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抑郁症中异质性的食欲模式:营养内感受、奖赏处理和决策的计算模型

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

作者信息

Uchida Yuuki, Hikida Takatoshi, Honda Manabu, Yamashita Yuichi

机构信息

Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan.

Graduate School of Medical and Dental Sciences, Institute of Science Tokyo, Tokyo, Japan.

出版信息

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 and delay discount ), 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.

摘要

决策过程中准确的内感受处理对于维持体内平衡和整体健康至关重要。这一过程的紊乱与包括抑郁症在内的各种精神疾病有关。最近的研究集中在抑郁症中的营养稳态失调,以实现有效的亚型分类和治疗。神经生理学研究将抑郁症患者的食欲变化与中脑边缘多巴胺系统和内感受区域(如岛叶皮质)的激活改变联系起来,这表明奖励处理和内感受的紊乱驱动了营养稳态和食欲的变化。本研究旨在探索计算精神病学在解决这些问题方面的潜力。通过使用一个将内部状态与行为控制之间的联系形式化的稳态强化学习模型,我们通过模拟实验研究了内感受改变影响稳态行为和奖励系统活动的机制。内感受改变的模拟显示出与抑郁症亚型相似的行为,如食欲失调。具体而言,内感受降低导致奖励系统活动减少和惩罚增加,这与抑郁症患者食欲下降的神经影像学研究结果相符。相反,内感受增加与奖励活动增强和目标导向行为受损有关,反映出食欲增加。此外,将内感受操纵的效果与代表抑郁症认知行为特征的传统强化学习参数(如逆温度和延迟折扣)进行了比较。结果表明,这些参数的紊乱通过影响潜在的稳态调节导致抑郁症状。总体而言,本研究结果强调了将内感受和稳态整合到决策框架中以加强亚型分类并促进有效治疗策略发展的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/085f/11683075/7ec851490ead/fnhum-18-1502508-g001.jpg

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