Computational Modelling for Alcohol Use Disorder

Colombo, Matteo

doi:10.1007/s10670-022-00533-x

Cited by 4 publications

(1 citation statement)

References 68 publications

(71 reference statements)

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“…We believe all of these are important factors in developing a more thorough model of alcohol use disorder, and we aim to include them in the future. Other researchers have included some of these features in models of alcohol use disorder [38][39][40], but these other models were not structured as neural networks making it difficult to understand how modifications to the models could translate to potential treatments. In this study, by ignoring these complicating factors, we were able to improve generalizability and simplicity, which we felt was paramount to our research program at this stage.…”

Section: Limitations and Advantages Of The Modelmentioning

confidence: 99%

Reducing maladaptive behavior in neuropsychiatric disorders using network modification

Timme

2024

Preprint

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Neuropsychiatric disorders are caused by many factors and produce a wide range of symptomatic maladaptive behaviors in patients. Despite this great variance in causes and resulting behavior, we believe the maladaptive behaviors that characterize neuropsychiatric disorders are most proximally determined by networks of neurons and that this forms a common conceptual link between these disorders. Operating from this premise, it follows that treating neuropsychiatric disorders to reduce maladaptive behavior can be accomplished by modifying the patient’s network of neurons. In this proof-of-concept computational psychiatry study, we tested this approach in a simple neural network model that produces aversion-resistant alcohol drinking – a key maladaptive behavior associated with alcohol use disorder. We demonstrated that it was possible to predict personalized network modifications that substantially reduced maladaptive behavior without inducing side effects. Furthermore, we found that it was possible to predict effective treatments with limited knowledge of the model and that information about neural activity during certain types of trials was more helpful in predicting treatment than information about model parameters. We hypothesize that this is a general feature of developing effective treatment strategies for networks of neurons. This computational study lays the groundwork for future studies utilizing more biologically realistic network models in conjunction within vivodata.

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Section: Limitations and Advantages Of The Modelmentioning

confidence: 99%

Reducing maladaptive behavior in neuropsychiatric disorders using network modification

Timme

2024

Preprint

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Computational psychiatry and the evolving concept of a mental disorder

Genin,

Grote,

Wolfers

2024

Synthese

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As a discipline, psychiatry is in the process of finding the right set of concepts to organize research and guide treatment. Dissatisfaction with the status quo as expressed in standard manuals has animated a number of computational paradigms, each proposing to rectify the received concept of mental disorder. We explore how different computational paradigms: normative modeling, network theory and learning-theoretic approaches like reinforcement learning and active inference, reconceptualize mental disorders. Although each paradigm borrows heavily from machine learning, they differ significantly in their methodology, their preferred level of description, the role they assign to the environment and, especially, the degree to which they aim to assimilate psychiatric disorders to a standard medical disease model. By imagining how these paradigms might evolve, we bring into focus three rather different visions for the future of psychiatric research. Although machine learning plays a crucial role in the articulation of these paradigms, it is clear that we are far from automating the process of conceptual revision. The leading role continues to be played by the theoretical, metaphysical and methodological commitments of the competing paradigms.

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The Computational Theory of Mind

Colombo,

Piccinini

2023

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The Computational Theory of Mind says that the mind is a computing system. It has a long history going back to the idea that thought is a kind of computation. Its modern incarnation relies on analogies with contemporary computing technology and the use of computational models. It comes in many versions, some more plausible than others. This Element supports the theory primarily by its contribution to solving the mind-body problem, its ability to explain mental phenomena, and the success of computational modelling and artificial intelligence. To be turned into an adequate theory, it needs to be made compatible with the tractability of cognition, the situatedness and dynamical aspects of the mind, the way the brain works, intentionality, and consciousness.

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Computational Modelling for Alcohol Use Disorder

Cited by 4 publications

References 68 publications

Reducing maladaptive behavior in neuropsychiatric disorders using network modification

Reducing maladaptive behavior in neuropsychiatric disorders using network modification

Computational psychiatry and the evolving concept of a mental disorder

The Computational Theory of Mind

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