The final, formatted version of the article will be published soon.
ORIGINAL RESEARCH article
Front. Hum. Neurosci.
Sec. Brain-Computer Interfaces
Volume 18 - 2024 |
doi: 10.3389/fnhum.2024.1502508
This article is part of the Research Topic Neurocomputational models of decision-making and cognitive processes View all articles
Heterogeneous Appetite Patterns in Depression: Computational Modeling of Nutritional Interoception, Reward Processing, and Decision-Making
Provisionally accepted- 1 National Institute of Neuroscience, National Center of Neurology and Psychiatry (Japan), Kodaira, Japan
- 2 Tokyo Medical and Dental University, Tokyo, Japan
- 3 Laboratory for Advanced Brain Functions, Institute for Protein Research, Osaka University, Suita, Osaka, Japan
Accurate interoceptive processing in decision-making is essential to maintain homeostasis and overall health. Disruptions in this process have been associated with various psychiatric conditions, including depression. Recent studies have focused on nutrient homeostatic dysregulation in depression for effective subtype classification and treatment. Neurophysiological studies have associated changes in appetite in depression with altered activation of the mesolimbic dopamine system and interoceptive regions, such as the insular cortex, suggesting that disruptions in reward processing and interoception drive changes in nutrient homeostasis and appetite. This study aimed to explore the potential of computational psychiatry in addressing these issues. Using a homeostatic reinforcement learning model formalizing the link between internal states and behavioral control, we investigated the mechanisms by which altered interoception affects homeostatic behavior and reward system activity via simulation experiments. Simulations of altered interoception demonstrated behaviors similar to those of depression subtypes, such as appetite dysregulation. Specifically, reduced interoception led to decreased reward system activity and increased punishment, mirroring the neuroimaging study findings of decreased appetite in depression. Conversely, increased interoception was associated with heightened reward activity and impaired goal-directed behavior, reflecting an increased appetite. Furthermore, effects of interoception manipulation were compared with traditional reinforcement learning parameters (e.g., inverse temperature β and delay discount γ), which represent cognitivebehavioral features of depression. The results suggest that disruptions in these parameters contribute to depressive symptoms by affecting the underlying homeostatic regulation. Overall, this study findings emphasize the importance of integrating interoception and homeostasis into decision-making frameworks to enhance subtype classification and facilitate the development of effective therapeutic strategies.
Keywords: Appetite, computational neuroscience, Computational Psychiatry, decision-making, Dopamine, Homeostasis, Homeostatic reinforcement learning
Received: 27 Sep 2024; Accepted: 02 Dec 2024.
Copyright: © 2024 Uchida, Hikida, Honda and Yamashita. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yuichi Yamashita, National Institute of Neuroscience, National Center of Neurology and Psychiatry (Japan), Kodaira, Japan
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.