Scalable Biomarkers and Generative Digital Twins for Personalized Neurostimulation in Depression

项目摘要/摘要 美国目前有超过1亿人表现出临床抑郁症的迹象，大约 是新冠肺炎危机爆发前的三倍。目前，此类疾病的主要治疗方案 抑郁症患者包括药物和心理干预，急性和长期 其疗效明显有限：多达三分之一的患者出现治疗耐药 抑郁症。重复经颅磁刺激等非侵入性神经刺激疗法 (RTMS)--在大脑皮质上放置一个磁线圈用来集中刺激大脑--最近有了 被认为是治疗难治性抑郁症的有希望的低风险干预措施。然而，这些机制和 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 戏剧性的效果，在几个小时内改变了病人的生活进程。然而，在许多情况下，它几乎没有 可衡量的影响。这提出了一个显而易见的问题：为什么当前的rTMS协议在某些情况下工作得很好 个人而不是其他人？我们是否可以调整协议以使其适用于每个人，从而潜在地提供可靠的 个性化治疗，甚至是治标不治本？越来越多的文献表明，如果我们学会裁剪，这是可能的 人类神经生理学中个体差异的治疗。在这里，我们提出了一种创新和独特的 实现精确精神神经刺激的方法：使用 “多产数字双胞胎”。为了经济实惠地构建和扩展生成性数字双胞胎，我们建议将HIGH 密度脑电(HD-EEG)--非侵入性、廉价且易于部署-- 在加速rTMS治疗方案期间测量大脑连接性的纵向变化 抑郁症。然后，使用可控的生成性神经网络，允许对 接受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。

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.