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: 10685558
• 负责人: SAHIB KHALSA
• 金额: $ 58.57万
• 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/10685558
• 关键词: Affective; Age; Anorexia Nervosa; Anxiety; Arbitration; Arousal; Attention; Attenuated; Automobile Driving; Awareness; Behavior; Binge eating disorder; Biological; Biological Assay; Body Weight decreased; Body mass index; Brain; Bulimia; Calibration; Case/Control Studies; Characteristics; Clinical; Cognitive; Computer Models; Cues; Data; Desire for food; Development; Diagnosis; Diagnostic; Disease; Disease remission; Eating; Eating Behavior; Eating Disorders; Electroencephalography; 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; Modeling; Motivation; Nervous System; 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; Training; Weight; anxious; attenuation; 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; neural circuit; neurophysiology; new technology; novel; predict clinical outcome; prediction algorithm; predictive marker; relapse risk; response; risk prediction; sex; success; tool; vibration

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.

项目摘要 鉴于厌食症（AN）在任何精神病和当前都具有最高的死亡率 治疗表明效率有限，至关重要的是更好地了解驱动大脑机制 这种疾病的病理生理。该建议结合了一种实验医学方法，重点是 胃肠道（GI）截距，用于计算建模，以探测拦截和 与食欲相关的胃处理。目的是确定AT的感知和神经标记 个体患者水平并应用机器学习方法以纵向测试临床结果预测。 在我们的初步数据的支持下，该提案基于以下前提： 包括通过认知抑制传入精度表现出的计算功能障碍 与饥饿相关的互感信号，这减少了他们的动机影响力并促进 适应不良并避免饮食行为。我们提出了一项对65 A和65健康的病例对照研究 比较将使用新型的胃肠道间介质探针进行广泛的基线测试 症状，行为，电路和生理学的测量。感官刺激将在 预先实时的时期，将与该疾病相关的预期环境锚定。这些 将遵循180天的个人检查临床结果。计算方法将检查 基本的假设是个体具有较低的胃肠道感官的感觉精度和程度 感觉的实现与临床特征有关。此外，我们将研究此关系的关系 电路和物理措施的印象。然后，我们将把机器学习方法应用于这些方法 神经生理和感知测量，以纵向测试临床结局的预测。实现 该项目的目的将通过仲裁是否为 神经系统中“自上而下”或“自下而上”失调的结果，这可能会改变我们的 了解内在的互感障碍如何导致AN。务实地将导致新的 在个人层面识别互感障碍的技术，允许精神病学发展 An的诊断和预测生物标志物。胃肠道的神经计算分析 AN中的感受可用于开发低成本，可扩展和客观的工具来识别功能障碍 在个别患者中，以促进基于神经生物学的恢复定义，并预测 治疗后复发。最后，该提议为未来的精确发展奠定了基础 精神病干预措施，例如靶向（和重新校准）异常脑的感知再培训疗法 身体相互作用。

项目成果

Highway to the danger zone? A cautionary account that GLP-1 receptor agonists may be too effective for unmonitored weight loss.

通往危险区的高速公路？

• DOI: 10.1111/obr.13709
• 发表时间: 2024
• 期刊: Obesity reviews : an official journal of the International Association for the Study of Obesity
• 作者: Richards,JesseR;Khalsa,SahibS
• 通讯作者: Khalsa,SahibS

Parieto-occipital ERP indicators of gut mechanosensation in humans.

• DOI: 10.1038/s41467-023-39058-4
• 发表时间: 2023-06-13
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Mayeli, Ahmad;Al Zoubi, Obada;White, Evan J.;Chappelle, Sheridan;Kuplicki, Rayus;Morton, Alexa;Bruce, Jaimee;Smith, Ryan;Feinstein, Justin S.;Bodurka, Jerzy;Paulus, Martin P.;Khalsa, Sahib S.
• 通讯作者: Khalsa, Sahib S.