Self-Attachment to Treat Chronic Anxiety and/or Depression: History
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor: , , , ,

Anxiety and depression are debilitating conditions which, together with substance abuse, are considered globally to represent the most common psychological disorders; they are correlated with each other and are also common features of many severe psychological disorders. Attachment theory was introduced by John Bowlby in the 1960s and 1970s and has since developed into a main paradigm in developmental psychology with wide impact in many related areas including psychotherapy.

  • attachment theory
  • affectional bond
  • attachment object
  • childhood self
  • adult self
  • re-parenting
  • affect regulation

1. Introduction

Anxiety and depression are debilitating conditions which, together with substance abuse, are considered globally to represent the most common psychological disorders; they are correlated with each other and are also common features of many severe psychological disorders [1]. Based on the findings of a comprehensive assessment of prevalence and incidence of psychological disorders conducted in 197 countries between 1990 and 2013, depression is the predominant mental health problem worldwide, followed by anxiety [2]. In another systematic and meta-analysis of data spanning over three decades (1980–2013), based on 174 surveys in 63 countries, women had higher rates of mood and anxiety disorders, whereas men had higher rates of substance abuse [3]. Due to their prevalence, anxiety and depression have been proposed as the two axes of a two-dimensional model of neurotic (non-psychotic) mental illness in which common diagnostic concepts are identified as points in the two-dimensional space [1].
According to the report of Lancet Commission on global mental health and sustainability [4], “common symptoms of mental distress such as anxiety or low mood are associated with more total disability at a population level than diagnostically defined mental disorders”. The report furthermore stresses that the burden of mental health relative to physical health has risen in all countries in the past decades and that “when it comes to mental health, all countries can be thought of as developing countries”.
It is therefore instructive to consider the problem in a European country where data has been available. In UK primary care clinics in the 1990s, between a third and a quarter of all patients suffered from mental illness, and approximately half of patients with non-psychotic mental illnesses suffered from an undiagnosed mental illness, most commonly depression or anxiety [5]. These individuals may not have met the diagnosis for a definite category of mental illness, but had symptoms of both anxiety and depression. In the UK, there has been a poor prognosis of anxiety and depression: one half of sufferers experience relapse and a quarter become chronic, with the index episode lasting for at least two years [6]. Most patients with chronic mental disorders suffer from depression and anxiety [7]. Unsatisfactory short-term outcomes are associated with initial severity of symptoms, significant concomitant physical illnesses, severe social problems and material circumstances, as well as genetic risk scores and personality factors [6].
Psychotherapy and pharmacological interventions are the two fundamental treatment methods available for mental disorders. A comprehensive meta-analysis of studies to compare the results of cognitive behavioural therapy (CBT) and pharmacological interventions has established that these two treatment methods have largely the same level of efficacy; however, a moderator analysis showed that participants in anxiety studies that included comorbid depression were more likely to benefit from pharmacotherapy, whereas participants in anxiety studies that excluded depression were more likely to benefit from CBT [8]. A meta-analysis of a large number of studies comparing the preferences of psychiatric patients for psychotherapeutic interventions versus pharmacological interventions has concluded that 75% of patients prefer psychotherapy; and a sensitivity analysis shows that younger patients and women are significantly more likely to choose psychological treatments [9].
Patient preference for psychotherapy has driven research investigating new and more effective psychotherapeutic interventions, specifically for chronic and resistant conditions. In the research literature, chronic depression is differentiated from non-chronic major depressive disorder (MDD) on many clinical, psychosocial, and familial variables, since it is usually associated with higher comorbidity, functional impairment, suicidality, personality disturbance, childhood adversity/maltreatment, and familial liability for mood disorders; see [10]. In the Diagnostic and Statistical Manual of Mental Disorders (DSM-V), persistent depressive disorder (dysthymia) (PDD) is introduced as “a consolidation of DSM-IV-defined chronic major depressive disorder and dysthymic disorder” [11]. DSM-IV also categorises Generalized Anxiety Disorder (GAD) in a person who reports chronic, excessive worry which is not related to particular circumstances, and symptoms of physiological arousal such as restlessness, insomnia, and muscle tension [11].
In recent decades, an increasing number of psychological interventions—in particular derivatives of CBT including group CBT and mindfulness-based CBT, but also several other therapies such as interpersonal therapy (IPT), dialectical behavioural therapy (DBT), schema-focused therapy (SFT), compassion focused therapy (CFT), mentalisation based therapy (MDT), structured clinical management, and acceptance and commitment therapy (ACT)—have been introduced to tackle such chronic conditions with a number of Randomised Clinical Trials measuring the efficacy of these treatments [12][13][14][15][16][17].
These therapies aim to identify and redress cognitive defects, as in classical CBT, or to regulate emotions, as in CFT or ACT. They are generally focused on the ‘here and now’. In contrast to psychodynamic or psychoanalytical interventions, they do not generally examine the childhood environment and experiences, with the notable exception of SFT, which is highly influenced by John Bowlby’s attachment theory and has been shown to be effective in tackling chronic conditions such as personality disorders [14].

2. Attachment Theory

The basic tenets of attachment theory are supported by a large number of studies over many decades [21]. A wide-ranging secondary analysis on attachment insecurities, based on hundreds of cross-sectional, longitudinal, and prospective studies of both clinical and non-clinical populations, concluded: “[A]ttachment insecurity was common among people with a wide variety of mental disorders, ranging from mild distress to severe personality disorders and even schizophrenia” and that “attachment insecurity is a major contributor to mental disorders” [22][23]. In addition to attachment insecurity, neglect and abuse by primary attachment figures become the source of great distress, insecurity and instability for children who then question the trustworthiness of their caregiver. These children often continue to experience repeated trauma and thus acquire the expectation that these episodes do recur, resulting in hypervigilance and chronic anxiety and leading to the maldevelopment of a brain “for survival”, without acquiring the capacity for self-regulation of emotions [24][25][26].
On the other hand, several experiments—using a technique called ‘security priming’—have been able to artificially activate mental representations of supportive attachment figures and thereby improve the mental health of individuals suffering from various mental disorders [22].
John Bowlby himself believed that attachment continues in one way or another later in life and can include attachment to abstract concepts rather than just to individuals: “Probably in all normal people [attachment] continues in one form or another throughout life and, although in many ways transformed, underlies many of our attachments to country, sovereign, or church” [27].
In fact, in the past few decades, the notion of a religion or a deity as an attachment object has been extensively examined in Christianity, Judaism and Islam by several groups of researchers [28][29][30][31].

3. Self-Attachment Technique

The self-attachment technique (SAT), as introduced in [32][33][34], is informed by John Bowlby’s attachment theory. It is based on establishing an affectional bond with one’s childhood self—subjectively experienced as falling in love with the child—and subsequently ‘re-parenting’ that child to emotional maturity by emulating the optimal parent-child interactions at the time of the child’s distress. This procedure, in practice, means learning to take care of oneself in a mature way; in SAT, this caring behaviour is conceived as the interaction of two actors within the individual, resembling a parent raising a child.
The individual’s mindset and behaviour are considered as the interaction of two agents, the childhood self and the adult self: The childhood self, mentally represented by the individual’s childhood photos, is conceived as the emotional self which is usually dominant when the individual is under stress. The adult self is conceived as the rational self which is usually dominant in the absence of stress. The adult self establishes first a compassionate connection with their childhood self, using their favourite childhood photos. Then, by reciting their favourite love and jolly song while looking at their favourite childhood photo and focusing on what they cherish and celebrate about their childhood, the adult self creates a passionate, imaginative bond with that child.
A vow is then made by the adult self to look after the child whenever they are in distress. This requires the adult taking up the challenge to comfort the child and moderate their arousal level whenever the individual is overwhelmed or affected by negative emotions. In practice, this comforting role is played by emulating the actions of a ‘good enough’ primary caregiver when their child is distressed [35]. It consists of projecting one’s negative emotions to one’s childhood photo, imaginatively embracing the child, loudly reassuring them, and giving oneself a head or neck self-massage (a close counterpart for cuddling a real child in distress). There are also several self-administrable protocols in SAT that mimic the actions of a primary caregiver, based on singing, dancing, laughing and playing, to enhance and maximize positive affects.
SAT thus represents an algorithm to re-run the emotional development of the individual while closely following the optimal parent-child interactions in real life. Since the affectional bond between the adult self and the childhood self is imaginative, SAT promotes a type of spirituality for emotional growth and well-being. To make a comparison with religion as an attachment object, in SAT the adult self rather than a deity becomes the attachment object for (the childhood self of) the individual.
The theoretical basis of the bond-making in SAT is the self-love of any individual which Freud considered a common component of human psyche and called primary narcissism [36]. Bowlby argued that separation anxiety is an injury to primary narcissism [18]. It is hypothesized in SAT that this affectional bond with the childhood self induces dopamine in the brain’s reward circuitry as is the case in romantic love [37][38], maternal love [39] and love of God [40].
Dopamine in the brain is thought to invoke response–reward and stimulus–reward associations for the control of motivated behaviour by past experience [41]. Based on this hypothesis, the researchers can expect that by activating the reward circuitry of the brain, SAT can lead to more hope, energy and incentive in the individual for practicing its protocols to improve mental health. In this way, it is hypothesized that SAT can redress problems in the early attachment interaction of an infant with their primary caregivers in the preverbal period of life, when the parent maximises the positive affects in the infant by singing, dancing and play and minimises their negative affects by embracing, cuddling and affection [24][25].
SAT consists of a set of practical exercises that are learned in 8 to 12 weeks and are intended to be practised for 20 min twice a day in this period; the list of exercises can be found in [33]. It is hypothesized that repetition of these protocols, by long term potentiation and neuroplasticity, will in time create optimal and robust neural activation patterns that challenge the suboptimal circuits in the brain, resulting in more adaptive cognitive and behavioural prototype patterns corresponding to secure attachment. The exercises can eventually be integrated in the individual’s routine schedule by turning the adult self into an attachment object for the childhood self to earn secure attachment and emotion self-regulation.
SAT has been supported by various computational models [34]. These include the basic frameworks (i) and (ii) below for the overall concept of SAT, as well as the additional models (iii), (iv) and (v) that correspond respectively to the three stages of SAT, namely connecting compassionately with the childhood self, creating an affectional bond with the childhood self and practicing protocols to enhance positive affects and reduce negative affects, respectively.
(i) In [32][42][43], the Hopfield network, the first artificial neural model of associative memory, has been used to show how behavioural and cognitive prototypes can be modelled in artificial neural networks as strong patterns stored in the artificial brain. The dominant sub-optimal prototype can undergo a fundamental change by creating through repeated learning—corresponding to neuroplasticity and long-term potentiation—a new strong pattern that represents a more optimal behavioural and cognitive prototype. The overall process thus reflects the psychotherapeutic process.
(ii) In [44], the various styles of attachment in the relationship between a parent agent and a child agent have been modelled in the setting of active inference and one-player game theory. In the simplest model, the parent’s behaviour is governed by their probability of attending to the distressed child, whereas the child has one of three kinds of possible actions: seeking proximity, guarded seeking and avoidance, each with a specific pay-off for the child. The system has three equilibria which can be identified, respectively, as (1) avoidant attachment, (2) anxious attachment and (3) secure attachment, corresponding, respectively, to low, moderate and high probabilities of parental attendance. A more complex model for disorganised attachment has also been developed in the above paper. This framework provides a model of SAT in which the adult self, by gradually increasing the probability of attending to the needs of the childhood self throughout the intervention, turns avoidant and then anxious attachment of the childhood self to secure attachment. In case the parameter interval for anxious attachment is very short, the avoidant attachment can turn directly to secure attachment.
(iii) The compassionate attitude towards the childhood self in the first stage of SAT has been modelled based on a framework by Numan [45] in the neural circuits in the brain using available data on caregiving behaviour in human and animal studies [46].
(iv) In [47], a neural model of bonding circuitry based on the orbital prefrontal cortex (OFC) is considered, in which the OFC mediates between facilitative and stress reactivity to social stimuli, via the dorsomedial and paraventricular nucleus of the hypothalamus. By integrating more recent neuroscientific results, a computational model is developed and, using simulations of the model, it is postulated that introducing additional reward could drive a further mechanism: a counter-conditioning and re-balancing mechanism between activation of these networks, via increasing prefrontal-driven inhibition of the central nucleus of the amygdala. It is hypothesised in the above paper that such a process may be involved in self-bonding that takes place in SAT.
(v) In [34], the practice of SAT has been modelled in the brain by reinforcement learning (Q-learning), based on a neural model of pathways for emotional-cognitive decision making developed by Levine. This framework is then integrated with a competitive Hopfield network built from strong patterns for the six basic emotions and the core SAT protocols. A successful SAT intervention is then considered as the process that brings about change, induced by reinforcement learning, from strong patterns of negative emotions to strong patterns of positive emotion.
Another significant aspect of the new intervention is that SAT is ultimately a self-administrable and algorithmic technique that can be available to patients by technological tools such as virtual reality (VR) environments and chatbots, thus potentially reducing the need for client-therapist interactions and making the intervention scalable. The researchers briefly describe these two technological tools that have so far been developed.
The interactive VR platform for SAT [48] features a virtual assistant and a customised child avatar that is created from the user’s favourite childhood photo. The virtual agent interacts with the user and based on an emotion recognition algorithm provides suggestions for the user to undertake an appropriate self-attachment sub-protocol. The user can also interact with the child avatar for example by embracing them.
The chatbot [49] is designed to coach the user in practicing the SAT protocols; it is a rule-based framework that is augmented with an AI-platform for engaging with the user in an empathetic, safe, fluent and non-repetitive way. The AI agent suggests SAT protocols in different contexts that are informed by the user’s current emotion and their past interactions with the protocols.

This entry is adapted from the peer-reviewed paper 10.3390/ijerph19116376

References

  1. Goldberg, D.; Huxley, P. Common Mental Disorders: A Bio-Social Model; Tavistock/Routledge: Abingdon, UK, 1992.
  2. Vos, T.; Allen, C.; Arora, M.; Barber, R.M.; Bhutta, Z.A.; Brown, A.; Liang, X.; Kawashima, T.; Coggeshall, M. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet 2015, 386, 743–800.
  3. Steel, Z.; Marnane, C.; Iranpour, C.; Chey, T.; Jackson, J.W.; Patel, V.; Silove, D. The global prevalence of common mental disorders: A systematic review and meta-analysis 1980–2013. Int. J. Epidemiol. 2014, 43, 476–493.
  4. Patel, V.; Saxena, S.; Lund, C.; Thornicroft, G.; Baingana, F.; Bolton, P.; Chisholm, D.; Collins, P.Y.; Cooper, J.L.; Eaton, J.; et al. The Lancet Commission on global mental health and sustainable development. Lancet 2018, 392, 1553–1598.
  5. Sharp, D.; Morrell, D. The Psychiatry of General Practice. In The Scope of Epidemiological Psychiatry; Routledge: Abingdon, UK, 2018; pp. 404–419.
  6. Lloyd, K.; Jenkins, R. Chronic depression and anxiety in primary care: Approaches to liaison. Adv. Psychiatr. Treat. 1995, 1, 192–198.
  7. Lyons, R.; Caroll, D.; Doherty, K.; Flynn, M.; Mason, J.; O’Brien, D.; O’Kelly, F. General practice estimates of the prevalence of common chronic conditions. Ir. Med. J. 1992, 85, 22–24.
  8. Moghaddam, B.R.; Pauly, M.C.; Atkins, D.C.; Baldwin, S.A.; Stein, M.B.; Roy-Byrne, P. Relative effects of CBT and pharmacotherapy in depression versus anxiety: Is medication somewhat better for depression, and CBT somewhat better for anxiety? Depress. Anxiety 2011, 28, 560–567.
  9. McHugh, R.K.; Whitton, S.W.; Peckham, A.D.; Welge, J.A.; Otto, M.W. Patient preference for psychological vs. pharmacologic treatment of psychiatric disorders: A meta-analytic review. J. Clin. Psychiatry 2013, 74, 13979.
  10. Torpey, D.; Klein, D. Chronic depression: Update on classification and treatment. Curr. Psychiatry Rep. 2008, 10, 458–464.
  11. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-5, 5th ed.; American Psychiatric Publishing: Washington, DC, USA, 2013.
  12. Klerman, G.; Weissman, M. Interpersonal Psychotherapy of Depression: A Brief, Focused, Specific Strategy; Jason Aronson, Incorporated: Lanham, MD, USA, 1994.
  13. Matusiewicz, A.K.; Hopwood, C.J.; Banducci, A.N.; Lejuez, C. The Effectiveness of Cognitive Behavioral Therapy for Personality Disorders. Psychiatr. Clin. N. Am. 2010, 33, 657–685.
  14. Van Vreeswijk, M.; Broersen, J.; Nadort, M. The Wiley-Blackwell Handbook of Schema Therapy: Theory, Research, and Practice; John Wiley & Sons: Hoboken, NJ, USA, 2012.
  15. Kahl, K.; Winter, L.; Schweiger, U. The third wave of cognitive behavioural therapies: What is new and what is effective? Curr. Opin. Psychiatry 2012, 25, 522–528.
  16. Jakobsen, J.C.; Gluud, C.; Kongerslev, M.; Larsen, K.A.; Sørensen, P.; Winkel, P.; Lange, T.; Søgaard, U.; Simonsen, E. Third-wave cognitive therapy versus mentalisation-based treatment for major depressive disorder: A randomised clinical trial. BMJ Open 2014, 4, e004903.
  17. Allen, J.; Fonagy, P.; Bateman, A. Mentalizing in Clinical Practice; American Psychiatric Pub.: Washington, DC, USA, 2008.
  18. Bowlby, J. Attachment and Loss: Attachment; Basic Books: New York, NY, USA, 1969; Volume 1.
  19. Bowlby, J. Attachment and Loss: Volume II: Separation, Anxiety and Anger; The Hogarth Press and the Institute of Psycho-Analysis: London, UK, 1973; pp. 1–429.
  20. Bowlby, J. Attachment and Loss: Volume III: Loss, Sadness and Depression; The Hogarth Press and the Institute of Psycho-Analysis: London, UK, 1980; pp. 1–462.
  21. Cassidy, J.; Shaver, P. Handbook of Attachment: Theory, Research, and Clinical Applications; Rough Guides: London, UK, 1999.
  22. Mikulincer, M.; Shaver, P. Attachment in Adulthood: Structure, Dynamics, and Change; Guilford Press: New York, NY, USA, 2007.
  23. Mikulincer, M.; Shaver, P. An Attachment Perspective on Psychopathology. World Psychiatry 2012, 11, 11–15.
  24. Schore, A.N. Affect Dysregulation and Disorders of the Self; Norton Series on Interpersonal Neurobiology; WW Norton & Company: New York, NY, USA, 2003.
  25. Cozolino, L. The Neuroscience of Human Relationships: Attachment and the Developing Social Brain; Norton Series on Interpersonal Neurobiology; W.W. Norton & Company: New York, USA, 2014.
  26. Courtois, C.; Ford, J. Treatment of Complex Trauma: A Sequenced, Relationship-Based Approach; Guilford Press: New York, NY, USA, 2012.
  27. Bowlby, J. The Growth of Independence in the Young Child. J. R. Soc. Health 1955, 76, 587–591.
  28. Kirkpatrick, L.; Shaver, P.R. Attachment theory and religion: Childhood attachments, religious beliefs, and conversion. J. Sci. Study Relig. 1990, 29, 315–334.
  29. Kirkpatrick, L. Attachment, Evolution, and the Psychology of Religion; Guilford Press: New York, NY, USA, 2005.
  30. Granqvist, P.; Mikulincer, M.; Shaver, P. Religion as attachment: Normative processes and individual differences. Personal. Soc. Psychol. Rev. 2010, 14, 49–59.
  31. Bonab, B.G.; Miner, M.; Proctor, M. Attachment to God in Islamic spirituality. J. Muslim Ment. Health 2013, 7, 77–104.
  32. Edalat, A. Introduction to self-attachment and its neural basis. In Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, 12–17 July 2015; IEEE: New York, NY, USA, 2015.
  33. Edalat, A. Self-Attachment: A Self-Administrable Intervention for Chronic Anxiety and Depression. 2017. Technical Report, Department of Computing, Imperial College London. Available online: https://www.doc.ic.ac.uk/research/technicalreports/2017/DTRS17-3.pdf (accessed on 14 February 2022).
  34. Edalat, A. Self-attachment: A holistic approach to computational psychiatry. In Computational Neurology and Psychiatry; Springer: Berlin/Heidelberg, Germany, 2017; pp. 273–314.
  35. Winnicott, D.W. The Child, the Family, and the Outside World; Penguin: London, UK, 2021.
  36. Freud, S. The Future of an Illusion; Broadview Press: Peterborough, ON, Canada, 2012.
  37. Bartels, A.; Zeki, S. The neural basis of romantic love. Neuroreport 2000, 11, 3829–3834.
  38. Acevedo, B.P.; Aron, A.; Fisher, H.E.; Brown, L.L. Neural correlates of long-term intense romantic love. Soc. Cogn. Affect. Neurosci. 2012, 7, 145–159.
  39. Bartels, A.; Zeki, S. The neural correlates of maternal and romantic love. Neuroimage 2004, 21, 1155–1166.
  40. Schjødt, U.; Stødkilde-Jørgensen, H.; Geertz, A.W.; Roepstorff, A. Rewarding prayers. Neurosci. Lett. 2008, 443, 165–168.
  41. Wise, R. Dopamine, learning and motivation. Nat. Rev. Neurosci. 2004, 5, 483–494.
  42. Edalat, A.; Mancinelli, F. Strong attractors of Hopfield neural networks to model attachment types and behavioural patterns. In Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX, USA, 4–9 August 2013; IEEE: New York, NY, USA, 2013.
  43. Edalat, A. Capacity of strong attractor patterns to model behavioural and cognitive prototypes. In Advances in Neural Information Processing Systems 26; Curran Associates, Inc.: Red Hook, NY, USA, 2013.
  44. Cittern, D.; Nolte, T.; Friston, K.; Edalat, A. Intrinsic and extrinsic motivators of attachment under active inference. PLoS ONE 2018, 13, e0193955.
  45. Numan, M. Neurobiology of Social Behavior: Toward an Understanding of the Prosocial and Antisocial Brain; Academic Press: Cambridge, MA, USA, 2014.
  46. Cittern, D.; Edalat, A. A neural model of empathic states in attachment-based psychotherapy. Comput. Psychiatry 2017, 1, 132–167.
  47. Cittern, D.; Edalat, A. Towards a neural model of bonding in self-attachment. In Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, 12–17 July 2015; IEEE: New York, NY, USA, 2015.
  48. Polydorou, N.; Edalat, A. An interactive VR platform with emotion recognition for self-attachment intervention. EAI PHAT 2021, 21, e5.
  49. Alazraki, L.; Ghachem, A.; Polydorou, N.; Khosmood, F.; Edalat, A. An empathetic AI coach for self-attachment therapy. In Proceedings of the 2021 IEEE Third International Conference on Cognitive Machine Intelligence (CogMI), Virtual Conference. 13–15 December 2021; IEEE: New York, NY, USA, 2021.
More
This entry is offline, you can click here to edit this entry!
Video Production Service