Eating Disorders Center for Treatment and Research, University of California San Diego, San Diego, California, USA.
Curr Opin Psychiatry. 2019 Nov;32(6):478-483. doi: 10.1097/YCO.0000000000000544.
Eating disorders are severe psychiatric disorders with a suspected complex biopsychosocial cause. The purpose of this review is to synthesize the recent literature on brain imaging in eating disorders.
Food restriction as well as binge eating and purging behaviors are associated with lower regional brain volumes or cortical thickness, but those changes largely return to normal with normalization of weight and eating behavior. Computational modeling has started to identify patterns of structural and functional imaging data that classify eating disorder subtypes, which could be used in the future, diagnostically and to better understand disorder-specific psychopathology. The prediction error model, a computational approach to assess dopamine-related brain reward function, helped support a brain-based model for anorexia nervosa. In that model, the conscious motivation to restrict conflicts with body signals that stimulate eating. This conflict causes anxiety and drives a vicious cycle of food restriction.
Novel brain research supports the notion that eating disorders have distinct neurobiological underpinnings. This new knowledge can be used to describe disease models to patients and develop novel treatments.
饮食失调是一种严重的精神疾病,其病因可能涉及复杂的生物心理社会因素。本文旨在综合近期有关饮食失调的脑影像学研究文献。
饮食限制以及暴食和催吐行为与脑区体积或皮质厚度减小有关,但随着体重和饮食行为的正常化,这些变化在很大程度上会恢复正常。计算模型已经开始识别结构和功能影像学数据的模式,这些模式可以对饮食失调的亚型进行分类,未来有望用于诊断和更好地理解特定于障碍的精神病理学。预测误差模型是一种评估多巴胺相关脑奖励功能的计算方法,有助于支持神经性厌食症的基于大脑的模型。在该模型中,有意识地限制食物摄入的动机与刺激进食的身体信号相冲突。这种冲突会引起焦虑,并导致食物限制的恶性循环。
新的脑研究支持这样一种观点,即饮食失调具有不同的神经生物学基础。这些新知识可用于向患者描述疾病模型并开发新的治疗方法。