Computational Mechanisms of Addiction and Anxiety: A Developmental Perspective

Goldway, Noam; Eldar, Eran; Shoval, Gal; Hartley, Catherine A.

doi:10.1016/j.biopsych.2023.02.004

Cited by 7 publications

(6 citation statements)

References 185 publications

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“…This suggests a parallel between FA and substance use disorders. Precisely, as indicated, FA and drug use disorders share analogous etiological variables such as impulsivity ( 85 ) and anxiety ( 30 , 86 ). This result demonstrates that high drive levels are related to higher values in FA ( 87 ).…”

Section: Discussionmentioning

confidence: 95%

Factors increasing the risk for food addiction in Ecuadorian students

Reivan Ortiz,

Campoverde,

Vinañzaca

et al. 2024

Front. Psychiatry

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BackgroundFood addiction (FA) is a construct that has gained interest in recent years, but its relevance in the Ecuadorian population has not yet been explored. The aims of this study were to explore the differences in the psychological profile (including FA) between university students from Ecuador and to identify the underlying structure of the relationships of the FA severity level through a mediational model.MethodsThe sample consisted of 972 university students, women and men (mean age: 20.1 years old, SD = 2.6), recruited from four Ecuadorian regions. The assessment tools included a unidimensional scale of FA, eating-related measures, emotion regulation state, impulsivity, and psychopathology state. Path analysis modeled the direct and indirect effects explaining the FA severity level.ResultsThe results indicated that higher psychopathological levels were associated with FA. Similarly, no differences in FA were observed between the Ecuadorian regions. The path analysis suggested that older age, female sex, and higher difficulties in emotional regulation, impulsivity, negative mood, and anxiety trigger disordered eating; subsequently, more impaired eating behavior impacted the FA level.ConclusionFA is a complex clinical entity that includes multiple components related to eating disorders (EDs) and other mental health problems. The results of this study provide empirical knowledge for designing evidence-based prevention and treatment strategies.

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Section: Discussionmentioning

confidence: 95%

Factors increasing the risk for food addiction in Ecuadorian students

Reivan Ortiz,

Campoverde,

Vinañzaca

et al. 2024

Front. Psychiatry

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“…In autism spectrum disorder, predictive coding theories have been used to explain the social deficits observed in these individuals [41] . Theories of anhedonia, a core symptom of depression, have been mapped onto reinforcement learning models [38] .…”

Section: Resultsmentioning

confidence: 99%

Current status, challenges and future prospects in computational psychiatry: a narrative review

Vasilchenko,

Chumakov

2023

Consortium Psychiatricum

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BACKGROUND: Computational psychiatry is an area of scientific knowledge which lies at the intersection of neuroscience,psychiatry, and computer science. It employs mathematical models and computational simulations to shed light onthe complexities inherent to mental disorders. AIM: The aim of this narrative review is to offer insight into the current landscape of computational psychiatry, todiscuss its significant challenges, as well as the potential opportunities for the fields growth. METHODS: The authors have carried out a narrative review of the scientific literature published on the topic ofcomputational psychiatry. The literature search was performed in the PubMed, eLibrary, PsycINFO, and Google Scholardatabases. A descriptive analysis was used to summarize the published information on the theoretical and practicalaspects of computational psychiatry. RESULTS: The article relates the development of the scientific approach in computational psychiatry since the mid-1980s.The data on the practical application of computational psychiatry in modeling psychiatric disorders and explaining themechanisms of how psychopathological symptomatology develops (in schizophrenia, attention-deficit/hyperactivitydisorder, autism spectrum disorder, anxiety disorders, obsessive-compulsive disorder, substance use disorders) aresummarized. Challenges, limitations, and the prospects of computational psychiatry are discussed.CONCLUSION: The capacity of current computational technologies in psychiatry has reached a stage where its integrationinto psychiatric practice is not just feasible but urgently needed. The hurdles that now need to be addressed are nolonger rooted in technological advancement, but in ethics, education, and understanding.

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“…More generally, although we focused on how computational approaches can be used to capture processes of aberrant decision-making in major depressive disorder and anxiety disorders, they have also been applied to a whole host of other neuropsychiatric disorders (Series, 2020 ), including substance use disorders (Goldway et al, 2023 ; Gueguen et al, 2021 ), attention deficit hyperactivity disorder (ADHD) (Ging-Jehli et al, 2021 ), obsessive–compulsive disorder (OCD) (Fradkin et al, 2020 ; Loosen & Hauser, 2020 ), schizophrenia (Valton et al, 2017 ), and Tourette syndrome (Rae et al, 2019 ; Schüller et al, 2020 ). All of these could also benefit from the translational approach we are advocating.…”

Section: Discussionmentioning

confidence: 99%

A primer on the use of computational modelling to investigate affective states, affective disorders and animal welfare in non-human animals

Neville,

Mendl,

Paul

et al. 2023

Cogn Affect Behav Neurosci

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Objective measures of animal emotion-like and mood-like states are essential for preclinical studies of affective disorders and for assessing the welfare of laboratory and other animals. However, the development and validation of measures of these affective states poses a challenge partly because the relationships between affect and its behavioural, physiological and cognitive signatures are complex. Here, we suggest that the crisp characterisations offered by computational modelling of the underlying, but unobservable, processes that mediate these signatures should provide better insights. Although this computational psychiatry approach has been widely used in human research in both health and disease, translational computational psychiatry studies remain few and far between. We explain how building computational models with data from animal studies could play a pivotal role in furthering our understanding of the aetiology of affective disorders, associated affective states and the likely underlying cognitive processes involved. We end by outlining the basic steps involved in a simple computational analysis.

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Computational Mechanisms of Addiction and Anxiety: A Developmental Perspective

Cited by 7 publications

References 185 publications

Factors increasing the risk for food addiction in Ecuadorian students

Factors increasing the risk for food addiction in Ecuadorian students

Current status, challenges and future prospects in computational psychiatry: a narrative review

A primer on the use of computational modelling to investigate affective states, affective disorders and animal welfare in non-human animals

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