A neurocomputational assay of gastrointestinal interoception in anorexia nervosa

神经性厌食症胃肠道内感受的神经计算分析

• 批准号: 10278979
• 负责人: SAHIB KHALSA
• 金额: $ 72.49万
• 依托单位: LAUREATE INSTITUTE FOR BRAIN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10278979
• 关键词: Affective; Age; Anorexia Nervosa; Anxiety; Arbitration; Arousal; Attention; Attenuated; Automobile Driving; Awareness; Bayesian Modeling; Behavior; Binge eating disorder; Biological; Biological Assay; Body Weight decreased; Body mass index; Brain; Bulimia; Case-Control Studies; Characteristics; Clinical; Cognitive; Computer Models; Cues; Data; Desire for food; Development; Diagnosis; Disease; Disease remission; Eating; Eating Behavior; Eating Disorders; Electroencephalogram; Esthesia; Exhibits; Female; Food; Functional disorder; Future; Goals; Hunger; Individual; Individual Differences; Ingestion; Inpatients; Interoception; Intervention; Knowledge; Lead; Learning; Machine Learning; Measurement; Measures; Medical; Medicine; Mental disorders; Motivation; Nervous system structure; Neurobiology; Outcome; Pattern; Physiological; Physiology; Process; Prognosis; Prognostic Marker; Psychiatry; Recovery; Relapse; Research; Role; Sampling; Sensory; Severities; Severity of illness; Signal Transduction; Stomach; Symptoms; Testing; Time; Weight; anxious; attenuation; base; brain abnormalities; capsule; classifier algorithm; clinically relevant; cost; diagnostic biomarker; effective therapy; expectation; food restriction; gastrointestinal; gastrointestinal system; improved; individual patient; insight; machine learning method; mind body interaction; minimally invasive; mortality; neural circuit; neurophysiology; new technology; novel; predict clinical outcome; prediction algorithm; predictive marker; relapse risk; relating to nervous system; response; sex; success; tool

PROJECT SUMMARY Given that anorexia nervosa (AN) has the highest mortality rate of any psychiatric illness and current treatments show limited efficacy, there is a crucial need to better understand the brain mechanisms driving the pathophysiology of this disorder. This proposal combines an experimental medicine approach focused on gastrointestinal (GI) interoception with computational modeling to probe neural circuits of interoception and appetite-related gastric processing in AN. The goal is to identify perceptual and neural markers for AN at the individual patient level and apply machine learning methods to test clinical outcomes prediction longitudinally. Supported by our preliminary data, this proposal is based on the premise that the pathophysiology of AN includes a computational dysfunction manifested by cognitive suppression of the expected precision of afferent interoceptive signals associated with hunger, which reduces their motivational influence and facilitates maladaptive and avoidant eating behaviors. We propose a case-control study with 65 AN and 65 healthy comparisons who will undergo extensive baseline testing using a novel GI interoception probe during measurement of symptoms, behavior, circuits, and physiology. Sensory stimulation will occur during the premeal period, anchoring responses to an anticipatory context with high relevance to the disorder. These individuals will be followed for 180 days to examine clinical outcomes. A computational approach will examine the basic hypothesis that AN individuals have lower sensory precision for GI interoception and that the degree of sensory imprecision is related to clinical characteristics. Moreover, we will examine the relationship of this imprecision to circuit and physiological measures. We will then apply machine learning approaches to these neurophysiological and perceptual measures to longitudinally test the prediction of clinical outcomes. Achieving the aims of this project will provide unique insights into the pathophysiology of AN by arbitrating whether AN is a consequence of “top-down” or “bottom-up” dysregulation in the nervous system, which could transform our understanding of how intrinsic interoceptive disturbances lead to AN. Pragmatically, it will result in new technologies for identifying interoceptive dysfunction at the individual level, allowing psychiatry to develop diagnostic and predictive biomarkers of AN. Thus the neurocomputational assay of gastrointestinal interoception in AN could be used to develop low-cost, scalable, and objective tools for identifying dysfunction in individual patients, to facilitate neurobiologically-based definitions of recovery, and to predict the risk of relapse following treatment. Finally, this proposal lays the groundwork for the future development of precision psychiatric interventions such as perceptual retraining therapies to target (and recalibrate) abnormal brain- body interactions.

项目总结 鉴于神经性厌食症(AN)是所有精神疾病中死亡率最高的，目前 治疗效果有限，因此迫切需要更好地了解推动 这种疾病的病理生理学。这项建议结合了一种实验医学方法，重点是 胃肠(GI)内感觉，用计算模型探索内感觉和神经回路 食欲相关的胃加工在一个。我们的目标是识别感知和神经标记，以便在 个体患者水平，并应用机器学习方法来纵向测试临床结果预测。 在我们的初步数据支持下，这一建议是基于这样一个前提，即 包括计算功能障碍，表现为对预期传入精确度的认知抑制 与饥饿相关的相互感觉信号，这降低了它们的动机影响，并促进了 不适应和回避的进食行为。我们提出了一项病例对照研究，研究对象为65名健康女性和65名健康女性 将使用新型胃肠道内感觉探头进行广泛基线测试的比较 对症状、行为、回路和生理的测量。感官刺激将发生在 饭前阶段，锚定对与疾病高度相关的预期背景的反应。这些 患者将接受为期180天的跟踪调查，以检查临床结果。一种计算方法将检验 基本假设是个体对GI内感的感觉精确度较低，且 感觉不精确的程度与临床特征有关。此外，我们还将研究这一关系 对电路和生理测量不精确。然后，我们将把机器学习方法应用于这些 神经生理学和知觉测量纵向测试临床结果的预测。实现 该项目的目的将通过仲裁AN是否是AN来提供对AN的病理生理学的独特见解 神经系统“自上而下”或“自下而上”失调的后果，这可能会改变我们的 理解内在的内感干扰是如何导致一种。务实地说，它将导致新的 在个体水平上识别感觉间功能障碍的技术，使精神病学得以发展 AN的诊断和预测性生物标志物。因此，胃肠道的神经计算分析 AN中的内感可以用来开发低成本、可扩展和客观的工具来识别功能障碍 在个别患者中，促进基于神经生物学的康复定义，并预测 治疗后复发。最后，这一建议为精度的未来发展奠定了基础 精神干预，如知觉再训练疗法，以瞄准(和重新校准)异常的大脑- 身体互动。