Suppr超能文献

伴有和不伴有暴饮暴食的肥胖症中基于输赢的差异强化学习的神经计算机制

Neurocomputational Mechanisms Underlying Differential Reinforcement Learning From Wins and Losses in Obesity With and Without Binge Eating.

作者信息

Waltmann Maria, Herzog Nadine, Reiter Andrea M F, Villringer Arno, Horstmann Annette, Deserno Lorenz

机构信息

Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Centre of Mental Health, University of Würzburg, Würzburg, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.

Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.

出版信息

Biol Psychiatry Cogn Neurosci Neuroimaging. 2024 Dec;9(12):1281-1290. doi: 10.1016/j.bpsc.2024.06.002. Epub 2024 Jun 21.

Abstract

BACKGROUND

Binge-eating disorder (BED) is thought of as a disorder of cognitive control, but evidence regarding its neurocognitive mechanisms is inconclusive. Key limitations of previous research include a lack of consistent separation between effects of BED and obesity and a disregard for self-report evidence suggesting that neurocognitive alterations may emerge primarily in loss- or harm-avoidance contexts.

METHODS

To address these gaps, in this longitudinal study we investigated behavioral flexibility and its underlying neurocomputational processes in reward-seeking and loss-avoidance contexts. Obese participants with BED, obese participants without BED, and healthy normal-weight participants (n = 96) performed a probabilistic reversal learning task during functional imaging, with different blocks focused on obtaining wins or avoiding losses. They were reinvited for a 6-month follow-up assessment.

RESULTS

Analyses informed by computational models of reinforcement learning showed that unlike obese participants with BED, obese participants without BED performed worse in the win than in the loss condition. Computationally, this was explained by differential learning sensitivities in the win versus loss conditions in the groups. In the brain, this was echoed in differential neural learning signals in the ventromedial prefrontal cortex per condition. The differences were subtle but scaled with BED symptoms, such that more severe BED symptoms were associated with increasing bias toward improved learning from wins versus losses. Across conditions, obese participants with BED switched more between choice options than healthy normal-weight participants. This was reflected in diminished representation of choice certainty in the ventromedial prefrontal cortex.

CONCLUSIONS

Our study highlights the importance of distinguishing between obesity with and without BED to identify unique neurocomputational alterations underlying different styles of maladaptive eating behavior.

摘要

背景

暴饮暴食症(BED)被认为是一种认知控制障碍,但其神经认知机制的证据尚无定论。以往研究的主要局限性包括未能始终如一地区分BED和肥胖的影响,以及忽视了自我报告证据,即神经认知改变可能主要出现在避免损失或伤害的情境中。

方法

为了填补这些空白,在这项纵向研究中,我们调查了在寻求奖励和避免损失情境中的行为灵活性及其潜在的神经计算过程。患有BED的肥胖参与者、未患BED的肥胖参与者和健康的正常体重参与者(n = 96)在功能成像期间执行了概率反转学习任务,不同的模块侧重于获得胜利或避免损失。他们被重新邀请进行为期6个月的随访评估。

结果

强化学习计算模型的分析表明,与患有BED的肥胖参与者不同,未患BED的肥胖参与者在获胜条件下的表现比在损失条件下更差。从计算角度来看,这可以通过两组在获胜与损失条件下不同的学习敏感性来解释。在大脑中,这反映在每种条件下腹内侧前额叶皮质不同的神经学习信号中。这些差异很细微,但与BED症状相关,即更严重的BED症状与从胜利中学习优于从损失中学习的偏差增加有关。在所有条件下,患有BED的肥胖参与者在选择选项之间的切换比健康的正常体重参与者更多。这反映在腹内侧前额叶皮质中选择确定性的表征减少。

结论

我们的研究强调了区分伴有和不伴有BED的肥胖的重要性,以识别不同类型适应不良饮食行为背后独特的神经计算改变。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验