Cognitive-emotion processing in psychogenic nonepileptic seizures

Cognitive-emotion processing in psychogenic nonepileptic seizures

Epilepsy & Behavior 102 (2020) 106639 Contents lists available at ScienceDirect Epilepsy & Behavior journal homepage: ...

311KB Sizes 0 Downloads 12 Views

Epilepsy & Behavior 102 (2020) 106639

Contents lists available at ScienceDirect

Epilepsy & Behavior journal homepage:

Cognitive-emotion processing in psychogenic nonepileptic seizures Rachael Rosales a,⁎, Barbara Dworetzky b, Gaston Baslet a a b

Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA

a r t i c l e

i n f o

Article history: Received 28 August 2019 Revised 22 September 2019 Accepted 26 September 2019 Available online xxxx Keywords: Psychogenic nonepileptic seizures Functional neurological disorder Emotions Emotion processing Psychopathology

a b s t r a c t Background: Previous literature suggests that cognitive-emotion processing contributes to the pathogenesis of psychogenic nonepileptic seizures (PNES). Characterization of alterations in cognitive-emotion processing in PNES could inform treatment. Methods: In this descriptive, cross-sectional study, 143 patients with video electroencephalogram (EEG) confirmed PNES were prospectively recruited. Patients completed self-report questionnaires on emotion perception (Trait Meta-Mood Scale (TMMS) attention and clarity subscales) and coping style (Affective Styles Questionnaire [ASQ] concealing, adjusting, and tolerating subscales) at the time of their initial evaluation for PNES. Demographic, clinical data and measures of psychopathology severity were also obtained. The TMMS and ASQ subscale scores were compared to available normative data and between PNES subgroups (based on presence of trauma-related factors). Correlation coefficients were obtained to evaluate associations between subscale scores and measures of psychopathology. Results: Mean scores on both TMMS subscales (attention 47.0 [SD 7.4] and clarity 37.5 [SD 8.0]) and the ASQ adjusting subscale (22.2 [SD 6.3]) were significantly lower than available normative data (p b .001). Among patients with PNES, those with a history of childhood abuse or active posttraumatic stress disorder (PTSD) were found to have significantly lower scores on emotion clarity, adjustment, and tolerance subscales than those without such histories (p b .05). Degree of clarity of emotions correlated negatively with severity of depression, anxiety, stress, and illness perception (p ≤ .001). Adjustment to and tolerance of emotional states correlated negatively with severity of depression and stress (p b .01). Conclusions: Patients with PNES, especially those with active PTSD and childhood trauma, have lower clarity of their emotions and lower ability to adjust to emotional states than healthy individuals. These cognitiveemotion processing deficits are more pronounced in patients with more severe depression and reported stress. This study characterizes alterations in cognitive-emotion processing in PNES that are well-suited therapeutic targets and can therefore inform treatment interventions. © 2019 Published by Elsevier Inc.

1. Introduction Psychogenic nonepileptic seizures (PNES) are paroxysmal involuntary episodes that resemble epileptic seizures but lack corresponding epileptiform electroencephalographic abnormalities. Despite increasing awareness about PNES, early diagnosis and evidence-based treatments are not consistently available, leading to significant morbidity from inappropriate treatment, increased costs, and stigmatization [1].

⁎ Corresponding author at: Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA. E-mail addresses: [email protected] (R. Rosales), [email protected] (B. Dworetzky), [email protected] (G. Baslet). 1525-5050/© 2019 Published by Elsevier Inc.

Previous research on PNES has identified associations with certain psychological traits, including alexithymia, emotion dysregulation and avoidance [2,3]. Proposed etiologic models in PNES emphasize deficits in cognitive-emotion processing, which are the target of psychotherapeutic modalities, such as cognitive-behavioral therapy (CBT) and mindfulness-based psychotherapy [4]. In this study, we aim to confirm that patients with PNES display deficits in cognitive-emotion processing compared to a presumed healthy population by using validated measures of emotion processing and management. Additionally, we aim to identify clinical factors more strongly associated with these cognitive-emotion processing deficits in PNES. We hypothesize that (1) patients with PNES will score lower on measures of cognitive-emotion processing, (2) patients with PNES and comorbid trauma-related histories and diagnoses will score lower


R. Rosales et al. / Epilepsy & Behavior 102 (2020) 106639

on measures of cognitive-emotion processing than PNES patients without such histories, and (3) more severe deficits in cognitive-emotion processing will be associated with higher scores on validated measures of psychiatric severity and illness perception. 2. Materials and methods 2.1. Participant recruitment One hundred and forty-three adult patients with video electroencephalogram (v-EEG) confirmed PNES were consecutively and prospectively recruited during their first neuropsychiatric diagnostic evaluation (either during the diagnostic long-term monitoring (LTM) admission or the first outpatient visit after LTM). All recruited patients had to be English proficient and intellectually capable of completing study questionnaires, which was based on patients' observed vocabulary and education history. No patients were excluded for clinical reasons. Recruitment took place from November 2014 to May 2018 at Brigham and Women's Hospital in Boston, Massachusetts. All subjects provided informed consent for research participation in accord with the local institutional review board (IRB).

Table 2a Medical and psychiatric comorbid conditions (n-143). Diagnosis

n (%)

Comorbid epilepsy Depressive disorder (current or past) Any anxiety disorder Panic disorder Specific phobia Generalized anxiety disorder Cluster B traits Borderline personality disorder Pain syndrome Any substance use disorder (current or past) Current substance use disorder Trauma related diagnoses Past PTSD Active PTSD Past dissociative disorder Current dissociative disorder History of any abuse Childhood abuse, any kind Adult abuse only, any kind Physical abuse Sexual abuse Emotional/verbal abuse

21 (14.8) 106 (74.1) 124 (86.7) 69 (48.3) 59 (41) 80 (56) 25 (18) 11 (7.7) 87 (61) 25 (18) 13 (9) 17 (11.9) 56 (39) 13 (9.1) 21 (14.7) 89 (63) 58 (41) 31 (22) 58 (41) 62 (43) 64 (45)

2.2. Study design

PTSD: Posttraumatic stress disorder.

This is a descriptive cross-sectional study. Data on demographic and clinical characteristics, including comorbid psychiatric diagnoses, were obtained through a semi-structured neuropsychiatric interview by a board-certified neuropsychiatrist (GB). Patients indicated via selfreport during the semi-structured interview whether they had a history of abuse, including type of abuse and whether any abuse occurred during childhood or adolescence. Self-rating questionnaires were used to obtain measurements of psychopathology severity and underlying psychological mechanisms. All data were collected during the initial neuropsychiatric evaluation. Table 1 and 2a list the demographic and clinical variables that were collected.

attention, clarity, and repair. The attention subscale assesses an individual's ability to perceive emotional experiences. The clarity subscale measures a person's confidence in understanding and discriminating emotions. The repair subscale assesses beliefs around how to respond to and change moods. Each statement of the questionnaire is selfrated on a Likert-like scale from 1 (“completely disagree”) to 5 (“completely agree”). Each TMMS subscale contains some items that are worded positively and some that are worded negatively, and the negatively worded items are reverse scored. Please see Table 3 for examples of selected items from each TMMS subscale. Each TMMS subscale score results from adding the responses of each individual item. There is no standardized or combined total score. The TMMS has been shown to have both convergent and discriminant validity with other scales, including the Toronto Alexithymia Scale (TAS-20), a commonly cited instrument in the literature on PNES [5–7]. We used the shorter 30-item version, which has been recommended for its higher efficiency and comparable validity to the 48-item version [5]. Furthermore, we eliminated the repair subscale items given that we utilized another questionnaire to specifically assess affective coping styles. Therefore, the total number of items in our study is limited to 24 (13 for the attention subscale and 11 for the clarity subscale). The Affective Styles Questionnaire (ASQ) is a 20-item self-report questionnaire that characterizes an individual's emotional coping styles via three domains (concealing, adjusting, tolerating). The three domains constitute the ASQ subscales. “Concealing” comprises defenses like suppression and avoidance. “Adjusting” includes ways in which people adapt to and problem solve in response to emotions. “Tolerating” refers to acceptance of and comfort in experiencing emotions [8]. Each statement in the ASQ subscales is self-rated on a Likert-like scale from 1 (“not true of me at all”) to 5 (“extremely true of me”). There is no reverse scoring for the ASQ items. Please see Table 3 for examples of selected items from each ASQ subscale. All subscale score results from adding the responses of each individual item. There is no standardized or combined total score. The ASQ has been shown to have convergent and discriminant validity with the TAS-20 [8–10]. The internal consistency for each subscale of the ASQ is satisfactory [8]. The Brief Illness Perception Questionnaire (BIPQ) is a 9-item selfreport questionnaire adapted from the Illness Perception Questionnaire (IPQ), which was designed to assess illness cognitive representations. All but one of the items uses a 0- to 10-response scale, and the last question has an open-ended response in which patients list the three most important causal factors in their illness [11]. The questionnaire

2.3. Measures The cognitive-emotion processing measures included the Trait Meta Mood Scale (TMMS) and the Affective Styles Questionnaire (ASQ). The TMMS is a self-report questionnaire that assesses an individual's capacity to attend to and regulate emotions via three domains: Table 1 Demographic characteristics of participants (n = 143). Age at evaluation, mean (SD) Female sex, n (%) Ethnicity, n (%) White, Non-Hispanic African American Hispanic Asian Other Marital status, n (%) Never married Married/live-in partner Widowed Separated/divorced Employment status, n (%) Full-time employment Part-time employment On disability benefits Unemployed Student Retired Educational level, n (%) 12 years or less 13–15 years College graduate and above

39 (14) 118 (83) 104 (73) 15 (11) 21 (15) 1 (0.7) 2 (1.4) 53 (37.1) 68 (47.6) 3 (2.1) 19 (13.3) 20 (14) 9 (6) 57 (40) 35 (25) 19 (13) 3 (2.1) 36 (25) 74 (52) 33 (23)

R. Rosales et al. / Epilepsy & Behavior 102 (2020) 106639

has been validated in numerous inpatient and outpatient settings for multiple diagnoses, and it has been used in other studies on PNES [11–13]. The clinical questionnaires included the commonly used Beck Depression Inventory-II (BDI-II) and the Patient Health Questionnaire 15 (PHQ-15), a measure of somatic distress. The Depression, Anxiety and Stress Symptoms scale (DASS) is a 42-item self-report questionnaire that measures severity of depression (DASS-D), anxiety (DASS-A), and stress (DASS-S) levels over the preceding week. We only scored the 14 items specific for the anxiety subscale (DASS-A) and the 14 items specific for the stress subscale (DASS-S), as the BDI-II already captured severity of depressive symptoms. The DASS was designed to assess for separate constructs of depression, anxiety, and tension/stress [14]. These clinical questionnaires have all been validated both in healthy and clinical populations [14–16]. 2.4. Statistical analyses Statistical analyses were conducted using IBM SPSS Statistics software version 23.0. The internal reliability of TMMS and ASQ subscales in the respondents ranged from acceptable to very good (Cronbach's alpha 0.65–0.87). Normative data from TMMS and ASQ validation studies were used to make comparisons [5,8]. This normative data was based on responses from college students, but the validation studies made no mention of any psychiatric screening. Independent t-tests were used to compare mean scores in patients versus normative mean scores, and to compare mean scores between subsets of patients with PNES with and without trauma-related diagnoses and histories. Pearson correlation coefficients were used to evaluate associations between continuous variables. 3. Results One hundred and forty-three patients were recruited, and their data were included in the final analysis. Demographic and clinical characteristics of participants in this study are summarized in Tables 1 and 2, respectively. The cutoff values included in Table 2b are based on validation studies and questionnaire manuals [17–19]. Patients with PNES had mean scores on TMMS attention and clarity subscales of 47.0 (SD 7.4) and 37.5 (SD 8.0), respectively. Their mean scores on ASQ concealing, adjusting and tolerating subscales were 22.2 (SD 6.3), 17.4 (SD 5.7), and 15.5 (SD 4.1), respectively. Independent

Table 2b Mean scores on clinical scales. Clinical scale

Mean score (SD)



15 (11.2)


12 (9.8)

0–13 = minimal depression 14–19 = mild depression 20–28 = moderate depression 29–63 = severe depression 0–7 = normal 8–9 = mild 10–14 = moderate 15–19 = severe 20+ = extremely severe 0–14 = normal 15–18 = mild 19–25 = Moderate 26–33 = severe 34+ = extremely severe 5 = low severity 10 = medium severity 15 = high severity No cutoffs used


13 (11.2)


12 (5.1)


49 (11.4)

BDI-II: Beck Depression Inventory-II; DASS: Depression, Anxiety and Stress Symptoms Scale (DASS-A: Anxiety subscale; DASS-S: Stress subscale); PHQ-15: Patient Health Questionnaire 15; BIPQ: Brief Illness Perception Questionnaire.


Table 3 Sample measures from TMMS and ASQ subscales. Trait Meta Mood Scale (TMMS)a People would be better off if they felt less and thought more.c Attention Feelings give direction in life. I am rarely confused about how I feel. Clarity I can't make sense out of my feelings.c Affective Styles Questionnaire (ASQ)b I often suppress my emotional reactions to things. Concealing People usually can't tell when I am sad. I know exactly what to do to get in a better mood Adjusting I am able to let go of my feelings. It's okay if people see me being upset. Tolerating There is nothing wrong with feeling very emotional. a Individual item score based on the following scale: 1 = strongly disagree, 2 = somewhat disagree, 3 = neither agree nor disagree, 4 = somewhat agree, 5 = strongly agree. b Individual item score based on the following scale: 1 = not true of me at all, 2 = a little bit, 3 = moderately, 4 = quite a bit, 5 = extremely true of me. c Indicates items that are reverse scored.

t-tests showed significantly lower means (p b 0.0001) in patients with PNES compared to normative data for both TMMS subscales and the ASQ adjusting subscale, as shown in more detail in Table 4. There was no significant difference for the concealing and tolerating subscales of the ASQ between patients with PNES and normative data. Within the cohort of patients with PNES, those with a history of childhood abuse versus those without had lower mean scores on emotion attention (p = .04), clarity (p = .001), adjusting (p = .02), and tolerating (p = .02). Physical abuse led to lower scores on emotion clarity (p = .02) and higher scores on emotion concealing (p = .01), but sexual abuse did not lead to significant differences in subscale scores. Patients with active posttraumatic stress disorder (PTSD) had lower scores in the emotion clarity (p = .02), adjusting (p = .009), and tolerating (p = .01) subscales, and they had higher scores in the emotion concealing subscale (p = 0. 02). Further detail of the differences in cognitiveemotion processing measures based on trauma-related factors is detailed in Table 5. Correlations between cognitive-emotion processing scales and psychopathology measures and illness perception within the cohort of patients with PNES are displayed in Table 6. Higher level of depression was significantly correlated with lower degree of clarity of emotions (r = − 0.41; p b .001) and adjustment to (r = − 0.39; p ≤.001) and tolerance of (r = 0.31; p = .001) emotional states. Higher level of stress was also correlated with lower degree of clarity of emotions (r = − 0.47; p b .001) and adjustment to (r = − 0.54; p b .001) and tolerance of (r = 0.26; p = .004) emotional states. Higher severity of anxiety, somatization, and illness perception were significantly associated with lower degree of clarity about emotions (respectively, r = − 0.44 and p b .001; r = − 0.19 and p = .045; r = − 0.34 and p = .001). More anxiety was also associated with significantly lower ability to tolerate emotional states. 4. Discussion Results from this analysis are consistent with our hypotheses that patients with PNES have cognitive-emotion processing and emotion coping styles that differ from a general population sample (in this case represented by the available normative data for each subscale). These changes in cognitive-emotion processing are more pronounced among patients with PNES and trauma-related histories and diagnoses (particularly those with active PTSD and history of childhood abuse), depression, and anxiety. The higher degree of cognitive-emotion processing deficits in patients with PNES and trauma-related histories and diagnoses is not surprising given the known negative effects of trauma on emotion regulation [20–22]. It is possible that difficulties with cognitive-emotion processing in PNES are primarily driven by


R. Rosales et al. / Epilepsy & Behavior 102 (2020) 106639

Table 4 Comparison of TMMS and ASQ subscale scores between participants and normative data. Scale



PNES subjects

Attention Clarity Concealing Adjusting Tolerating

Normative data


Mean (SD)

Mean (SD)

116 122 127 131 128

47.0 (7.4) 37.5 (8) 22.2 (6.3) 17.4 (5.7) 15.5 (4.1)

51 (7.9) 42.5 (7.9) 22.6 (6.3) 21.0 (5.2) 15.5 (3.4)

95% CI


P value for difference

−5.67 to −2.35 −6.66 to −3.34 −1.66 to 0.79 −4.6 to −2.56. −0.71 to 0.69

−4.74 −5.91 −0.69 −6.91 −0.03

P b .0001 P b .0001 P = .5 P b .0001 P = .98

TMMS: Trait Meta-Mood Scale; ASQ: Affective Style Questionnaire.

these identified underlying psychopathological processes. Patients with PNES without a history of trauma have previously been characterized by psychological profiles with less psychiatric comorbidity and lower dissociative tendencies [23]. It is, therefore, possible that in patients with PNES without a history of trauma, cognitive-emotion processing is impacted in a way that is not captured by measures that traditionally evaluate emotion management. In this study, recruited participants had to be intellectually capable of understanding and responding to questionnaires. Therefore, this study could not explore whether developmental delay or significant cognitive deficits can contribute to cognitive-emotion processing abnormalities in PNES. Cognitive impairment has been speculated to be an important factor in a subgroup of patients with PNES [24–26]. The associations between cognitive-emotion processing and trauma-related factors in PNES require careful consideration. The finding that childhood abuse had an effect on most subscales of cognitiveemotion processing within our sample highlights the long-term impact of early experiences on cognitive and behavioral responses to emotions. Many previous studies have emphasized the profound neurobiological and behavioral effects of childhood trauma, including changes in cortical thickness and functional activity in brain regions involved in emotion regulation [27–29]. It is also important to note that sexual abuse did not have a statistically significant impact on the measured variables of cognitive-emotion processing in our sample. There is increasing recognition that some stressful life events, such as, childhood emotional neglect, have a stronger association with the development of functional neurological disorders than the traditionally emphasized physical or sexual abuse [30]. Nevertheless, many previous studies suggested that a history of sexual abuse is associated with more severe PNES symptoms and psychopathology, and for some subgroups of

Table 5 Comparison of cognitive-emotion processing subscale scores (TMMS and ASQ) between PNES patients with and without a trauma-related history or diagnosis. Trauma-related characteristic that defined PNES subgroup

TMMS TMMS Attention Clarity

Female sex Past PTSD Active PTSD Any anxiety disorder History of trauma/abuse Childhood abuse Physical abuse Sexual abuse Emotional/verbal abuse Borderline Personality Disorder


ASQ ASQ ASQ Concealing Adjusting Tolerating


− − −


− −

patients (especially women) with PNES, sexual abuse seems to have a more prominent role [23,31–33]. It is possible that with our limited sample, the broader definition of early childhood trauma was found to have broader impact on cognitive-emotion processing than the specific kinds of traumatic experiences, such as sexual abuse, which may have been detected in a larger sample. Furthermore, if we had classified sexual abuse by age of occurrence during our semi-structured interview, it is possible that we might have found a relationship that we did not find when looking broadly at the impact of sexual abuse at any point in life. The selected TMMS and ASQ subscales represent different stages of cognitive-emotion processing and affective coping strategies. It was, therefore, expected that comparison between PNES patients and normative data (and between subgroups of PNES patients) were not going to show universally similar results across all subscales. The TMMS clarity and the ASQ adjusting were the two subscales that more consistently showed a pattern of deficit in PNES and stronger correlations with trauma-related factors and psychiatric severity. The deficits in emotion clarity are consistent with the known literature on alexithymia in PNES [2,9]. Deficits in adjustment to emotional states have also been previously described in PNES with an emotion regulation scale [34]. However, the lack of a statistically significant difference between patients and normative data in the ASQ concealing and tolerating subscales conflict with theories that suppression (overmodulation) and undermodulation of distressing emotions play a role in the underlying psychopathology of PNES [34]. This could be, in part, due to an artifact of using self-report questionnaires in which patients who utilize unconscious suppression as a defense mechanism are asked to assess their own emotion suppression. The correlation of TMMS and ASQ subscale scores with other clinical variables provides further evidence for the broader psychiatric impact of alterations in cognitive-emotion processing in PNES. Particularly, lower scores on the clarity subscale of the TMMS were negatively associated with scores on every clinical scale (depression, anxiety, stress, somatic distress, illness perception), suggesting that difficulty in identifying specific emotions has broad psychological and clinical impact. Further development of treatments for PNES should focus on addressing these alterations in cognitive-emotion processing. Skill-based psychotherapies, such as CBT and mindfulness-based psychotherapies are well-suited since they can help address deficits in attention and

Table 6 Pearson correlations between cognitive-emotion processing subscale scores (TMMS and ASQ) and scores in psychiatric severity scales/subscales. Scale

TMMS: Trait Meta-Mood Scale; ASQ: Affective Style Questionnaire; PTSD: Posttraumatic stress disorder. + or – represent that PNES patients with the trauma characteristic or diagnosis had higher (+) or lower (−) scores on the corresponding TMMS or ASQ subscale compared to PNES patients who did not have such trauma characteristic or diagnosis. For example, PNES subjects with a history of physical abuse had less clarity of emotions and used more concealing strategies than those PNES subjects without such history.



BDI (p)

DASS anxiety

DASS stress



Attention Clarity Concealing Adjusting Tolerating

−0.07 −0.41⁎ 0.02 −0.39⁎ −0.31⁎

−0.16 −0.44⁎ 0.00 −0.39 −0.19⁎

−0.05 −0.47⁎ −0.04 −0.54⁎ −0.26⁎

0.05 −0.34⁎ −0.05 −0.35 −0.15

0.03 −0.19⁎ −0.15 −0.17 −0.15

TMMS: Trait Meta-Mood Scale; ASQ: Affective Style Questionnaire; BDI-II: Beck Depression Inventory-II; DAAS: Depression, Anxiety and Stress Symptoms Scale (DASS-A: Anxiety subscale; S: Stress subscale); PHQ-15: Patient Health Questionnaire 15; BIPQ: Brief Illness Perception Questionnaire. ⁎ p b .05.

R. Rosales et al. / Epilepsy & Behavior 102 (2020) 106639

clarity of emotions and can help with the development of effective emotion adjusting strategies [4]. Treatments that address PTSD-related symptoms, such as prolonged exposure, may also represent a suitable form of treatment in patients with comorbid PNES and PTSD. Prolonged exposure has been shown to improve emotion regulation related to PTSD symptoms and also to reduce PNES episodes [35]. In the future, it will be informative to track changes in cognitive-emotion processing during intensive treatment and evaluate if this change correlates with substantive clinical improvement. There are several limitations to this study. First, it is a relatively small cohort from one center that may not broadly represent all patients with PNES. Second, we did not have a control group. Although we did compare TMMS and ASQ subscale scores with normative data, use of a matched healthy control sample would have allowed a more accurate analysis of between-group differences. It would also be informative to compare scores from PNES subjects to a more clinically relevant control group (such as patients with epilepsy or another type of functional neurological disorder) to identify more specific and nuanced deficits in PNES. Third, using self-report questionnaires can be challenging, since identification of cognitive-emotion processing deficits becomes dependent on some degree of awareness of such problems. Despite this, TMMS and ASQ are validated instruments that have shown correlations with other well-established measures of cognitive-emotion processing [2,9,10,36–40]. Finally, while we did subcategorize trauma related histories by time of occurrence (childhood versus adult) and subtypes of trauma, we did not differentiate subtypes of trauma based on age. This may have obscured some findings given that certain types of childhood abuse could theoretically have more profound effects when separated from other subtypes. This study adds to the existing literature by more specifically characterizing cognitive-emotion processing and coping styles in PNES. Identifying alterations in emotion processing and behavioral response can help guide treatment by identifying the treatment modality that best fits underlying psychological mechanisms. Future studies will assess whether effective treatments are associated with changes in these deficits. Additionally, deficits in cognitive-emotion processing should be explored in other functional neurological disorders to better understand differences and similarities between phenotypes in this group of disorders and to eventually adjust treatment accordingly.

Declaration of competing interest The authors report no competing interest.

References [1] Baslet G, Seshadri A, Bermeo-Ovalle A, Willment K, Myers L. Psychogenic non-epileptic seizures: an updated primer. Psychosomatics 2016;57(1):1–17. [2] Williams IA, Levita L, Reuber M. Emotion dysregulation in patients with psychogenic nonepileptic seizures: a systematic review based on the extended process model. Epilepsy Behav 2018;86:37–48. [3] Urbanek M, Harvey M, McGowan J, Agrawal N. Regulation of emotions in psychogenic nonepileptic seizures. Epilepsy Behav 2014;37:110–5. [4] Baslet G, Dworetzky B, Perez DL, Oser M. Treatment of psychogenic nonepileptic seizures: updated review and findings from a mindfulness-based intervention case series. Clin EEG Neurosci 2015;46(1):54–64. [5] Salovey P, Mayer JD, Goldman SL, Turvey C, Palfai TP. Emotional attention, clarity, and repair: exploring emotional intelligence using the Trait Meta-Mood Scale. In: Pennebaker JW, editor. Emotion, disclosure, & health. American Psychological Association; 1995. p. 125–54. [6] Palmer B, Gignac G, Bates T, Stough C. Examining the structure of the Trait MetaMood Scale. Australian J Psychol 2003;55(3):154–8. [7] Fernandez-Berrocal P, Natalio E. A review of trait meta-mood research. Int J Psychol Res 2008;2(1):39–67. [8] Hofmann SG, Kashdan TB. The affective style questionnaire: development and psychometric properties. J Psychopathol Behav Assess 2010;32(2):255–63.


[9] Brown RJ, Bouska JF, Frow A, Kirkby A, Baker GA, Kemp S, et al. Emotional dysregulation, alexithymia, and attachment in psychogenic nonepileptic seizures. Epilepsy Behav 2013;29(1):178–83. [10] Martino I, Bruni A, Labate A, Vasta R, Cerasa A, Borzì G, et al. Psychopathological constellation in patients with PNES: a new hypothesis. Epilepsy Behav 2018;78: 297–301. [11] Broadbent E, Petrie KJ, Main J, Weinman J. The brief illness perception questionnaire. J Psychosom Res 2006;60(6):631–7. [12] Basu S, Poole J. The brief illness perception questionnaire. Occup Med 2016;66(5): 419–20. [13] Novakova B, Howlett S, Baker R, Reuber M. Emotion processing and psychogenic non-epileptic seizures: a cross-sectional comparison of patients and healthy controls. Seizure 2015;29:4–10. [14] Lovibond P, Lovibond S. The structure of negative emotional states: comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav Res Ther 1995;33:335–43. [15] Beck A, Steer R, Ball R, Ranieri W. Comparison of the Beck Depression Inventories IA and II in psychiatric outpatients. J Pers Assess 1996;67:588–97. [16] Interian A, Allen L, Gara M, Escobar J, Diaz-Martinez A. Somatic complaints in primary care: further examining the validity of the Patient Health Questionnaire (PHQ-15). Psychosomatics 2006;47:392–8. [17] Kroenke K, Spitzer RL, Williams JB. The PHQ-15: validity of a new measure for evaluating the severity of somatic symptoms. Psychosom Med 2002;64(2):258–66. [18] Lovibond S, Lovibond P. Manual for the Depression Anxiety Stress Scales. . 2nd ed. Psychology Foundation: Sydney; 1995. [19] Beck A, Steer R, Brown G. Manual for the Beck Depression Inventory-II. San Antonio, TX: Psychological Corporation; 1996. [20] McLean CP, Foa EB. Emotions and emotion regulation in posttraumatic stress disorder. Curr Opin Psychol 2017;14:72–7. [21] Chesney SA, Gordon NS. Profiles of emotion regulation: understanding regulatory patterns and the implications for posttraumatic stress. Cogn Emot 2017;31(3): 598–606. [22] Seligowski AV, Lee DJ, Bardeen JR, Orcutt HK. Emotion regulation and posttraumatic stress symptoms: a meta-analysis. Cogn Behav Ther 2015;44(2):87–102. [23] Hingray C, Maillard L, Hubsch C, Vignal JP, Bourgognon F, Laprevote V, et al. Psychogenic nonepileptic seizures: characterization of two distinct patient profiles on the basis of trauma history. Epilepsy Behav 2011;22(3):532–6. [24] Popkirov S, Carson A, Stone J. Scared or scarred: could ‘dissocionegic’ lesions predispose to nonepileptic seizures after head trauma? Seizure 2018;58:127–32. [25] Chapman M, Iddon P, Atkinson K, Brodie C, Mitchell D, Parvin G, et al. The misdiagnosis of epilepsy in people with intellectual disabilities: a systematic review. Seizure 2011;20(2):101–6. [26] Salinsky M, Storzbach D, Goy E, Evrard C. Traumatic brain injury and psychogenic seizures in veterans. J Head Trauma Rehabil 2015;30(1):E65–70. [27] Gold AL, Sheridan MA, Peverill M, Busso DS, Lambert HK, Alves S, et al. Childhood abuse and reduced cortical thickness in brain regions involved in emotional processing. J Child Psychol Psychiatry 2016;57(10):1154–64. [28] Marusak HA, Martin KR, Etkin A, Thomason ME. Childhood trauma exposure disrupts the automatic regulation of emotional processing. Neuropsychopharmacology 2015;40(5):1250–8. [29] Zeanah CH, Humphreys KL. Child abuse and neglect. J Am Acad Child Adolesc Psychiatry 2018;57(9):637–44. [30] Ludwig L, Pasman JA, Nicholson T, Aybek S, David AS, Tuck S, et al. Stressful life events and maltreatment in conversion (functional neurological) disorder: systematic review and meta-analysis of case-control studies. Lancet Psychiatry 2018;5(4):307–20. [31] Selkirk M, Duncan R, Oto M, Pelosi A. Clinical differences between patients with nonepileptic seizures who report antecedent sexual abuse and those who do not. Epilepsia 2008;49(8):1446–50. [32] Reuber M. Psychogenic nonepileptic seizures: answers and questions. Epilepsy Behav 2008;12(4):622–35. [33] Beghi M, Cornaggia I, Magaudda A, Perin C, Peroni F, Cornaggia CM. Childhood trauma and psychogenic nonepileptic seizures: a review of findings with speculations on the underlying mechanisms. Epilepsy Behav 2015;52(Pt A):169–73. [34] Uliaszek AA, Prensky E, Baslet G. Emotion regulation profiles in psychogenic nonepileptic seizures. Epilepsy Behav 2012;23(3):364–9. [35] Myers L, Vaidya-Mathur U, Lancman M. Prolonged exposure therapy for the treatment of patients diagnosed with psychogenic non-epileptic seizures (PNES) and post-traumatic stress disorder (PTSD). Epilepsy Behav 2017;66:86–92. [36] Aradilla-Herrero A, Tomas-Sabado J, Gomez-Benito J. Associations between emotional intelligence, depression and suicide risk in nursing students. Nurse Educ Today 2014;34(4):520–5. [37] Extremera N, Fernandez-Berrocal P. Relation of perceived emotional intelligence and health-related quality of life of middle-aged women. Psychol Rep 2002;91(1):47–59. [38] Fernandez-Abascal EG, Martin-Diaz MD. Dimensions of emotional intelligence related to physical and mental health and to health behaviors. Front Psychol 2015;6:317. [39] Tabak NT, Green MF, Wynn JK, Proudfit GH, Altshuler L, Horan WP. Perceived emotional intelligence is impaired and associated with poor community functioning in schizophrenia and bipolar disorder. Schizophr Res 2015;162(1–3):189–95. [40] Totzeck C, Teismann T, Hofmann S, von Brachel R, Zhang XC, Pflug V, et al. Affective styles in mood and anxiety disorders — clinical validation of the “Affective Style Questionnaire” (ASQ). J Affect Disord 2018;238:392–8.