Heterogeneous appetite patterns in depression: computational modeling of nutritional interoception, reward processing, and decision-making

Front Hum Neurosci. 2024 Dec 16:18:1502508. doi: 10.3389/fnhum.2024.1502508. eCollection 2024.

Abstract

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 cognitive-behavioral 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.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the following funding sources: JST SPRING, grant number JPMJSP2120 (YU), JSPS KAKENHI (JP23K24205, JP22H00494, and JP23K18163), AMED under grant number (JP21wm0425010 and JP21gm1510006), Salt Science Research Foundation Grant (2438), the Collaborative Research Program of Institute for Protein Research, Osaka University (ICR-24-03) (TH), JSPS KAKENHI (JP20H00625, JP24H00076, and JP24K00499), JST CREST (JPMJCR21P4), Intramural Research Grant (4–6, 6–9) for Neurological and Psychiatric Disorders of NCNP (YY).