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.

项目概要 鉴于神经性厌食症 (AN) 是所有精神疾病中死亡率最高的疾病，并且目前 治疗效果有限，因此迫切需要更好地了解驱动该疾病的大脑机制 该提案结合了侧重于这种疾病的病理生理学方法。 胃肠道（GI）内感受与计算模型来探测内感受的神经回路 AN 中与食欲相关的胃加工的目标是识别 AN 的感知和神经标记。 个体患者水平并应用机器学习方法纵向测试临床结果预测。 在我们的初步数据支持下，该提议的前提是 AN 的病理生理学 包括计算功能障碍，表现为对传入神经的预期精度的认知抑制 与饥饿相关的内感受信号，减少了它们的动机影响并促进 我们提出了一项针对 65 名 AN 和 65 名健康人的病例对照研究。 比较谁将使用新型胃肠道内感受探针进行广泛的基线测试 症状、行为、回路和生理学的测量将在过程中发生。 餐前阶段，将反应锚定于与疾病高度相关的预期环境。 将对个体进行 180 天的跟踪，以检查临床结果。 基本假设是 AN 个体对 GI 内感受的感觉精确度较低，并且程度 此外，我们将研究这种感觉不精确的关系。 然后，我们将应用机器学习方法来解决这些问题。 神经生理学和感知测量来纵向测试临床结果的预测。 该项目的目标是通过仲裁 AN 是否为 AN 的病理生理学提供独特的见解 神经系统“自上而下”或“自下而上”失调的结果，这可能会改变我们的 理解内在的内感受障碍如何导致 AN 实际上，它将导致新的结果。 用于识别个体水平内感受功能障碍的技术，使精神病学得以发展 AN 的诊断和预测生物标志物，因此胃肠道的神经计算分析。 AN 中的内感受可用于开发低成本、可扩展且客观的工具来识别功能障碍 在个体患者中，促进基于神经生物学的康复定义，并预测风险 最后，该建议为今后精准治疗的发展奠定了基础。 精神科干预措施，例如针对（和重新校准）异常大脑的知觉再训练疗法 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？

• 发表时间: 2024-05
• 作者: Richards, Jesse R;Khalsa, Sahib S
• 通讯作者: Khalsa, Sahib S

Parieto-occipital ERP indicators of gut mechanosensation in humans.

人类肠道机械感觉的顶枕 ERP 指标。

• 发表时间: 2023-06-13
• 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