et al., 2009; Walum et al., 2008, 2012) systems. Here, the oxytocin system deserves special mention, given its therapeutic promise in a variety of psychiatric conditions (MacDonald and Feifel, 2013), recent reports indicating that its effects in humans and peripheral levels vary based on attachment (Bartz et al., 2010b, 2011; De Dreu, 2012; Love et al., 2012; Marazziti et al., 2006), and its association with temperament and personality (Kawamura et al., 2010; Kogan et al., 2011; Montag et al., 2011; Saphire-Bernstein et al., 2011; Strathearn et al., 2012; Chen et al., 2011; De Dreu et al., 2013), mood disorders (Costa et al., 2009; Thompson et al., 2011) (Slattery and Neumann, 2010 for review) and brain structures related to both social function and mood (Zink and Meyer-Lindenberg, 2012). In toto, then, neurobiological evidence from multiple levels of analysis suggests that correlations between adult attachment, temperament, and personality have shared roots in central systems important to clinical psychiatry. Ultimately, our growing understanding of these systems may improve our ability to individually tailor and improve both psychopharmacological treatment and therapeutic relationships. 4.3. Limitations This study's limitations include a monomethod bias and resultant shared methods variance. Related to this – and germane to the issue of PDs – is the fact that self-report scales may overdiagnose personality psychopathology relative to semi-structured interviews (Zimmerman, 1994). We note, however, that using our combined criteria, our rates of PDs are consistent with results from other outpatient psychiatric samples (Zimmerman et al., 2005). We also highlight that we organized data from the ECR-R both continuously and categorically, and that a bulk of evidence suggests the former model is most reliable (Fraley and Spieker, 2003). As mentioned above, our suggestion that the ECR-R may function well a screen for PDs is preliminary, and should be explored in prospective studies using gold-standard PD assessments. Lastly, though we believe our population is representative of a large swath of psychiatric outpatients, it is a largely helpseeking, ambulatory sample, many of whom were in relationships. As such, our results may not generalize to other psychiatric populations with a greater level of relationship difficulties or higher level of functional impairment. 4.4. Implications In this study – one of the largest groups of psychiatric outpatients examined with the ECR-R – we explored correlations between adult attachment, temperament and personality.

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Consistent with both prior research and theoretical models, we found significant correlations between attachment anxiety, negative affective temperaments, and several markers of personality pathology. We also found that the ECR-R performed quite well as an initial screening test for personality disorder. Clinically, these results support the assertion that assessment of adult attachment and an understanding of attachment theory add considerable value to outpatient mental health care.

Role of funding source The study had no funding source.

Conflict of interest All of the authors declare they have no financial conflicts of interest.

Acknowledgments Thanks to Shah Golshan, Ph.D. and Tina MacDonald, RN, who helped with data collection, statistical analysis and editorial support.

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