Scalable Biomarkers and Generative Digital Twins for Personalized Neurostimulation in Depression

用于抑郁症个性化神经刺激的可扩展生物标志物和生成数字双胞胎

• 批准号: 10556838
• 负责人: LOGAN GROSENICK
• 金额: $ 63.43万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10556838
• 关键词: Acute; Affect; Aftercare; American; Biological; Biological Markers; Brain; COVID-19; COVID-19 pandemic; Chronic stress; Clinical; Clinical Trials; Custom; Data; Depressed mood; Development; Disease remission; Effectiveness; Electroencephalography; Functional Magnetic Resonance Imaging; Gold; Hour; Human; Individual; Individual Differences; Intervention; Learning; Life; Literature; Magnetism; Major Depressive Disorder; Measurable; Measurement; Measures; Mental Depression; Modality; Modeling; Neurobiology; Patients; Pattern; Persons; Pharmacology; Prevalence; Protocols documentation; Research; Rest; Risk; Social isolation; Symptoms; Techniques; Time; Training; Trauma; Treatment Protocols; Twin Multiple Birth; United States; Woman; Work; base; density; depressed patient; digital; disability; generative adversarial network; improved; in silico; individual response; individualized medicine; innovation; insight; machine learning framework; neural network; neurophysiology; novel strategies; outcome prediction; personalized medicine; physical model; psychologic; repetitive transcranial magnetic stimulation; simulation; socioeconomics; treatment response; treatment-resistant depression; trend

Project Summary/Abstract More than 100 million people in the United States currently show signs of clinical depression, approximately three times more than before the onset of the COVID-19 crisis. Currently, the main treatment options for such depressed individuals include pharmacological and psychological interventions, the acute and long-term effectiveness of which are significantly limited: up to one-third of patients develop treatment-resistant depression. Noninvasive neurostimulation therapies such as repetitive Transcranial Magnetic Stimulation (rTMS)–where a magnetic coil placed over the cortex is used to focally stimulate the brain–have recently emerged as promising low-risk interventions for treatment-resistant depression. However, the mechanisms and appropriate parameters for this treatment remain poorly understood. In the best cases, rTMS can have dramatic effects, changing the course of a patient's life in hours. In many cases, however, it has little to no measurable effect. This raises the obvious question: why do current rTMS protocols work well for some individuals but not for others? Could we adapt protocols to work well for everyone, potentially providing reliable personalized treatment or even a lasting cure? A growing literature suggests this is possible if we learn to tailor treatment to individual differences in human neurophysiology. Here we propose an innovative and unique approach towards precision psychiatric neurostimulation: personalized modeling of treatment using “Generative Digital Twins”. To affordably build and scale Generative Digital Twins, we propose combining high density electroencephalography (HD-EEG)–which is non-invasive, inexpensive, and easily deployable–to measure longitudinal changes in brain connectivity during an accelerated rTMS treatment protocol for depression. Then, using controllable generative neural networks that allow detailed predictive simulations of individual response trajectories given rTMS treatment, we can begin to predict outcomes prior to treatment, understand individual responses, and personalize treatment parameters.

项目概要/摘要 目前美国有超过 1 亿人表现出临床抑郁症的迹象，大约 目前，这种情况的主要治疗方案是 COVID-19 危机爆发前的三倍。 抑郁症患者的治疗包括药物和心理干预、急性和长期的干预 其效果非常有限：多达三分之一的患者出现治疗耐药 无创神经刺激疗法，例如重复经颅磁刺激。 （rTMS）——放置在皮层上的磁线圈用于集中刺激大脑——最近已经 已成为治疗难治性抑郁症的有希望的低风险干预措施，但其机制和机制尚不清楚。 对于这种治疗的适当参数仍然知之甚少，在最好的情况下，rTMS 可以有。 然而，在许多情况下，它几乎没有什么作用。 这提出了一个明显的问题：为什么当前的 rTMS 协议对某些人效果很好。 我们能否调整协议以适合每个人，从而提供可靠的服务？ 越来越多的文献表明，如果我们学会量身定制，这是可能的？ 在这里，我们提出了一种创新且独特的针对人类神经生理学个体差异的治疗方法。 精神神经刺激的精确度：个性化治疗建模 “生成数字孪生”为了以经济实惠的方式构建和扩展生成数字孪生，我们建议将高成本结合起来。 密度脑电图（HD-EEG）——非侵入性、廉价且易于部署—— 测量加速 rTMS 治疗方案期间大脑连接的纵向变化 然后，使用可控制的生成神经网络来进行详细的预测模拟。 rTMS 治疗的个体反应轨迹，我们可以在治疗前开始预测结果， 了解个体反应并个性化治疗参数。