The algorithmic agent perspective and computational neuropsychiatry: from etiology to advanced therapy in major depressive disorder
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- dc.contributor.author Ruffini, Giulio
- dc.contributor.author Castaldo, Francesca
- dc.contributor.author Lopez-Sola, Edmundo
- dc.contributor.author Sanchez Todo, Roser
- dc.contributor.author Vohryzek, Jakub
- dc.date.accessioned 2025-05-29T08:59:45Z
- dc.date.available 2025-05-29T08:59:45Z
- dc.date.issued 2024
- dc.description.abstract Major Depressive Disorder (MDD) is a complex, heterogeneous condition affecting millions worldwide. Computational neuropsychiatry offers potential breakthroughs through the mechanistic modeling of this disorder. Using the Kolmogorov theory (KT) of consciousness, we developed a foundational model where algorithmic agents interact with the world to maximize an Objective Function evaluating affective valence. Depression, defined in this context by a state of persistently low valence, may arise from various factors—including inaccurate world models (cognitive biases), a dysfunctional Objective Function (anhedonia, anxiety), deficient planning (executive deficits), or unfavorable environments. Integrating algorithmic, dynamical systems, and neurobiological concepts, we map the agent model to brain circuits and functional networks, framing potential etiological routes and linking with depression biotypes. Finally, we explore how brain stimulation, psychotherapy, and plasticity-enhancing compounds such as psychedelics can synergistically repair neural circuits and optimize therapies using personalized computational models.
- dc.description.sponsorship This research was partially funded by the European Commission under European Union’s Horizon 2020 research and innovation programme Grant Number 101017716 (Neurotwin) and European Research Council (ERC Synergy Galvani) under the European Union’s Horizon 2020 research and innovation programme Grant Number 855109.
- dc.format.mimetype application/pdf
- dc.identifier.citation Ruffini G, Castaldo F, Lopez-Sola E, Sanchez-Todo R, Vohryzek J. The algorithmic agent perspective and computational neuropsychiatry: from etiology to advanced therapy in major depressive disorder. Entropy (Basel). 2024 Nov;26(11):953. DOI: 10.3390/e26110953
- dc.identifier.doi http://dx.doi.org/10.3390/e26110953
- dc.identifier.issn 1099-4300
- dc.identifier.uri http://hdl.handle.net/10230/70556
- dc.language.iso eng
- dc.publisher MDPI
- dc.relation.ispartof Entropy. 2024 Nov;26(11):953
- dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/101017716
- dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/855109
- dc.rights © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri https://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Algorithmic information theory
- dc.subject.keyword Computational neuroscience
- dc.subject.keyword Depression
- dc.subject.keyword Kolmogorov theory of consciousness
- dc.subject.keyword Biotypes
- dc.subject.keyword Brain stimulation
- dc.subject.keyword tDCS
- dc.subject.keyword Artificial intelligence
- dc.subject.keyword Free energy principle
- dc.subject.keyword Digital twins
- dc.title The algorithmic agent perspective and computational neuropsychiatry: from etiology to advanced therapy in major depressive disorder
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion