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Background:
Hypothesis

Affective Neuroscience Personality Scales and Early Maladaptive Schemas in Depressive Disorders

by
Monika Talarowska
1,*,
Grzegorz Wysiadecki
1,2 and
Jan Chodkiewicz
1
1
Institute of Psychology, Faculty of Educational Sciences, University of Lodz, Smugowa 10/12, 90-433 Łódź, Poland
2
Department of Normal and Clinical Anatomy, Medical University of Lodz, 90-419 Łódź, Poland
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(13), 8062; https://doi.org/10.3390/ijerph19138062
Submission received: 15 May 2022 / Revised: 22 June 2022 / Accepted: 24 June 2022 / Published: 30 June 2022
(This article belongs to the Special Issue Cognitive Functioning in Depressive Disorders – New Challenges)
Review Reports Versions Notes

Abstract

:
Aim: The aim of this study was to assess the interrelationships of Young’s early maladaptive schemas with indicators of specific neural emotional systems conceptualized in Panksepp’s theory in a group of people suffering from depressive disorders. Materials and methods: The Affective Neuroscience Personality Scales (ANPS) v. 2.4. and J. Young’s Early Maladaptive Schema Questionnaire (YSQ-S3-PL) were used. Ninety (90) individuals aged 18–58, including 45 people treated for depression (DD group), were qualified to participate in the experiment. Results: The subjects in the DD group scored statistically significantly lower than the subjects from the control group (CG group) on the three ANPS scale domains, namely SEEKING, PLAY, and ANGER. The subjects with depressive symptoms scored significantly higher in the YSQ-S3-PL questionnaire on two domains of early maladaptive schemas, i.e., “Impaired autonomy and performance” and “Other-directedness”. Regression analysis results indicate that impairment of the emotional SEEKING system explains most of the variability in the following typical domains of depression: “Disconnection and rejection”, “Impaired autonomy and performance”, and “Other-directedness”. For score variability in the domain area of “Impaired limits”, the ANGER system was found to be most significant, and the FEAR system proved the same for “Overvigilance and Inhibition”. Conclusions: 1. Two domains of early maladaptive schemas are significant for the onset of depressive symptoms, namely “Impaired autonomy and performance” and “Other-directedness”, linked to difficulties in engaging in behaviors to meet one’s own needs. 2. Impairment of the neural emotional SEEKING system most significantly explains the variability in depression-typical areas of early maladaptive schemas.

1. Introduction

The theory of early maladaptive schemas (EMS), created by Jeffrey E. Young, seems to comprehensively explain the mechanism and, above all, the dynamics of mental disorders arising from early childhood experiences [1,2].
According to the assumptions of EMS, the experiences we make in our earliest stages of development shape relatively stable patterns of functioning and beliefs about ourselves, other people, and the surrounding world. These patterns are referred to as schemas. The primary source of dysfunctional schemas is the inability to meet or inadequately meet one (or more) of the child’s basic developmental needs (so-called core needs) [3,4]. Failure to meet these needs causes emotions that are difficult for the child, such as anxiety, anger, shame, or guilt; to avoid experiencing them, individuals engage in a variety of behavioral and coping strategies, which—while reducing tension—also contribute to the perpetuation of certain schemas [5]. Over the past few years, Young’s theory of schemas has become a focal point for many studies that have attempted to explain the etiology of numerous psychiatric disorders [6].
The achievements of modern medicine and neuroscience allow us to assume that biological factors play a significant role in the formation of human personality [7]. One theory that seeks to understand the neural foundations of emotion, and thus human personality, is the Affective Neuroscience Personality Theory (ANPT) by Panksepp [8]. According to Panksepp’s view, words (narratives) and environmental influences do not fully explain animal and human behavior. However, they may be explained by processes that originate in human brain activity [9].
Panksepp suggests the existence of six neural emotional, and motivational systems related to older subcortical structures of the mammalian brain that were developed by nature over millions of years of evolution as adaptations crucial to animals’ survival [10,11]. These systems are mostly homologous in all mammals. Furthermore, the neurochemistry of these systems is similar in all mammals [12]. According to the cited authors, in the face of external and internal environmental challenges, primary emotional systems allow for the rapid arousal and coordination of dynamic forms of brain organization. Emotions, in this view, are adaptive and innate, and the neural circuits associated with them have evolved to ensure that individuals are highly efficient at surviving and passing on genes to their offspring [13].
According to Panksepp, primary emotional processes, which are instinctive and represent a form of evolutionary “memory”, underpin the emotional and drive-related activity of the brain. This adaptation is essential for mammals to survive in the environment. Primary processes are associated with subcortical centers in the lower brain, mainly in its medial part [14]. Lower brain areas not only have an evolutionary advantage in creating primary emotions but also generate instinctive behavioral responses that are closely related to primary effects. According to Panksepp, emotions are an important foundation of personality, and personality assessment can serve as information regarding the aforementioned subcortical effects [15]. Additionally, differences in the response of primary emotional systems underlie human personality [16].
The location of specific neural systems was determined by observing the instinctive responses of animals that occur under electrical or chemical stimulation of particular brain regions [17]. The neural systems listed by Panksepp include SEEKING (interest), ANGER (rage), FEAR (anxiety), CARE (caring/nurturance), SADNESS/PANIC (separation distress/grief), and PLAY (playfulness/joy) [18,19]. Each of these systems can be activated by stimulating separate (although overlapping) areas of the brain. However, they usually work together to increase the adaptability of feelings, thoughts, and behaviors of the individual [20]. The location in the brain and characteristics of the neural emotional systems described by Panksepp are shown in Table 1.
Panksepp assumes that emotions are the basis of personality; hence the tendency to respond with a specific effect associated with the activation of the relevant neural emotional system can be associated with the formation of a specific response pattern or personality trait [9]. Moreover, the operation of these systems may help in understanding the etiology and course of many psychiatric disorders [21].

Aim

Until the moment of compiling this article, the authors did not confirm the presence of other studies linking Jeffrey Young’s theory of early maladaptive schemas with J. Panksepp’s Affective Neuroscience Personality Theory. The study presented herein is, therefore, the first attempt to deal with this matter. It is hoped that the results obtained lead to a better understanding of the biological basis of early maladaptive schemas and, in the future, contribute to an increase in the effectiveness of applied therapeutic interventions.
The authors attempted to assess the relationship between early schemas and neural emotional systems in the depressive disorders group. These disorders were selected due to the frequency of their prevalence in the population and the magnitude of the clinical, social, and economic costs they generate [23].
Bearing in mind the aforementioned correlations, the aim of the presented study was to investigate and assess the interrelationships between J. Young’s early maladaptive schemas and indicators of individual emotional systems distinguished in J. Panksepp’s theory in a group of patients with depressive disorders.

2. Material

Ninety (90) individuals aged 18–58 took part in the study. The sample size was estimated using the G*Power program [24]. Forty-five (45) subjects in the study group had been diagnosed with a depressive disorder by a psychiatrist (depressive disorder group, DD group, F33) [25]. The individuals in the DD group were matched by taking into account the pharmacological treatment applied. Only the patients taking SSRI medications at the time of the study and who were receiving standard treatment with SSRIs were eligible to participate [26]. Subjects who had been taking SSRIs for no more than two weeks prior to the start of the study were eligible to participate. Only the subjects who scored above seven on the HDRS scale, indicating the presence of depressive symptoms, were qualified for the DD group. The mean severity of depressive symptoms as measured by the Hamilton Depression Rating Scale in the DD group was M = 25.07 (SD = 7.91), which indicates a significant intensification of symptoms of the disease.
The comparison group consisted of 45 healthy subjects (CG group) recruited by the snowball method, matched for gender and education. There were no statistically significant differences in gender between the groups analyzed (chi2 = 0.498, p = 0.481), but there were statistically significant differences in age (t = 11.174, p < 0.001). The respondents also performed the BDI-II test to exclude people with symptoms of depression. In each case, the mental condition of the respondents was assessed by a psychiatrist. The social and demographic characteristics of the study group are presented in Table 2.
In the DD group, the subjects diagnosed with disorders other than axis disorders (F33) were excluded from the study.

2.1. Method

A self-reported survey, enabling the collection of sociodemographic data, as well as the following questionnaires, were used in the study:
J. Young’s early maladaptive schemas questionnaire (YSQ-S3-PL) in the Polish adaptation by [27].
The method examined the intensity of each of the 18 schemas based on a self-report of the respondent, who was asked to respond to the highlighted statements. This allows the pattern of schemas characteristic of the particular person to be identified. The questionnaire consists of 90 test items (five for each schema). The scores for each schema are in the range of 5 to 30. The arithmetic mean for each schema and the total score for all were also calculated. The Polish version of the method has acceptable psychometric properties [27].
When analyzing the results obtained in the YSQ-S3-PL questionnaire, the authors used the division into five schema areas (domains) distinguished by its author, namely: “Disconnection and rejection”, “Impaired autonomy and performance”, “Impaired limits”, “Other-directedness”, and “Overvigilance and inhibition”. These domains were distinguished by the degree of unmet needs that are crucial to normal human development (respectively), i.e., secure attachment to others; autonomy; realistic boundaries and self-control; freedom to express needs; and spontaneity and play [28].

2.1.1. The Affective Neuroscience Personality Scales (ANPS) v. 2.4

The ANPS version 2.4 of 2004, developed by Davis et al. (2011), was used in the study. This tool is designed to assess endophenotypes associated with the activity in core emotional systems that emerged from research in affective neuroscience [18,19]. This scale, with the permission of its author, was translated into Polish by competent specialists.
The ANPS consists of 112 statements rated on a 4-point Likert scale (strongly disagree (1) to strongly agree (4)). The results represent the intensity of six emotional systems: SEEKING (interest), ANGER (rage), FEAR (anxiety), CARE (caring/nurturance), SADNESS/PANIC (separation distress/grief), and PLAY (playfulness/joy). Of those cited so far, LUST was excluded due to its lesser relevance in light of current conceptions of human personality [29,30,31]. Detailed characteristics of the neural emotional systems are shown in Table 1.

2.1.2. Hamilton Depression Rating Scale (HDRS)

The 21-item Hamilton Depression Scale [32] was used to assess the severity of depression in the DD group. It consists of items that assess the degree of depressed mood; the presence of psychomotor retardation and/or inhibition; the severity of experienced guilt; the presence of sleep and/or appetite disturbances; the presence of anxiety symptoms; the presence of suicidal thoughts, tendencies, and attempts; and critical attitudes toward disease symptoms.
The intensity of depressive disorders was classified based on the grades distinguished in Demyttenaere et al. [33]: <7—no depressive symptoms; 8–12—mild intensity of depressive symptoms; 13–17—moderate intensity of depressive symptoms; 18–29—severe intensity of depressive symptoms; >30—highly severe intensity of depressive symptoms.

2.2. Study Procedure

The study presented herein was conducted between 2019 and 2022. In each case, an assessment of the severity of depressive disorders and an assessment of psychological test performance were conducted by the same person, i.e., a psychiatrist and a clinical psychologist, respectively.
In the DD group, HDRS scale testing was performed on the day the subjects were eligible to participate in the experiment (at most after week 2 of treatment).
In both study groups, an assessment of functioning with the ANPS and YSQ-S3-PL scales took place on the day the participants were eligible to participate in the study.
Participation in the study was voluntary, and the subjects were recruited after they had given written informed consent to participate. The study was approved by the Bioethics Committee No. RNN/136/17/KE and RNN/37/22/KE.

2.3. Methods of Statistical Analysis

Selected descriptive methods and methods of statistical inference were applied to analyze the data. The first step was to use descriptive statistics for all quantitative parameters of the interpreted variables. The arithmetic mean (M) and standard deviation (SD) were calculated, and the symmetry of the distribution was verified. The normality of the distribution was checked using the Shapiro–Wilk test and the Lilliefors test. They allowed the hypothesis of normality of distribution to be rejected (p < 0.001). The Mann–Whitney U test was used to evaluate differences between independent variables. Spearman’s rank correlation coefficient and stepwise multiple regression coefficients were used to measure the relationship between the analyzed variables. In the conducted analyses, the adopted level of significance was p < 0.05 [34,35]. All statistical calculations were performed using STATISTICA PL software (version 13.3).

3. Results

3.1. Neural Emotional Systems and Early Maladaptive Schemas—Differences across Study Groups

The results recorded in the ANPS and YSQ-S3-PL questionnaires in the studied group are presented in Table 3.
For the ANPS scale, the authors confirmed significant statistical differences for three neural emotional systems, namely SEEKING, PLAY, and ANGER. In each of the three cases, depressed patients scored lower than healthy subjects.
The subjects with depressive symptoms scored significantly higher in the YSQ-S3-PL questionnaire on two domains of early maladaptive schemas, i.e., “Impaired autonomy and performance” and “Other-directedness”.

3.2. Neural Emotional Systems and Early Maladaptive Schemas—Interrelationships

In order to look at the associations of neural emotional systems with early maladaptive schemas, a correlational analysis was conducted using Spearman’s rho correlation coefficient. Given the breadth of the data collected and because previous analyses showed significant differences between DD and CG subjects in only two domains of the YSQ-S3-PL questionnaire, the authors focused on analyses for the five domains of early maladaptive schemas (Table 4) and resigned from additional analyses for individual 18 schemas. An analysis of the entire study group (n = 90) was performed for the same reasons.
As indicated in Table 4, the YSQ-S3-PL total score and individual domains of early maladaptive schemas are strongly correlated with individual dimensions of the ANPS scale. Only the CARE system was found to be poorly associated with most domains of early maladaptive schemas.
The following systems are relevant for the “Disconnection and rejection” area: SEEKING, PLAY (negative correlation), ANGER, FEAR, and SADNESS (positive correlation). These links mean that individuals with impaired feelings of safety, stability, caring, and empathy in their relationships with others have an impaired tendency to seek out stimuli that provide a sense of security, a tendency to experience anger, fear, and sadness, and difficulties relating to others. Similar relationships apply to the area of “Impaired autonomy and performance”, linked to a lack of confidence in one’s own abilities and difficulties with emotional separation from significant others.
In the case of the “Impaired limits” domain, statistical significance was obtained for two emotional systems, namely CARE (negative relationship) and ANGER (positive relationship). This means that people in this group, who, according to J. Young, are characterized by difficulty in respecting the rights of others, have a strong tendency to experience feelings of anger and difficulty in showing closeness to others or accepting care from them.
The “Other-directedness” area, on the other hand, correlates with SEEKING (negative correlation), FEAR, and SADNESS (positive correlations). This means that excessive focus on other people’s emotions and desires is associated with a tendency to experience feelings of sadness and fear (these emotions are linked to fear of rejection) and a low attitude towards seeking positive sensations.
Interestingly, the last of the YSQ-S3-PL domains, namely “Overvigilance and inhibition”, centered around difficulty recognizing one’s own emotional states and over-suppressing them, is linked significantly positively to two neural emotional systems, i.e., FEAR and ANGER. This may mean that the overuse of denial and displacement mechanisms in social relationships—at the neural level—does not cancel out the emotions experienced.

3.3. Regression Analysis

The next step in the statistical analyses was to assess the significance of the six dimensions of the ANPS for the severity of the early maladaptive schema domains. The progressive stepwise multiple regression method was used for statistical calculations (Table 5).
The results of the regression analysis indicate that impairment of the neural emotional SEEKING system explains the variability of the “Disconnection and rejection”, “Impaired autonomy and performance”, and “Other-directedness” domains to the largest extent. For score variability in the domain area of “Impaired limits”, the ANGER system was found to be most significant, and it was the FEAR system for “Overvigilance and inhibition”.
Thus, the most important neural emotional system for patients with depressive symptoms may be the SEEKING system.

4. Discussion

Numerous studies confirm the association of cognitive maladaptive schemas distinguished by Jeffrey Young with the occurrence of not only symptoms of the recurrent depressive disorder but also bipolar disorder, suicidal phobia, obsessive-compulsive disorder, social phobia, addictions, and of course, personality disorders and self-harm tendencies (among others Marteinsdottir et al. [36]; Pinto-Gouveia et al. [37]; Unoka et al. [38]; Hawke and Provencher [39]; Kim et al. [40]; Kwak and Lee [41]; Khosravani et al. [42]; Munuera et al. [43]; Nicol et al. [44].
In contrast, neuroimaging studies indicate the involvement of analogous dysfunctions between the amygdala (the so-called emotional brain) and frontal lobes (the so-called rational brain) in the etiology of the same groups of disorders as mentioned above, i.e., generalized anxiety disorder (GAD) [45], borderline personality disorder [46], depressive disorders [47], bipolar affective disorder [48], substance abuse [49], or behavioral addictions [50]. However, not enough research focusing on the neurobiological basis of early maladaptive schemas has been conducted so far [51].
As with personality traits, schemas are an indispensable part of a person’s mental structure. They have the nature of unconditional beliefs, not questioned by a given person; they constitute an important part of people’s identity and their knowledge about themselves, other people, as well as about the surrounding world. Their strength, reinforcement, and frequency of activation determine the impact they have on the daily functioning of the given person [52]. A key component of early maladaptive schemas includes emotions, and differences in the expression and regulation of emotions account for a large range of individual differences in personality [53]. Following J. Panksepp, the authors treat neural emotional systems as emotional endophenotypes (emotional markers of underlying neuropsychological activity, which mediate between epigenetics and human behavior) and consider them as a component of early maladaptive schemas. These primary affective networks condition the development of higher-order mental processes and are central to the formation of an individual’s behavior and relationships when interacting with others [54,55,56].
Moreover, when faced with new challenges, different types of schemas may be activated to effectively deal with the difficult situation and the emotions that result [57]. Schemas—similarly to responses activated by neural emotional systems—are designed to help us survive in adverse environmental conditions [11].
In the authors’ opinion, investigating this issue in more detail may allow for a better understanding of the etiology and course of most mental disorders (including depression) and to link the achievements of psychology and neuroscience in the design of new therapeutic techniques [58].

4.1. Neurobiology of Depressive Disorders

According to Li et al. [59], dysfunctions of functional connectivity (FC) between the amygdala, the insular cortex, the anterior cingulate cortex (ACC), and the prefrontal cortex (PFC) are observed in depressive disorders. These changes primarily affect the following connections between ACC and left precuneus, ACC and left amygdala, ACC and left dorsolateral PFC, left subgenual ACC and left cerebellar, left PFC and anterior subcallosal area, and left precuneus and left pulvinar of the thalamus. Wang et al. [60], on the other hand, observed that the right anterior cingulated cortex (ACC) in depressed patients was involved not only in the regulation of emotional functions (inversely proportional relationship) but also in the normal course of executive functions (directly proportional relationship). These dysfunctions resolve to some extent as a consequence of antidepressant pharmacotherapy [59], psychotherapeutic interventions, or a series of electric shocks [61], but significant changes in the aforementioned areas are observed during subsequent episodes of the illness [62,63]. Additionally, the changes described are more severe in depressed patients who make suicide attempts compared to treated patients with the same diagnosis and no history of suicide attempts [64]. Increasingly for depressive disorders, it is emphasized that emotional and cognitive dysregulation is attributed to structural and functional abnormalities in the affective network (AN) and cognitive control network (CCN) [62].

4.2. Early Maladaptive Schemas and Depressive Disorders

The authors devoted their previous work to a detailed analysis of the relationship between early maladaptive schemas and depressive disorders [65]. At this point, it would be appropriate to mention the most relevant content related to this issue.
According to numerous authors, scores obtained in the early maladaptive schema questionnaire are a reliable and relatively stable marker of depressive disorders [66,67,68,69], and approximately 60% of patients with depressive symptoms achieve a symptomatic improvement following the use of psychotherapy in the form of schema therapy [70,71,72]. Kindyis et al. [73] also confirmed the effectiveness of schema therapy in alleviating depressive symptoms in older adults.
According to Cormier et al. [66], the severity of early maladaptive schemas increases with the severity of depressive symptoms, and three schemas are characteristic of its occurrence (regardless of symptoms intensity), namely “Defectiveness/shame”, “Dependence/incompetence”, and “Vulnerability to harm or illness”. In contrast, the least characteristic schema for depressed patients is “Entitlement/Grandiosity”. Interestingly, the aforementioned schemas also achieve the highest intensity in the subjects during the remission period of the disease [67], which makes it possible to consider them as indicators of vulnerability to the onset of depressive symptoms.
The classic symptoms of depression, regardless of gender, are associated with the following domains: “Disconnection and rejection”, “Impaired autonomy and performance”, and “Other-directedness” [67,74,75]. The authors observed results consistent with those cited (areas of “Impaired autonomy and performance” and “Other-directedness” differentiated the studied groups to the largest extent).
For experiences associated with early childhood trauma, the “Disconnection and rejection” area is a particularly important moderator of depressive symptoms [76]. In a longitudinal, 9-year study involving patients with a diagnosis of depressive disorder, Wang et al. [69] found a nearly 60% correlation between the areas of “Disconnection and rejection” and “Impaired limits” and the severity of depressive symptoms. In contrast, the following schemas are associated with the risk of suicide attempts in the course of the disease: “Emotional deprivation”, “Defectiveness/shame”, “Abandonment/instability”, and “Social isolation/alienation” [77].

4.3. Neural Emotional Systems and Early Maladaptive Schemas in Depressive Disorders

In recent years, J. Panksepp’s theory has been linked [29] to, among others, P. Costa and T. McCrae’s BIG FIVE theory [78], Cloninger’s Temperament and Character Theory [79,80], and even A. Maslow’s theory of needs [81]. In their meta-analysis, Marengo et al. [82] found that high SEEKING relates to high Openness to Experience, high PLAY to high Extraversion, high CARE/low ANGER to high Agreeableness, and high FEAR/SADNESS/ANGER to high Neuroticism. Similar results were obtained in German [21], Spanish [20], Italian [83], and Serbian (Montag et al., 2019) studies. However, as noted earlier, there is a lack of research linking the theory of neural emotional systems to J. Young’s EMS.
According to Panksepp [84], two of the emotional systems—SADNESS and SEEKING—are particularly relevant to the development of depressive disorders. They are stimulated in case of separation from a loved one (which continues as mourning if the separation is sustained) [84]. This phase may also be characterized by significant arousal in the SEEKING system region (which is supposed to facilitate a future encounter with the beloved object). If separation is maintained, the role of the SEEKING system decreases, and the activity of the SADNESS system increases (this condition resembles depressive disorders in clinical terms) [16]. Moreover, anhedonia typical for the course of depression is associated by J. Panksepp with high SADNESS and diminished SEEKING activity. In contrast, high SEEKING activity with high SADNESS was linked by authors to the risk of engaging in self-aggressive behavior, including suicide attempts [16].
An attempt to assess the severity of individual neural emotional systems in depression was made by Montag et al. (55). A group of 669 individuals, including 55 patients diagnosed with depression, took part in the study. Those authors found a statistically significant association between lower SEEKING system scores and higher depressive tendencies among healthy individuals and those treated for depressive symptoms. Starting from this assumption, Panksepp highlights the role of overactive SEEKING in psychosis and its deficit in depression and addiction [16]. Furthermore, low SEEKING, high FEAR, and high SADNESS scores were associated with higher scores in the Beck Depression Inventory questionnaire (BDI-II) [12].
Fuchshuber et al. [85] also evaluated the relationship between the severity of depressive symptoms and subjects’ scores on the ANPS test. The cited authors indicated associations of depression with SADNESS (β = 0.53), FEAR (β = 0.10), SEEKING (β = −0.10), and PLAY (β = −0.15). Comparable results were obtained in the pre-COVID-19 study conducted by Sanwald et al. [86]. There were 44 patients treated for depression and 49 healthy controls in that project. Inpatients suffering from major depressive disorder (MDD) scored significantly lower on the primary emotion SEEKING and PLAY than controls did. Inpatients, as compared to healthy controls, had significantly higher scores with respect to FEAR and SADNESS [86]. The study was repeated during the first year of the SARS-CoV-2 pandemic (116 depressed patients and 91 healthy subjects). As a result, the primary PLAY emotion was significantly negatively associated with fear of COVID-19. Interestingly, while there was a (non-significant) positive association between SADNESS and fear of COVID-19 in the healthy controls, SADNESS was negatively associated with fear of COVID-19 in the former inpatients [86]. Sanwald et al. [87] also confirmed the increased activity of the SADNESS system with decreased activity of the SEEKING system among young women suffering from depressive disorders compared to male subjects. Montag et al. [88] also observed strong associations between higher FEAR and SADNESS scores and depressive tendencies in healthy subjects and depressed individuals.
In the presented study, the SEEKING system was also found to be most strongly associated with key early maladaptive schemas for depressive symptoms. This primary and oldest motivational system triggers actions related to the exploration of the world, interest in reality, and the search for and anticipation of positive experiences. The arousal of this system leads to intense learning processes and the production of adaptive behaviors and allows for the acquisition of knowledge [80]. Weakening of its activity would therefore imply a risk of depressive symptoms. Are we dealing with the occurrence of neural changes at the most basic level of our brain functioning in patients with symptoms of this disease? [89,90]. Answering this question certainly requires further research, but the results presented here bring us closer to an answer.

5. Summary

Panksepp’s model brilliantly integrates knowledge from such disparate scientific fields as neuroscience, behavioral psychology, cognitive psychology, psychoanalysis, evolutionary psychology, and attachment theory. It appears to be inspiring to both clinicians and researchers [13]. What is more, in a cross-cultural project involving 520 Canadian subjects and 830 French subjects, Orri et al. [91] demonstrated the temporal stability of personality profiles as assessed by the ANPS scale. Thus, a conclusion can be made that there is temporal stability of the human personality dimensions associated with these systems.
Despite several limitations (indicated below), this paper represents the first attempt systematically investigate the early maladaptive schemas in disease conditions. It may constitute a vital contribution to the innovation and practice of depression treatment.

6. Limitations

  • The study presented here does not provide direct evidence (e.g., in the form of functional imaging findings) to explain the mechanisms underlying the association between primary emotional systems and early maladaptive schemas. However, it allows for the formulation of hypotheses to explain the relationships obtained, which can inspire further extended research;
  • The size of the group of depressed individuals does not allow for additional analyses that would indicate the functioning of neural emotional systems for different severity of depressive symptoms or different clinical courses of depression;
  • Nevertheless, the work highlights the usefulness of the Affective Neuroscience Personality Scales for psychiatric diagnostics and extended studies on treatment responses in depression.

7. Conclusions

Two domains of early maladaptive schemas are significant for the onset of depressive symptoms, namely “Impaired autonomy and performance” and “Other-directedness”, linked to difficulties in engaging in behaviors to meet one’s own needs.
Impairment of the neural and emotional SEEKING system most significantly explains the variability in depression-typical areas of early maladaptive schemas.

Author Contributions

Conceptualization, M.T..; methodology, M.T., J.C..; formal analysis, M.T., G.W., J.C.; investigation, M.T., G.W.; writing—original draft preparation, M.T.; writing—review and editing, M.T., G.W., J.C.; funding acquisition, M.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by grant from University of Lodz grant number B2211802000104.07.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and ap-proved by the Institutional Review Board (or Ethics Committee) The study was ap-proved by the Bioethics Committee No. RNN/136/17/KE and RNN/37/22/KE.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Masley, S.A.; Gillanders, D.T.; Simpson, S.G.; Taylor, M.A. A systematic review of the evidence base for schema therapy. Cogn. Behav. Ther. 2012, 41, 185–202. [Google Scholar] [CrossRef]
  2. Young, J.E.; Klosko, J.S.; Weishaar, M.E. Schema Therapy: A Practitioner’s Guide; Guilford Publications: New York, NY, USA, 2006. [Google Scholar]
  3. Bach, B.; Lockwood, G.; Young, J.E. A new look at the schema therapy model: Organization and role of early maladaptive schemas. Cogn. Behav. Ther. 2017, 47, 328–349. [Google Scholar] [CrossRef]
  4. Batool, N.; Shehzadi, H.; Riaz, M.N.; Riaz, M.A. Paternal malparenting and offspring personality disorders: Mediating effect of early maladaptive schemas. J. Pak. Med. Assoc. 2017, 67, 556–560. [Google Scholar]
  5. Renner, F.; van Goor, M.; Huibers, M.; Arntz, A.; Butz, B.; Bernstein, D. Short-term group schema cognitive-behavioral therapy for young adults with personality disorders and personality disorder features: Associations with changes in symptomatic distress, schemas, schema modes and coping styles. Behav. Res. Ther. 2013, 51, 487–492. [Google Scholar] [CrossRef]
  6. Esmaeilian, N.; Dehghani, M.; Koster, E.H.; Hoorelbeke, K. Early maladaptive schemas and borderline personality disorder features in a nonclinical sample: A network analysis. Clin. Psychol. Psychother. 2019, 26, 388–398. [Google Scholar] [CrossRef] [Green Version]
  7. Bouchard, T.J., Jr.; Loehlin, J.C. Genes, evolution, and personality. Behav. Genet. 2011, 31, 243–273. [Google Scholar] [CrossRef]
  8. Panksepp, J. Affective Neuroscience; Oxford University Press: New York, NY, USA, 1998. [Google Scholar]
  9. Panksepp, J. Emotional endophenotypes in evolutionary psychiatry. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 2006, 30, 774–784. [Google Scholar] [CrossRef]
  10. Davis, K.L.; Panksepp, J. The Emotional Foundations of Personality: A Neurobiological and Evolutionary Approach; W.W. Norton: New York, NY, USA, 2018. [Google Scholar]
  11. Deris, N.; Montag, C.; Reuter, M.; Weber, B.; Markett, S. Functional connectivity in the resting brain as biological correlate of the Affective Neuroscience Personality Scales. NeuroImage 2017, 147, 423–431. [Google Scholar] [CrossRef]
  12. Montag, C.; Panksepp, J. Primary emotional systems and personality: An evolutionary perspective. Front. Psychol. 2017, 8, 464. [Google Scholar] [CrossRef] [Green Version]
  13. Zechowski, C. Theory of drives and emotions—From Sigmund Freud to Jaak Panksepp. Psychiatr. Polska 2017, 51, 1181–1189. [Google Scholar] [CrossRef]
  14. Panksepp, J. A synopsis of affective neuroscience—Naturalizing the mammalian mind. J. Conscious Stud. 2012, 19, 6–48. [Google Scholar]
  15. Panksepp, J. At the interface of the affective, behavioral, and cognitive neurosciences: Decoding the emotional feelings of the brain. Brain Cogn. 2003, 52, 4–14. [Google Scholar] [CrossRef]
  16. Panksepp, J.; Watt, D. Why does depression hurt? Ancestral primary-process separation-distress (PANIC/GRIEF) and diminished brain reward (SEEKING) processes in the genesis of depressive affect. Psychiatry 2011, 74, 5–13. [Google Scholar] [CrossRef] [Green Version]
  17. Barrett, F.S.; Robins, R.W.; Janata, P. A brief form of the Affective Neuroscience Personality Scales. Psychol. Assess. 2013, 25, 826–843. [Google Scholar] [CrossRef]
  18. Pingault, J.-B.; Falissard, B.; Cote, S.M.; Berthoz, S. A new approach of personality and psychiatric disorders: A short version of the affective neuroscience personality scales. PLoS ONE 2012, 7, e41489. [Google Scholar] [CrossRef]
  19. Pingault, J.-B.; Pouga, L.; Grèzes, J.; Berthoz, S. Determination of emotional endophenotypes: A validation of the Affective Neuroscience Personality Scales and further perspectives. Psychol. Assess. 2012, 24, 375–385. [Google Scholar] [CrossRef]
  20. Abella, V.; Panksepp, J.; Manga, D.; Bárcena, C.; Iglesias, J.A. Spanish validation of the affective neuroscience personality scales. Span. J. Psychol. 2011, 14, 926–935. [Google Scholar] [CrossRef]
  21. Davis, K.L.; Panksepp, J.; Normansell, L. The affective neuroscience personality scales: Normative data and impli-cations. Neuro-Psychoanalysis 2003, 5, 57–69. [Google Scholar]
  22. Pascazio, L.; Bembich, S.; Nardone, I.B.; Vecchiet, C.; Guarino, G.; Clarici, A. Validation of the Italian translation of the affective neuroscience personality scales. Psychol. Rep. 2015, 116, 97–115. [Google Scholar] [CrossRef]
  23. Celik, F.G.H.; Hocaoglu, C. Implications of childhood trauma on suicidal behavior and deliberate self-harm in patients with major depressive disorder. Psychiatr. Danub. 2022, 34, 57–63. [Google Scholar] [CrossRef]
  24. Faul, F.; Erdfelder, E.; Lang, A.-G.; Buchner, A.G. Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 2007, 39, 175–191. [Google Scholar] [CrossRef]
  25. WHO. ICD-10 Classification of Mental and Behavioural Disorders; WHO: Geneva, Switzerland, 1992. [Google Scholar]
  26. Martin, C.; Hunter, L.; Vinood Patel, V.; Preedy, V.; Rajendram, R. The Neuroscience of Depression. Features, Diagnosis, and Treatent; Elsevier: Amsterdam, The Netherlands, 2021. [Google Scholar]
  27. Oettingen, J.; Chodkiewicz, J.; Mącik, R.; Gruszczyńska, E.; Oetingen, J. Polish adaptation of the Young Schema Questionnaire 3 Short Form (YSQ-S3-PL). Psychiatr. Polska 2018, 52, 707–718. [Google Scholar] [CrossRef]
  28. Young, J. Young Schema Questionnaire—Short Form 3 (YSQ-S3); Cognitive Therapy Center: New York, NY, USA, 2005. [Google Scholar]
  29. Montag, C.; Davis, K.L. Affective neuroscience theory and personality: An update. Personal. Neurosci. 2018, 1, e12. [Google Scholar] [CrossRef] [Green Version]
  30. Montag, C.; Elhai, J.D.; Davis, K.L. A comprehensive review of studies using the Affective Neuroscience Personality Scales in the psychological and psychiatric sciences. Neurosci. Biobehav. Rev. 2021, 125, 160–167. [Google Scholar] [CrossRef]
  31. Panksepp, J. The basic emotional circuits of mammalian brains: Do animals have affective lives? Neurosci. Biobehav. Rev. 2011, 35, 1791–1804. [Google Scholar] [CrossRef]
  32. Hamilton, M. A rating scale for depression. J. Neurol. Neurosurg. Psychiatry 1960, 23, 56–62. [Google Scholar] [CrossRef] [Green Version]
  33. Demyttenaere, K.; De Fruyt, J. Getting what you ask for: On the selectivity of depression rating scales. Psychother. Psychosom. 2003, 72, 61–70. [Google Scholar] [CrossRef]
  34. Kirkwood, B.; Sterne, J. Essential Medical Statistics, 2nd ed.; Wiley-Bleckwell: Hoboken, NJ, USA, 2003. [Google Scholar]
  35. Peacock, J.; Peacock, P. Oxford Handbook of Medical Statistics; Oxford University Press: New York, NY, USA, 2010. [Google Scholar] [CrossRef]
  36. Marteinsdottir, I.; Furmark, T.; Tillfors, M.; Fredrikson, M.; Ekselius, L. Personality traits in social phobia. Eur. Psychiatry 2001, 16, 143–150. [Google Scholar] [CrossRef]
  37. Pinto-Gouveia, J.; Castilho, P.; Galhardo, A.; Cunha, M. Early maladaptive schemas and social phobia. Cogn. Ther. Res. 2006, 30, 571–584. [Google Scholar] [CrossRef] [Green Version]
  38. Unoka, Z.; Tölgyes, T.; Czobor, P. Early maladaptive schemas and body mass index in subgroups of eating disorders: A differential association. Compr. Psychiatry 2007, 48, 199–204. [Google Scholar] [CrossRef]
  39. Hawke, L.D.; Provencher, M.D. Early maladaptive schemas among patients diagnosed with bipolar disorder. J. Affect. Disord. 2012, 136, 803–811. [Google Scholar] [CrossRef]
  40. Kim, J.E.; Lee, S.W.; Lee, S.J. Relationship between early maladaptive schemas and symptom dimensions in patients with obsessive-compulsive disorder. Psychiatry Res. 2014, 215, 134–140. [Google Scholar] [CrossRef]
  41. Kwak, K.-H.; Lee, S.J. A comparative study of early maladaptive schemas in obsessive–compulsive disorder and panic disorder. Psychiatry Res. 2015, 230, 757–762. [Google Scholar] [CrossRef]
  42. Khosravani, V.; Alvani, A.; Seidisarouei, M. The relation of early maladaptive schemas and behavioral inhibi-tion/approach systems to defense styles in abusers of synthetic drug: A cross-sectional study. J. Subst. Use 2016, 21, 652–661. [Google Scholar] [CrossRef]
  43. Munuera, C.; Roux, P.; Weil, F.; Passerieux, C.; M’Bailara, K. Determinants of the remission heterogeneity in bipolar disorders: The importance of early maladaptive schemas (EMS). J. Affect. Disord. 2020, 277, 857–868. [Google Scholar] [CrossRef]
  44. Nicol, A.; Mak, A.S.; Murray, K.; Kavanagh, P.S. Early maladaptive schemas in young people who self-injure. J. Clin. Psychol. 2021, 77, 1745–1762. [Google Scholar] [CrossRef]
  45. Fitzgerald, J.M.; Phan, K.L.; Kennedy, A.E.; Shankman, S.A.; Langenecker, S.A.; Klumpp, H. Prefrontal and amygdala engagement during emotional reactivity and regulation in generalized anxiety disorder. J. Affect. Disord. 2017, 218, 398–406. [Google Scholar] [CrossRef]
  46. Baczkowski, B.M.; Van Zutphen, L.; Siep, N.; Jacob, G.A.; Domes, G.; Maier, S.; Sprenger, A.; Senft, A.; Willenborg, B.; Tüscher, O.; et al. Deficient amygdala–prefrontal intrinsic connectivity after effortful emotion regulation in borderline personality disorder. Eur. Arch. Psychiatry Clin. Neurosci. 2016, 267, 551–565. [Google Scholar] [CrossRef] [Green Version]
  47. Miller, C.H.; Hamilton, J.P.; Sacchet, M.D.; Gotlib, I.H. Meta-analysis of functional neuroimaging of major depressive disorder in youth. JAMA Psychiatry 2015, 72, 1045–1053. [Google Scholar] [CrossRef]
  48. Rive, M.M.; Mocking, R.J.T.; Koeter, M.W.J.; Van Wingen, G.; De Wit, S.J.; Heuvel, O.A.V.D.; Veltman, D.J.; Ruhé, H.G.; Schene, A.H. State-dependent differences in emotion regulation between unmedicated bipolar disorder and major depressive disorder. JAMA Psychiatry 2015, 72, 687–696. [Google Scholar] [CrossRef] [Green Version]
  49. Wilcox, C.E.; Pommy, J.M.; Adinoff, B. Neural circuitry of impaired emotion regulation in substance use disorders. Am. J. Psychiatry 2016, 173, 344–361. [Google Scholar] [CrossRef] [Green Version]
  50. Rozgonjuk, D.; Davis, K.L.; Montag, C. The roles of primary emotional systems and need satisfaction in problematic internet and smartphone use: A network perspective. Front. Psychol. 2021, 12, 709805. [Google Scholar] [CrossRef]
  51. Lim, C.R.; Barlas, J.; Ho, R.C.M. The effects of temperament on depression according to the schema model: A scoping review. Int. J. Environ. Res. Public Health 2018, 15, 1231. [Google Scholar] [CrossRef] [Green Version]
  52. Leppänen, V.; Kärki, A.; Saariaho, T.; Lindeman, S.; Hakko, H. Changes in schemas of patients with severe border-line personality disorder: The Oulu BPD study. Scand. J. Psychol. 2015, 56, 78–85. [Google Scholar] [CrossRef]
  53. Reisenzein, R.; Hannelore, W. The Cambridge Handbook of Personality Psychology; Cambridge University Press: Cambridge, UK, 2009. [Google Scholar]
  54. Davis, K.L.; Montag, C. Selected principles of pankseppian affective neuroscience. Front. Neurosci. 2019, 12, 1025. [Google Scholar] [CrossRef]
  55. Montag, C.; Davis, K.L.; Lazarevic, L.; Knezevic, G. A Serbian version of the ANPS and its link to the five-factor model of personality. Open Psychol. 2019, 1, 303–316. [Google Scholar] [CrossRef]
  56. Panksepp, J.; Panksepp, J.B. Toward a cross-species understanding of empathy. Trends Neurosci. 2013, 36, 489–496. [Google Scholar] [CrossRef] [Green Version]
  57. Bishop, A.; Younan, R.; Low, J.; Pilkington, P.D. Early maladaptive schemas and depression in adulthood: A systematic review and meta-analysis. Clin. Psychol. Psychother. 2021, 29, 111–130. [Google Scholar] [CrossRef]
  58. Markett, S.; Wudarczyk, O.A.; Biswal, B.B.; Jawinski, P.; Montag, C. Affective network neuroscience. Front. Neurosci. 2018, 12, 895. [Google Scholar] [CrossRef] [Green Version]
  59. Li, J.; Chen, J.; Kong, W.; Li, X.; Hu, B. Abnormal core functional connectivity on the pathology of MDD and antidepressant treatment: A systematic review. J. Affect. Disord. 2021, 296, 622–634. [Google Scholar] [CrossRef]
  60. Wang, Y.-L.; Yang, S.-Z.; Sun, W.-L.; Shi, Y.-Z.; Duan, H.-F. Altered functional interaction hub between affective network and cognitive control network in patients with major depressive disorder. Behav. Brain Res. 2016, 298, 301–309. [Google Scholar] [CrossRef] [PubMed]
  61. Brakowski, J.; Spinelli, S.; Dörig, N.; Bosch, O.G.; Manoliu, A.; Holtforth, M.G.; Seifritz, E. Resting state brain network function in major depression—Depression symptomatology, antidepressant treatment effects, future research. J. Psychiatr. Res. 2017, 92, 147–159. [Google Scholar] [CrossRef] [PubMed]
  62. Jiao, K.; Xu, H.; Teng, C.; Song, X.; Xiao, C.; Fox, P.T.; Zhang, N.; Wang, C.; Zhong, Y. Connectivity patterns of cognitive control network in first episode medication-naive depression and remitted depression. Behav. Brain Res. 2020, 379, 112381. [Google Scholar] [CrossRef]
  63. Liu, G.; Jiao, K.; Zhong, Y.; Hao, Z.; Wang, C.; Xu, H.; Teng, C.; Song, X.; Xiao, C.; Fox, P.T.; et al. The alteration of cognitive function networks in remitted patients with major depressive disorder: An independent component analysis. Behav. Brain Res. 2020, 400, 113018. [Google Scholar] [CrossRef]
  64. Kang, S.-G.; Na, K.-S.; Choi, J.-W.; Kim, J.-H.; Son, Y.-D.; Lee, Y.J. Resting-state functional connectivity of the amygdala in suicide attempters with major depressive disorder. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 2017, 77, 222–227. [Google Scholar] [CrossRef]
  65. Chodkiewicz, J.; Wydrzyński, M.; Talarowska, M.J. Young’s early maladaptive schemas and symptoms of male depression. Life 2022, 12, 167. [Google Scholar] [CrossRef]
  66. Cormier, A.; Jourda, B.; Laros, C.; Walburg, V.; Callahan, S. Influence between early maladaptive schemas and depression. L’Encéphale 2011, 37, 293–298. [Google Scholar] [CrossRef]
  67. Halvorsen, M.; Wang, C.E.; Richter, J.; Myrland, I.; Pedersen, S.K.; Eisemann, M.; Waterloo, K. Early maladaptive schemas, temperament and character traits in clinically depressed and previously depressed subjects. Clin. Psychol. Psychother. 2009, 16, 394–407. [Google Scholar] [CrossRef] [Green Version]
  68. Nilsson, K.K.; Straarup, K.N.; Halvorsen, M. Early maladaptive schemas: A comparison between bipolar disorder and major depressive disorder. Clin. Psychol. Psychother. 2014, 22, 387–391. [Google Scholar] [CrossRef]
  69. Wang, C.E.; Halvorsen, M.; Eisemann, M.; Waterloo, K. Stability of dysfunctional attitudes and early maladaptive schemas: A 9-year follow-up study of clinically depressed subjects. J. Behav. Ther. Exp. Psychiatry 2010, 41, 389–396. [Google Scholar] [CrossRef]
  70. Atalay, H.; Atalay, F.; Bağdaçiçek, S. Effect of short-term antidepressant treatment on early maladaptive schemas in patients with major depressive and panic disorder. Int. J. Psychiatry Clin. Pract. 2011, 15, 97–105. [Google Scholar] [CrossRef] [PubMed]
  71. Malogiannis, I.A.; Arntz, A.; Spyropoulou, A.; Tsartsara, E.; Aggeli, A.; Karveli, S.; Vlavianou, M.; Pehlivanidis, A.; Papadimitriou, G.N.; Zervas, I. Schema therapy for patients with chronic depression: A single case series study. J. Behav. Ther. Exp. Psychiatry 2014, 45, 319–329. [Google Scholar] [CrossRef] [PubMed]
  72. Renner, F.; Arntz, A.; Peeters, F.; Lobbestael, J.; Huibers, M.J. Schema therapy for chronic depression: Results of a multiple single case series. J. Behav. Ther. Exp. Psychiatry 2016, 51, 66–73. [Google Scholar] [CrossRef] [PubMed]
  73. Kindynis, S.; Burlacu, S.; Louville, P.; Limosin, F. Thérapie des schémas du sujet âgé: Impact sur la dépression, l’anxiété et les schémas cognitifs typiques. L’Encéphale 2013, 39, 393–400. [Google Scholar] [CrossRef]
  74. Davoodi, E.; Wen, A.; Dobson, K.S.; Noorbala, A.A.; Mohammadi, A.; Farahmand, Z. Early maladaptive schemas in depression and somatization disorder. J. Affect. Disord. 2018, 235, 82–89. [Google Scholar] [CrossRef]
  75. Tariq, A.; Reid, C.; Chan, S.W.Y. A meta-analysis of the relationship between early maladaptive schemas and depression in adolescence and young adulthood. Psychol. Med. 2021, 51, 1233–1248. [Google Scholar] [CrossRef]
  76. Rezaei, M.; Ghazanfari, F.; Rezaee, F. The role of childhood trauma, early maladaptive schemas, emotional schemas and experimental avoidance on depression: A structural equation modeling. Psychiatry Res. 2016, 246, 407–414. [Google Scholar] [CrossRef]
  77. Ahmadpanah, M.; Astinsadaf, S.; Akhondi, A.; Haghighi, M.; Bahmani, D.S.; Nazaribadie, M.; Jahangard, L.; Holsboer-Trachsler, E.; Brand, S. Early maladaptive schemas of emotional deprivation, social isolation, shame and abandonment are related to a history of suicide attempts among patients with major depressive disorders. Compr. Psychiatry 2017, 77, 71–79. [Google Scholar] [CrossRef]
  78. Costa, P.T.; McCrae, R.R. The revised NEO personality inventory (NEO-PI-R). In The SAGE Handbook of Personality Theory and Assessment: Volume 2—Personality Measurement and Testing; SAGE Publications Ltd.: Thousand Oaks, CA, USA, 2008; pp. 179–198. [Google Scholar] [CrossRef]
  79. da Costa, I.F.; Tomaz, M.P.; Pessoa, G.D.N.; Miranda, H.D.S.; Galdino, M.K. Early maladaptive schemas and harm avoidance as mediating factors between early life stress and psychiatric symptoms in adults. Rev. Bras. Psiquiatr. 2020, 42, 489–495. [Google Scholar] [CrossRef]
  80. Davis, K.L.; Panksepp, J. The brain’s emotional foundations of human personality and the Affective Neuroscience Personality Scales. Neurosci. Biobehav. Rev. 2011, 35, 1946–1958. [Google Scholar] [CrossRef]
  81. Montag, C.; Sindermann, C.; Lester, D.; Davis, K.L. Linking individual differences in satisfaction with each of Maslow’s needs to the Big Five personality traits and Panksepp’s primary emotional systems. Heliyon 2020, 6, e04325. [Google Scholar] [CrossRef] [PubMed]
  82. Marengo, D.; Davis, K.L.; Gradwohl, G.; Montag, C. A meta-analysis on individual differences in primary emotional systems and Big Five personality traits. Sci. Rep. 2021, 11, 7453. [Google Scholar] [CrossRef] [PubMed]
  83. Giacolini, T.; Ardizzone, I.; Davis, K.L.; Ferrara, M.; Picconi, L.; Terrinoni, A.; Sabatello, U. Brain emotional systems: The Italian version of the ANPS-Affective Neuroscience Personality Scales 2.4 (reliability and validity). Clin. Neuropsychiatry J. Treat. Eval. 2017, 14, 263–274. [Google Scholar]
  84. Watt, D.F.; Panksepp, J. Depression: An evolutionarily conserved mechanism to terminate separation-distress? A review of aminergic, peptidergic, and neural network perspectives. Neuropsychoanalysis 2009, 11, 5–104. [Google Scholar] [CrossRef]
  85. Fuchshuber, J.; Hiebler-Ragger, M.; Kresse, A.; Kapfhammer, H.-P.; Unterrainer, H.F. Do primary emotions predict psychopathological symptoms? A multigroup path analysis. Front. Psychiatry 2019, 10, 610. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  86. Sanwald, S.; Widenhorn-Müller, K.; Montag, C.; Kiefer, M.; GenEmo Research Group. Primary emotions as predictors for fear of COVID-19 in former inpatients with Major Depressive Disorder and healthy control participants. BMC Psychiatry 2022, 22, 94. [Google Scholar] [CrossRef]
  87. Sanwald, S.; Widenhorn-Müller, K.; Schönfeldt-Lecuona, C.; Montag, C.; Kiefer, M.; GenEmo Research Group. Factors related to age at depression onset: The role of SLC6A4 methylation, sex, exposure to stressful life events and personality in a sample of inpatients suffering from major depression. BMC Psychiatry 2021, 21, 167. [Google Scholar] [CrossRef]
  88. Montag, C.; Widenhorn-Müller, K.; Panksepp, J.; Kiefer, M. Individual differences in Affective Neuroscience Personality Scale (ANPS) primary emotional traits and depressive tendencies. Compr. Psychiatry 2017, 73, 136–142. [Google Scholar] [CrossRef]
  89. Gałecki, P.; Talarowska, M. Neurodevelopmental theory of depression. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 2018, 80, 267–272. [Google Scholar] [CrossRef]
  90. Gałecki, P.; Talarowska, M. The evolutionary theory of depression. Med. Sci. Monit. 2017, 23, 2267–2274. [Google Scholar] [CrossRef] [Green Version]
  91. Orri, M.; Pingault, J.-B.; Rouquette, A.; Lalanne, C.; Falissard, B.; Herba, C.; Cote, S.M.; Berthoz, S. Identifying affective personality profiles: A latent profile analysis of the Affective Neuroscience Personality Scales. Sci. Rep. 2017, 7, 4548. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Table
Table 1. Characteristics and biological basis of Panksepp’s neuroaffective emotional systems [8,9,11,15,21,22].
Table 1. Characteristics and biological basis of Panksepp’s neuroaffective emotional systems [8,9,11,15,21,22].
System NameCharacteristicsBrain Location
POSITIVE AFFECTSEEKING
(interest) *		The main and oldest motivational system.
It stimulates activities related to the exploration of the world, interest in reality, and seeking and anticipating positive experiences. Arousal of this system leads to intense learning processes, production of adaptive behavior (basal nuclei), and knowledge (neocortex). The SEEKING neural system includes the reward system-in terms of enthusiasm and euphoria of engaging, but not hedonistic satisfaction.		It is associated with the activity of, among others, the nucleus accumbens, ventral tegmental area, lateral hypothalamic area, periaqueductal gray (PAG), as well as mesolimbic and mesocortical pathways.
PLAY
(playfulness/joy)		It controls responses related to social adaptation, formation of social patterns, and prosocial attitudes.
The PLAY system is otherwise known as the physical and social engagement system. Play, as animal and human research shows, shapes social patterns that have no prior representation in the brain. PLAY reduces negative affect (e.g., anger), reinforces prosocial attitudes, influences brain neuroplasticity, and modifies the functions of other emotional systems.		The brain areas involved in this system are the dorsomedial part of the midbrain, parafascicular thalamic nucleus, and PAG.
CARE
(caring/nurturance)		It controls responses associated with maternal and nurturing behaviors and feelings and with the development of interpersonal relationships.
It plays an important role in early childhood development and is linked with the activation of the opioids, oxytocin, and prolactin systems in the brain.		The areas significant for this system include, among others, the anterior cingulate cortex and bed nucleus of the stria terminalis, preoptic area, ventral tegmental area, and PAG.
NEGATIVE AFFECTANGER
(rage)		It is responsible for reactions associated with experiencing feelings of anger and rage and with a tendency to exhibit aggressive behavior.
The ANGER system activates when the SEEKING system is disabled.		It is located in the middle parts of the amygdala, in the bed nucleus of the stria terminalis (sometimes referred to as the extended amygdala), in the central parts of the hypothalamus, and PAG.
The areas of the brain activated in RAGE include, among others, the amygdala, stria terminalis, medial hypothalamus, and PAG. This system also projects to the frontal and insular cortex.
FEAR
(anxiety)		Its activation is associated with experiencing feelings of anxiety, a tendency to worry, difficulty making decisions, frequent ruminations, and a sense of internal tension. Stimulation of this system elicits an escape or refrain response. This system is also linked to a reduction in pain sensation.It is associated with the activity of the central and posterior amygdala, the medial part of the hypothalamus, and the dorsal part of PAG.
SADNESS
(panic/separation distress/grief)		It forms the basis of the attachment response and is activated in situations of separation from meaningful objects. It involves experiencing a sense of loneliness.Higher SADNESS scores may be associated with lower functional connectivity between the left basolateral amygdala and the right postcentral gyrus and between the right basolateral amygdala and a so-called “subgyral” cluster in the parietal lobe (as well as the right superior parietal lobe)—see Deris et al. [11].
*—other names for particular neural emotional systems that can be found in the literature.
Table
Table 2. The social and demographic characteristics of the study group.
Table 2. The social and demographic characteristics of the study group.
VariablesDD n = 45CG n = 45All Subjects n = 90
MSDMSDMSD
Age (years)41.2611.6821.522.0231.3912.97
Severity of depressive disorder symptoms as measured by HDRS25.077.91--
GenderFemalesN%N%N%
3168.893168.896268.89
Males1431.111431.112831.11
DD—depressive disorder group; CG—comparison group; N—size; M—mean; SD—standard deviation; %—percentage; HDRS—Hamilton Depression Rating Scale.
Table
Table 3. Neural emotional systems and early maladaptive schemas across the studied groups.
Table 3. Neural emotional systems and early maladaptive schemas across the studied groups.
VariablesDD n = 45GC n = 45All Subjects n = 90DD v GC
MSDMinMax.MSDMinMax.MSDMinMaxMann–Whitney U testp
ANPS
SEEKING21.047.1653927.936.01114224.497.43542469.510.001 *
PLAY19.896.2473026.988.0894023.438.01740478.510.001 *
CARE24.187.2274226.617.0163825.397.18642789.010.071
ANGER17.276.2912722.427.6264219.847.42142652.510.003 **
FEAR23.476.06123925.787.9474224.627.12742808.010.09
SADNESS22.516.31113922.497.4523722.516.86239980.510.791
YSQ-S3-PL
Sum222.71118.2431436241.3656.91122343232.5890.9831436829.510.537
DISCONNECTION AND REJECTION
Sum76.8836.793014464.3121.222610669.5329.1826144605.510.238
Emotional deprivation14.668.2752910.425.4952512.187.05529503.510.021 **
Abandonment/instability17.476.7973016.076.5652916.656.65529644.510.438
Mistrust/abuse15.377.9352914.045.7752314.616.74530672.010.623
Defectiveness/shame14.478.1653010.416.1352612.097.28530515.010.034 **
Social isolation/alienation14.917.7952813.386.0652914.016.82529651.510.482
IMPAIRED AUTONOMY AND PERFORMANCE
Sum58.5926.332310743.8915.56207450.0121.8220107508.510.029 **
Dependence/incompetence14.726.975269.613.2951711.735.71526434.010.003 **
Vulnerability to harm
or illness14.446.8652813.246.0852613.746.41528662.010.552
Enmeshment/undeveloped self13.947.785289.765.0952511.496.63528523.010.042 **
Failure to achieve15.517.7653011.294.7352313.046.47530511.510.031 **
IMPAIRED LIMITS
Sum29.2211.85125328.476.78247228.789.171272690.510.764
Entitlement/grandiosity13.636.7162615.163.6792614.525.17626587.010.171
Insufficient self-control/self-discipline15.595.9252913.315.3252714.265.78529542.010.067
OTHER-DIRECTEDNESS
Sum52.9413.36368245.6410.25247248.6812.112482508.010.028 **
Subjugation16.946.9672911.223.8552113.616.03529396.510.001 *
Self-sacrifice18.695.1392816.535.1982817.435.24828546.010.072
Approval-seeking/recognition-seeking17.314.7172817.895.0892817.654.91728662.510.555
OVERVIGILANCE AND INHIBITION
Sum65.2222.312810859.9413.91308660.5518.8728108610.010.357
Negativity/pessimism16.567.0152815.115.5652715.716.21528630.010.354
Emotional inhibition16.116.2962713.095.1952614.315.82527521.510.041 **
Unrelenting standards17.165.0191717.714.7582717.484.83827686.010.729
Punitiveness15.516.6252713.134.5452314.125.59527573.010.131
ANPS—Affective Neuroscience Personality Scale; YSQ-S3-PL—Young Schema Questionnaire; DD—depressive disorders; CG—comparison group; N—size; M—mean; SD—standard deviation; *—p ≤ 0.001; **—p ≤ 0.05.
Table
Table 4. Values of Spearman’s rho correlation coefficient for the ANPS scale scores and the five domains of the YSQ-S3-PL questionnaire for the study group (n = 90).
Table 4. Values of Spearman’s rho correlation coefficient for the ANPS scale scores and the five domains of the YSQ-S3-PL questionnaire for the study group (n = 90).
All Subjects n = 90
ANPS
SEEKINGPLAYCAREANGERFEARSADNESS
RpRpRpRpRpRp
YSQ-S3-PL Sum−0.2240.039 **−0.0870.428−0.1730.133 0.4280.001 *0.3480.001 *
Disconnection and rejection−0.5460.001 *−0.4480.001 *0.0030.9790.3140.005 **0.5110.001 *0.4640.001 *
Impaired autonomy and performance−0.5480.001 *−0.3780.001 *−0.01760.1260.3010.008 **0.5160.001 *0.5280.001 *
Impaired limits−0.2170.0590.0360.756−0.3250.004 **0.5940.001 *0.1360.2370.0610.603
Other-directedness−0.4480.001 *−0.2070.0710.0760.5140.1920.0940.4570.001 *0.5280.001 *
Overvigilance and inhibition−0.1050.364−0.1010.384−0.0680.5570.2330.043 **0.2640.021 **0.1860.107
ANPS—Affective Neuroscience Personality Scale; YSQ-S3-PL—Young Schema Questionnaire; *—p ≤ 0.001; **—p ≤ 0.05.
Table
Table 5. Progressive stepwise regression coefficient results for the five domains of the YSQ-S3-PL questionnaire (dependent variables) and for the ANPS dimensions (independent variables) for the entire study group (n = 90).
Table 5. Progressive stepwise regression coefficient results for the five domains of the YSQ-S3-PL questionnaire (dependent variables) and for the ANPS dimensions (independent variables) for the entire study group (n = 90).
VariableYSQ-S3-PL
Disconnection and Rejection
R2bp
Absolute term 15.794
SEEKING −0.381
SADNESS 0.241
ANGER 0.252
PLAY0.437−0.2110.001 *
Impaired autonomy and performance
R2bp
Absolute term 12.287
SEEKING −0.481
ANGER0.3940.2570.001 *
SADNESS 0.205
Impaired limits
R2bp
Absolute term 5.778
PLAY 0.164
SEEKING −0.371
CARE −0.081
FEAR −0.111
ANGER0.3720.5340.001 *
Other-directedness
R2bp
Absolute term0.3397.6760.001 *
SEEKING−0.571
FEAR0.061
CARE0.305
ANGER0.262
Overvigilance and inhibition
R2bp
Absolute term 13.03
PLAY −0.021
SEEKING 0.041
CARE −0.14
FEAR0.1030.3270.001 *
ANGER 0.021
ANPS—Affective Neuroscience Personality Scale; YSQ-S3-PL—Young Schema Questionnaire; *—p ≤ 0.001; gray color indicates strongest relationships.
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Talarowska, M.; Wysiadecki, G.; Chodkiewicz, J. Affective Neuroscience Personality Scales and Early Maladaptive Schemas in Depressive Disorders. Int. J. Environ. Res. Public Health 2022, 19, 8062. https://doi.org/10.3390/ijerph19138062

AMA Style

Talarowska M, Wysiadecki G, Chodkiewicz J. Affective Neuroscience Personality Scales and Early Maladaptive Schemas in Depressive Disorders. International Journal of Environmental Research and Public Health. 2022; 19(13):8062. https://doi.org/10.3390/ijerph19138062

Chicago/Turabian Style

Talarowska, Monika, Grzegorz Wysiadecki, and Jan Chodkiewicz. 2022. "Affective Neuroscience Personality Scales and Early Maladaptive Schemas in Depressive Disorders" International Journal of Environmental Research and Public Health 19, no. 13: 8062. https://doi.org/10.3390/ijerph19138062

APA Style

Talarowska, M., Wysiadecki, G., & Chodkiewicz, J. (2022). Affective Neuroscience Personality Scales and Early Maladaptive Schemas in Depressive Disorders. International Journal of Environmental Research and Public Health, 19(13), 8062. https://doi.org/10.3390/ijerph19138062

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